TidyTuesday
    • About TidyTuesday
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    On this page

    • Ask a Manager Survey
      • Get the data here
      • Data Dictionary
    • survey.csv
      • Cleaning Script

    Logo for the Ask a Manager blog which is a white red-haired woman next to the words “Ask a Manager, and if you don’t I’ll tell you anyway”

    Please note that the image above belongs to the Ask a Manager blog/Alison Green.

    Ask a Manager Survey

    The data this week comes from the Ask a Manager Survey. H/t to Kaija Gahm for sharing it as an issue!

    The salary survey a few weeks ago got a huge response — 24,000+ people shared their salaries and other info, which is a lot of raw data to sift through. Reader Elisabeth Engl kindly took the raw data and analyzed some of the trends in it and here’s what she found. (She asked me to note that she did this as a fun project to share some insights from the survey, rather than as a paid engagement.)

    This data does not reflect the general population; it reflects Ask a Manager readers who self-selected to respond, which is a very different group (as you can see just from the demographic breakdown below, which is very white and very female).

    Elisabeth Engl prepped some plots for the Ask a Manager blog using this data.

    The survey itself is available here.

    Get the data here

    # Get the Data
    
    # Read in with tidytuesdayR package 
    # Install from CRAN via: install.packages("tidytuesdayR")
    # This loads the readme and all the datasets for the week of interest
    
    # Either ISO-8601 date or year/week works!
    
    tuesdata <- tidytuesdayR::tt_load('2021-05-18')
    tuesdata <- tidytuesdayR::tt_load(2021, week = 21)
    
    survey <- tuesdata$survey
    
    # Or read in the data manually
    
    survey <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-05-18/survey.csv')

    Data Dictionary

    survey.csv

    variable class description
    timestamp character Timestamp when survey submitted
    how_old_are_you character How old are you (bracket range)
    industry character Industry
    job_title character Job title
    additional_context_on_job_title character Additional context on job, free text
    annual_salary double Annual salary in local currency
    other_monetary_comp character Additional other monetary comp
    currency character Local currency
    currency_other character Currency for other compensation
    additional_context_on_income character Additional context on income (free text)
    country character Country currently working in
    state character State
    city character City
    overall_years_of_professional_experience character Overall years of professional experience (bracketed)
    years_of_experience_in_field character Years of experience in field (bracketed)
    highest_level_of_education_completed character Highest level of education completed
    gender character Gender
    race character Race

    Cleaning Script