if (interactive()){
outdir <- here::here()
} else {
outdir <- here::here(params$outdir)
}Cleaning r/biotech Salary Data
set out dir
libraries
library(readr)
library(here)
library(purrr)
library(glue)
library(lubridate)
library(forcats)
library(dplyr)
library(tidyr)
library(gt)
library(ggplot2)
library(skimr)
library(janitor)
library(stringr)
library(tictoc)
library(usethis)
library(reactable)preprocess excel data
I extract the sheets into separate csv files so that they can display on github and be more easily used.
# run this once, manually
library(googlesheets4)
gs4_deauth()
google_sheets_url <- 'https://docs.google.com/spreadsheets/d/1G0FmJhkOME_sv66hWmhnZS5qR2KMTY7nzkxksv46bfk/edit#gid=491268892'
sheet_names <- googlesheets4::sheet_names(google_sheets_url)yearly survey version differences
sal <- sheet_names |>
# read it as character values to avoid list-column headaches
map(
\(x) read_sheet(google_sheets_url, sheet = x, col_types = 'c')
) ✔ Reading from "r/biotech salary and company survey".✔ Range ''2025''.✔ Reading from "r/biotech salary and company survey".✔ Range ''2024''.✔ Reading from "r/biotech salary and company survey".✔ Range ''2023''.✔ Reading from "r/biotech salary and company survey".✔ Range ''2022''.sal <- sal |>
set_names(sheet_names) |>
bind_rows(.id = 'sheet_name')
sal |> glimpse()Rows: 3,664
Columns: 68
$ sheet_name <chr> …
$ Timestamp <chr> …
$ `Email Address` <chr> …
$ `Which country do you work in?` <chr> …
$ `What City do you work in?` <chr> …
$ `Which US state do you work in?` <chr> …
$ `Which Canadian Province do you work in?` <chr> …
$ `Company or Institution Name` <chr> …
$ `Which of the following best describes your company?` <chr> …
$ `Biotech sub industry?` <chr> …
$ `Company Detail - Approximate Company Size` <chr> …
$ `Provide a review and rate your company/institution and experience` <chr> …
$ `Work Life Balance - On average, how many hours do you work per week` <chr> …
$ `What is your official job title?\n\nThis should match the title that was present on your offer letter. \n\nSome examples: Research Associate, Scientist, Scientist I/II/III, Senior Scientist, Principal Scientist, Manager, Lab Technician, VP, Director etc.\n\nIf your job title includes additional detail, please include e.g. Director of Bioinformatics` <chr> …
$ `Which department best describes your role? If you can't find one that fits your specific role, select 'Other' and describe` <chr> …
$ `Briefly describe your position and responsibilities` <chr> …
$ `Do you work In-Person, Remote, or Hybrid?` <chr> …
$ `How many days on average per week do you work from home?` <chr> …
$ `What degrees do you have?` <chr> …
$ `How many total years of experience in post-docs do you have? (If none, enter 0)` <chr> …
$ `List other relevant and recognized certifications` <chr> …
$ `Please enter your total years of experience in the field\n\nDO NOT INCLUDE years in BSc, Masters, PhD, or post-bacc/post-doc` <chr> …
$ `What currency will you be answering these questions in?` <chr> …
$ `Are you a salaried or hourly employee` <chr> …
$ `If you are an hourly employee, how many hours per week do you work?` <chr> …
$ `Annual Base Salary \n\nWhat is your annual BASE salary? Do not include bonus or stocks\n\nIf you are paid hourly, multiply by 2080 to convert to yearly (assuming you work 40hr/week)` <chr> …
$ `Overtime Pay\n\nHow is overtime handled on a per hour basis? (1.5x base, 2x base, etc)` <chr> …
$ `Annual Target Bonus in PERCENTAGE (%)\n\nThis should be stipulated prior to your employment. DO NOT include one-time bonuses (spot bonuses, signing bonuses, referral bonuses).` <chr> …
$ `Commission\n\nIf you are compensated with commission how much is your commission? (Please indicate if answering in percentage or absolute monetary value)` <chr> …
$ `Annual Equity/Stock Option\n\nPlease enter the grant/market value of your long-term incentive. Use the annualized amount if distributed over several years. Enter 0 if none or not applicable. \n\nDO NOT include equity when signing on or one-time equity bonuses` <chr> …
$ `Most recent annual yearly raise (%)\n\nCompanies typically do yearly raises across the board for all employees. This is not to be confused with promotions or raises that were requested.` <chr> …
$ `[Sign on] Bonus Value\nThis is a one-time bonus you may have received when signing-on. This is not a bonus that reoccurs` <chr> …
$ `[Sign on] Stock/Equity Options\nBriefly describe sign on stock/equity options that is available` <chr> …
$ `[Sign on] Relocation Assistance Total Value\n\nThis is the total value of the relocation assistance including but not limited to tax-true ups company may have paid, movers expenses, shipping of vehicles, temporary living, etc.` <chr> …
$ `Retirement Benefits (free text)\nBriefly describe retirement benefits. For example 401k match %, pension, etc` <chr> …
$ `Healthcare Benefits (free text)\nDescribe healthcare benefits - costs, deductibles, outfamily plan, insurance provider, total out of pocket, HSA contributions, etc` <chr> …
$ `Select the highest level of education that you have that's relevant to your occupation. If you have multiple (e.g. PhD + MD) please select "Other" and describe` <chr> …
$ `How many years have you been working at this current position?` <chr> …
$ `What currency will you be answering these quesitons in?` <chr> …
$ `Survey feedback` <chr> …
$ `Any other type of annual bonus? Please describe.\n\n DO NOT include one-time bonuses (spot bonuses, signing bonuses, referral bonuses).` <chr> …
$ `How much of your bonus did you receive in the last cycle?\n\ne.g. maybe your target bonus was 10%, but you actually got 8% this year. So here you would write "8%"` <chr> …
$ `What country do you work in?` <chr> …
$ `Where is the closest major city or hub?` <chr> …
$ `Where are you located?` <chr> …
$ `Company Details - public/private/start-up/ subsidiary of` <chr> …
$ `Role / Title of current position` <chr> …
$ `[Optional] Briefly describe your position` <chr> …
$ `Years of Experience` <chr> …
$ `Compensation - Annual Base Salary/Pay` <chr> …
$ `Compensation - Overtime Pay` <chr> …
$ `Compensation - Annual Target Bonus ($)` <chr> …
$ `Compensation - Annual Equity/Stock Option` <chr> …
$ `Compensation - Most recent annual yearly raise (%)` <chr> …
$ `Compensation - Sign on Bonus Value` <chr> …
$ `Compensation - Sign on Stock/Equity Options` <chr> …
$ `Compensation - Retirement Benefits (free text)` <chr> …
$ `Compensation - Retirement Percent Match (free text)` <chr> …
$ `Compensation - Healthcare Benefits (free text)` <chr> …
$ `[OPTIONAL] Sign on - Relocation Assistance Total Value` <chr> …
$ `[Optional] Company Review` <chr> …
$ `[Optional] Work Life Balance - On average, how many hours do you work per week` <chr> …
$ `Survey Feedback` <chr> …
$ `Highest achieved Formal Education` <chr> …
$ `Compensation - Annual Equity` <chr> …
$ `Compensation - Stock Options` <chr> …
$ `Compensation - Options` <chr> …
$ `Have you one a post-doc?` <chr> …sal |> count(sheet_name)# A tibble: 4 × 2
sheet_name n
<chr> <int>
1 2022 576
2 2023 670
3 2024 1750
4 2025 668missing_variables_by_year <- sal |>
summarize(
.by = sheet_name,
across(everything(), ~sum(is.na(.)))
) |>
pivot_longer(
cols = -c(sheet_name),
names_to = 'variable',
values_to = 'n_missing'
) |>
arrange(desc(sheet_name), desc(n_missing))
missing_variables_by_year |> reactable(groupBy = 'sheet_name')Initial exploratory
Completeness
Look at response rate per question (number of NAs):
# assess NAs
percent_na <- function(x, invert = FALSE) {
p <- sum(is.na(x)) / length(x)
if (invert) { p <- 1 - p}
p
}
completeness <- sal |>
summarize(
.by = sheet_name,
across(everything(), ~percent_na(.x, invert = TRUE))
) |>
mutate(across(everything(), ~ifelse(. == 0, NA, .))) |>
pivot_longer(-sheet_name, names_to = 'variable', values_to = '% responded') |>
pivot_wider(names_from = sheet_name, values_from = '% responded')
reactable_na <- function(df) {
df |>
gt() |>
fmt_percent(columns = -variable, decimals = 0) |>
data_color(
columns = -variable,
palette = c('white', 'blue'),
na_color = 'white',
domain = c(0, 1)) |>
sub_missing(missing_text = '0') |>
tab_options(table.width = px(500))
}
reactable_na(completeness) | variable | 2025 | 2024 | 2023 | 2022 |
|---|---|---|---|---|
| Timestamp | 100% | 100% | 100% | 100% |
| Email Address | 0 | 0 | 0 | 0 |
| Which country do you work in? | 100% | 0 | 0 | 0 |
| What City do you work in? | 10% | 0 | 0 | 0 |
| Which US state do you work in? | 87% | 0 | 0 | 0 |
| Which Canadian Province do you work in? | 3% | 0 | 0 | 0 |
| Company or Institution Name | 100% | 100% | 100% | 26% |
| Which of the following best describes your company? | 100% | 0 | 0 | 0 |
| Biotech sub industry? | 100% | 100% | 100% | 100% |
| Company Detail - Approximate Company Size | 100% | 99% | 100% | 99% |
| Provide a review and rate your company/institution and experience | 35% | 0 | 0 | 0 |
| Work Life Balance - On average, how many hours do you work per week | 100% | 0 | 0 | 0 |
| What is your official job title? This should match the title that was present on your offer letter. Some examples: Research Associate, Scientist, Scientist I/II/III, Senior Scientist, Principal Scientist, Manager, Lab Technician, VP, Director etc. If your job title includes additional detail, please include e.g. Director of Bioinformatics | 100% | 0 | 0 | 0 |
| Which department best describes your role? If you can't find one that fits your specific role, select 'Other' and describe | 100% | 0 | 0 | 0 |
| Briefly describe your position and responsibilities | 57% | 0 | 0 | 0 |
| Do you work In-Person, Remote, or Hybrid? | 100% | 0 | 0 | 0 |
| How many days on average per week do you work from home? | 100% | 0 | 0 | 0 |
| What degrees do you have? | 0 | 100% | 0 | 0 |
| How many total years of experience in post-docs do you have? (If none, enter 0) | 100% | 0 | 0 | 0 |
| List other relevant and recognized certifications | 9% | 100% | 100% | 0 |
| Please enter your total years of experience in the field DO NOT INCLUDE years in BSc, Masters, PhD, or post-bacc/post-doc | 100% | 0 | 0 | 0 |
| What currency will you be answering these questions in? | 0 | 0 | 0 | 0 |
| Are you a salaried or hourly employee | 100% | 0 | 0 | 0 |
| If you are an hourly employee, how many hours per week do you work? | 100% | 0 | 0 | 0 |
| Annual Base Salary What is your annual BASE salary? Do not include bonus or stocks If you are paid hourly, multiply by 2080 to convert to yearly (assuming you work 40hr/week) | 100% | 0 | 0 | 0 |
| Overtime Pay How is overtime handled on a per hour basis? (1.5x base, 2x base, etc) | 100% | 0 | 0 | 0 |
| Annual Target Bonus in PERCENTAGE (%) This should be stipulated prior to your employment. DO NOT include one-time bonuses (spot bonuses, signing bonuses, referral bonuses). | 86% | 0 | 0 | 0 |
| Commission If you are compensated with commission how much is your commission? (Please indicate if answering in percentage or absolute monetary value) | 100% | 0 | 0 | 0 |
| Annual Equity/Stock Option Please enter the grant/market value of your long-term incentive. Use the annualized amount if distributed over several years. Enter 0 if none or not applicable. DO NOT include equity when signing on or one-time equity bonuses | 100% | 0 | 0 | 0 |
| Most recent annual yearly raise (%) Companies typically do yearly raises across the board for all employees. This is not to be confused with promotions or raises that were requested. | 100% | 0 | 0 | 0 |
| [Sign on] Bonus Value This is a one-time bonus you may have received when signing-on. This is not a bonus that reoccurs | 41% | 0 | 0 | 0 |
| [Sign on] Stock/Equity Options Briefly describe sign on stock/equity options that is available | 36% | 0 | 0 | 0 |
| [Sign on] Relocation Assistance Total Value This is the total value of the relocation assistance including but not limited to tax-true ups company may have paid, movers expenses, shipping of vehicles, temporary living, etc. | 34% | 0 | 0 | 0 |
| Retirement Benefits (free text) Briefly describe retirement benefits. For example 401k match %, pension, etc | 70% | 0 | 0 | 0 |
| Healthcare Benefits (free text) Describe healthcare benefits - costs, deductibles, outfamily plan, insurance provider, total out of pocket, HSA contributions, etc | 47% | 0 | 0 | 0 |
| Select the highest level of education that you have that's relevant to your occupation. If you have multiple (e.g. PhD + MD) please select "Other" and describe | 100% | 0 | 0 | 0 |
| How many years have you been working at this current position? | 100% | 0 | 0 | 0 |
| What currency will you be answering these quesitons in? | 100% | 0 | 0 | 0 |
| Survey feedback | 9% | 0 | 0 | 0 |
| Any other type of annual bonus? Please describe. DO NOT include one-time bonuses (spot bonuses, signing bonuses, referral bonuses). | 21% | 0 | 0 | 0 |
| How much of your bonus did you receive in the last cycle? e.g. maybe your target bonus was 10%, but you actually got 8% this year. So here you would write "8%" | 57% | 0 | 0 | 0 |
| What country do you work in? | 0 | 10% | 100% | 0 |
| Where is the closest major city or hub? | 0 | 10% | 100% | 100% |
| Where are you located? | 0 | 90% | 0 | 0 |
| Company Details - public/private/start-up/ subsidiary of | 0 | 100% | 100% | 100% |
| Role / Title of current position | 0 | 100% | 100% | 100% |
| [Optional] Briefly describe your position | 0 | 48% | 52% | 0 |
| Years of Experience | 0 | 100% | 100% | 100% |
| Compensation - Annual Base Salary/Pay | 0 | 100% | 100% | 100% |
| Compensation - Overtime Pay | 0 | 100% | 100% | 0 |
| Compensation - Annual Target Bonus ($) | 0 | 100% | 100% | 100% |
| Compensation - Annual Equity/Stock Option | 0 | 100% | 0 | 0 |
| Compensation - Most recent annual yearly raise (%) | 0 | 100% | 100% | 0 |
| Compensation - Sign on Bonus Value | 0 | 50% | 51% | 37% |
| Compensation - Sign on Stock/Equity Options | 0 | 38% | 0 | 0 |
| Compensation - Retirement Benefits (free text) | 0 | 97% | 64% | 0 |
| Compensation - Retirement Percent Match (free text) | 0 | 0 | 0 | 0 |
| Compensation - Healthcare Benefits (free text) | 0 | 44% | 41% | 0 |
| [OPTIONAL] Sign on - Relocation Assistance Total Value | 0 | 21% | 18% | 24% |
| [Optional] Company Review | 0 | 23% | 23% | 0 |
| [Optional] Work Life Balance - On average, how many hours do you work per week | 0 | 94% | 0 | 0 |
| Survey Feedback | 0 | 6% | 0 | 0 |
| Highest achieved Formal Education | 0 | 0 | 100% | 100% |
| Compensation - Annual Equity | 0 | 0 | 100% | 100% |
| Compensation - Stock Options | 0 | 0 | 41% | 0 |
| Compensation - Options | 0 | 0 | 0 | 0 |
| Have you one a post-doc? | 0 | 0 | 0 | 0 |
Remove those with 0 answers:
# remove variables that are all NA:
var_all_na <- sal |>
skim() |>
filter(n_missing == nrow(sal)) |>
pull(skim_variable)
message('removing variables that are have all missing values')removing variables that are have all missing valuessal_clean <- sal |>
select(-any_of(var_all_na))
message(glue(
"{length(var_all_na)}/{ncol(sal)} columns removed that are all NA"
))5/68 columns removed that are all NAprint(var_all_na)[1] "Email Address"
[2] "What currency will you be answering these questions in?"
[3] "Compensation - Retirement Percent Match (free text)"
[4] "Compensation - Options"
[5] "Have you one a post-doc?" Rename column names
I rename column names to make them easier to call:
# view changed colnames
tibble(original = colnames(sal)) |>
mutate(new = janitor::make_clean_names(original)) |>
gt() | original | new |
|---|---|
| sheet_name | sheet_name |
| Timestamp | timestamp |
| Email Address | email_address |
| Which country do you work in? | which_country_do_you_work_in |
| What City do you work in? | what_city_do_you_work_in |
| Which US state do you work in? | which_us_state_do_you_work_in |
| Which Canadian Province do you work in? | which_canadian_province_do_you_work_in |
| Company or Institution Name | company_or_institution_name |
| Which of the following best describes your company? | which_of_the_following_best_describes_your_company |
| Biotech sub industry? | biotech_sub_industry |
| Company Detail - Approximate Company Size | company_detail_approximate_company_size |
| Provide a review and rate your company/institution and experience | provide_a_review_and_rate_your_company_institution_and_experience |
| Work Life Balance - On average, how many hours do you work per week | work_life_balance_on_average_how_many_hours_do_you_work_per_week |
| What is your official job title? This should match the title that was present on your offer letter. Some examples: Research Associate, Scientist, Scientist I/II/III, Senior Scientist, Principal Scientist, Manager, Lab Technician, VP, Director etc. If your job title includes additional detail, please include e.g. Director of Bioinformatics | what_is_your_official_job_title_this_should_match_the_title_that_was_present_on_your_offer_letter_some_examples_research_associate_scientist_scientist_i_ii_iii_senior_scientist_principal_scientist_manager_lab_technician_vp_director_etc_if_your_job_title_includes_additional_detail_please_include_e_g_director_of_bioinformatics |
| Which department best describes your role? If you can't find one that fits your specific role, select 'Other' and describe | which_department_best_describes_your_role_if_you_cant_find_one_that_fits_your_specific_role_select_other_and_describe |
| Briefly describe your position and responsibilities | briefly_describe_your_position_and_responsibilities |
| Do you work In-Person, Remote, or Hybrid? | do_you_work_in_person_remote_or_hybrid |
| How many days on average per week do you work from home? | how_many_days_on_average_per_week_do_you_work_from_home |
| What degrees do you have? | what_degrees_do_you_have |
| How many total years of experience in post-docs do you have? (If none, enter 0) | how_many_total_years_of_experience_in_post_docs_do_you_have_if_none_enter_0 |
| List other relevant and recognized certifications | list_other_relevant_and_recognized_certifications |
| Please enter your total years of experience in the field DO NOT INCLUDE years in BSc, Masters, PhD, or post-bacc/post-doc | please_enter_your_total_years_of_experience_in_the_field_do_not_include_years_in_b_sc_masters_ph_d_or_post_bacc_post_doc |
| What currency will you be answering these questions in? | what_currency_will_you_be_answering_these_questions_in |
| Are you a salaried or hourly employee | are_you_a_salaried_or_hourly_employee |
| If you are an hourly employee, how many hours per week do you work? | if_you_are_an_hourly_employee_how_many_hours_per_week_do_you_work |
| Annual Base Salary What is your annual BASE salary? Do not include bonus or stocks If you are paid hourly, multiply by 2080 to convert to yearly (assuming you work 40hr/week) | annual_base_salary_what_is_your_annual_base_salary_do_not_include_bonus_or_stocks_if_you_are_paid_hourly_multiply_by_2080_to_convert_to_yearly_assuming_you_work_40hr_week |
| Overtime Pay How is overtime handled on a per hour basis? (1.5x base, 2x base, etc) | overtime_pay_how_is_overtime_handled_on_a_per_hour_basis_1_5x_base_2x_base_etc |
| Annual Target Bonus in PERCENTAGE (%) This should be stipulated prior to your employment. DO NOT include one-time bonuses (spot bonuses, signing bonuses, referral bonuses). | annual_target_bonus_in_percentage_percent_this_should_be_stipulated_prior_to_your_employment_do_not_include_one_time_bonuses_spot_bonuses_signing_bonuses_referral_bonuses |
| Commission If you are compensated with commission how much is your commission? (Please indicate if answering in percentage or absolute monetary value) | commission_if_you_are_compensated_with_commission_how_much_is_your_commission_please_indicate_if_answering_in_percentage_or_absolute_monetary_value |
| Annual Equity/Stock Option Please enter the grant/market value of your long-term incentive. Use the annualized amount if distributed over several years. Enter 0 if none or not applicable. DO NOT include equity when signing on or one-time equity bonuses | annual_equity_stock_option_please_enter_the_grant_market_value_of_your_long_term_incentive_use_the_annualized_amount_if_distributed_over_several_years_enter_0_if_none_or_not_applicable_do_not_include_equity_when_signing_on_or_one_time_equity_bonuses |
| Most recent annual yearly raise (%) Companies typically do yearly raises across the board for all employees. This is not to be confused with promotions or raises that were requested. | most_recent_annual_yearly_raise_percent_companies_typically_do_yearly_raises_across_the_board_for_all_employees_this_is_not_to_be_confused_with_promotions_or_raises_that_were_requested |
| [Sign on] Bonus Value This is a one-time bonus you may have received when signing-on. This is not a bonus that reoccurs | sign_on_bonus_value_this_is_a_one_time_bonus_you_may_have_received_when_signing_on_this_is_not_a_bonus_that_reoccurs |
| [Sign on] Stock/Equity Options Briefly describe sign on stock/equity options that is available | sign_on_stock_equity_options_briefly_describe_sign_on_stock_equity_options_that_is_available |
| [Sign on] Relocation Assistance Total Value This is the total value of the relocation assistance including but not limited to tax-true ups company may have paid, movers expenses, shipping of vehicles, temporary living, etc. | sign_on_relocation_assistance_total_value_this_is_the_total_value_of_the_relocation_assistance_including_but_not_limited_to_tax_true_ups_company_may_have_paid_movers_expenses_shipping_of_vehicles_temporary_living_etc |
| Retirement Benefits (free text) Briefly describe retirement benefits. For example 401k match %, pension, etc | retirement_benefits_free_text_briefly_describe_retirement_benefits_for_example_401k_match_percent_pension_etc |
| Healthcare Benefits (free text) Describe healthcare benefits - costs, deductibles, outfamily plan, insurance provider, total out of pocket, HSA contributions, etc | healthcare_benefits_free_text_describe_healthcare_benefits_costs_deductibles_outfamily_plan_insurance_provider_total_out_of_pocket_hsa_contributions_etc |
| Select the highest level of education that you have that's relevant to your occupation. If you have multiple (e.g. PhD + MD) please select "Other" and describe | select_the_highest_level_of_education_that_you_have_thats_relevant_to_your_occupation_if_you_have_multiple_e_g_ph_d_md_please_select_other_and_describe |
| How many years have you been working at this current position? | how_many_years_have_you_been_working_at_this_current_position |
| What currency will you be answering these quesitons in? | what_currency_will_you_be_answering_these_quesitons_in |
| Survey feedback | survey_feedback |
| Any other type of annual bonus? Please describe. DO NOT include one-time bonuses (spot bonuses, signing bonuses, referral bonuses). | any_other_type_of_annual_bonus_please_describe_do_not_include_one_time_bonuses_spot_bonuses_signing_bonuses_referral_bonuses |
| How much of your bonus did you receive in the last cycle? e.g. maybe your target bonus was 10%, but you actually got 8% this year. So here you would write "8%" | how_much_of_your_bonus_did_you_receive_in_the_last_cycle_e_g_maybe_your_target_bonus_was_10_percent_but_you_actually_got_8_percent_this_year_so_here_you_would_write_8_percent |
| What country do you work in? | what_country_do_you_work_in |
| Where is the closest major city or hub? | where_is_the_closest_major_city_or_hub |
| Where are you located? | where_are_you_located |
| Company Details - public/private/start-up/ subsidiary of | company_details_public_private_start_up_subsidiary_of |
| Role / Title of current position | role_title_of_current_position |
| [Optional] Briefly describe your position | optional_briefly_describe_your_position |
| Years of Experience | years_of_experience |
| Compensation - Annual Base Salary/Pay | compensation_annual_base_salary_pay |
| Compensation - Overtime Pay | compensation_overtime_pay |
| Compensation - Annual Target Bonus ($) | compensation_annual_target_bonus |
| Compensation - Annual Equity/Stock Option | compensation_annual_equity_stock_option |
| Compensation - Most recent annual yearly raise (%) | compensation_most_recent_annual_yearly_raise_percent |
| Compensation - Sign on Bonus Value | compensation_sign_on_bonus_value |
| Compensation - Sign on Stock/Equity Options | compensation_sign_on_stock_equity_options |
| Compensation - Retirement Benefits (free text) | compensation_retirement_benefits_free_text |
| Compensation - Retirement Percent Match (free text) | compensation_retirement_percent_match_free_text |
| Compensation - Healthcare Benefits (free text) | compensation_healthcare_benefits_free_text |
| [OPTIONAL] Sign on - Relocation Assistance Total Value | optional_sign_on_relocation_assistance_total_value |
| [Optional] Company Review | optional_company_review |
| [Optional] Work Life Balance - On average, how many hours do you work per week | optional_work_life_balance_on_average_how_many_hours_do_you_work_per_week |
| Survey Feedback | survey_feedback_2 |
| Highest achieved Formal Education | highest_achieved_formal_education |
| Compensation - Annual Equity | compensation_annual_equity |
| Compensation - Stock Options | compensation_stock_options |
| Compensation - Options | compensation_options |
| Have you one a post-doc? | have_you_one_a_post_doc |
sal_clean <- janitor::clean_names(sal_clean)Now I begin cleaning the variable values
Column cleaning
It would be a massive effort to clean every column. Let’s prioritize to most important ones:
- Salary
- base
- target bonus (%)
- equity
- Title
- Experience
- years
- degree
- Location
- country
- city
If I have time, can look also at:
- 401k match
salary
- base
- target bonus (%)
- equity
There are a handful of responses with annual salary reportedly less than $5000. There is one person that responded with “$1” - we remove them. And then the remaining report a range from 105-210, which likely represent 105k - 210k, I multiple these ones by 1000.
base
Which column corresponds to base salary?
completeness |> filter(str_detect(tolower(variable), 'salary')) |> reactable_na()| variable | 2025 | 2024 | 2023 | 2022 |
|---|---|---|---|---|
| Annual Base Salary What is your annual BASE salary? Do not include bonus or stocks If you are paid hourly, multiply by 2080 to convert to yearly (assuming you work 40hr/week) | 100% | 0 | 0 | 0 |
| Compensation - Annual Base Salary/Pay | 0 | 100% | 100% | 100% |
2025 we changed the question, added data validation so that it has to be digits
before 2025, it was a short-form text, so it needed a lot of data cleaning.
2025 looks like this
I rename to compensation_annual_base_salary_pay_2025 first
sal_clean <- sal_clean |> rename(
compensation_annual_base_salary_pay_2025 = annual_base_salary_what_is_your_annual_base_salary_do_not_include_bonus_or_stocks_if_you_are_paid_hourly_multiply_by_2080_to_convert_to_yearly_assuming_you_work_40hr_week
)
sal_clean <- sal_clean |> mutate(compensation_annual_base_salary_pay_2025 = as.numeric(compensation_annual_base_salary_pay_2025))
sal_clean |>
select(compensation_annual_base_salary_pay_2025 ) |>
ggplot(aes(x = compensation_annual_base_salary_pay_2025 )) +
geom_histogram() +
scale_x_continuous(labels = scales::dollar)`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 2996 rows containing non-finite outside the scale range
(`stat_bin()`).
Since it looks good I will coaelesce 2025 with previous years:
sal_clean <- sal_clean |>
mutate(compensation_annual_base_salary_pay = ifelse(
is.na(compensation_annual_base_salary_pay),
compensation_annual_base_salary_pay_2025,
compensation_annual_base_salary_pay
)) |>
select(-compensation_annual_base_salary_pay_2025)Now what follows is cleaning the data: - remove too low salaries (<5k)
# make numeric
sal_clean <- sal_clean |>
mutate(
across(
c(compensation_annual_base_salary_pay,
compensation_annual_target_bonus), ~as.numeric(.x))
)Warning: There was 1 warning in `mutate()`.
ℹ In argument: `across(...)`.
Caused by warning:
! NAs introduced by coercionsal_clean |>
ggplot(aes(x = compensation_annual_base_salary_pay)) +
geom_histogram() +
scale_x_continuous(labels = scales::number)`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# a bunch of salary entries that are <1000:
sal_clean |>
filter(compensation_annual_base_salary_pay < 5000) |>
count(compensation_annual_base_salary_pay)# A tibble: 39 × 2
compensation_annual_base_salary_pay n
<dbl> <int>
1 0 1
2 1 1
3 15 1
4 23.6 1
5 24 1
6 28.4 1
7 38 1
8 58 1
9 62 1
10 70 1
# ℹ 29 more rows# remove guy with "1" in base
sal_clean_filt <- sal_clean |>
filter(compensation_annual_base_salary_pay > 1)
n_removed <- nrow(sal_clean) - nrow(sal_clean_filt)
# 1 guy added extra 0
sal_clean_filt <- sal_clean_filt |>
mutate(compensation_annual_base_salary_pay = ifelse(
compensation_annual_base_salary_pay == 1350000 &
sheet_name == '2023',
1350000/10,
compensation_annual_base_salary_pay
))
# for the remainder of individuals with salary < 5000 (all in hundreds),
# multiply by 1000
sal_clean_filt <- sal_clean_filt |>
mutate(
salary_base = ifelse(
compensation_annual_base_salary_pay < 1000,
compensation_annual_base_salary_pay*1000,
compensation_annual_base_salary_pay
))
n_changed <- sal_clean_filt |>
filter(salary_base != compensation_annual_base_salary_pay) |>
nrow() We removed 2 data point, and changed / cleaned 41
sal_clean_filt |>
filter(salary_base != compensation_annual_base_salary_pay) |>
select(compensation_annual_base_salary_pay, salary_base) |>
gt()| compensation_annual_base_salary_pay | salary_base |
|---|---|
| 200.00 | 200000 |
| 100.00 | 100000 |
| 168.00 | 168000 |
| 195.00 | 195000 |
| 175.00 | 175000 |
| 77.00 | 77000 |
| 120.00 | 120000 |
| 102.00 | 102000 |
| 38.00 | 38000 |
| 210.00 | 210000 |
| 170.00 | 170000 |
| 198.00 | 198000 |
| 105.00 | 105000 |
| 127.00 | 127000 |
| 126.00 | 126000 |
| 110.00 | 110000 |
| 160.00 | 160000 |
| 62.00 | 62000 |
| 15.00 | 15000 |
| 118.00 | 118000 |
| 220.00 | 220000 |
| 115.00 | 115000 |
| 87.00 | 87000 |
| 100.00 | 100000 |
| 58.00 | 58000 |
| 23.65 | 23650 |
| 109.00 | 109000 |
| 276.00 | 276000 |
| 155.00 | 155000 |
| 170.00 | 170000 |
| 100.00 | 100000 |
| 165.00 | 165000 |
| 123.00 | 123000 |
| 125.00 | 125000 |
| 28.37 | 28370 |
| 115.00 | 115000 |
| 70.00 | 70000 |
| 170.00 | 170000 |
| 125.00 | 125000 |
| 24.00 | 24000 |
| 350.00 | 350000 |
target bonus
Which column corresponds to bonus?
completeness |> filter(str_detect(tolower(variable), 'bonus')) |> reactable_na()| variable | 2025 | 2024 | 2023 | 2022 |
|---|---|---|---|---|
| Annual Base Salary What is your annual BASE salary? Do not include bonus or stocks If you are paid hourly, multiply by 2080 to convert to yearly (assuming you work 40hr/week) | 100% | 0 | 0 | 0 |
| Annual Target Bonus in PERCENTAGE (%) This should be stipulated prior to your employment. DO NOT include one-time bonuses (spot bonuses, signing bonuses, referral bonuses). | 86% | 0 | 0 | 0 |
| Annual Equity/Stock Option Please enter the grant/market value of your long-term incentive. Use the annualized amount if distributed over several years. Enter 0 if none or not applicable. DO NOT include equity when signing on or one-time equity bonuses | 100% | 0 | 0 | 0 |
| [Sign on] Bonus Value This is a one-time bonus you may have received when signing-on. This is not a bonus that reoccurs | 41% | 0 | 0 | 0 |
| Any other type of annual bonus? Please describe. DO NOT include one-time bonuses (spot bonuses, signing bonuses, referral bonuses). | 21% | 0 | 0 | 0 |
| How much of your bonus did you receive in the last cycle? e.g. maybe your target bonus was 10%, but you actually got 8% this year. So here you would write "8%" | 57% | 0 | 0 | 0 |
| Compensation - Annual Target Bonus ($) | 0 | 100% | 100% | 100% |
| Compensation - Sign on Bonus Value | 0 | 50% | 51% | 37% |
sal_clean_filt |> colnames() |> str_subset('bonus')[1] "annual_target_bonus_in_percentage_percent_this_should_be_stipulated_prior_to_your_employment_do_not_include_one_time_bonuses_spot_bonuses_signing_bonuses_referral_bonuses"
[2] "annual_equity_stock_option_please_enter_the_grant_market_value_of_your_long_term_incentive_use_the_annualized_amount_if_distributed_over_several_years_enter_0_if_none_or_not_applicable_do_not_include_equity_when_signing_on_or_one_time_equity_bonuses"
[3] "sign_on_bonus_value_this_is_a_one_time_bonus_you_may_have_received_when_signing_on_this_is_not_a_bonus_that_reoccurs"
[4] "any_other_type_of_annual_bonus_please_describe_do_not_include_one_time_bonuses_spot_bonuses_signing_bonuses_referral_bonuses"
[5] "how_much_of_your_bonus_did_you_receive_in_the_last_cycle_e_g_maybe_your_target_bonus_was_10_percent_but_you_actually_got_8_percent_this_year_so_here_you_would_write_8_percent"
[6] "compensation_annual_target_bonus"
[7] "compensation_sign_on_bonus_value" 2025: annual_target_bonus_in_percentage_percent_this_should_be_stipulated_prior_to_your_employment_do_not_include_one_time_bonuses_spot_bonuses_signing_bonuses_referral_bonuses
2024: compensation_annual_target_bonus
sal_clean_filt |>
count(annual_target_bonus_in_percentage_percent_this_should_be_stipulated_prior_to_your_employment_do_not_include_one_time_bonuses_spot_bonuses_signing_bonuses_referral_bonuses) |>
set_names(nm = c('bonus', 'n')) |>
gt()| bonus | n |
|---|---|
| 0 | 65 |
| 1 | 1 |
| 10 | 122 |
| 100 | 1 |
| 11 | 2 |
| 11.5 | 1 |
| 12 | 34 |
| 12.5 | 12 |
| 13 | 6 |
| 14 | 8 |
| 14.5 | 1 |
| 15 | 101 |
| 16 | 7 |
| 17 | 4 |
| 17.5 | 6 |
| 18 | 11 |
| 19 | 1 |
| 2 | 4 |
| 2.5 | 1 |
| 20 | 53 |
| 21 | 1 |
| 22 | 3 |
| 22.5 | 1 |
| 23 | 5 |
| 24 | 2 |
| 25 | 16 |
| 26 | 1 |
| 3 | 9 |
| 30 | 9 |
| 33 | 1 |
| 35 | 2 |
| 4 | 2 |
| 4.5 | 1 |
| 40 | 1 |
| 45 | 1 |
| 5 | 25 |
| 50 | 1 |
| 6 | 9 |
| 6.5 | 1 |
| 7 | 6 |
| 7.5 | 3 |
| 75 | 1 |
| 8 | 24 |
| 9 | 6 |
| 9.5 | 1 |
| NA | 3089 |
2025 looks good, combine it
leave it in the data, but rename to shorter: bonus_pct_2025
sal_clean_filt <- sal_clean_filt |>
rename(bonus_pct_2025 = annual_target_bonus_in_percentage_percent_this_should_be_stipulated_prior_to_your_employment_do_not_include_one_time_bonuses_spot_bonuses_signing_bonuses_referral_bonuses) |>
mutate(compensation_annual_target_bonus = case_when(
!is.na(bonus_pct_2025) ~ as.numeric(bonus_pct_2025),
TRUE ~ compensation_annual_target_bonus
)) Look at the completeness of bonus now:
sal_clean_filt |>
count(.by = sheet_name, missing_bonus = is.na(compensation_annual_target_bonus)) |>
mutate(.by = .by, p = n / sum(n)) |>
pivot_wider(
names_from = .by, values_from = c(n, p), names_vary = 'slowest'
) |>
gt() |>
fmt_percent(contains('p_')) |>
data_color(
columns = contains('p_'),
palette = c('grey', 'forestgreen'),
na_color = 'white',
domain = c(0, 1)) |>
tab_spanner_delim(columns = -missing_bonus, delim = '_', reverse = TRUE)| missing_bonus |
2022
|
2023
|
2024
|
2025
|
||||
|---|---|---|---|---|---|---|---|---|
| n | p | n | p | n | p | n | p | |
| FALSE | 303 | 52.70% | 346 | 51.64% | 912 | 52.14% | 573 | 85.78% |
| TRUE | 272 | 47.30% | 324 | 48.36% | 837 | 47.86% | 95 | 14.22% |
Before 2025:
is a mix of % and $ amount
I combine this with annual base to create two new variables: bonus_pct, bonus (raw)
My strategy:
- Remove special characters, except periods which represent decimals
- convert to numeric, which has two impacts:
- “Not Applicable/None” entries will be converted to NAs
- percentages will be converted from those values that are less than “50”
- raw currency values will be simply those that are above 1000
- If there is a range of values, then I keep the higher value, since bonus is typically interpreted as the maximum bonus amount, depending on company and individual performance
sal_clean_filt |>
select(compensation_annual_base_salary_pay,
compensation_annual_target_bonus) |>
head(n = 20) |> gt() | compensation_annual_base_salary_pay | compensation_annual_target_bonus |
|---|---|
| 150000 | NA |
| 150000 | NA |
| 123000 | 15.0 |
| 77000 | NA |
| 165000 | 15.0 |
| 126000 | 10.0 |
| 78000 | NA |
| 136000 | 12.0 |
| 180000 | 20.0 |
| 95000 | 12.0 |
| 132500 | 10.0 |
| 115000 | NA |
| 122500 | 12.5 |
| 159000 | 20.0 |
| 98000 | 0.0 |
| 167900 | NA |
| 190000 | 15.0 |
| 78400 | 10.0 |
| 28500 | 10.0 |
| 260000 | 25.0 |
sal_clean_filt <- sal_clean_filt |>
mutate(
bonus_cleaned = compensation_annual_target_bonus |>
# remove special characters
str_remove_all('[\\$,\\%]') |>
# remove any non-digits at beginning or end of string
str_remove('^[^\\d]+') |>
str_remove('[^\\d]+$') |>
# if there is text between numbers,
# split it as likely this represents a range
str_split('[^\\d\\.]+') |>
# convert to numeric
# if it is a range, keep the highest value
map_dbl(\(x) x |> as.numeric() |> max())
) |>
mutate(bonus_pct = case_when(
bonus_cleaned < 50 ~ bonus_cleaned/100,
bonus_cleaned > 1000 ~ bonus_cleaned / salary_base,
TRUE ~ 0
),
bonus = case_when(
bonus_cleaned < 50 ~ bonus_pct * salary_base,
bonus_cleaned > 1000 ~ bonus_cleaned,
TRUE ~ 0
))Let’s look at the cleaned bonus data vs the original:
sal_clean_filt |>
# take 12 rows, 6 that are different, and then 6 not
mutate(different = compensation_annual_target_bonus != bonus_cleaned) |>
slice_sample(n = 6, by = different) |>
count(salary_base, compensation_annual_target_bonus,
bonus_cleaned, bonus_pct, bonus) |>
arrange(salary_base) |>
gt() |>
fmt_percent(bonus_pct) |>
fmt_currency(c(salary_base, bonus)) |>
sub_missing()| salary_base | compensation_annual_target_bonus | bonus_cleaned | bonus_pct | bonus | n |
|---|---|---|---|---|---|
| $65,000.00 | — | — | 0.00% | $0.00 | 1 |
| $70,000.00 | 7000 | 7000 | 10.00% | $7,000.00 | 1 |
| $72,000.00 | — | — | 0.00% | $0.00 | 1 |
| $79,000.00 | — | — | 0.00% | $0.00 | 1 |
| $97,000.00 | — | — | 0.00% | $0.00 | 1 |
| $99,750.00 | — | — | 0.00% | $0.00 | 1 |
| $101,000.00 | 8080 | 8080 | 8.00% | $8,080.00 | 1 |
| $105,000.00 | 5000 | 5000 | 4.76% | $5,000.00 | 1 |
| $113,000.00 | — | — | 0.00% | $0.00 | 1 |
| $127,500.00 | 19125 | 19125 | 15.00% | $19,125.00 | 1 |
| $155,000.00 | 23000 | 23000 | 14.84% | $23,000.00 | 1 |
| $190,000.00 | 100000 | 5 | 5.00% | $9,500.00 | 1 |
| $200,000.00 | 30000 | 30000 | 15.00% | $30,000.00 | 1 |
| $275,000.00 | 100000 | 5 | 5.00% | $13,750.00 | 1 |
| $280,000.00 | 100000 | 5 | 5.00% | $14,000.00 | 1 |
| $300,000.00 | 100000 | 5 | 5.00% | $15,000.00 | 1 |
| $350,000.00 | 100000 | 5 | 5.00% | $17,500.00 | 1 |
n_changed_bonus <- sal_clean_filt |>
filter(salary_base != compensation_annual_base_salary_pay) |>
nrow() I converted bonus into % and raw $. This involved modifying 41 bonus data points.
filter
These are the responses that put a bonus amount that is nonsensical (> 100%)
I remove these responses completely
# bonus > 100% of base
sal_clean_filt |>
filter(bonus_pct >= 1) |>
select(bonus_pct, bonus_pct_2025, bonus_cleaned, compensation_annual_base_salary_pay)# A tibble: 8 × 4
bonus_pct bonus_pct_2025 bonus_cleaned compensation_annual_base_salary_pay
<dbl> <chr> <dbl> <dbl>
1 1 <NA> 11100 11100
2 1.2 <NA> 169200 141000
3 1.15 <NA> 93000 81000
4 1.71 <NA> 240000 140000
5 1 <NA> 125000 125000
6 1 <NA> 130000 130000
7 1 <NA> 13500 13500
8 1 <NA> 140000 140000sal_clean_filt <- sal_clean_filt |>
filter(bonus_pct < 1) total comp
sal_clean_filt <- sal_clean_filt |>
mutate(salary_total = salary_base + bonus)
sal_clean_filt |> count(is.na(salary_total))# A tibble: 1 × 2
`is.na(salary_total)` n
<lgl> <int>
1 FALSE 3654sal_clean_filt |> count(is.na(salary_base))# A tibble: 1 × 2
`is.na(salary_base)` n
<lgl> <int>
1 FALSE 3654sal_clean_filt |> count(is.na(bonus))# A tibble: 1 × 2
`is.na(bonus)` n
<lgl> <int>
1 FALSE 3654equity
NOT CURRENTLY ATTEMPTED. Prioritize other data first.
Equity is a little bit complicated:
Some are reported as options, some are reported in 1000s e.g. “15k” vs “15000” Some are reported in $ amount Some say “it varies per year” There are a few that report %, is that amount percentage from base in $ or in options??
- We set “Not Applicable/None” to 0
- Set responses that report %, to NA (I don’t want to deal with this and it is a small number of responses)
- We then create two variables, equity_options, equity_currency_value
equity_currency_valuetakes those data points that start with a dollar sign $equity_optionsis everything else- treat this the same as base salary:
- clean and then:
- where if it is less than 1000, multiple by 1000
- Convert everything to numeric
sal_clean_filt |>
pull(compensation_annual_equity_stock_option) |>
unique() |>
head(n = 20) [1] NA
[2] "Not Applicable/None"
[3] "70000"
[4] "5000"
[5] "Stock options awarded every year."
[6] "~2500 in options"
[7] "$2,500"
[8] "20000"
[9] "Depends on market value (stock options)"
[10] "IDK … 10s of thousands"
[11] "$20,000"
[12] "85000"
[13] "13000"
[14] "40000"
[15] "0"
[16] "12000"
[17] "15000"
[18] "15k"
[19] "29000"
[20] "22000" Title
What variables correspond to job title?
completeness |> filter(str_detect(tolower(variable), 'title')) |> reactable_na()| variable | 2025 | 2024 | 2023 | 2022 |
|---|---|---|---|---|
| What is your official job title? This should match the title that was present on your offer letter. Some examples: Research Associate, Scientist, Scientist I/II/III, Senior Scientist, Principal Scientist, Manager, Lab Technician, VP, Director etc. If your job title includes additional detail, please include e.g. Director of Bioinformatics | 100% | 0 | 0 | 0 |
| Role / Title of current position | 0 | 100% | 100% | 100% |
2025: What is your official job title? This should match the title that was present on your offer letter. Some examples: Research Associate, Scientist, Scientist I/II/III, Senior Scientist, Principal Scientist, Manager, Lab Technician, VP, Director etc. If your job title includes additional detail, please include e.g. Director of Bioinformatics
2024: Role / Title of current position
rename 2025 variable to title_2025
And then merge with 2024 to: role_title_of_current_position
sal_clean_filt <- sal_clean_filt |>
rename(title_2025 = what_is_your_official_job_title_this_should_match_the_title_that_was_present_on_your_offer_letter_some_examples_research_associate_scientist_scientist_i_ii_iii_senior_scientist_principal_scientist_manager_lab_technician_vp_director_etc_if_your_job_title_includes_additional_detail_please_include_e_g_director_of_bioinformatics) |>
mutate(role_title_of_current_position = case_when(
is.na(role_title_of_current_position) ~ title_2025,
TRUE ~ role_title_of_current_position
))pre-2025 cleaning:
Title is very important, but also very messy. Titles can have different meanings (compensation, experience, responsibilities) between companies, and location.
sal_clean_filt |>
count(role_title_of_current_position) |>
slice_sample(n=10) |>
gt() | role_title_of_current_position | n |
|---|---|
| VP R&D | 1 |
| Associate Scientist, Bio-Analytics | 1 |
| Strategy Lead | 1 |
| Pathology Director | 1 |
| Senior Scientist II Process Development | 1 |
| Sr. Clinical Trials Associate | 1 |
| Lead | 1 |
| Associate Scientist 1 | 1 |
| Staff Applications Scientist | 1 |
| Quality Associate | 1 |
filtering
Remove some obvious rows
sal_clean_filt <- sal_clean_filt |>
filter(role_title_of_current_position != 'pimp')Standardize titles
It’s a lot of work to standardize titles, so I focus on what I know best, scientist-relaeta Let me intialize some empty columns
sal_clean_filt <- sal_clean_filt |>
mutate(
title_category = NA_character_,
title_general = NA_character_,
title_detail = NA_character_
)Scientist
It’s a lot of work to standardize titles, so I focus on what I know best, scientist
Scientist:
- Research Associate
- Associate Scientist
- Scientist
- Senior Scientist
- Staff Scientist
- Principal Scientist
I’m going to create two new variables
title_category- Set this toScientisttitle_general- A variable that includes general scientist + levels, e.g. Sci III, and Associate Scititle_detail- Contains the other detail of job title, e.g. Research, Clinical
# all roles matching "Scientist"
sal_clean_filt |>
filter(str_detect(
role_title_of_current_position, regex('Scientist', ignore_case = FALSE))) |>
count(role_title_of_current_position) |> head(25) |> gt()| role_title_of_current_position | n |
|---|---|
| Advisor (Scientist 1) | 1 |
| Advisor - Quantitative Scientist | 1 |
| Analytical Development Scientist I | 1 |
| Analytical Scientist | 1 |
| Application Development Scientist | 1 |
| Application Scientist | 1 |
| Ass. Scientist | 1 |
| Assistant Scientist | 2 |
| Associate Clinical Scientist | 1 |
| Associate Clinical Trial Scientist | 1 |
| Associate Principal Scientist | 9 |
| Associate Process Scientist | 1 |
| Associate Research Scientist | 3 |
| Associate Scientist | 97 |
| Associate Scientist (R1) | 1 |
| Associate Scientist 1 | 1 |
| Associate Scientist 2 | 2 |
| Associate Scientist Doctoral Student | 1 |
| Associate Scientist I | 5 |
| Associate Scientist I/Chemist I | 1 |
| Associate Scientist II | 18 |
| Associate Scientist III | 1 |
| Associate Scientist, Bio-Analytics | 1 |
| Associate Scientist, QC Flow Cytometry | 1 |
| Associate Scientist, QC Microbiology | 1 |
Try a different approach:
Hierarchical, see case_when
The order of the statements are impactful e.g.
- If they have associate, they will associate scientists
- If they have associate and “principal” they will be principal scientist
sal_clean_filt <- sal_clean_filt |>
mutate(
# title_category
title_category =
ifelse(
str_detect(
role_title_of_current_position, regex('Scientist', ignore_case = TRUE)),
'Scientist',
title_category
),
# scratch
a = title_category == 'Scientist',
b = role_title_of_current_position |>
str_replace_all('sr\\.?', 'Senior'),
# title detail
title_detail = ifelse(
a,
role_title_of_current_position |>
str_remove_all(
regex('(Associate|Scientist|Principal|Senior)*', ignore_case = TRUE)) |>
str_replace_all('[:punct:]+', ' ') # |>
# str_replace_all('\\s{2,}', ' ')
,
NA
)
) |>
# title_gen most specific to most general
mutate(title_general = case_when(
a & str_detect(
b, regex('Principal', ignore_case = TRUE)) ~ 'Principal Scientist',
a & str_detect(
b, regex('Senior', ignore_case = TRUE)) ~ 'Senior Scientist',
a & str_detect(
b, regex('Associate', ignore_case = TRUE)) ~ 'Associate Scientist',
a ~ 'Scientist',
)) |>
# clean
select(-a, -b)
sal_clean_filt |>
count(role_title_of_current_position, title_general, title_detail) |>
slice_sample(n = 20) |> gt()| role_title_of_current_position | title_general | title_detail | n |
|---|---|---|---|
| Director of Commercialization Analytics | NA | NA | 1 |
| Research technologist II | NA | NA | 1 |
| Senior Research Associate I | NA | NA | 3 |
| Director Regulatory | NA | NA | 1 |
| Regulatory strategy post-doc | NA | NA | 1 |
| Automation Engineer I | NA | NA | 1 |
| Lab Assistant (Accessioner) | NA | NA | 1 |
| Senior scientist 1 | Senior Scientist | 1 | 4 |
| Senior Clinical Scientist | Senior Scientist | Clinical | 2 |
| Director of BD | NA | NA | 1 |
| Technical Service Manager | NA | NA | 1 |
| Director Marketing | NA | NA | 1 |
| Project engineer | NA | NA | 1 |
| Pharmacokineticist | NA | NA | 1 |
| Manager | NA | NA | 35 |
| Senior Bioinformatician | NA | NA | 2 |
| Quality Assurance Manager | NA | NA | 1 |
| Lead QA Technician | NA | NA | 1 |
| Senior Principal Scienist | NA | NA | 1 |
| Associate Director of Bioinformatics | NA | NA | 2 |
sal_clean_filt |> count(title_general) |> gt()| title_general | n |
|---|---|
| Associate Scientist | 173 |
| Principal Scientist | 186 |
| Scientist | 856 |
| Senior Scientist | 283 |
| NA | 2155 |
filter
remove some outliers upon inspection post deployment
# salary > 800k as scientist
remove <- sal_clean_filt |>
filter(
year(mdy_hms(timestamp)) == 2024,
month(mdy_hms(timestamp)) == 5,
day(mdy_hms(timestamp)) == 22,
role_title_of_current_position == 'Scientist'
) |>
pull(timestamp)
# removed
sal_clean_filt |> filter(timestamp == remove)# A tibble: 1 × 70
sheet_name timestamp which_country_do_you_wo…¹ what_city_do_you_wor…²
<chr> <chr> <chr> <chr>
1 2024 5/22/2024 17:53:21 <NA> <NA>
# ℹ abbreviated names: ¹which_country_do_you_work_in, ²what_city_do_you_work_in
# ℹ 66 more variables: which_us_state_do_you_work_in <chr>,
# which_canadian_province_do_you_work_in <chr>,
# company_or_institution_name <chr>,
# which_of_the_following_best_describes_your_company <chr>,
# biotech_sub_industry <chr>, company_detail_approximate_company_size <chr>,
# provide_a_review_and_rate_your_company_institution_and_experience <chr>, …sal_clean_filt <- sal_clean_filt |> filter(timestamp != remove)Directors and VP
Same approach as for Scientist, but these are the levels:
sal_clean_filt |>
filter(str_detect(
role_title_of_current_position, regex('(Dir(ector)*)|(AD)', ignore_case = TRUE))) |>
count(role_title_of_current_position) |> slice_sample(n = 25) |> gt()| role_title_of_current_position | n |
|---|---|
| Senior Director Operations | 1 |
| Clinical Development Director | 1 |
| Assoc Director Publications | 1 |
| Group Leader | 1 |
| IT Director | 1 |
| Group Lead, Principal Scientist | 1 |
| Executive Administrator | 1 |
| Pathology Director | 1 |
| Associate Director, Scientific Communications | 1 |
| Associate director | 8 |
| Associate Director of Alliance Management | 1 |
| Executive Director, Global Regulatory Operations | 1 |
| Global Director | 1 |
| Sr Scientist 2 (AD equivalent) | 1 |
| Associate Director, Clinical Science | 1 |
| Associate Director, Legal | 1 |
| Director of Alliance Management | 1 |
| Associate Director, Clinical Supplies | 1 |
| Product development lead | 1 |
| Dir, CLinical Programming | 1 |
| Director Biology | 2 |
| Director of Engineering | 2 |
| Senior Scientist /Team Lead | 1 |
| Senior Director Program Management | 1 |
| Director of Bioinformatics | 1 |
sal_clean_filt <- sal_clean_filt |>
mutate(
# title category
title_category =
ifelse(
str_detect(
role_title_of_current_position, regex('(Director|\\bAD\\b|\\bDir\\b)', ignore_case = TRUE)),
'Director',
title_category
),
# scratch for concise coding
a = title_category == 'Director',
# preprocessed
b = role_title_of_current_position |>
str_replace_all('sr\\.?', 'Senior') |>
str_replace_all('\\bAD\\b', 'Associate Director') |>
str_replace_all('\\bAssoc\\.?\\b', 'Associate') |>
str_replace_all('\\bDir\\.?\\b', 'Director')
,
# title detail
title_detail = ifelse(
a,
b |>
# remove the level part + director
str_remove_all(
regex('(Associate|Director|Senior|Executive)*', ignore_case = TRUE)) |>
str_replace_all('[:punct:]+', ' ') # |>
# str_replace_all('\\s{2,}', ' ')
,
title_detail
)) |>
# title_gen most specific to most general
mutate(title_general = case_when(
a & str_detect(
b, regex('Executive', ignore_case = TRUE)) ~ 'Executive Director',
a & str_detect(
b, regex('Senior', ignore_case = TRUE)) ~ 'Senior Director',
a & str_detect(
b, regex('Associate', ignore_case = TRUE)) ~ 'Associate Director',
a ~ 'Director',
.default = title_general
)) |>
# clean
select(-a, -b)
sal_clean_filt |>
count(
role_title_of_current_position, title_category,
title_general, title_detail) |>
slice_sample(n = 20) |>
gt()| role_title_of_current_position | title_category | title_general | title_detail | n |
|---|---|---|---|---|
| Process Engineer | NA | NA | NA | 6 |
| Associate Director, Process Development | Director | Associate Director | Process Development | 1 |
| Chemist III | NA | NA | NA | 1 |
| FAS | NA | NA | NA | 1 |
| Assoc Dir, Supply | Director | Associate Director | Supply | 1 |
| Senior Associate Scientist | Scientist | Senior Scientist | 18 | |
| Lead Analyst | NA | NA | NA | 1 |
| Director, Clinical Sciences Lead | Director | Director | Clinical Sciences Lead | 1 |
| Client Account Director | Director | Director | Client Account | 1 |
| Process Development Sr. Associate | NA | NA | NA | 1 |
| Vice President | NA | NA | NA | 1 |
| Senior Manager, Commercial | NA | NA | NA | 1 |
| Subject Matter Expert | NA | NA | NA | 1 |
| Senior Program and Alliance Manager | NA | NA | NA | 1 |
| Supervisor I | NA | NA | NA | 1 |
| Bioinformatics Consultant | NA | NA | NA | 1 |
| QA | NA | NA | NA | 1 |
| Clinical Research Scientist | Scientist | Scientist | Clinical Research | 1 |
| MSAT | NA | NA | NA | 1 |
| Clinical Scientist II | Scientist | Scientist | Clinical II | 1 |
sal_clean_filt |>
filter(title_category == 'Director') |> count(title_general) |> gt()| title_general | n |
|---|---|
| Associate Director | 193 |
| Director | 200 |
| Executive Director | 13 |
| Senior Director | 41 |
- Associate Director
- Director
- Senior Director
- Executive Director
VP
There’s not many VPs
sal_clean_filt |>
filter(str_detect(
role_title_of_current_position, regex('(VP|Vice Principal)', ignore_case = TRUE))) |>
count(role_title_of_current_position) |> gt()| role_title_of_current_position | n |
|---|---|
| SVP | 1 |
| SVP, clinical operations | 1 |
| Svp Marketing and BD | 1 |
| VP | 9 |
| VP / CIO | 1 |
| VP Finance | 1 |
| VP Medical Affairs | 1 |
| VP R&D | 1 |
| VP Regulatory | 1 |
| VP clin ops | 1 |
| VP medical affairs | 1 |
| VP of Digital | 1 |
| VP, Translational Genetics | 1 |
| Vp | 1 |
sal_clean_filt <- sal_clean_filt |>
mutate(
title_category =
ifelse(
str_detect(
role_title_of_current_position,
regex('(VP|Vice Principal)', ignore_case = TRUE)),
'VP',
title_category
),
# scratch for concise coding
a = title_category == 'VP',
# preprocessed
b = role_title_of_current_position |>
str_replace_all(regex('vp', ignore_case = TRUE), 'VP')
,
# title detail
title_detail = ifelse(
a,
b |>
# remove the level part + title_category
str_remove_all(
regex('S?VP', ignore_case = TRUE)) |>
str_replace_all('[:punct:]+', ' ') |>
str_replace_all('\\s{2,}', ' ') |>
str_trim()
,
title_detail
)) |>
# title_gen most specific to most general
mutate(title_general = case_when(
a & str_detect(
b, regex('SVP', ignore_case = TRUE)) ~ 'SVP',
a ~ 'VP',
.default = title_general
)) |>
# clean
select(-a, -b)
sal_clean_filt |>
filter(str_detect(
role_title_of_current_position, regex('vp', ignore_case = TRUE))) |>
count(
role_title_of_current_position, title_category,
title_general, title_detail) |>
gt()| role_title_of_current_position | title_category | title_general | title_detail | n |
|---|---|---|---|---|
| SVP | VP | SVP | 1 | |
| SVP, clinical operations | VP | SVP | clinical operations | 1 |
| Svp Marketing and BD | VP | SVP | Marketing and BD | 1 |
| VP | VP | VP | 9 | |
| VP / CIO | VP | VP | CIO | 1 |
| VP Finance | VP | VP | Finance | 1 |
| VP Medical Affairs | VP | VP | Medical Affairs | 1 |
| VP R&D | VP | VP | R D | 1 |
| VP Regulatory | VP | VP | Regulatory | 1 |
| VP clin ops | VP | VP | clin ops | 1 |
| VP medical affairs | VP | VP | medical affairs | 1 |
| VP of Digital | VP | VP | of Digital | 1 |
| VP, Translational Genetics | VP | VP | Translational Genetics | 1 |
| Vp | VP | VP | 1 |
sal_clean_filt |>
filter(title_category == 'VP') |> count(title_general) |> gt()| title_general | n |
|---|---|
| SVP | 3 |
| VP | 19 |
Research Associate
sal_clean_filt |>
filter(str_detect(
role_title_of_current_position,
regex('(Research Associate|\\bRA\\b)', ignore_case = TRUE)
)) |>
count(role_title_of_current_position) |> gt()| role_title_of_current_position | n |
|---|---|
| Advanced Research Associate | 1 |
| Bioinformatics Research Associate | 1 |
| Bioinformatics Senior Research Associate | 1 |
| Clinical Research Associate | 2 |
| Contract Research Associate | 1 |
| Manufacturing Research Associate I I | 1 |
| Principal Research Associate | 9 |
| Principal Research Associate II | 1 |
| Principal research associate | 2 |
| RA | 2 |
| RA 1 | 1 |
| RA II | 2 |
| Research Associate | 75 |
| Research Associate 2 | 1 |
| Research Associate (Engineer) | 1 |
| Research Associate 1 | 5 |
| Research Associate 111 | 1 |
| Research Associate 2 | 8 |
| Research Associate I | 31 |
| Research Associate I - Protein Purification | 1 |
| Research Associate I, In Vivo | 1 |
| Research Associate II | 56 |
| Research Associate II, Contract | 1 |
| Research Associate III | 11 |
| Research Associate Ii | 1 |
| Research Associate Scientist | 1 |
| Research Associate lvl II | 1 |
| Research Associate, Process Development (Tech Dev Rotational Program) | 1 |
| Research associate | 13 |
| Research associate 2 | 1 |
| Research associate I | 3 |
| Research associate II | 5 |
| Research associate Ii | 1 |
| SENIOR RESEARCH ASSOCIATE | 1 |
| Senior Clinical Research Associate I | 1 |
| Senior RA | 1 |
| Senior Research Associate | 61 |
| Senior Research Associate 1 | 3 |
| Senior Research Associate 2 | 1 |
| Senior Research Associate I | 3 |
| Senior Research Associate II | 3 |
| Senior Research Associate, Platform Development | 1 |
| Senior Research Associate, Strain Engineering | 1 |
| Senior Research Associates | 1 |
| Senior research associate | 9 |
| Senior research associate I | 2 |
| Sr Research Associate | 5 |
| Sr research associate | 1 |
| Sr. RA | 2 |
| Sr. Research Associate | 16 |
| Sr. Research Associate II | 1 |
| research associate | 1 |
| research associate I | 1 |
| senior research associate | 1 |
sal_clean_filt <- sal_clean_filt |>
mutate(
# title_category
title_category =
ifelse(
str_detect(
role_title_of_current_position,
regex('(Research Associate|\\bRA\\b)', ignore_case = TRUE)),
'Research Associate',
title_category
),
# scratch
a = title_category == 'Research Associate',
b = role_title_of_current_position |>
str_replace_all('[Ss]r\\.?', 'Senior'),
# title detail
title_detail = ifelse(
a,
role_title_of_current_position |>
str_remove_all(
regex('(Research|Associate|Scientist|Senior|\\bRA\\b)*',
ignore_case = TRUE)) |>
str_replace_all('[:punct:]+', ' ') |>
str_replace_all('\\s{2,}', ' ')
,
title_detail
)
) |>
# title_gen most specific to most general
mutate(title_general = case_when(
a & str_detect(
b, regex('Senior', ignore_case = TRUE)) ~ 'Senior Research Associate',
a ~ 'Research Associate',
.default = title_general
)) |>
# clean
select(-a, -b)
sal_clean_filt |>
filter(str_detect(
role_title_of_current_position, regex('(Research Associate|\\bRA\\b)', ignore_case = TRUE))) |>
count(
role_title_of_current_position, title_category,
title_general, title_detail) |>
gt()| role_title_of_current_position | title_category | title_general | title_detail | n |
|---|---|---|---|---|
| Advanced Research Associate | Research Associate | Research Associate | Advanced | 1 |
| Bioinformatics Research Associate | Research Associate | Research Associate | Bioinformatics | 1 |
| Bioinformatics Senior Research Associate | Research Associate | Senior Research Associate | Bioinformatics | 1 |
| Clinical Research Associate | Research Associate | Research Associate | Clinical | 2 |
| Contract Research Associate | Research Associate | Research Associate | Contract | 1 |
| Manufacturing Research Associate I I | Research Associate | Research Associate | Manufacturing I I | 1 |
| Principal Research Associate | Research Associate | Research Associate | Principal | 9 |
| Principal Research Associate II | Research Associate | Research Associate | Principal II | 1 |
| Principal research associate | Research Associate | Research Associate | Principal | 2 |
| RA | Research Associate | Research Associate | 2 | |
| RA 1 | Research Associate | Research Associate | 1 | 1 |
| RA II | Research Associate | Research Associate | II | 2 |
| Research Associate | Research Associate | Research Associate | 75 | |
| Research Associate 2 | Research Associate | Research Associate | 2 | 1 |
| Research Associate (Engineer) | Research Associate | Research Associate | Engineer | 1 |
| Research Associate 1 | Research Associate | Research Associate | 1 | 5 |
| Research Associate 111 | Research Associate | Research Associate | 111 | 1 |
| Research Associate 2 | Research Associate | Research Associate | 2 | 8 |
| Research Associate I | Research Associate | Research Associate | I | 31 |
| Research Associate I - Protein Purification | Research Associate | Research Associate | I Protein Purification | 1 |
| Research Associate I, In Vivo | Research Associate | Research Associate | I In Vivo | 1 |
| Research Associate II | Research Associate | Research Associate | II | 56 |
| Research Associate II, Contract | Research Associate | Research Associate | II Contract | 1 |
| Research Associate III | Research Associate | Research Associate | III | 11 |
| Research Associate Ii | Research Associate | Research Associate | Ii | 1 |
| Research Associate Scientist | Research Associate | Research Associate | 1 | |
| Research Associate lvl II | Research Associate | Research Associate | lvl II | 1 |
| Research Associate, Process Development (Tech Dev Rotational Program) | Research Associate | Research Associate | Process Development Tech Dev Rotational Program | 1 |
| Research associate | Research Associate | Research Associate | 13 | |
| Research associate 2 | Research Associate | Research Associate | 2 | 1 |
| Research associate I | Research Associate | Research Associate | I | 3 |
| Research associate II | Research Associate | Research Associate | II | 5 |
| Research associate Ii | Research Associate | Research Associate | Ii | 1 |
| SENIOR RESEARCH ASSOCIATE | Research Associate | Senior Research Associate | 1 | |
| Senior Clinical Research Associate I | Research Associate | Senior Research Associate | Clinical I | 1 |
| Senior RA | Research Associate | Senior Research Associate | 1 | |
| Senior Research Associate | Research Associate | Senior Research Associate | 61 | |
| Senior Research Associate 1 | Research Associate | Senior Research Associate | 1 | 3 |
| Senior Research Associate 2 | Research Associate | Senior Research Associate | 2 | 1 |
| Senior Research Associate I | Research Associate | Senior Research Associate | I | 3 |
| Senior Research Associate II | Research Associate | Senior Research Associate | II | 3 |
| Senior Research Associate, Platform Development | Research Associate | Senior Research Associate | Platform Development | 1 |
| Senior Research Associate, Strain Engineering | Research Associate | Senior Research Associate | Strain Engineering | 1 |
| Senior Research Associates | Research Associate | Senior Research Associate | s | 1 |
| Senior research associate | Research Associate | Senior Research Associate | 9 | |
| Senior research associate I | Research Associate | Senior Research Associate | I | 2 |
| Sr Research Associate | Research Associate | Senior Research Associate | Sr | 5 |
| Sr research associate | Research Associate | Senior Research Associate | Sr | 1 |
| Sr. RA | Research Associate | Senior Research Associate | Sr | 2 |
| Sr. Research Associate | Research Associate | Senior Research Associate | Sr | 16 |
| Sr. Research Associate II | Research Associate | Senior Research Associate | Sr II | 1 |
| research associate | Research Associate | Research Associate | 1 | |
| research associate I | Research Associate | Research Associate | I | 1 |
| senior research associate | Research Associate | Senior Research Associate | 1 |
sal_clean_filt |>
filter(title_category == 'Research Associate') |> count(title_general) |> gt()| title_general | n |
|---|---|
| Research Associate | 244 |
| Senior Research Associate | 115 |
filter
I filter out an outlier sample, looks like they incorrectly inputted bonus:
n_1 <- nrow(sal_clean_filt)
sal_clean_filt |>
dplyr::filter((
str_detect(timestamp, '10/31/2023') &
role_title_of_current_position == 'Sr Clinical Research Associate')
) |>
gt::gt()
sal_clean_filt <- sal_clean_filt |>
dplyr::filter(!(
str_detect(timestamp, '10/31/2023') &
role_title_of_current_position == 'Sr Clinical Research Associate')
)
.diff <- nrow(sal_clean_filt) - n_1
stopifnot(.diff == -1)Roles that have not been processed
Look at the remaining roles that I have not tried to process.
sal_clean_filt |>
filter(is.na(title_category)) |>
distinct(role_title_of_current_position) |>
reactable()Experience
What variables correspond to years of experience?
completeness |> filter(str_detect(tolower(variable), 'experience')) |> reactable_na()| variable | 2025 | 2024 | 2023 | 2022 |
|---|---|---|---|---|
| Provide a review and rate your company/institution and experience | 35% | 0 | 0 | 0 |
| How many total years of experience in post-docs do you have? (If none, enter 0) | 100% | 0 | 0 | 0 |
| Please enter your total years of experience in the field DO NOT INCLUDE years in BSc, Masters, PhD, or post-bacc/post-doc | 100% | 0 | 0 | 0 |
| Years of Experience | 0 | 100% | 100% | 100% |
2025: Please enter your total years of experience in the field DO NOT INCLUDE years in BSc, Masters, PhD, or post-bacc/post-doc
2024: Years of Experience
rename 2025 variable to yoe_2025
And then merge with 2024 to: years_of_experience
sal_clean_filt <- sal_clean_filt |>
rename(yoe_2025 = please_enter_your_total_years_of_experience_in_the_field_do_not_include_years_in_b_sc_masters_ph_d_or_post_bacc_post_doc) |>
mutate(years_of_experience = case_when(
is.na(years_of_experience) ~ yoe_2025,
TRUE ~ years_of_experience
))pre-2025 cleaning:
sal_clean_filt |>
count(years_of_experience) |> gt()| years_of_experience | n |
|---|---|
| 0 | 251 |
| 1 | 328 |
| 10 | 197 |
| 11 | 73 |
| 12 | 98 |
| 13 | 42 |
| 14 | 40 |
| 15 | 106 |
| 16 | 25 |
| 17 | 26 |
| 18 | 37 |
| 19 | 11 |
| 2 | 379 |
| 20 | 42 |
| 21 | 8 |
| 22 | 8 |
| 23 | 9 |
| 24 | 8 |
| 25 | 19 |
| 26 | 1 |
| 27 | 3 |
| 3 | 410 |
| 30 | 3 |
| 30 or more | 1 |
| 32 | 2 |
| 4 | 361 |
| 45 | 1 |
| 5 | 321 |
| 6 | 256 |
| 7 | 251 |
| 8 | 212 |
| 9 | 123 |
only 1 will be converted to NA
n_before <- sum(is.na(sal_clean_filt$years_of_experience))
sal_clean_filt <- sal_clean_filt |>
mutate(years_of_experience = as.numeric(years_of_experience)) Warning: There was 1 warning in `mutate()`.
ℹ In argument: `years_of_experience = as.numeric(years_of_experience)`.
Caused by warning:
! NAs introduced by coercionn_after <- sum(is.na(sal_clean_filt$years_of_experience))
message(glue('# NAs\nbefore: {n_before}\nafter: {n_after}'))# NAs
before: 0
after: 1degree
What variables correspond to education?
completeness |> filter(str_detect(tolower(variable), '(degree|edu)')) |> reactable_na()| variable | 2025 | 2024 | 2023 | 2022 |
|---|---|---|---|---|
| What degrees do you have? | 0 | 100% | 0 | 0 |
| Healthcare Benefits (free text) Describe healthcare benefits - costs, deductibles, outfamily plan, insurance provider, total out of pocket, HSA contributions, etc | 47% | 0 | 0 | 0 |
| Select the highest level of education that you have that's relevant to your occupation. If you have multiple (e.g. PhD + MD) please select "Other" and describe | 100% | 0 | 0 | 0 |
| Highest achieved Formal Education | 0 | 0 | 100% | 100% |
2025: Select the highest level of education that you have that's relevant to your occupation. If you have multiple (e.g. PhD + MD) please select "Other" and describe
2024: Select the highest level of education that you have that's relevant to your occupation. If you have multiple (e.g. PhD + MD) please select "Other" and describe
2023: Highest achieved Formal Education
rename 2025 variable to highest_degree_2025
And then merge with 2024 to: what_degrees_do_you_have
sal_clean_filt |> colnames() |> str_subset('education')[1] "select_the_highest_level_of_education_that_you_have_thats_relevant_to_your_occupation_if_you_have_multiple_e_g_ph_d_md_please_select_other_and_describe"
[2] "highest_achieved_formal_education" sal_clean_filt <- sal_clean_filt |>
rename(highest_degree_2025 = select_the_highest_level_of_education_that_you_have_thats_relevant_to_your_occupation_if_you_have_multiple_e_g_ph_d_md_please_select_other_and_describe) |>
mutate(what_degrees_do_you_have = case_when(
#2025
is.na(what_degrees_do_you_have) &
is.na(highest_achieved_formal_education) ~ highest_degree_2025,
# 2023 2022
is.na(what_degrees_do_you_have) &
is.na(highest_degree_2025) ~ highest_achieved_formal_education,
#2024
TRUE ~ what_degrees_do_you_have
))pre-2025 cleaning:
sal_clean_filt |>
count(what_degrees_do_you_have) |> gt()| what_degrees_do_you_have | n |
|---|---|
| Associates Degree | 3 |
| Associate’s Degree or Equivalent | 30 |
| Associate’s Degree or Equivalent, Bachelors or Equivalent | 4 |
| Bachelor's in pharmacy | 1 |
| Bachelors | 216 |
| Bachelors or Equivalent | 1026 |
| Bachelors or Equivalent, Masters or Equivalent | 46 |
| Bachelors or Equivalent, Masters or Equivalent, MD or Equivalent | 2 |
| Bachelors or Equivalent, Masters or Equivalent, PhD or Equivalent | 16 |
| Bachelors or Equivalent, PhD or Equivalent | 10 |
| Bachelors or Equivalent, PharmD or Equivalent | 1 |
| DVM + MS | 2 |
| High School | 3 |
| High School or Equivalent | 20 |
| High School or Equivalent, Associate’s Degree or Equivalent | 2 |
| High School or Equivalent, Associate’s Degree or Equivalent, Bachelors or Equivalent | 20 |
| High School or Equivalent, Associate’s Degree or Equivalent, Bachelors or Equivalent, Masters or Equivalent | 12 |
| High School or Equivalent, Associate’s Degree or Equivalent, Bachelors or Equivalent, Masters or Equivalent, PhD or Equivalent | 2 |
| High School or Equivalent, Associate’s Degree or Equivalent, Bachelors or Equivalent, PhD or Equivalent | 2 |
| High School or Equivalent, Bachelors or Equivalent | 37 |
| High School or Equivalent, Bachelors or Equivalent, Masters or Equivalent | 40 |
| High School or Equivalent, Bachelors or Equivalent, Masters or Equivalent, JD or Equivalent | 2 |
| High School or Equivalent, Bachelors or Equivalent, Masters or Equivalent, PhD or Equivalent | 13 |
| High School or Equivalent, Bachelors or Equivalent, PhD or Equivalent | 17 |
| High School or Equivalent, Bachelors or Equivalent, PhD or Equivalent, JD or Equivalent | 1 |
| High School or Equivalent, Bachelors or Equivalent, PhD or Equivalent, MD or Equivalent | 3 |
| High School or Equivalent, Bachelors or Equivalent, PharmD or Equivalent | 1 |
| High School or Equivalent, Masters or Equivalent | 1 |
| High School or Equivalent, Masters or Equivalent, PhD or Equivalent | 1 |
| High School or Equivalent, Masters or Equivalent, PharmD or Equivalent | 1 |
| High School or Equivalent, PhD or Equivalent | 1 |
| High School or Equivalent, PharmD or Equivalent | 1 |
| JD | 2 |
| JD + MBA | 1 |
| JD or Equivalent | 1 |
| M.D./PharmD/D.D.S. or Equivalent | 27 |
| MBA | 1 |
| MD | 1 |
| MD or Equivalent | 10 |
| MD, PhD | 1 |
| MS MPH PhD | 1 |
| Masters | 163 |
| Masters or Equivalent | 572 |
| Masters or Equivalent, PhD or Equivalent | 8 |
| Masters or Equivalent, PharmD or Equivalent | 3 |
| PhD | 258 |
| PhD + postdoc + PMP | 1 |
| PhD or Equivalent | 1022 |
| PhD or Equivalent, MD or Equivalent | 3 |
| PhD or Equivalent, PharmD or Equivalent | 2 |
| PharmD | 11 |
| PharmD + Masters in Clinical Research + MBA | 1 |
| PharmD PhD | 1 |
| PharmD or Equivalent | 24 |
| TAFE Diploma | 1 |
contains all degrees, but let’s simplify to the highest degree:
e.g.
- “High School or Equivalent, Bachelors or Equivalent, Masters or Equivalent, PhD or Equivalent”
- “Bachelors or Equivalent, Masters or Equivalent, PhD or Equivalent”
- “Bachelors or Equivalent, PhD or Equivalent”
- “PhD or Equivalent”
should all just be “PhD”
Below code assumes highest degree is the last listed degree
sal_clean_filt <- sal_clean_filt |>
mutate(experience_highest_degree = str_extract(
what_degrees_do_you_have,
'(?<=,|^)[^,]+$'
) |>
str_replace('M\\.D\\.', 'MD') |>
str_remove('or Equivalent') |>
str_replace('MD/Pharm.*', 'MD')
)
# View Changes
sal_clean_filt |>
count(is.na(experience_highest_degree), is.na(what_degrees_do_you_have))# A tibble: 1 × 3
`is.na(experience_highest_degree)` `is.na(what_degrees_do_you_have)` n
<lgl> <lgl> <int>
1 FALSE FALSE 3652sal_clean_filt |>
count(experience_highest_degree, what_degrees_do_you_have) |>
gt()| experience_highest_degree | what_degrees_do_you_have | n |
|---|---|---|
| Associate’s Degree | High School or Equivalent, Associate’s Degree or Equivalent | 2 |
| Bachelors | Associate’s Degree or Equivalent, Bachelors or Equivalent | 4 |
| Bachelors | High School or Equivalent, Associate’s Degree or Equivalent, Bachelors or Equivalent | 20 |
| Bachelors | High School or Equivalent, Bachelors or Equivalent | 37 |
| JD | High School or Equivalent, Bachelors or Equivalent, Masters or Equivalent, JD or Equivalent | 2 |
| JD | High School or Equivalent, Bachelors or Equivalent, PhD or Equivalent, JD or Equivalent | 1 |
| MD | Bachelors or Equivalent, Masters or Equivalent, MD or Equivalent | 2 |
| MD | High School or Equivalent, Bachelors or Equivalent, PhD or Equivalent, MD or Equivalent | 3 |
| MD | PhD or Equivalent, MD or Equivalent | 3 |
| Masters | Bachelors or Equivalent, Masters or Equivalent | 46 |
| Masters | High School or Equivalent, Associate’s Degree or Equivalent, Bachelors or Equivalent, Masters or Equivalent | 12 |
| Masters | High School or Equivalent, Bachelors or Equivalent, Masters or Equivalent | 40 |
| Masters | High School or Equivalent, Masters or Equivalent | 1 |
| PhD | MD, PhD | 1 |
| PhD | Bachelors or Equivalent, Masters or Equivalent, PhD or Equivalent | 16 |
| PhD | Bachelors or Equivalent, PhD or Equivalent | 10 |
| PhD | High School or Equivalent, Associate’s Degree or Equivalent, Bachelors or Equivalent, Masters or Equivalent, PhD or Equivalent | 2 |
| PhD | High School or Equivalent, Associate’s Degree or Equivalent, Bachelors or Equivalent, PhD or Equivalent | 2 |
| PhD | High School or Equivalent, Bachelors or Equivalent, Masters or Equivalent, PhD or Equivalent | 13 |
| PhD | High School or Equivalent, Bachelors or Equivalent, PhD or Equivalent | 17 |
| PhD | High School or Equivalent, Masters or Equivalent, PhD or Equivalent | 1 |
| PhD | High School or Equivalent, PhD or Equivalent | 1 |
| PhD | Masters or Equivalent, PhD or Equivalent | 8 |
| PharmD | Bachelors or Equivalent, PharmD or Equivalent | 1 |
| PharmD | High School or Equivalent, Bachelors or Equivalent, PharmD or Equivalent | 1 |
| PharmD | High School or Equivalent, Masters or Equivalent, PharmD or Equivalent | 1 |
| PharmD | High School or Equivalent, PharmD or Equivalent | 1 |
| PharmD | Masters or Equivalent, PharmD or Equivalent | 3 |
| PharmD | PhD or Equivalent, PharmD or Equivalent | 2 |
| Associates Degree | Associates Degree | 3 |
| Associate’s Degree | Associate’s Degree or Equivalent | 30 |
| Bachelor's in pharmacy | Bachelor's in pharmacy | 1 |
| Bachelors | Bachelors | 216 |
| Bachelors | Bachelors or Equivalent | 1026 |
| DVM + MS | DVM + MS | 2 |
| High School | High School | 3 |
| High School | High School or Equivalent | 20 |
| JD | JD | 2 |
| JD | JD or Equivalent | 1 |
| JD + MBA | JD + MBA | 1 |
| MBA | MBA | 1 |
| MD | M.D./PharmD/D.D.S. or Equivalent | 27 |
| MD | MD | 1 |
| MD | MD or Equivalent | 10 |
| MS MPH PhD | MS MPH PhD | 1 |
| Masters | Masters | 163 |
| Masters | Masters or Equivalent | 572 |
| PhD | PhD | 258 |
| PhD | PhD or Equivalent | 1022 |
| PhD + postdoc + PMP | PhD + postdoc + PMP | 1 |
| PharmD | PharmD | 11 |
| PharmD | PharmD or Equivalent | 24 |
| PharmD + Masters in Clinical Research + MBA | PharmD + Masters in Clinical Research + MBA | 1 |
| PharmD PhD | PharmD PhD | 1 |
| TAFE Diploma | TAFE Diploma | 1 |
Location
What variables correspond to location?
completeness |> filter(str_detect(tolower(variable), '(located|city|country|state|province)')) |> reactable_na()| variable | 2025 | 2024 | 2023 | 2022 |
|---|---|---|---|---|
| Which country do you work in? | 100% | 0 | 0 | 0 |
| What City do you work in? | 10% | 0 | 0 | 0 |
| Which US state do you work in? | 87% | 0 | 0 | 0 |
| Which Canadian Province do you work in? | 3% | 0 | 0 | 0 |
| What country do you work in? | 0 | 10% | 100% | 0 |
| Where is the closest major city or hub? | 0 | 10% | 100% | 100% |
| Where are you located? | 0 | 90% | 0 | 0 |
sal_clean_filt |> colnames() |> str_subset('(country|city|state|provin)')[1] "which_country_do_you_work_in"
[2] "what_city_do_you_work_in"
[3] "which_us_state_do_you_work_in"
[4] "which_canadian_province_do_you_work_in"
[5] "what_country_do_you_work_in"
[6] "where_is_the_closest_major_city_or_hub"2025: Which country do you work in?, What City do you work in?, Which US state do you work in?, Which Canadian Province do you work in?
2024: Where are you located?
<2023: What country do you work in?, Where is the closest major city or hub?
pre-2025 cleaning:
sal_clean_filt |>
count(where_is_the_closest_major_city_or_hub, where_are_you_located) |>
gt()| where_is_the_closest_major_city_or_hub | where_are_you_located | n |
|---|---|---|
| Alachua | NA | 1 |
| Albany | NA | 1 |
| Ames, IA NADC | NA | 1 |
| Amsterdam | NA | 1 |
| Ann Arbor | NA | 1 |
| Athens | NA | 1 |
| Atlanta | NA | 2 |
| Atlanta, GA | NA | 1 |
| Austin (Remote) | NA | 1 |
| Austin, TX | NA | 2 |
| Austria | NA | 1 |
| BOSTON | NA | 1 |
| Baltimore | NA | 2 |
| Baltimore, MD | NA | 2 |
| Baltimore/Washington DC | NA | 1 |
| Bangalore | NA | 1 |
| Basel | NA | 1 |
| Bay Are | NA | 1 |
| Bay Area | NA | 185 |
| Bay Area - Mid Penninsula | NA | 1 |
| Bay area | NA | 21 |
| Belgium | NA | 1 |
| Berkeley, CA | NA | 1 |
| Berlin | NA | 1 |
| Bloomington, IN, USA | NA | 1 |
| Boca Raton | NA | 1 |
| Bosto | NA | 1 |
| Boston | NA | 356 |
| Boston (company location) - company is WFH | NA | 1 |
| Boston / Remote | NA | 1 |
| Boston MA | NA | 1 |
| Boston, MA | NA | 4 |
| Boston, US | NA | 1 |
| Boston/Cambridge | NA | 2 |
| Bosyon | NA | 1 |
| Boulder | NA | 1 |
| Boulder (remote, company in Bay) | NA | 1 |
| Boulder, CO | NA | 3 |
| Brazil | NA | 1 |
| Brighton, UK | NA | 1 |
| Buffalo | NA | 1 |
| CA | NA | 2 |
| Calgary | NA | 1 |
| California | NA | 2 |
| Cambridge | NA | 7 |
| Cambridge Massachusetts | NA | 1 |
| Cambridge UK | NA | 1 |
| Cambridge, MA | NA | 7 |
| Cambridge, UK | NA | 3 |
| Cambridge,MA | NA | 1 |
| Canada | NA | 6 |
| Canada, Ottawa | NA | 1 |
| Carlsbad | NA | 1 |
| Central Maine | NA | 1 |
| Central/North Jersey | NA | 1 |
| Chicago | NA | 18 |
| Chicago IL | NA | 1 |
| Chicago, IL | NA | 1 |
| Chicagoland | NA | 1 |
| Cincinnati, Ohio | NA | 1 |
| Cleveland | NA | 2 |
| College Station TX | NA | 1 |
| Cologne | NA | 1 |
| Colorado | NA | 1 |
| Columbus | NA | 2 |
| Columbus, OH | NA | 1 |
| Columbus, Ohio | NA | 1 |
| Copenhagen | NA | 1 |
| DC | NA | 5 |
| DC Area/I-270 | NA | 1 |
| DC area | NA | 1 |
| DMV | NA | 1 |
| Dc | NA | 1 |
| Denver | NA | 10 |
| Detroit | NA | 1 |
| Devens | NA | 1 |
| Dublin | NA | 2 |
| Dundee | NA | 1 |
| Durham, NC | NA | 2 |
| Edinburgh | NA | 1 |
| Florida | NA | 1 |
| France | NA | 1 |
| Frankfurt am Main | NA | 1 |
| Gaithersburg | NA | 1 |
| Gaithersburg, Maryland | NA | 1 |
| Georgia | NA | 2 |
| Germany | NA | 1 |
| Greater Boston | NA | 2 |
| Groton | NA | 1 |
| HOUSTON | NA | 1 |
| Halifax, Nova Scotia | NA | 1 |
| Hamburg | NA | 2 |
| Hartford, CT | NA | 1 |
| Hayward, CA | NA | 2 |
| Houston | NA | 6 |
| Houston, TX | NA | 5 |
| Houston,TX | NA | 2 |
| I-270 corridor | NA | 1 |
| Illinois | NA | 1 |
| Indianapolis | NA | 7 |
| Indianapolis, IN | NA | 1 |
| Ireland | NA | 3 |
| Irvine, CA | NA | 1 |
| Kansas City | NA | 4 |
| Kansas City, MO | NA | 1 |
| Kansas City, Missouri, United States | NA | 1 |
| Knoxville, TN | NA | 1 |
| LA | NA | 3 |
| La Jolla | NA | 1 |
| Limerick | NA | 1 |
| Liverpool | NA | 1 |
| Ljubljana | NA | 1 |
| London | NA | 5 |
| Los Angeles | NA | 34 |
| Los Angeles (company is in Bay) | NA | 1 |
| Los Angeles, CA | NA | 1 |
| Los Ángeles | NA | 1 |
| MVA | NA | 1 |
| Macclesfield, UK | NA | 1 |
| Madison | NA | 1 |
| Madison, Wisconsin | NA | 1 |
| Manchester | NA | 1 |
| Maryland | NA | 6 |
| Maryland/DC | NA | 1 |
| Melbourne | NA | 1 |
| Miami | NA | 3 |
| Michigan, USA | NA | 1 |
| Midwest | NA | 6 |
| Milwaukee | NA | 1 |
| Minneapolis | NA | 5 |
| Minneapolis, mn | NA | 1 |
| Minnesota | NA | 1 |
| Montreal | NA | 3 |
| Munich | NA | 2 |
| N/A | NA | 1 |
| NJ | NA | 5 |
| NJ-Philadelphia | NA | 1 |
| NJ-Philly-DE | NA | 1 |
| NRW | NA | 1 |
| NY | NA | 1 |
| NY Metro | NA | 1 |
| NY, NY | NA | 1 |
| NYC | NA | 17 |
| Near boston | NA | 1 |
| Netherlands | NA | 1 |
| New England, not Boston | NA | 1 |
| New Haven | NA | 2 |
| New Jersey | NA | 15 |
| New Jersey, USA | NA | 1 |
| New York | NA | 16 |
| New York City | NA | 11 |
| New York Metro Area | NA | 1 |
| New York metropolitan area | NA | 1 |
| Newark, CA | NA | 1 |
| North Carolina | NA | 1 |
| Nyc | NA | 1 |
| Nyc metro | NA | 1 |
| Oakland | NA | 1 |
| Ohio | NA | 2 |
| Oklahoma City | NA | 1 |
| Orange County | NA | 1 |
| Oxford | NA | 2 |
| PA | NA | 1 |
| Pacific NW | NA | 1 |
| Pennsylvania | NA | 1 |
| Philadelphia | NA | 59 |
| Philadelphia Metro Area | NA | 1 |
| Philadelphia metro | NA | 1 |
| Philadelphia, PA | NA | 2 |
| Phoenix | NA | 3 |
| Pittsburgh | NA | 3 |
| Portland | NA | 4 |
| Portland Oregon | NA | 1 |
| Portland, OR | NA | 3 |
| Porto | NA | 1 |
| Providence | NA | 1 |
| RDU | NA | 2 |
| RESEARCH TRIANGLE | NA | 1 |
| RTP | NA | 12 |
| RTP NC | NA | 1 |
| RTP North Carolina | NA | 1 |
| RTP, NC | NA | 3 |
| RTP, North Carolina | NA | 1 |
| RTP/Raleigh, NC | NA | 1 |
| Raleigh | NA | 2 |
| Raleigh, NC | NA | 1 |
| Raleigh-Durham | NA | 1 |
| Redwood City | NA | 1 |
| Rehovot | NA | 1 |
| Remote | NA | 3 |
| Remote, HQ in San Diego | NA | 1 |
| Remote, TX, US | NA | 1 |
| Remote, office based in Bay Area | NA | 1 |
| Research Triangle | NA | 41 |
| Research Triangle Park | NA | 1 |
| Research Triangle, NC | NA | 1 |
| Ridgefield, CT | NA | 1 |
| Rockville | NA | 1 |
| Rockville, MD | NA | 2 |
| SF Bay Area | NA | 5 |
| Sacramento | NA | 1 |
| Sacramento, CA | NA | 2 |
| Saint Louis | NA | 1 |
| Salt Lake City | NA | 7 |
| Salt Lake City, UT | NA | 1 |
| Salt lake city | NA | 1 |
| San Antonio | NA | 1 |
| San Antonio, texas | NA | 1 |
| San Diego | NA | 111 |
| San Diego, CA | NA | 3 |
| San Francisco | NA | 18 |
| San Francisco Bay Area | NA | 2 |
| San Francisco CA | NA | 1 |
| San Francisco ca | NA | 2 |
| San diego | NA | 3 |
| San francisco | NA | 2 |
| Santa Barbara | NA | 1 |
| Santa Barbara, CA | NA | 1 |
| Saxony | NA | 1 |
| Seattle | NA | 50 |
| Seattle Area | NA | 1 |
| Seattle WA | NA | 2 |
| Seattle but I work remotely from FL | NA | 1 |
| Seattle, WA | NA | 2 |
| Shanghai | NA | 1 |
| Shanghai/Chengdu | NA | 1 |
| SoCal | NA | 2 |
| South San Francisco | NA | 4 |
| Southeast | NA | 1 |
| St Louis | NA | 2 |
| St. Louis | NA | 4 |
| Stockholm | NA | 1 |
| Switzerland | NA | 3 |
| Syracuse | NA | 2 |
| Szeged | NA | 1 |
| Tampa | NA | 1 |
| Thessaloniki | NA | 1 |
| Thousand Oaks | NA | 2 |
| Thousand Oaks (Los Angeles) | NA | 1 |
| Thousand Oaks, CA | NA | 1 |
| Toronto | NA | 16 |
| Triangle | NA | 1 |
| Tucson, Az. | NA | 1 |
| U.K | NA | 2 |
| U.K. | NA | 1 |
| U.k. | NA | 1 |
| UK | NA | 3 |
| USA, ALBANY | NA | 1 |
| Uppsala | NA | 1 |
| Vancouver | NA | 6 |
| Vancouver, Canada | NA | 1 |
| Vienna | NA | 2 |
| Waco | NA | 1 |
| Waco, TX | NA | 1 |
| Waltham | NA | 1 |
| Washington D.C. Metro | NA | 1 |
| Washington DC | NA | 7 |
| Washington DC (Rockville MD) USA | NA | 1 |
| Washington DC Capitol Region | NA | 1 |
| Washington, DC | NA | 3 |
| Wilmington,DE | NA | 1 |
| Worcester MA | NA | 1 |
| bay area | NA | 6 |
| boston | NA | 2 |
| great lakes / Midwest | NA | 1 |
| nj | NA | 1 |
| philadelphia | NA | 1 |
| philly | NA | 2 |
| san diego | NA | 2 |
| seattle | NA | 1 |
| NA | Argentine | 1 |
| NA | Australia | 6 |
| NA | Austria | 2 |
| NA | Belgium | 9 |
| NA | Benelux | 1 |
| NA | Boston | 3 |
| NA | CO | 1 |
| NA | Canada | 49 |
| NA | Carolinas & Southeast (From NC to AR, South FL and LA) | 83 |
| NA | Central VA | 1 |
| NA | Chile | 1 |
| NA | DC Metro Area (DC, VA, MD, DE) | 79 |
| NA | Denmark | 4 |
| NA | EU | 2 |
| NA | Elkton, Virginia | 1 |
| NA | FRANCE | 1 |
| NA | Finland | 1 |
| NA | France | 7 |
| NA | Germany | 27 |
| NA | India | 1 |
| NA | Iraq | 1 |
| NA | Kenya | 1 |
| NA | Midwest (From OH to KS, North to ND) | 113 |
| NA | NL | 1 |
| NA | Netherlands | 3 |
| NA | New England (MA, CT, RI, NH, VT, ME) | 401 |
| NA | Other US Location (HI, AK, PR, etc.) | 1 |
| NA | Pharma Central (NY, NJ, PA) | 220 |
| NA | Poland | 2 |
| NA | Remote - US | 1 |
| NA | Research Triangle Park, NC | 1 |
| NA | San Diego | 1 |
| NA | Singapore | 2 |
| NA | South & Mountain West (TX to AZ, North to MT) | 58 |
| NA | Spain | 5 |
| NA | Spain (Barcelona) | 1 |
| NA | Sweden | 2 |
| NA | Switzerland | 10 |
| NA | Tennessee | 1 |
| NA | United Kingdom and Ireland | 62 |
| NA | West Coast (California & Pacific Northwest) | 396 |
| NA | australia | 1 |
| NA | denmark | 1 |
| NA | india | 1 |
| NA | russia | 1 |
| NA | spain | 1 |
| NA | NA | 671 |
sal_clean_filt |>
count(
where_are_you_located, what_country_do_you_work_in,
where_is_the_closest_major_city_or_hub) |>
gt()| where_are_you_located | what_country_do_you_work_in | where_is_the_closest_major_city_or_hub | n |
|---|---|---|---|
| Argentine | NA | NA | 1 |
| Australia | NA | NA | 6 |
| Austria | NA | NA | 2 |
| Belgium | NA | NA | 9 |
| Benelux | NA | NA | 1 |
| Boston | NA | NA | 3 |
| CO | NA | NA | 1 |
| Canada | NA | NA | 49 |
| Carolinas & Southeast (From NC to AR, South FL and LA) | NA | NA | 83 |
| Central VA | NA | NA | 1 |
| Chile | NA | NA | 1 |
| DC Metro Area (DC, VA, MD, DE) | NA | NA | 79 |
| Denmark | NA | NA | 4 |
| EU | NA | NA | 2 |
| Elkton, Virginia | NA | NA | 1 |
| FRANCE | NA | NA | 1 |
| Finland | NA | NA | 1 |
| France | NA | NA | 7 |
| Germany | NA | NA | 27 |
| India | NA | NA | 1 |
| Iraq | NA | NA | 1 |
| Kenya | NA | NA | 1 |
| Midwest (From OH to KS, North to ND) | NA | NA | 113 |
| NL | NA | NA | 1 |
| Netherlands | NA | NA | 3 |
| New England (MA, CT, RI, NH, VT, ME) | NA | NA | 401 |
| Other US Location (HI, AK, PR, etc.) | NA | NA | 1 |
| Pharma Central (NY, NJ, PA) | NA | NA | 220 |
| Poland | NA | NA | 2 |
| Remote - US | NA | NA | 1 |
| Research Triangle Park, NC | NA | NA | 1 |
| San Diego | NA | NA | 1 |
| Singapore | NA | NA | 2 |
| South & Mountain West (TX to AZ, North to MT) | NA | NA | 58 |
| Spain | NA | NA | 5 |
| Spain (Barcelona) | NA | NA | 1 |
| Sweden | NA | NA | 2 |
| Switzerland | NA | NA | 10 |
| Tennessee | NA | NA | 1 |
| United Kingdom and Ireland | NA | NA | 62 |
| West Coast (California & Pacific Northwest) | NA | NA | 396 |
| australia | NA | NA | 1 |
| denmark | NA | NA | 1 |
| india | NA | NA | 1 |
| russia | NA | NA | 1 |
| spain | NA | NA | 1 |
| NA | America | Bay Area | 1 |
| NA | America | Philadelphia | 1 |
| NA | Australia | Melbourne | 1 |
| NA | Austria | Vienna | 2 |
| NA | Canada | Montreal | 2 |
| NA | Canada | Toronto | 11 |
| NA | Canada | Vancouver | 6 |
| NA | China | Shanghai | 1 |
| NA | China | Shanghai/Chengdu | 1 |
| NA | England | London | 1 |
| NA | Germany | Berlin | 1 |
| NA | Germany | Cologne | 1 |
| NA | Germany | Frankfurt am Main | 1 |
| NA | Germany | Hamburg | 2 |
| NA | Germany | Munich | 1 |
| NA | Germany | N/A | 1 |
| NA | Germany | NRW | 1 |
| NA | Germany | Saxony | 1 |
| NA | Greece | Thessaloniki | 1 |
| NA | Hungary | Szeged | 1 |
| NA | India | Bangalore | 1 |
| NA | Ireland | Dublin | 1 |
| NA | Ireland | Limerick | 1 |
| NA | Netherlands | Amsterdam | 1 |
| NA | Portugal | Porto | 1 |
| NA | Slovenia | Ljubljana | 1 |
| NA | Sweden | Copenhagen | 1 |
| NA | Sweden | Stockholm | 1 |
| NA | Sweden | Uppsala | 1 |
| NA | Switzerland | Basel | 1 |
| NA | U.S.A. | Albany | 1 |
| NA | UK | Cambridge | 1 |
| NA | UK | Cambridge, UK | 2 |
| NA | UK | London | 1 |
| NA | US | Bay Area | 9 |
| NA | US | Bay area | 1 |
| NA | US | Boston | 11 |
| NA | US | Chicago | 2 |
| NA | US | Columbus | 1 |
| NA | US | DC | 1 |
| NA | US | DMV | 1 |
| NA | US | Denver | 1 |
| NA | US | Los Angeles | 1 |
| NA | US | Los Angeles, CA | 1 |
| NA | US | NJ | 2 |
| NA | US | NY Metro | 1 |
| NA | US | NYC | 5 |
| NA | US | New Jersey | 1 |
| NA | US | New York | 1 |
| NA | US | Philadelphia | 2 |
| NA | US | Remote, office based in Bay Area | 1 |
| NA | US | Research Triangle | 3 |
| NA | US | San Diego | 5 |
| NA | US | Seattle | 1 |
| NA | US | St. Louis | 1 |
| NA | US | Washington DC | 1 |
| NA | USA | Alachua | 1 |
| NA | USA | Austin (Remote) | 1 |
| NA | USA | BOSTON | 1 |
| NA | USA | Baltimore | 1 |
| NA | USA | Bay Area | 54 |
| NA | USA | Bay area | 7 |
| NA | USA | Bosto | 1 |
| NA | USA | Boston | 80 |
| NA | USA | Boston/Cambridge | 1 |
| NA | USA | Bosyon | 1 |
| NA | USA | Boulder | 1 |
| NA | USA | Buffalo | 1 |
| NA | USA | Chicago | 5 |
| NA | USA | Cleveland | 1 |
| NA | USA | Columbus | 1 |
| NA | USA | Columbus, OH | 1 |
| NA | USA | DC | 1 |
| NA | USA | DC Area/I-270 | 1 |
| NA | USA | Denver | 3 |
| NA | USA | Georgia | 1 |
| NA | USA | Houston | 2 |
| NA | USA | I-270 corridor | 1 |
| NA | USA | Indianapolis | 2 |
| NA | USA | Kansas City | 1 |
| NA | USA | LA | 1 |
| NA | USA | La Jolla | 1 |
| NA | USA | Los Angeles | 9 |
| NA | USA | Miami | 1 |
| NA | USA | Milwaukee | 1 |
| NA | USA | Minneapolis | 1 |
| NA | USA | NYC | 8 |
| NA | USA | New Haven | 1 |
| NA | USA | New Jersey | 2 |
| NA | USA | New York | 3 |
| NA | USA | New York City | 3 |
| NA | USA | Pacific NW | 1 |
| NA | USA | Philadelphia | 16 |
| NA | USA | Portland | 2 |
| NA | USA | RESEARCH TRIANGLE | 1 |
| NA | USA | RTP | 2 |
| NA | USA | RTP, NC | 1 |
| NA | USA | Remote | 1 |
| NA | USA | Research Triangle | 13 |
| NA | USA | Rockville, MD | 1 |
| NA | USA | SF Bay Area | 1 |
| NA | USA | Salt Lake City | 2 |
| NA | USA | San Diego | 26 |
| NA | USA | San Francisco | 4 |
| NA | USA | San diego | 1 |
| NA | USA | Seattle | 18 |
| NA | USA | SoCal | 1 |
| NA | USA | Southeast | 1 |
| NA | USA | St Louis | 1 |
| NA | USA | Syracuse | 1 |
| NA | USA | Thousand Oaks | 1 |
| NA | USA | Washington DC | 1 |
| NA | USA | Washington, DC | 1 |
| NA | USA | philadelphia | 1 |
| NA | United Kingdom | Cambridge | 2 |
| NA | United Kingdom | Edinburgh | 1 |
| NA | United Kingdom | Liverpool | 1 |
| NA | United Kingdom | London | 3 |
| NA | United Kingdom | Manchester | 1 |
| NA | United Kingdom | Oxford | 2 |
| NA | United Stated | Bay Area | 1 |
| NA | United Stated of Americ | Bay Area | 1 |
| NA | United States | Ann Arbor | 1 |
| NA | United States | Atlanta | 2 |
| NA | United States | Atlanta, GA | 1 |
| NA | United States | Bay Area | 44 |
| NA | United States | Bay area | 2 |
| NA | United States | Boston | 118 |
| NA | United States | Boston (company location) - company is WFH | 1 |
| NA | United States | Boston / Remote | 1 |
| NA | United States | CA | 1 |
| NA | United States | California | 1 |
| NA | United States | Cambridge | 2 |
| NA | United States | Chicago | 7 |
| NA | United States | Cleveland | 1 |
| NA | United States | Columbus, Ohio | 1 |
| NA | United States | Denver | 3 |
| NA | United States | Detroit | 1 |
| NA | United States | Gaithersburg | 1 |
| NA | United States | Groton | 1 |
| NA | United States | Hartford, CT | 1 |
| NA | United States | Houston | 2 |
| NA | United States | Indianapolis | 3 |
| NA | United States | Kansas City | 2 |
| NA | United States | LA | 1 |
| NA | United States | Los Angeles | 13 |
| NA | United States | Maryland/DC | 1 |
| NA | United States | Miami | 2 |
| NA | United States | Midwest | 4 |
| NA | United States | Minneapolis | 4 |
| NA | United States | Minneapolis, mn | 1 |
| NA | United States | NJ | 1 |
| NA | United States | NYC | 3 |
| NA | United States | New Jersey | 2 |
| NA | United States | New York | 6 |
| NA | United States | New York City | 5 |
| NA | United States | Nyc | 1 |
| NA | United States | Oklahoma City | 1 |
| NA | United States | Philadelphia | 23 |
| NA | United States | Phoenix | 1 |
| NA | United States | Pittsburgh | 1 |
| NA | United States | Portland | 2 |
| NA | United States | Portland, OR | 1 |
| NA | United States | Providence | 1 |
| NA | United States | Redwood City | 1 |
| NA | United States | Research Triangle | 20 |
| NA | United States | SF Bay Area | 1 |
| NA | United States | Salt Lake City | 3 |
| NA | United States | San Antonio | 1 |
| NA | United States | San Diego | 36 |
| NA | United States | San Francisco | 6 |
| NA | United States | San Francisco Bay Area | 1 |
| NA | United States | San diego | 1 |
| NA | United States | Seattle | 13 |
| NA | United States | St Louis | 1 |
| NA | United States | St. Louis | 1 |
| NA | United States | Syracuse | 1 |
| NA | United States | Tampa | 1 |
| NA | United States | Washington DC | 4 |
| NA | United States | Washington, DC | 2 |
| NA | United States | Wilmington,DE | 1 |
| NA | United States | bay area | 1 |
| NA | United States (Remote) | New Jersey | 1 |
| NA | United States - Florida | Boca Raton | 1 |
| NA | United States of America | Bay area | 1 |
| NA | United States of America | Boston | 5 |
| NA | United States of America | Madison | 1 |
| NA | United States of America | Philadelphia | 1 |
| NA | United States of America | Research Triangle | 3 |
| NA | United States of America | Salt Lake City | 1 |
| NA | United States of America | San Diego | 4 |
| NA | United States of america | Boston | 1 |
| NA | United states | Bay Are | 1 |
| NA | United states | Bay area | 2 |
| NA | United states | Boston | 1 |
| NA | United states | Cambridge, MA | 1 |
| NA | United states | Chicago | 1 |
| NA | United states | Denver | 1 |
| NA | United states | Philadelphia | 1 |
| NA | United states | Research Triangle | 2 |
| NA | United states | Washington DC Capitol Region | 1 |
| NA | Us | Boston | 1 |
| NA | Us | Seattle | 1 |
| NA | Usa | Bay Area | 2 |
| NA | Usa | Boston | 5 |
| NA | Usa | Philadelphia | 2 |
| NA | Usa | Portland Oregon | 1 |
| NA | Usa | Salt lake city | 1 |
| NA | Usa | San Diego | 1 |
| NA | Usa | philly | 2 |
| NA | united states | DC | 1 |
| NA | united states | Philadelphia | 1 |
| NA | united states | Washington D.C. Metro | 1 |
| NA | usa | bay area | 2 |
| NA | usa | boston | 2 |
| NA | usa | great lakes / Midwest | 1 |
| NA | usa | san diego | 1 |
| NA | usa | seattle | 1 |
| NA | NA | Ames, IA NADC | 1 |
| NA | NA | Athens | 1 |
| NA | NA | Austin, TX | 2 |
| NA | NA | Austria | 1 |
| NA | NA | Baltimore | 1 |
| NA | NA | Baltimore, MD | 2 |
| NA | NA | Baltimore/Washington DC | 1 |
| NA | NA | Bay Area | 73 |
| NA | NA | Bay Area - Mid Penninsula | 1 |
| NA | NA | Bay area | 8 |
| NA | NA | Belgium | 1 |
| NA | NA | Berkeley, CA | 1 |
| NA | NA | Bloomington, IN, USA | 1 |
| NA | NA | Boston | 134 |
| NA | NA | Boston MA | 1 |
| NA | NA | Boston, MA | 4 |
| NA | NA | Boston, US | 1 |
| NA | NA | Boston/Cambridge | 1 |
| NA | NA | Boulder (remote, company in Bay) | 1 |
| NA | NA | Boulder, CO | 3 |
| NA | NA | Brazil | 1 |
| NA | NA | Brighton, UK | 1 |
| NA | NA | CA | 1 |
| NA | NA | Calgary | 1 |
| NA | NA | California | 1 |
| NA | NA | Cambridge | 2 |
| NA | NA | Cambridge Massachusetts | 1 |
| NA | NA | Cambridge UK | 1 |
| NA | NA | Cambridge, MA | 6 |
| NA | NA | Cambridge, UK | 1 |
| NA | NA | Cambridge,MA | 1 |
| NA | NA | Canada | 6 |
| NA | NA | Canada, Ottawa | 1 |
| NA | NA | Carlsbad | 1 |
| NA | NA | Central Maine | 1 |
| NA | NA | Central/North Jersey | 1 |
| NA | NA | Chicago | 3 |
| NA | NA | Chicago IL | 1 |
| NA | NA | Chicago, IL | 1 |
| NA | NA | Chicagoland | 1 |
| NA | NA | Cincinnati, Ohio | 1 |
| NA | NA | College Station TX | 1 |
| NA | NA | Colorado | 1 |
| NA | NA | DC | 2 |
| NA | NA | DC area | 1 |
| NA | NA | Dc | 1 |
| NA | NA | Denver | 2 |
| NA | NA | Devens | 1 |
| NA | NA | Dublin | 1 |
| NA | NA | Dundee | 1 |
| NA | NA | Durham, NC | 2 |
| NA | NA | Florida | 1 |
| NA | NA | France | 1 |
| NA | NA | Gaithersburg, Maryland | 1 |
| NA | NA | Georgia | 1 |
| NA | NA | Germany | 1 |
| NA | NA | Greater Boston | 2 |
| NA | NA | HOUSTON | 1 |
| NA | NA | Halifax, Nova Scotia | 1 |
| NA | NA | Hayward, CA | 2 |
| NA | NA | Houston | 2 |
| NA | NA | Houston, TX | 5 |
| NA | NA | Houston,TX | 2 |
| NA | NA | Illinois | 1 |
| NA | NA | Indianapolis | 2 |
| NA | NA | Indianapolis, IN | 1 |
| NA | NA | Ireland | 3 |
| NA | NA | Irvine, CA | 1 |
| NA | NA | Kansas City | 1 |
| NA | NA | Kansas City, MO | 1 |
| NA | NA | Kansas City, Missouri, United States | 1 |
| NA | NA | Knoxville, TN | 1 |
| NA | NA | LA | 1 |
| NA | NA | Los Angeles | 11 |
| NA | NA | Los Angeles (company is in Bay) | 1 |
| NA | NA | Los Ángeles | 1 |
| NA | NA | MVA | 1 |
| NA | NA | Macclesfield, UK | 1 |
| NA | NA | Madison, Wisconsin | 1 |
| NA | NA | Maryland | 6 |
| NA | NA | Michigan, USA | 1 |
| NA | NA | Midwest | 2 |
| NA | NA | Minnesota | 1 |
| NA | NA | Montreal | 1 |
| NA | NA | Munich | 1 |
| NA | NA | NJ | 2 |
| NA | NA | NJ-Philadelphia | 1 |
| NA | NA | NJ-Philly-DE | 1 |
| NA | NA | NY | 1 |
| NA | NA | NY, NY | 1 |
| NA | NA | NYC | 1 |
| NA | NA | Near boston | 1 |
| NA | NA | Netherlands | 1 |
| NA | NA | New England, not Boston | 1 |
| NA | NA | New Haven | 1 |
| NA | NA | New Jersey | 9 |
| NA | NA | New Jersey, USA | 1 |
| NA | NA | New York | 6 |
| NA | NA | New York City | 3 |
| NA | NA | New York Metro Area | 1 |
| NA | NA | New York metropolitan area | 1 |
| NA | NA | Newark, CA | 1 |
| NA | NA | North Carolina | 1 |
| NA | NA | Nyc metro | 1 |
| NA | NA | Oakland | 1 |
| NA | NA | Ohio | 2 |
| NA | NA | Orange County | 1 |
| NA | NA | PA | 1 |
| NA | NA | Pennsylvania | 1 |
| NA | NA | Philadelphia | 12 |
| NA | NA | Philadelphia Metro Area | 1 |
| NA | NA | Philadelphia metro | 1 |
| NA | NA | Philadelphia, PA | 2 |
| NA | NA | Phoenix | 2 |
| NA | NA | Pittsburgh | 2 |
| NA | NA | Portland, OR | 2 |
| NA | NA | RDU | 2 |
| NA | NA | RTP | 10 |
| NA | NA | RTP NC | 1 |
| NA | NA | RTP North Carolina | 1 |
| NA | NA | RTP, NC | 2 |
| NA | NA | RTP, North Carolina | 1 |
| NA | NA | RTP/Raleigh, NC | 1 |
| NA | NA | Raleigh | 2 |
| NA | NA | Raleigh, NC | 1 |
| NA | NA | Raleigh-Durham | 1 |
| NA | NA | Rehovot | 1 |
| NA | NA | Remote | 2 |
| NA | NA | Remote, HQ in San Diego | 1 |
| NA | NA | Remote, TX, US | 1 |
| NA | NA | Research Triangle Park | 1 |
| NA | NA | Research Triangle, NC | 1 |
| NA | NA | Ridgefield, CT | 1 |
| NA | NA | Rockville | 1 |
| NA | NA | Rockville, MD | 1 |
| NA | NA | SF Bay Area | 3 |
| NA | NA | Sacramento | 1 |
| NA | NA | Sacramento, CA | 2 |
| NA | NA | Saint Louis | 1 |
| NA | NA | Salt Lake City | 1 |
| NA | NA | Salt Lake City, UT | 1 |
| NA | NA | San Antonio, texas | 1 |
| NA | NA | San Diego | 39 |
| NA | NA | San Diego, CA | 3 |
| NA | NA | San Francisco | 8 |
| NA | NA | San Francisco Bay Area | 1 |
| NA | NA | San Francisco CA | 1 |
| NA | NA | San Francisco ca | 2 |
| NA | NA | San diego | 1 |
| NA | NA | San francisco | 2 |
| NA | NA | Santa Barbara | 1 |
| NA | NA | Santa Barbara, CA | 1 |
| NA | NA | Seattle | 17 |
| NA | NA | Seattle Area | 1 |
| NA | NA | Seattle WA | 2 |
| NA | NA | Seattle but I work remotely from FL | 1 |
| NA | NA | Seattle, WA | 2 |
| NA | NA | SoCal | 1 |
| NA | NA | South San Francisco | 4 |
| NA | NA | St. Louis | 2 |
| NA | NA | Switzerland | 3 |
| NA | NA | Thousand Oaks | 1 |
| NA | NA | Thousand Oaks (Los Angeles) | 1 |
| NA | NA | Thousand Oaks, CA | 1 |
| NA | NA | Toronto | 5 |
| NA | NA | Triangle | 1 |
| NA | NA | Tucson, Az. | 1 |
| NA | NA | U.K | 2 |
| NA | NA | U.K. | 1 |
| NA | NA | U.k. | 1 |
| NA | NA | UK | 3 |
| NA | NA | USA, ALBANY | 1 |
| NA | NA | Vancouver, Canada | 1 |
| NA | NA | Waco | 1 |
| NA | NA | Waco, TX | 1 |
| NA | NA | Waltham | 1 |
| NA | NA | Washington DC | 1 |
| NA | NA | Washington DC (Rockville MD) USA | 1 |
| NA | NA | Worcester MA | 1 |
| NA | NA | bay area | 3 |
| NA | NA | nj | 1 |
| NA | NA | san diego | 1 |
| NA | NA | NA | 671 |
Location is quite messy data consisting of free-form responses spread between 3 variables:
where_are_you_locatedcontains Countries (USA, Canada, etc.), states (CO, Carolinas, etc.)
This is not a required question so there are 174 responses that did not respond, and this also included an “other” freeform option.
- Pharma Central (NY, NJ, PA)
- New England (MA, CT, RI, NH, VT, ME)
- DC Metro Area (DC, VA, MD, DE)
- Carolinas & Southeast (From NC to AR, South FL and LA)
- Midwest (From OH to KS, North to ND)
- South & Mountain West (TX to AZ, North to MT)
- West Coast (California & Pacific Northwest)
- Other US Location (HI, AK, PR, etc.)
- Canada
- United Kingdom and Ireland
- Germany
- Other:
The 174 missing responses have values for these other two variables. I think this is due to the survey changing the question at some point, probably due to feedback.
what_country_do_you_work_inseems to all be countries, but not harmonized e.g.: Usa, usa, United Stateswhere_is_the_closest_major_city_or_hubmixed bag: Bay area, Boston, san diego, Research Triangle,
Here I create one new location variable location_country, that, unlike the other variables, cover >90% of the data
USA <- c(
"Pharma Central (NY, NJ, PA)",
"New England (MA, CT, RI, NH, VT, ME)",
"DC Metro Area (DC, VA, MD, DE)",
"Carolinas & Southeast (From NC to AR, South FL and LA)",
"Midwest (From OH to KS, North to ND)",
"South & Mountain West (TX to AZ, North to MT)",
"West Coast (California & Pacific Northwest)",
"Other US Location (HI, AK, PR, etc.)"
)
usa_locations <- c(
'baltimore',
'boston',
'bay area',
'baltimore',
'boulder',
'california',
'cambridge',
'carlsbad',
'jersey', 'maine',
'chicago',
'\\bdc\\b',
'denver',
'durham',
'hayward',
'hungary',
'indianapolis',
'kansas',
'\\bla\\b',
'los angeles',
'maryland',
'midwest',
'new york',
'\\bnj\\b',
'\\bny[c]?\\b',
'ohio',
'philadelphia',
'phoenix',
'pittsburgh',
'raleigh',
'san diego',
'ridgefield',
'rockville',
'rtp\\b',
'sacramento',
'salt lake',
'seattle',
'san francisco',
'socal',
'united states',
'u\\.s\\.a',
'washington'
)
usa_locations <- str_c('(', str_c(usa_locations, collapse = '|'), ')')
sal_clean_filt <- sal_clean_filt |>
mutate(
# prep raw location data
location_raw_data = case_when(
!is.na(what_country_do_you_work_in) ~ what_country_do_you_work_in,
!is.na(where_are_you_located) ~ where_are_you_located,
!is.na(where_is_the_closest_major_city_or_hub) ~ where_is_the_closest_major_city_or_hub,
.default = where_is_the_closest_major_city_or_hub
) |>
tolower(),
# matching
location_country = case_when(
### 2025 data
!is.na(which_country_do_you_work_in) ~ which_country_do_you_work_in |>
str_replace('United States', 'United States of America'),
### usa
location_raw_data %in% c(
tolower(USA),
'co',
'san diego, ca',
'united states'
) ~ 'United States of America',
location_raw_data |>
str_detect(regex('usa?', ignore_case = TRUE)) ~
'United States of America',
location_raw_data |>
str_detect(regex(usa_locations, ignore_case = TRUE), ) ~
'United States of America',
### UK
location_raw_data |>
str_detect('(u\\.k|dundee|ireland)') ~ 'United Kingdom',
location_raw_data %in% c(
'united kingdom and ireland', 'uk') ~ 'United Kingdom',
### other
location_raw_data %in%
c('canada', 'united states of america',
'belgium', 'france', 'spain', 'united kingdom', 'benelux', 'germany',
'sweden', 'switzerland', 'denmark',
'singapore', 'india', 'australia') ~ location_raw_data,
location_raw_data |> str_detect('toronto') ~ 'Canada',
TRUE ~ NA
),
location_country = str_to_title(location_country)
)
sal_clean_filt |>
count(location_country, location_raw_data,
where_are_you_located, what_country_do_you_work_in,
where_is_the_closest_major_city_or_hub) |>
arrange(location_country) |> gt()| location_country | location_raw_data | where_are_you_located | what_country_do_you_work_in | where_is_the_closest_major_city_or_hub | n |
|---|---|---|---|---|---|
| Australia | NA | NA | NA | NA | 3 |
| Belgium | belgium | Belgium | NA | NA | 9 |
| Belgium | belgium | NA | NA | Belgium | 1 |
| Belgium | NA | NA | NA | NA | 1 |
| Benelux | benelux | Benelux | NA | NA | 1 |
| Canada | canada | Canada | NA | NA | 49 |
| Canada | canada | NA | Canada | Montreal | 2 |
| Canada | canada | NA | Canada | Toronto | 11 |
| Canada | canada | NA | Canada | Vancouver | 6 |
| Canada | canada | NA | NA | Canada | 6 |
| Canada | toronto | NA | NA | Toronto | 5 |
| Canada | NA | NA | NA | NA | 21 |
| Denmark | denmark | Denmark | NA | NA | 4 |
| Denmark | denmark | denmark | NA | NA | 1 |
| Denmark | NA | NA | NA | NA | 2 |
| Egypt | NA | NA | NA | NA | 1 |
| France | france | FRANCE | NA | NA | 1 |
| France | france | France | NA | NA | 7 |
| France | france | NA | NA | France | 1 |
| France | NA | NA | NA | NA | 2 |
| Germany | germany | Germany | NA | NA | 27 |
| Germany | germany | NA | Germany | Berlin | 1 |
| Germany | germany | NA | Germany | Cologne | 1 |
| Germany | germany | NA | Germany | Frankfurt am Main | 1 |
| Germany | germany | NA | Germany | Hamburg | 2 |
| Germany | germany | NA | Germany | Munich | 1 |
| Germany | germany | NA | Germany | N/A | 1 |
| Germany | germany | NA | Germany | NRW | 1 |
| Germany | germany | NA | Germany | Saxony | 1 |
| Germany | germany | NA | NA | Germany | 1 |
| Germany | NA | NA | NA | NA | 3 |
| India | india | India | NA | NA | 1 |
| India | india | india | NA | NA | 1 |
| India | india | NA | India | Bangalore | 1 |
| India | NA | NA | NA | NA | 2 |
| Ireland | NA | NA | NA | NA | 3 |
| Italy | NA | NA | NA | NA | 1 |
| Lithuania | NA | NA | NA | NA | 1 |
| Netherlands | NA | NA | NA | NA | 1 |
| Singapore | singapore | Singapore | NA | NA | 2 |
| Singapore | NA | NA | NA | NA | 1 |
| Slovenia | NA | NA | NA | NA | 1 |
| Spain | spain | Spain | NA | NA | 5 |
| Spain | spain | spain | NA | NA | 1 |
| Spain | NA | NA | NA | NA | 1 |
| Sweden | sweden | Sweden | NA | NA | 2 |
| Sweden | sweden | NA | Sweden | Copenhagen | 1 |
| Sweden | sweden | NA | Sweden | Stockholm | 1 |
| Sweden | sweden | NA | Sweden | Uppsala | 1 |
| Sweden | NA | NA | NA | NA | 1 |
| Switzerland | switzerland | Switzerland | NA | NA | 10 |
| Switzerland | switzerland | NA | Switzerland | Basel | 1 |
| Switzerland | switzerland | NA | NA | Switzerland | 3 |
| Switzerland | NA | NA | NA | NA | 13 |
| United Kingdom | dundee | NA | NA | Dundee | 1 |
| United Kingdom | ireland | NA | Ireland | Dublin | 1 |
| United Kingdom | ireland | NA | Ireland | Limerick | 1 |
| United Kingdom | ireland | NA | NA | Ireland | 3 |
| United Kingdom | u.k | NA | NA | U.K | 2 |
| United Kingdom | u.k. | NA | NA | U.K. | 1 |
| United Kingdom | u.k. | NA | NA | U.k. | 1 |
| United Kingdom | uk | NA | UK | Cambridge | 1 |
| United Kingdom | uk | NA | UK | Cambridge, UK | 2 |
| United Kingdom | uk | NA | UK | London | 1 |
| United Kingdom | uk | NA | NA | UK | 3 |
| United Kingdom | united kingdom | NA | United Kingdom | Cambridge | 2 |
| United Kingdom | united kingdom | NA | United Kingdom | Edinburgh | 1 |
| United Kingdom | united kingdom | NA | United Kingdom | Liverpool | 1 |
| United Kingdom | united kingdom | NA | United Kingdom | London | 3 |
| United Kingdom | united kingdom | NA | United Kingdom | Manchester | 1 |
| United Kingdom | united kingdom | NA | United Kingdom | Oxford | 2 |
| United Kingdom | united kingdom and ireland | United Kingdom and Ireland | NA | NA | 62 |
| United Kingdom | NA | NA | NA | NA | 29 |
| United States Of America | austin, tx | NA | NA | Austin, TX | 2 |
| United States Of America | australia | Australia | NA | NA | 6 |
| United States Of America | australia | australia | NA | NA | 1 |
| United States Of America | australia | NA | Australia | Melbourne | 1 |
| United States Of America | austria | Austria | NA | NA | 2 |
| United States Of America | austria | NA | Austria | Vienna | 2 |
| United States Of America | austria | NA | NA | Austria | 1 |
| United States Of America | baltimore | NA | NA | Baltimore | 1 |
| United States Of America | baltimore, md | NA | NA | Baltimore, MD | 2 |
| United States Of America | baltimore/washington dc | NA | NA | Baltimore/Washington DC | 1 |
| United States Of America | bay area | NA | NA | Bay Area | 73 |
| United States Of America | bay area | NA | NA | Bay area | 8 |
| United States Of America | bay area | NA | NA | bay area | 3 |
| United States Of America | bay area - mid penninsula | NA | NA | Bay Area - Mid Penninsula | 1 |
| United States Of America | bloomington, in, usa | NA | NA | Bloomington, IN, USA | 1 |
| United States Of America | boston | Boston | NA | NA | 3 |
| United States Of America | boston | NA | NA | Boston | 134 |
| United States Of America | boston ma | NA | NA | Boston MA | 1 |
| United States Of America | boston, ma | NA | NA | Boston, MA | 4 |
| United States Of America | boston, us | NA | NA | Boston, US | 1 |
| United States Of America | boston/cambridge | NA | NA | Boston/Cambridge | 1 |
| United States Of America | boulder (remote, company in bay) | NA | NA | Boulder (remote, company in Bay) | 1 |
| United States Of America | boulder, co | NA | NA | Boulder, CO | 3 |
| United States Of America | california | NA | NA | California | 1 |
| United States Of America | cambridge | NA | NA | Cambridge | 2 |
| United States Of America | cambridge massachusetts | NA | NA | Cambridge Massachusetts | 1 |
| United States Of America | cambridge uk | NA | NA | Cambridge UK | 1 |
| United States Of America | cambridge, ma | NA | NA | Cambridge, MA | 6 |
| United States Of America | cambridge, uk | NA | NA | Cambridge, UK | 1 |
| United States Of America | cambridge,ma | NA | NA | Cambridge,MA | 1 |
| United States Of America | carlsbad | NA | NA | Carlsbad | 1 |
| United States Of America | carolinas & southeast (from nc to ar, south fl and la) | Carolinas & Southeast (From NC to AR, South FL and LA) | NA | NA | 83 |
| United States Of America | central maine | NA | NA | Central Maine | 1 |
| United States Of America | central/north jersey | NA | NA | Central/North Jersey | 1 |
| United States Of America | chicago | NA | NA | Chicago | 3 |
| United States Of America | chicago il | NA | NA | Chicago IL | 1 |
| United States Of America | chicago, il | NA | NA | Chicago, IL | 1 |
| United States Of America | chicagoland | NA | NA | Chicagoland | 1 |
| United States Of America | cincinnati, ohio | NA | NA | Cincinnati, Ohio | 1 |
| United States Of America | co | CO | NA | NA | 1 |
| United States Of America | dc | NA | NA | DC | 2 |
| United States Of America | dc | NA | NA | Dc | 1 |
| United States Of America | dc area | NA | NA | DC area | 1 |
| United States Of America | dc metro area (dc, va, md, de) | DC Metro Area (DC, VA, MD, DE) | NA | NA | 79 |
| United States Of America | denver | NA | NA | Denver | 2 |
| United States Of America | durham, nc | NA | NA | Durham, NC | 2 |
| United States Of America | gaithersburg, maryland | NA | NA | Gaithersburg, Maryland | 1 |
| United States Of America | greater boston | NA | NA | Greater Boston | 2 |
| United States Of America | hayward, ca | NA | NA | Hayward, CA | 2 |
| United States Of America | houston | NA | NA | HOUSTON | 1 |
| United States Of America | houston | NA | NA | Houston | 2 |
| United States Of America | houston, tx | NA | NA | Houston, TX | 5 |
| United States Of America | houston,tx | NA | NA | Houston,TX | 2 |
| United States Of America | hungary | NA | Hungary | Szeged | 1 |
| United States Of America | indianapolis | NA | NA | Indianapolis | 2 |
| United States Of America | indianapolis, in | NA | NA | Indianapolis, IN | 1 |
| United States Of America | kansas city | NA | NA | Kansas City | 1 |
| United States Of America | kansas city, missouri, united states | NA | NA | Kansas City, Missouri, United States | 1 |
| United States Of America | kansas city, mo | NA | NA | Kansas City, MO | 1 |
| United States Of America | la | NA | NA | LA | 1 |
| United States Of America | los angeles | NA | NA | Los Angeles | 11 |
| United States Of America | los angeles (company is in bay) | NA | NA | Los Angeles (company is in Bay) | 1 |
| United States Of America | maryland | NA | NA | Maryland | 6 |
| United States Of America | michigan, usa | NA | NA | Michigan, USA | 1 |
| United States Of America | midwest | NA | NA | Midwest | 2 |
| United States Of America | midwest (from oh to ks, north to nd) | Midwest (From OH to KS, North to ND) | NA | NA | 113 |
| United States Of America | near boston | NA | NA | Near boston | 1 |
| United States Of America | new england (ma, ct, ri, nh, vt, me) | New England (MA, CT, RI, NH, VT, ME) | NA | NA | 401 |
| United States Of America | new england, not boston | NA | NA | New England, not Boston | 1 |
| United States Of America | new jersey | NA | NA | New Jersey | 9 |
| United States Of America | new jersey, usa | NA | NA | New Jersey, USA | 1 |
| United States Of America | new york | NA | NA | New York | 6 |
| United States Of America | new york city | NA | NA | New York City | 3 |
| United States Of America | new york metro area | NA | NA | New York Metro Area | 1 |
| United States Of America | new york metropolitan area | NA | NA | New York metropolitan area | 1 |
| United States Of America | nj | NA | NA | NJ | 2 |
| United States Of America | nj | NA | NA | nj | 1 |
| United States Of America | nj-philadelphia | NA | NA | NJ-Philadelphia | 1 |
| United States Of America | nj-philly-de | NA | NA | NJ-Philly-DE | 1 |
| United States Of America | ny | NA | NA | NY | 1 |
| United States Of America | ny, ny | NA | NA | NY, NY | 1 |
| United States Of America | nyc | NA | NA | NYC | 1 |
| United States Of America | nyc metro | NA | NA | Nyc metro | 1 |
| United States Of America | ohio | NA | NA | Ohio | 2 |
| United States Of America | other us location (hi, ak, pr, etc.) | Other US Location (HI, AK, PR, etc.) | NA | NA | 1 |
| United States Of America | pharma central (ny, nj, pa) | Pharma Central (NY, NJ, PA) | NA | NA | 220 |
| United States Of America | philadelphia | NA | NA | Philadelphia | 12 |
| United States Of America | philadelphia metro | NA | NA | Philadelphia metro | 1 |
| United States Of America | philadelphia metro area | NA | NA | Philadelphia Metro Area | 1 |
| United States Of America | philadelphia, pa | NA | NA | Philadelphia, PA | 2 |
| United States Of America | phoenix | NA | NA | Phoenix | 2 |
| United States Of America | pittsburgh | NA | NA | Pittsburgh | 2 |
| United States Of America | raleigh | NA | NA | Raleigh | 2 |
| United States Of America | raleigh, nc | NA | NA | Raleigh, NC | 1 |
| United States Of America | raleigh-durham | NA | NA | Raleigh-Durham | 1 |
| United States Of America | remote - us | Remote - US | NA | NA | 1 |
| United States Of America | remote, hq in san diego | NA | NA | Remote, HQ in San Diego | 1 |
| United States Of America | remote, tx, us | NA | NA | Remote, TX, US | 1 |
| United States Of America | ridgefield, ct | NA | NA | Ridgefield, CT | 1 |
| United States Of America | rockville | NA | NA | Rockville | 1 |
| United States Of America | rockville, md | NA | NA | Rockville, MD | 1 |
| United States Of America | rtp | NA | NA | RTP | 10 |
| United States Of America | rtp nc | NA | NA | RTP NC | 1 |
| United States Of America | rtp north carolina | NA | NA | RTP North Carolina | 1 |
| United States Of America | rtp, nc | NA | NA | RTP, NC | 2 |
| United States Of America | rtp, north carolina | NA | NA | RTP, North Carolina | 1 |
| United States Of America | rtp/raleigh, nc | NA | NA | RTP/Raleigh, NC | 1 |
| United States Of America | russia | russia | NA | NA | 1 |
| United States Of America | sacramento | NA | NA | Sacramento | 1 |
| United States Of America | sacramento, ca | NA | NA | Sacramento, CA | 2 |
| United States Of America | salt lake city | NA | NA | Salt Lake City | 1 |
| United States Of America | salt lake city, ut | NA | NA | Salt Lake City, UT | 1 |
| United States Of America | san diego | San Diego | NA | NA | 1 |
| United States Of America | san diego | NA | NA | San Diego | 39 |
| United States Of America | san diego | NA | NA | San diego | 1 |
| United States Of America | san diego | NA | NA | san diego | 1 |
| United States Of America | san diego, ca | NA | NA | San Diego, CA | 3 |
| United States Of America | san francisco | NA | NA | San Francisco | 8 |
| United States Of America | san francisco | NA | NA | San francisco | 2 |
| United States Of America | san francisco bay area | NA | NA | San Francisco Bay Area | 1 |
| United States Of America | san francisco ca | NA | NA | San Francisco CA | 1 |
| United States Of America | san francisco ca | NA | NA | San Francisco ca | 2 |
| United States Of America | seattle | NA | NA | Seattle | 17 |
| United States Of America | seattle area | NA | NA | Seattle Area | 1 |
| United States Of America | seattle but i work remotely from fl | NA | NA | Seattle but I work remotely from FL | 1 |
| United States Of America | seattle wa | NA | NA | Seattle WA | 2 |
| United States Of America | seattle, wa | NA | NA | Seattle, WA | 2 |
| United States Of America | sf bay area | NA | NA | SF Bay Area | 3 |
| United States Of America | socal | NA | NA | SoCal | 1 |
| United States Of America | south & mountain west (tx to az, north to mt) | South & Mountain West (TX to AZ, North to MT) | NA | NA | 58 |
| United States Of America | south san francisco | NA | NA | South San Francisco | 4 |
| United States Of America | thousand oaks | NA | NA | Thousand Oaks | 1 |
| United States Of America | thousand oaks (los angeles) | NA | NA | Thousand Oaks (Los Angeles) | 1 |
| United States Of America | thousand oaks, ca | NA | NA | Thousand Oaks, CA | 1 |
| United States Of America | u.s.a. | NA | U.S.A. | Albany | 1 |
| United States Of America | united states | NA | United States | Ann Arbor | 1 |
| United States Of America | united states | NA | United States | Atlanta | 2 |
| United States Of America | united states | NA | United States | Atlanta, GA | 1 |
| United States Of America | united states | NA | United States | Bay Area | 44 |
| United States Of America | united states | NA | United States | Bay area | 2 |
| United States Of America | united states | NA | United States | Boston | 118 |
| United States Of America | united states | NA | United States | Boston (company location) - company is WFH | 1 |
| United States Of America | united states | NA | United States | Boston / Remote | 1 |
| United States Of America | united states | NA | United States | CA | 1 |
| United States Of America | united states | NA | United States | California | 1 |
| United States Of America | united states | NA | United States | Cambridge | 2 |
| United States Of America | united states | NA | United States | Chicago | 7 |
| United States Of America | united states | NA | United States | Cleveland | 1 |
| United States Of America | united states | NA | United States | Columbus, Ohio | 1 |
| United States Of America | united states | NA | United States | Denver | 3 |
| United States Of America | united states | NA | United States | Detroit | 1 |
| United States Of America | united states | NA | United States | Gaithersburg | 1 |
| United States Of America | united states | NA | United States | Groton | 1 |
| United States Of America | united states | NA | United States | Hartford, CT | 1 |
| United States Of America | united states | NA | United States | Houston | 2 |
| United States Of America | united states | NA | United States | Indianapolis | 3 |
| United States Of America | united states | NA | United States | Kansas City | 2 |
| United States Of America | united states | NA | United States | LA | 1 |
| United States Of America | united states | NA | United States | Los Angeles | 13 |
| United States Of America | united states | NA | United States | Maryland/DC | 1 |
| United States Of America | united states | NA | United States | Miami | 2 |
| United States Of America | united states | NA | United States | Midwest | 4 |
| United States Of America | united states | NA | United States | Minneapolis | 4 |
| United States Of America | united states | NA | United States | Minneapolis, mn | 1 |
| United States Of America | united states | NA | United States | NJ | 1 |
| United States Of America | united states | NA | United States | NYC | 3 |
| United States Of America | united states | NA | United States | New Jersey | 2 |
| United States Of America | united states | NA | United States | New York | 6 |
| United States Of America | united states | NA | United States | New York City | 5 |
| United States Of America | united states | NA | United States | Nyc | 1 |
| United States Of America | united states | NA | United States | Oklahoma City | 1 |
| United States Of America | united states | NA | United States | Philadelphia | 23 |
| United States Of America | united states | NA | United States | Phoenix | 1 |
| United States Of America | united states | NA | United States | Pittsburgh | 1 |
| United States Of America | united states | NA | United States | Portland | 2 |
| United States Of America | united states | NA | United States | Portland, OR | 1 |
| United States Of America | united states | NA | United States | Providence | 1 |
| United States Of America | united states | NA | United States | Redwood City | 1 |
| United States Of America | united states | NA | United States | Research Triangle | 20 |
| United States Of America | united states | NA | United States | SF Bay Area | 1 |
| United States Of America | united states | NA | United States | Salt Lake City | 3 |
| United States Of America | united states | NA | United States | San Antonio | 1 |
| United States Of America | united states | NA | United States | San Diego | 36 |
| United States Of America | united states | NA | United States | San Francisco | 6 |
| United States Of America | united states | NA | United States | San Francisco Bay Area | 1 |
| United States Of America | united states | NA | United States | San diego | 1 |
| United States Of America | united states | NA | United States | Seattle | 13 |
| United States Of America | united states | NA | United States | St Louis | 1 |
| United States Of America | united states | NA | United States | St. Louis | 1 |
| United States Of America | united states | NA | United States | Syracuse | 1 |
| United States Of America | united states | NA | United States | Tampa | 1 |
| United States Of America | united states | NA | United States | Washington DC | 4 |
| United States Of America | united states | NA | United States | Washington, DC | 2 |
| United States Of America | united states | NA | United States | Wilmington,DE | 1 |
| United States Of America | united states | NA | United States | bay area | 1 |
| United States Of America | united states | NA | United states | Bay Are | 1 |
| United States Of America | united states | NA | United states | Bay area | 2 |
| United States Of America | united states | NA | United states | Boston | 1 |
| United States Of America | united states | NA | United states | Cambridge, MA | 1 |
| United States Of America | united states | NA | United states | Chicago | 1 |
| United States Of America | united states | NA | United states | Denver | 1 |
| United States Of America | united states | NA | United states | Philadelphia | 1 |
| United States Of America | united states | NA | United states | Research Triangle | 2 |
| United States Of America | united states | NA | United states | Washington DC Capitol Region | 1 |
| United States Of America | united states | NA | united states | DC | 1 |
| United States Of America | united states | NA | united states | Philadelphia | 1 |
| United States Of America | united states | NA | united states | Washington D.C. Metro | 1 |
| United States Of America | united states (remote) | NA | United States (Remote) | New Jersey | 1 |
| United States Of America | united states - florida | NA | United States - Florida | Boca Raton | 1 |
| United States Of America | united states of america | NA | United States of America | Bay area | 1 |
| United States Of America | united states of america | NA | United States of America | Boston | 5 |
| United States Of America | united states of america | NA | United States of America | Madison | 1 |
| United States Of America | united states of america | NA | United States of America | Philadelphia | 1 |
| United States Of America | united states of america | NA | United States of America | Research Triangle | 3 |
| United States Of America | united states of america | NA | United States of America | Salt Lake City | 1 |
| United States Of America | united states of america | NA | United States of America | San Diego | 4 |
| United States Of America | united states of america | NA | United States of america | Boston | 1 |
| United States Of America | us | NA | US | Bay Area | 9 |
| United States Of America | us | NA | US | Bay area | 1 |
| United States Of America | us | NA | US | Boston | 11 |
| United States Of America | us | NA | US | Chicago | 2 |
| United States Of America | us | NA | US | Columbus | 1 |
| United States Of America | us | NA | US | DC | 1 |
| United States Of America | us | NA | US | DMV | 1 |
| United States Of America | us | NA | US | Denver | 1 |
| United States Of America | us | NA | US | Los Angeles | 1 |
| United States Of America | us | NA | US | Los Angeles, CA | 1 |
| United States Of America | us | NA | US | NJ | 2 |
| United States Of America | us | NA | US | NY Metro | 1 |
| United States Of America | us | NA | US | NYC | 5 |
| United States Of America | us | NA | US | New Jersey | 1 |
| United States Of America | us | NA | US | New York | 1 |
| United States Of America | us | NA | US | Philadelphia | 2 |
| United States Of America | us | NA | US | Remote, office based in Bay Area | 1 |
| United States Of America | us | NA | US | Research Triangle | 3 |
| United States Of America | us | NA | US | San Diego | 5 |
| United States Of America | us | NA | US | Seattle | 1 |
| United States Of America | us | NA | US | St. Louis | 1 |
| United States Of America | us | NA | US | Washington DC | 1 |
| United States Of America | us | NA | Us | Boston | 1 |
| United States Of America | us | NA | Us | Seattle | 1 |
| United States Of America | usa | NA | USA | Alachua | 1 |
| United States Of America | usa | NA | USA | Austin (Remote) | 1 |
| United States Of America | usa | NA | USA | BOSTON | 1 |
| United States Of America | usa | NA | USA | Baltimore | 1 |
| United States Of America | usa | NA | USA | Bay Area | 54 |
| United States Of America | usa | NA | USA | Bay area | 7 |
| United States Of America | usa | NA | USA | Bosto | 1 |
| United States Of America | usa | NA | USA | Boston | 80 |
| United States Of America | usa | NA | USA | Boston/Cambridge | 1 |
| United States Of America | usa | NA | USA | Bosyon | 1 |
| United States Of America | usa | NA | USA | Boulder | 1 |
| United States Of America | usa | NA | USA | Buffalo | 1 |
| United States Of America | usa | NA | USA | Chicago | 5 |
| United States Of America | usa | NA | USA | Cleveland | 1 |
| United States Of America | usa | NA | USA | Columbus | 1 |
| United States Of America | usa | NA | USA | Columbus, OH | 1 |
| United States Of America | usa | NA | USA | DC | 1 |
| United States Of America | usa | NA | USA | DC Area/I-270 | 1 |
| United States Of America | usa | NA | USA | Denver | 3 |
| United States Of America | usa | NA | USA | Georgia | 1 |
| United States Of America | usa | NA | USA | Houston | 2 |
| United States Of America | usa | NA | USA | I-270 corridor | 1 |
| United States Of America | usa | NA | USA | Indianapolis | 2 |
| United States Of America | usa | NA | USA | Kansas City | 1 |
| United States Of America | usa | NA | USA | LA | 1 |
| United States Of America | usa | NA | USA | La Jolla | 1 |
| United States Of America | usa | NA | USA | Los Angeles | 9 |
| United States Of America | usa | NA | USA | Miami | 1 |
| United States Of America | usa | NA | USA | Milwaukee | 1 |
| United States Of America | usa | NA | USA | Minneapolis | 1 |
| United States Of America | usa | NA | USA | NYC | 8 |
| United States Of America | usa | NA | USA | New Haven | 1 |
| United States Of America | usa | NA | USA | New Jersey | 2 |
| United States Of America | usa | NA | USA | New York | 3 |
| United States Of America | usa | NA | USA | New York City | 3 |
| United States Of America | usa | NA | USA | Pacific NW | 1 |
| United States Of America | usa | NA | USA | Philadelphia | 16 |
| United States Of America | usa | NA | USA | Portland | 2 |
| United States Of America | usa | NA | USA | RESEARCH TRIANGLE | 1 |
| United States Of America | usa | NA | USA | RTP | 2 |
| United States Of America | usa | NA | USA | RTP, NC | 1 |
| United States Of America | usa | NA | USA | Remote | 1 |
| United States Of America | usa | NA | USA | Research Triangle | 13 |
| United States Of America | usa | NA | USA | Rockville, MD | 1 |
| United States Of America | usa | NA | USA | SF Bay Area | 1 |
| United States Of America | usa | NA | USA | Salt Lake City | 2 |
| United States Of America | usa | NA | USA | San Diego | 26 |
| United States Of America | usa | NA | USA | San Francisco | 4 |
| United States Of America | usa | NA | USA | San diego | 1 |
| United States Of America | usa | NA | USA | Seattle | 18 |
| United States Of America | usa | NA | USA | SoCal | 1 |
| United States Of America | usa | NA | USA | Southeast | 1 |
| United States Of America | usa | NA | USA | St Louis | 1 |
| United States Of America | usa | NA | USA | Syracuse | 1 |
| United States Of America | usa | NA | USA | Thousand Oaks | 1 |
| United States Of America | usa | NA | USA | Washington DC | 1 |
| United States Of America | usa | NA | USA | Washington, DC | 1 |
| United States Of America | usa | NA | USA | philadelphia | 1 |
| United States Of America | usa | NA | Usa | Bay Area | 2 |
| United States Of America | usa | NA | Usa | Boston | 5 |
| United States Of America | usa | NA | Usa | Philadelphia | 2 |
| United States Of America | usa | NA | Usa | Portland Oregon | 1 |
| United States Of America | usa | NA | Usa | Salt lake city | 1 |
| United States Of America | usa | NA | Usa | San Diego | 1 |
| United States Of America | usa | NA | Usa | philly | 2 |
| United States Of America | usa | NA | usa | bay area | 2 |
| United States Of America | usa | NA | usa | boston | 2 |
| United States Of America | usa | NA | usa | great lakes / Midwest | 1 |
| United States Of America | usa | NA | usa | san diego | 1 |
| United States Of America | usa | NA | usa | seattle | 1 |
| United States Of America | usa, albany | NA | NA | USA, ALBANY | 1 |
| United States Of America | washington dc | NA | NA | Washington DC | 1 |
| United States Of America | washington dc (rockville md) usa | NA | NA | Washington DC (Rockville MD) USA | 1 |
| United States Of America | west coast (california & pacific northwest) | West Coast (California & Pacific Northwest) | NA | NA | 396 |
| United States Of America | NA | NA | NA | NA | 581 |
| NA | america | NA | America | Bay Area | 1 |
| NA | america | NA | America | Philadelphia | 1 |
| NA | ames, ia nadc | NA | NA | Ames, IA NADC | 1 |
| NA | argentine | Argentine | NA | NA | 1 |
| NA | athens | NA | NA | Athens | 1 |
| NA | berkeley, ca | NA | NA | Berkeley, CA | 1 |
| NA | brazil | NA | NA | Brazil | 1 |
| NA | brighton, uk | NA | NA | Brighton, UK | 1 |
| NA | ca | NA | NA | CA | 1 |
| NA | calgary | NA | NA | Calgary | 1 |
| NA | canada, ottawa | NA | NA | Canada, Ottawa | 1 |
| NA | central va | Central VA | NA | NA | 1 |
| NA | chile | Chile | NA | NA | 1 |
| NA | china | NA | China | Shanghai | 1 |
| NA | china | NA | China | Shanghai/Chengdu | 1 |
| NA | college station tx | NA | NA | College Station TX | 1 |
| NA | colorado | NA | NA | Colorado | 1 |
| NA | devens | NA | NA | Devens | 1 |
| NA | dublin | NA | NA | Dublin | 1 |
| NA | elkton, virginia | Elkton, Virginia | NA | NA | 1 |
| NA | england | NA | England | London | 1 |
| NA | eu | EU | NA | NA | 2 |
| NA | finland | Finland | NA | NA | 1 |
| NA | florida | NA | NA | Florida | 1 |
| NA | georgia | NA | NA | Georgia | 1 |
| NA | greece | NA | Greece | Thessaloniki | 1 |
| NA | halifax, nova scotia | NA | NA | Halifax, Nova Scotia | 1 |
| NA | illinois | NA | NA | Illinois | 1 |
| NA | iraq | Iraq | NA | NA | 1 |
| NA | irvine, ca | NA | NA | Irvine, CA | 1 |
| NA | kenya | Kenya | NA | NA | 1 |
| NA | knoxville, tn | NA | NA | Knoxville, TN | 1 |
| NA | los ángeles | NA | NA | Los Ángeles | 1 |
| NA | macclesfield, uk | NA | NA | Macclesfield, UK | 1 |
| NA | madison, wisconsin | NA | NA | Madison, Wisconsin | 1 |
| NA | minnesota | NA | NA | Minnesota | 1 |
| NA | montreal | NA | NA | Montreal | 1 |
| NA | munich | NA | NA | Munich | 1 |
| NA | mva | NA | NA | MVA | 1 |
| NA | netherlands | Netherlands | NA | NA | 3 |
| NA | netherlands | NA | Netherlands | Amsterdam | 1 |
| NA | netherlands | NA | NA | Netherlands | 1 |
| NA | new haven | NA | NA | New Haven | 1 |
| NA | newark, ca | NA | NA | Newark, CA | 1 |
| NA | nl | NL | NA | NA | 1 |
| NA | north carolina | NA | NA | North Carolina | 1 |
| NA | oakland | NA | NA | Oakland | 1 |
| NA | orange county | NA | NA | Orange County | 1 |
| NA | pa | NA | NA | PA | 1 |
| NA | pennsylvania | NA | NA | Pennsylvania | 1 |
| NA | poland | Poland | NA | NA | 2 |
| NA | portland, or | NA | NA | Portland, OR | 2 |
| NA | portugal | NA | Portugal | Porto | 1 |
| NA | rdu | NA | NA | RDU | 2 |
| NA | rehovot | NA | NA | Rehovot | 1 |
| NA | remote | NA | NA | Remote | 2 |
| NA | research triangle park | NA | NA | Research Triangle Park | 1 |
| NA | research triangle park, nc | Research Triangle Park, NC | NA | NA | 1 |
| NA | research triangle, nc | NA | NA | Research Triangle, NC | 1 |
| NA | saint louis | NA | NA | Saint Louis | 1 |
| NA | san antonio, texas | NA | NA | San Antonio, texas | 1 |
| NA | santa barbara | NA | NA | Santa Barbara | 1 |
| NA | santa barbara, ca | NA | NA | Santa Barbara, CA | 1 |
| NA | slovenia | NA | Slovenia | Ljubljana | 1 |
| NA | spain (barcelona) | Spain (Barcelona) | NA | NA | 1 |
| NA | st. louis | NA | NA | St. Louis | 2 |
| NA | tennessee | Tennessee | NA | NA | 1 |
| NA | triangle | NA | NA | Triangle | 1 |
| NA | tucson, az. | NA | NA | Tucson, Az. | 1 |
| NA | united stated | NA | United Stated | Bay Area | 1 |
| NA | united stated of americ | NA | United Stated of Americ | Bay Area | 1 |
| NA | vancouver, canada | NA | NA | Vancouver, Canada | 1 |
| NA | waco | NA | NA | Waco | 1 |
| NA | waco, tx | NA | NA | Waco, TX | 1 |
| NA | waltham | NA | NA | Waltham | 1 |
| NA | worcester ma | NA | NA | Worcester MA | 1 |
| NA | NA | NA | NA | NA | 3 |
location granular
for selecting within country:
sal_clean_filt <- sal_clean_filt |>
mutate(
location_granular =
coalesce(
which_us_state_do_you_work_in,
which_canadian_province_do_you_work_in,
where_is_the_closest_major_city_or_hub, where_are_you_located)
)
sal_clean_filt |>
count(
location_granular,
which_us_state_do_you_work_in,
which_canadian_province_do_you_work_in,
where_is_the_closest_major_city_or_hub,
where_are_you_located) |> gt() | location_granular | which_us_state_do_you_work_in | which_canadian_province_do_you_work_in | where_is_the_closest_major_city_or_hub | where_are_you_located | n |
|---|---|---|---|---|---|
| Alabama | Alabama | NA | NA | NA | 2 |
| Alachua | NA | NA | Alachua | NA | 1 |
| Albany | NA | NA | Albany | NA | 1 |
| Ames, IA NADC | NA | NA | Ames, IA NADC | NA | 1 |
| Amsterdam | NA | NA | Amsterdam | NA | 1 |
| Ann Arbor | NA | NA | Ann Arbor | NA | 1 |
| Argentine | NA | NA | NA | Argentine | 1 |
| Arizona | Arizona | NA | NA | NA | 1 |
| Athens | NA | NA | Athens | NA | 1 |
| Atlanta | NA | NA | Atlanta | NA | 2 |
| Atlanta, GA | NA | NA | Atlanta, GA | NA | 1 |
| Austin (Remote) | NA | NA | Austin (Remote) | NA | 1 |
| Austin, TX | NA | NA | Austin, TX | NA | 2 |
| Australia | NA | NA | NA | Australia | 6 |
| Austria | NA | NA | Austria | NA | 1 |
| Austria | NA | NA | NA | Austria | 2 |
| BOSTON | NA | NA | BOSTON | NA | 1 |
| Baltimore | NA | NA | Baltimore | NA | 2 |
| Baltimore, MD | NA | NA | Baltimore, MD | NA | 2 |
| Baltimore/Washington DC | NA | NA | Baltimore/Washington DC | NA | 1 |
| Bangalore | NA | NA | Bangalore | NA | 1 |
| Basel | NA | NA | Basel | NA | 1 |
| Bay Are | NA | NA | Bay Are | NA | 1 |
| Bay Area | NA | NA | Bay Area | NA | 185 |
| Bay Area - Mid Penninsula | NA | NA | Bay Area - Mid Penninsula | NA | 1 |
| Bay area | NA | NA | Bay area | NA | 21 |
| Belgium | NA | NA | Belgium | NA | 1 |
| Belgium | NA | NA | NA | Belgium | 9 |
| Benelux | NA | NA | NA | Benelux | 1 |
| Berkeley, CA | NA | NA | Berkeley, CA | NA | 1 |
| Berlin | NA | NA | Berlin | NA | 1 |
| Bloomington, IN, USA | NA | NA | Bloomington, IN, USA | NA | 1 |
| Boca Raton | NA | NA | Boca Raton | NA | 1 |
| Bosto | NA | NA | Bosto | NA | 1 |
| Boston | NA | NA | Boston | NA | 356 |
| Boston | NA | NA | NA | Boston | 3 |
| Boston (company location) - company is WFH | NA | NA | Boston (company location) - company is WFH | NA | 1 |
| Boston / Remote | NA | NA | Boston / Remote | NA | 1 |
| Boston MA | NA | NA | Boston MA | NA | 1 |
| Boston, MA | NA | NA | Boston, MA | NA | 4 |
| Boston, US | NA | NA | Boston, US | NA | 1 |
| Boston/Cambridge | NA | NA | Boston/Cambridge | NA | 2 |
| Bosyon | NA | NA | Bosyon | NA | 1 |
| Boulder | NA | NA | Boulder | NA | 1 |
| Boulder (remote, company in Bay) | NA | NA | Boulder (remote, company in Bay) | NA | 1 |
| Boulder, CO | NA | NA | Boulder, CO | NA | 3 |
| Brazil | NA | NA | Brazil | NA | 1 |
| Brighton, UK | NA | NA | Brighton, UK | NA | 1 |
| British Columbia | NA | British Columbia | NA | NA | 7 |
| Buffalo | NA | NA | Buffalo | NA | 1 |
| CA | NA | NA | CA | NA | 2 |
| CO | NA | NA | NA | CO | 1 |
| Calgary | NA | NA | Calgary | NA | 1 |
| California | California | NA | NA | NA | 152 |
| California | NA | NA | California | NA | 2 |
| Cambridge | NA | NA | Cambridge | NA | 7 |
| Cambridge Massachusetts | NA | NA | Cambridge Massachusetts | NA | 1 |
| Cambridge UK | NA | NA | Cambridge UK | NA | 1 |
| Cambridge, MA | NA | NA | Cambridge, MA | NA | 7 |
| Cambridge, UK | NA | NA | Cambridge, UK | NA | 3 |
| Cambridge,MA | NA | NA | Cambridge,MA | NA | 1 |
| Canada | NA | NA | Canada | NA | 6 |
| Canada | NA | NA | NA | Canada | 49 |
| Canada, Ottawa | NA | NA | Canada, Ottawa | NA | 1 |
| Carlsbad | NA | NA | Carlsbad | NA | 1 |
| Carolinas & Southeast (From NC to AR, South FL and LA) | NA | NA | NA | Carolinas & Southeast (From NC to AR, South FL and LA) | 83 |
| Central Maine | NA | NA | Central Maine | NA | 1 |
| Central VA | NA | NA | NA | Central VA | 1 |
| Central/North Jersey | NA | NA | Central/North Jersey | NA | 1 |
| Chicago | NA | NA | Chicago | NA | 18 |
| Chicago IL | NA | NA | Chicago IL | NA | 1 |
| Chicago, IL | NA | NA | Chicago, IL | NA | 1 |
| Chicagoland | NA | NA | Chicagoland | NA | 1 |
| Chile | NA | NA | NA | Chile | 1 |
| Cincinnati, Ohio | NA | NA | Cincinnati, Ohio | NA | 1 |
| Cleveland | NA | NA | Cleveland | NA | 2 |
| College Station TX | NA | NA | College Station TX | NA | 1 |
| Cologne | NA | NA | Cologne | NA | 1 |
| Colorado | Colorado | NA | NA | NA | 6 |
| Colorado | NA | NA | Colorado | NA | 1 |
| Columbus | NA | NA | Columbus | NA | 2 |
| Columbus, OH | NA | NA | Columbus, OH | NA | 1 |
| Columbus, Ohio | NA | NA | Columbus, Ohio | NA | 1 |
| Connecticut | Connecticut | NA | NA | NA | 4 |
| Copenhagen | NA | NA | Copenhagen | NA | 1 |
| DC | NA | NA | DC | NA | 5 |
| DC Area/I-270 | NA | NA | DC Area/I-270 | NA | 1 |
| DC Metro Area (DC, VA, MD, DE) | NA | NA | NA | DC Metro Area (DC, VA, MD, DE) | 79 |
| DC area | NA | NA | DC area | NA | 1 |
| DMV | NA | NA | DMV | NA | 1 |
| Dc | NA | NA | Dc | NA | 1 |
| Delaware | Delaware | NA | NA | NA | 2 |
| Denmark | NA | NA | NA | Denmark | 4 |
| Denver | NA | NA | Denver | NA | 10 |
| Detroit | NA | NA | Detroit | NA | 1 |
| Devens | NA | NA | Devens | NA | 1 |
| Dublin | NA | NA | Dublin | NA | 2 |
| Dundee | NA | NA | Dundee | NA | 1 |
| Durham, NC | NA | NA | Durham, NC | NA | 2 |
| EU | NA | NA | NA | EU | 2 |
| Edinburgh | NA | NA | Edinburgh | NA | 1 |
| Elkton, Virginia | NA | NA | NA | Elkton, Virginia | 1 |
| FRANCE | NA | NA | NA | FRANCE | 1 |
| Finland | NA | NA | NA | Finland | 1 |
| Florida | NA | NA | Florida | NA | 1 |
| France | NA | NA | France | NA | 1 |
| France | NA | NA | NA | France | 7 |
| Frankfurt am Main | NA | NA | Frankfurt am Main | NA | 1 |
| Gaithersburg | NA | NA | Gaithersburg | NA | 1 |
| Gaithersburg, Maryland | NA | NA | Gaithersburg, Maryland | NA | 1 |
| Georgia | Georgia | NA | NA | NA | 4 |
| Georgia | NA | NA | Georgia | NA | 2 |
| Germany | NA | NA | Germany | NA | 1 |
| Germany | NA | NA | NA | Germany | 27 |
| Greater Boston | NA | NA | Greater Boston | NA | 2 |
| Groton | NA | NA | Groton | NA | 1 |
| HOUSTON | NA | NA | HOUSTON | NA | 1 |
| Halifax, Nova Scotia | NA | NA | Halifax, Nova Scotia | NA | 1 |
| Hamburg | NA | NA | Hamburg | NA | 2 |
| Hartford, CT | NA | NA | Hartford, CT | NA | 1 |
| Hayward, CA | NA | NA | Hayward, CA | NA | 2 |
| Houston | NA | NA | Houston | NA | 6 |
| Houston, TX | NA | NA | Houston, TX | NA | 5 |
| Houston,TX | NA | NA | Houston,TX | NA | 2 |
| I-270 corridor | NA | NA | I-270 corridor | NA | 1 |
| Idaho | Idaho | NA | NA | NA | 1 |
| Illinois | Illinois | NA | NA | NA | 8 |
| Illinois | NA | NA | Illinois | NA | 1 |
| India | NA | NA | NA | India | 1 |
| Indiana | Indiana | NA | NA | NA | 6 |
| Indianapolis | NA | NA | Indianapolis | NA | 7 |
| Indianapolis, IN | NA | NA | Indianapolis, IN | NA | 1 |
| Iraq | NA | NA | NA | Iraq | 1 |
| Ireland | NA | NA | Ireland | NA | 3 |
| Irvine, CA | NA | NA | Irvine, CA | NA | 1 |
| Kansas City | NA | NA | Kansas City | NA | 4 |
| Kansas City, MO | NA | NA | Kansas City, MO | NA | 1 |
| Kansas City, Missouri, United States | NA | NA | Kansas City, Missouri, United States | NA | 1 |
| Kenya | NA | NA | NA | Kenya | 1 |
| Knoxville, TN | NA | NA | Knoxville, TN | NA | 1 |
| LA | NA | NA | LA | NA | 3 |
| La Jolla | NA | NA | La Jolla | NA | 1 |
| Limerick | NA | NA | Limerick | NA | 1 |
| Liverpool | NA | NA | Liverpool | NA | 1 |
| Ljubljana | NA | NA | Ljubljana | NA | 1 |
| London | NA | NA | London | NA | 5 |
| Los Angeles | NA | NA | Los Angeles | NA | 34 |
| Los Angeles (company is in Bay) | NA | NA | Los Angeles (company is in Bay) | NA | 1 |
| Los Angeles, CA | NA | NA | Los Angeles, CA | NA | 1 |
| Los Ángeles | NA | NA | Los Ángeles | NA | 1 |
| MVA | NA | NA | MVA | NA | 1 |
| Macclesfield, UK | NA | NA | Macclesfield, UK | NA | 1 |
| Madison | NA | NA | Madison | NA | 1 |
| Madison, Wisconsin | NA | NA | Madison, Wisconsin | NA | 1 |
| Manchester | NA | NA | Manchester | NA | 1 |
| Manitoba | NA | Manitoba | NA | NA | 1 |
| Maryland | Maryland | NA | NA | NA | 25 |
| Maryland | NA | NA | Maryland | NA | 6 |
| Maryland/DC | NA | NA | Maryland/DC | NA | 1 |
| Massachusetts | Massachusetts | NA | NA | NA | 166 |
| Melbourne | NA | NA | Melbourne | NA | 1 |
| Miami | NA | NA | Miami | NA | 3 |
| Michigan | Michigan | NA | NA | NA | 6 |
| Michigan, USA | NA | NA | Michigan, USA | NA | 1 |
| Midwest | NA | NA | Midwest | NA | 6 |
| Midwest (From OH to KS, North to ND) | NA | NA | NA | Midwest (From OH to KS, North to ND) | 113 |
| Milwaukee | NA | NA | Milwaukee | NA | 1 |
| Minneapolis | NA | NA | Minneapolis | NA | 5 |
| Minneapolis, mn | NA | NA | Minneapolis, mn | NA | 1 |
| Minnesota | Minnesota | NA | NA | NA | 3 |
| Minnesota | NA | NA | Minnesota | NA | 1 |
| Mississippi | Mississippi | NA | NA | NA | 1 |
| Missouri | Missouri | NA | NA | NA | 10 |
| Montreal | NA | NA | Montreal | NA | 3 |
| Munich | NA | NA | Munich | NA | 2 |
| N/A | NA | NA | N/A | NA | 1 |
| NJ | NA | NA | NJ | NA | 5 |
| NJ-Philadelphia | NA | NA | NJ-Philadelphia | NA | 1 |
| NJ-Philly-DE | NA | NA | NJ-Philly-DE | NA | 1 |
| NL | NA | NA | NA | NL | 1 |
| NRW | NA | NA | NRW | NA | 1 |
| NY | NA | NA | NY | NA | 1 |
| NY Metro | NA | NA | NY Metro | NA | 1 |
| NY, NY | NA | NA | NY, NY | NA | 1 |
| NYC | NA | NA | NYC | NA | 17 |
| Near boston | NA | NA | Near boston | NA | 1 |
| Nebraska | Nebraska | NA | NA | NA | 1 |
| Netherlands | NA | NA | Netherlands | NA | 1 |
| Netherlands | NA | NA | NA | Netherlands | 3 |
| Nevada | Nevada | NA | NA | NA | 1 |
| New England (MA, CT, RI, NH, VT, ME) | NA | NA | NA | New England (MA, CT, RI, NH, VT, ME) | 401 |
| New England, not Boston | NA | NA | New England, not Boston | NA | 1 |
| New Hampshire | New Hampshire | NA | NA | NA | 2 |
| New Haven | NA | NA | New Haven | NA | 2 |
| New Jersey | New Jersey | NA | NA | NA | 28 |
| New Jersey | NA | NA | New Jersey | NA | 15 |
| New Jersey, USA | NA | NA | New Jersey, USA | NA | 1 |
| New Mexico | New Mexico | NA | NA | NA | 1 |
| New York | New York | NA | NA | NA | 29 |
| New York | NA | NA | New York | NA | 16 |
| New York City | NA | NA | New York City | NA | 11 |
| New York Metro Area | NA | NA | New York Metro Area | NA | 1 |
| New York metropolitan area | NA | NA | New York metropolitan area | NA | 1 |
| Newark, CA | NA | NA | Newark, CA | NA | 1 |
| North Carolina | North Carolina | NA | NA | NA | 32 |
| North Carolina | NA | NA | North Carolina | NA | 1 |
| North Dakota | North Dakota | NA | NA | NA | 1 |
| Nyc | NA | NA | Nyc | NA | 1 |
| Nyc metro | NA | NA | Nyc metro | NA | 1 |
| Oakland | NA | NA | Oakland | NA | 1 |
| Ohio | Ohio | NA | NA | NA | 6 |
| Ohio | NA | NA | Ohio | NA | 2 |
| Oklahoma | Oklahoma | NA | NA | NA | 1 |
| Oklahoma City | NA | NA | Oklahoma City | NA | 1 |
| Ontario | NA | Ontario | NA | NA | 7 |
| Orange County | NA | NA | Orange County | NA | 1 |
| Oregon | Oregon | NA | NA | NA | 3 |
| Other US Location (HI, AK, PR, etc.) | NA | NA | NA | Other US Location (HI, AK, PR, etc.) | 1 |
| Oxford | NA | NA | Oxford | NA | 2 |
| PA | NA | NA | PA | NA | 1 |
| Pacific NW | NA | NA | Pacific NW | NA | 1 |
| Pennsylvania | Pennsylvania | NA | NA | NA | 41 |
| Pennsylvania | NA | NA | Pennsylvania | NA | 1 |
| Pharma Central (NY, NJ, PA) | NA | NA | NA | Pharma Central (NY, NJ, PA) | 220 |
| Philadelphia | NA | NA | Philadelphia | NA | 59 |
| Philadelphia Metro Area | NA | NA | Philadelphia Metro Area | NA | 1 |
| Philadelphia metro | NA | NA | Philadelphia metro | NA | 1 |
| Philadelphia, PA | NA | NA | Philadelphia, PA | NA | 2 |
| Phoenix | NA | NA | Phoenix | NA | 3 |
| Pittsburgh | NA | NA | Pittsburgh | NA | 3 |
| Poland | NA | NA | NA | Poland | 2 |
| Portland | NA | NA | Portland | NA | 4 |
| Portland Oregon | NA | NA | Portland Oregon | NA | 1 |
| Portland, OR | NA | NA | Portland, OR | NA | 3 |
| Porto | NA | NA | Porto | NA | 1 |
| Providence | NA | NA | Providence | NA | 1 |
| Quebec | NA | Quebec | NA | NA | 6 |
| RDU | NA | NA | RDU | NA | 2 |
| RESEARCH TRIANGLE | NA | NA | RESEARCH TRIANGLE | NA | 1 |
| RTP | NA | NA | RTP | NA | 12 |
| RTP NC | NA | NA | RTP NC | NA | 1 |
| RTP North Carolina | NA | NA | RTP North Carolina | NA | 1 |
| RTP, NC | NA | NA | RTP, NC | NA | 3 |
| RTP, North Carolina | NA | NA | RTP, North Carolina | NA | 1 |
| RTP/Raleigh, NC | NA | NA | RTP/Raleigh, NC | NA | 1 |
| Raleigh | NA | NA | Raleigh | NA | 2 |
| Raleigh, NC | NA | NA | Raleigh, NC | NA | 1 |
| Raleigh-Durham | NA | NA | Raleigh-Durham | NA | 1 |
| Redwood City | NA | NA | Redwood City | NA | 1 |
| Rehovot | NA | NA | Rehovot | NA | 1 |
| Remote | NA | NA | Remote | NA | 3 |
| Remote - US | NA | NA | NA | Remote - US | 1 |
| Remote, HQ in San Diego | NA | NA | Remote, HQ in San Diego | NA | 1 |
| Remote, TX, US | NA | NA | Remote, TX, US | NA | 1 |
| Remote, office based in Bay Area | NA | NA | Remote, office based in Bay Area | NA | 1 |
| Research Triangle | NA | NA | Research Triangle | NA | 41 |
| Research Triangle Park | NA | NA | Research Triangle Park | NA | 1 |
| Research Triangle Park, NC | NA | NA | NA | Research Triangle Park, NC | 1 |
| Research Triangle, NC | NA | NA | Research Triangle, NC | NA | 1 |
| Rhode Island | Rhode Island | NA | NA | NA | 1 |
| Ridgefield, CT | NA | NA | Ridgefield, CT | NA | 1 |
| Rockville | NA | NA | Rockville | NA | 1 |
| Rockville, MD | NA | NA | Rockville, MD | NA | 2 |
| SF Bay Area | NA | NA | SF Bay Area | NA | 5 |
| Sacramento | NA | NA | Sacramento | NA | 1 |
| Sacramento, CA | NA | NA | Sacramento, CA | NA | 2 |
| Saint Louis | NA | NA | Saint Louis | NA | 1 |
| Salt Lake City | NA | NA | Salt Lake City | NA | 7 |
| Salt Lake City, UT | NA | NA | Salt Lake City, UT | NA | 1 |
| Salt lake city | NA | NA | Salt lake city | NA | 1 |
| San Antonio | NA | NA | San Antonio | NA | 1 |
| San Antonio, texas | NA | NA | San Antonio, texas | NA | 1 |
| San Diego | NA | NA | San Diego | NA | 111 |
| San Diego | NA | NA | NA | San Diego | 1 |
| San Diego, CA | NA | NA | San Diego, CA | NA | 3 |
| San Francisco | NA | NA | San Francisco | NA | 18 |
| San Francisco Bay Area | NA | NA | San Francisco Bay Area | NA | 2 |
| San Francisco CA | NA | NA | San Francisco CA | NA | 1 |
| San Francisco ca | NA | NA | San Francisco ca | NA | 2 |
| San diego | NA | NA | San diego | NA | 3 |
| San francisco | NA | NA | San francisco | NA | 2 |
| Santa Barbara | NA | NA | Santa Barbara | NA | 1 |
| Santa Barbara, CA | NA | NA | Santa Barbara, CA | NA | 1 |
| Saxony | NA | NA | Saxony | NA | 1 |
| Seattle | NA | NA | Seattle | NA | 50 |
| Seattle Area | NA | NA | Seattle Area | NA | 1 |
| Seattle WA | NA | NA | Seattle WA | NA | 2 |
| Seattle but I work remotely from FL | NA | NA | Seattle but I work remotely from FL | NA | 1 |
| Seattle, WA | NA | NA | Seattle, WA | NA | 2 |
| Shanghai | NA | NA | Shanghai | NA | 1 |
| Shanghai/Chengdu | NA | NA | Shanghai/Chengdu | NA | 1 |
| Singapore | NA | NA | NA | Singapore | 2 |
| SoCal | NA | NA | SoCal | NA | 2 |
| South & Mountain West (TX to AZ, North to MT) | NA | NA | NA | South & Mountain West (TX to AZ, North to MT) | 58 |
| South San Francisco | NA | NA | South San Francisco | NA | 4 |
| Southeast | NA | NA | Southeast | NA | 1 |
| Spain | NA | NA | NA | Spain | 5 |
| Spain (Barcelona) | NA | NA | NA | Spain (Barcelona) | 1 |
| St Louis | NA | NA | St Louis | NA | 2 |
| St. Louis | NA | NA | St. Louis | NA | 4 |
| Stockholm | NA | NA | Stockholm | NA | 1 |
| Sweden | NA | NA | NA | Sweden | 2 |
| Switzerland | NA | NA | Switzerland | NA | 3 |
| Switzerland | NA | NA | NA | Switzerland | 10 |
| Syracuse | NA | NA | Syracuse | NA | 2 |
| Szeged | NA | NA | Szeged | NA | 1 |
| Tampa | NA | NA | Tampa | NA | 1 |
| Tennessee | Tennessee | NA | NA | NA | 1 |
| Tennessee | NA | NA | NA | Tennessee | 1 |
| Texas | Texas | NA | NA | NA | 7 |
| Thessaloniki | NA | NA | Thessaloniki | NA | 1 |
| Thousand Oaks | NA | NA | Thousand Oaks | NA | 2 |
| Thousand Oaks (Los Angeles) | NA | NA | Thousand Oaks (Los Angeles) | NA | 1 |
| Thousand Oaks, CA | NA | NA | Thousand Oaks, CA | NA | 1 |
| Toronto | NA | NA | Toronto | NA | 16 |
| Triangle | NA | NA | Triangle | NA | 1 |
| Tucson, Az. | NA | NA | Tucson, Az. | NA | 1 |
| U.K | NA | NA | U.K | NA | 2 |
| U.K. | NA | NA | U.K. | NA | 1 |
| U.k. | NA | NA | U.k. | NA | 1 |
| UK | NA | NA | UK | NA | 3 |
| USA, ALBANY | NA | NA | USA, ALBANY | NA | 1 |
| United Kingdom and Ireland | NA | NA | NA | United Kingdom and Ireland | 62 |
| Uppsala | NA | NA | Uppsala | NA | 1 |
| Utah | Utah | NA | NA | NA | 3 |
| Vancouver | NA | NA | Vancouver | NA | 6 |
| Vancouver, Canada | NA | NA | Vancouver, Canada | NA | 1 |
| Vienna | NA | NA | Vienna | NA | 2 |
| Virginia | Virginia | NA | NA | NA | 3 |
| Waco | NA | NA | Waco | NA | 1 |
| Waco, TX | NA | NA | Waco, TX | NA | 1 |
| Waltham | NA | NA | Waltham | NA | 1 |
| Washington | Washington | NA | NA | NA | 18 |
| Washington D.C. Metro | NA | NA | Washington D.C. Metro | NA | 1 |
| Washington DC | NA | NA | Washington DC | NA | 7 |
| Washington DC (Rockville MD) USA | NA | NA | Washington DC (Rockville MD) USA | NA | 1 |
| Washington DC Capitol Region | NA | NA | Washington DC Capitol Region | NA | 1 |
| Washington, DC | NA | NA | Washington, DC | NA | 3 |
| West Coast (California & Pacific Northwest) | NA | NA | NA | West Coast (California & Pacific Northwest) | 396 |
| Wilmington,DE | NA | NA | Wilmington,DE | NA | 1 |
| Wisconsin | Wisconsin | NA | NA | NA | 5 |
| Worcester MA | NA | NA | Worcester MA | NA | 1 |
| australia | NA | NA | NA | australia | 1 |
| bay area | NA | NA | bay area | NA | 6 |
| boston | NA | NA | boston | NA | 2 |
| denmark | NA | NA | NA | denmark | 1 |
| great lakes / Midwest | NA | NA | great lakes / Midwest | NA | 1 |
| india | NA | NA | NA | india | 1 |
| nj | NA | NA | nj | NA | 1 |
| philadelphia | NA | NA | philadelphia | NA | 1 |
| philly | NA | NA | philly | NA | 2 |
| russia | NA | NA | NA | russia | 1 |
| san diego | NA | NA | san diego | NA | 2 |
| seattle | NA | NA | seattle | NA | 1 |
| spain | NA | NA | NA | spain | 1 |
| NA | NA | NA | NA | NA | 69 |
Completeness
Compare the missingness of original location variables vs the cleaned location_country variable:
sal_clean_filt |>
summarize(
across(c(
location_country,
location_granular,
where_are_you_located,
what_country_do_you_work_in,
where_is_the_closest_major_city_or_hub),
~ percent_na(.x))
) |>
pivot_longer(everything(), names_to = 'column', values_to = '% missing')# A tibble: 5 × 2
column `% missing`
<chr> <dbl>
1 location_country 0.0238
2 location_granular 0.0189
3 where_are_you_located 0.571
4 what_country_do_you_work_in 0.770
5 where_is_the_closest_major_city_or_hub 0.613 time
sal_clean_filt <- sal_clean_filt |>
mutate(timestamp = mdy_hms(timestamp),
year = year(timestamp),
month = month(timestamp),
day = day(timestamp)) |>
select(timestamp, year:day, everything())write out the data
sal_clean_filt |>
mutate(date = str_extract(as.character(timestamp), "^[-\\w]+")) |>
select(
date,
location_country,
title_category, title_general, title_detail,
salary_base, bonus_pct, bonus,
# need to fix these
years_of_experience, what_degrees_do_you_have,
# the original data
where_are_you_located, what_country_do_you_work_in,
role_title_of_current_position,
everything(),
-timestamp, -bonus_cleaned, -year, -month, -day
) |>
write_csv(here::here(outdir , 'salary_results_cleaned.csv'))