library(tidyverse)

Week 2 Workshop

In this workshop we will work on some data on the gender pay gap from the UK Government. Use functions from the dplyr library from the tidyverse.

Data are from https://gender-pay-gap.service.gov.uk/viewing/download, which you can download yourself but is also included in the materials.

Be sure to use the 2024 - 2025 data! The 2025 to 2026 dataset is not yet complete.

Information about the data collection are available at https://www.gov.uk/guidance/gender-pay-gap-reporting-overview and descriptions of the data fields can be found under ““About CSV Data” on https://gender-pay-gap.service.gov.uk/viewing/download.

Tasks

Open the rstudio project provided for this workshop
Make a new RMarkdown document to record, and be able to reproduce, everything you do
Within the new RMarkdown document, use R to:

1. calculate the square root of 44 and save the answer in a new variable
1. find the log of the following values and divide the result by 2. Save the result in a new variable. [12, 5, 29, 0.02, 3]
1. Multiply the result of 3a by 3b

Use dplyr functions to read in the data from CSV format
Write a brief summary of 100 - 200 words in your RMarkdown file that explains what one row of the data represents (e.g. is it one row per individual, or per country, or per gender etc.; and what ). Make sure you do so in a way that it will output as formatted text when you knit your RMarkdown file together.
Good practise is to make a data-dictionary table, to record your understanding of what each variable actually is (e.g. MaleBonusPercent could be described as “Percentage of males receiving a bonus”). This is a written table for a human to read, not a data table. It should output as formatted RMarkdown text (Hint, the RMarkdown Cheat Sheet has information on making tables). Usually you would include a description for all variables, but to save time in this workshop, only describe 5 variables - ideally the ones that you will make use of.

For questions 7–11, write a plain-English sentence describing the pattern of the results

Averaging across employers, what is the mean difference between male and female hourly pay? (Hint, the answer is 12%)
Find the entry for Vodafone (specifically the entry for Vodafone with the most employees). (Hint: use the grepl function. Use the help files to see how it works and how it will help (e.g. through the F1 button))

1. What is the pay gap at Vodafone?
1. Look at the quartile information. What does this tell you about the overall distributions of pay for males and for females?

Do public sector employers have a smaller gender pay gap than businesses/charities? Use CompanyNumber as an indicator: public organisations do not have one so the field is empty (NA) for them.
What is the mean pay gap for employers submitting late?
What is the mean pay gap for employers who change their name and those who do not?

Check you can knit your Rmarkdown document to produce an HTML file documenting everything you have done

Stretch Tasks

This is also a nice dataset to try new things with and practice using dplyr. Can you find interesting patterns that are surprising/interesting?

e.g. Can you find particular employers easily? How about using keywords in the name to find particular types of company, such as schools or universities?

Download the data from previous years. How easy is it to use your code to produce the same calculations and output for that different dataset? Good code should be easily adaptable to this.

Learning objectives for today

Know how to make a new RStudio project and organise files
Know the very basics of RMarkdown
Practice using dplyr

Example Answer

pay.gap <- read_csv("UK Gender Pay Gap Data - 2024 to 2025.csv") #correct approach

## Rows: 11240 Columns: 27
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): EmployerName, Address, PostCode, CompanyNumber, SicCodes, CompanyL...
## dbl (15): EmployerId, DiffMeanHourlyPercent, DiffMedianHourlyPercent, DiffMe...
## lgl  (1): SubmittedAfterTheDeadline
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

pay.gap.old <- read_csv("UK Gender Pay Gap Data - 2023 to 2024.csv", guess_max = 10000)

## Rows: 11069 Columns: 27
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): EmployerName, Address, PostCode, CompanyNumber, SicCodes, CompanyL...
## dbl (15): EmployerId, DiffMeanHourlyPercent, DiffMedianHourlyPercent, DiffMe...
## lgl  (1): SubmittedAfterTheDeadline
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Note autocomplete for file names

# We can see the structure and how the function has read in the variables
spec(pay.gap)

## cols(
##   EmployerName = col_character(),
##   EmployerId = col_double(),
##   Address = col_character(),
##   PostCode = col_character(),
##   CompanyNumber = col_character(),
##   SicCodes = col_character(),
##   DiffMeanHourlyPercent = col_double(),
##   DiffMedianHourlyPercent = col_double(),
##   DiffMeanBonusPercent = col_double(),
##   DiffMedianBonusPercent = col_double(),
##   MaleBonusPercent = col_double(),
##   FemaleBonusPercent = col_double(),
##   MaleLowerQuartile = col_double(),
##   FemaleLowerQuartile = col_double(),
##   MaleLowerMiddleQuartile = col_double(),
##   FemaleLowerMiddleQuartile = col_double(),
##   MaleUpperMiddleQuartile = col_double(),
##   FemaleUpperMiddleQuartile = col_double(),
##   MaleTopQuartile = col_double(),
##   FemaleTopQuartile = col_double(),
##   CompanyLinkToGPGInfo = col_character(),
##   ResponsiblePerson = col_character(),
##   EmployerSize = col_character(),
##   CurrentName = col_character(),
##   SubmittedAfterTheDeadline = col_logical(),
##   DueDate = col_character(),
##   DateSubmitted = col_character()
## )

# Lets compare this with the "dot" function
pay.gap_alt <- read.csv("UK Gender Pay Gap Data - 2024 to 2025.csv")
#spec(pay.gap_alt)

str(pay.gap_alt)

## 'data.frame':    11240 obs. of  27 variables:
##  $ EmployerName             : chr  "'PRIFYSGOL ABERYSTWYTH' AND 'ABERYSTWYTH UNIVERSITY'" "\"RED BAND\" CHEMICAL COMPANY, LIMITED" "10 TRINITY SQUARE HOTEL LIMITED" "10442565 LTD" ...
##  $ EmployerId               : int  19070 16879 19455 19491 15320 687 18136 17484 14399 21894 ...
##  $ Address                  : chr  "Aberystwyth University, Penglais, Ceredigion, SY23 3FL" "19 Smith's Place, Leith Walk, Edinburgh, EH6 8NU" "5 Market Yard Mews, 194-204 Bermondsey Street, London, United Kingdom, SE1 3TQ" "Compass House 17-19 Empringham Street, Kingston Upon, Hull, East Yorkshire, England, HU9 1RP" ...
##  $ PostCode                 : chr  "SY23 3FL" "EH6 8NU" "SE1 3TQ" "HU9 1RP" ...
##  $ CompanyNumber            : chr  "RC000641" "SC016876" "08064685" "10442565" ...
##  $ SicCodes                 : chr  "" "47730" "82990" "70229" ...
##  $ DiffMeanHourlyPercent    : num  9.17 8.75 -2.2 11 20.12 ...
##  $ DiffMedianHourlyPercent  : num  2.9 -4.96 8.4 3.6 17.48 ...
##  $ DiffMeanBonusPercent     : num  7.5 20.8 -93.9 26.1 NA ...
##  $ DiffMedianBonusPercent   : num  0 36.9 0 4.9 NA ...
##  $ MaleBonusPercent         : num  6 24.6 39.2 3 0 2.4 0 0 0 63 ...
##  $ FemaleBonusPercent       : num  3.3 75.4 58.6 2 0 1.4 0 0 0 61 ...
##  $ MaleLowerQuartile        : num  47.7 56.2 66 76.3 30.4 ...
##  $ FemaleLowerQuartile      : num  52.3 43.8 34 23.7 69.6 ...
##  $ MaleLowerMiddleQuartile  : num  40.2 23.9 46 69.6 39.7 ...
##  $ FemaleLowerMiddleQuartile: num  59.8 76.1 54 30.4 60.3 ...
##  $ MaleUpperMiddleQuartile  : num  39.7 10.5 66 66.8 47.1 ...
##  $ FemaleUpperMiddleQuartile: num  60.3 89.5 34 33.2 52.9 ...
##  $ MaleTopQuartile          : num  53.6 26.1 63 60 73.5 ...
##  $ FemaleTopQuartile        : num  46.4 73.9 37 40 26.5 ...
##  $ CompanyLinkToGPGInfo     : chr  "https://www.aber.ac.uk/en/equality/equality-reports/genderpaygapreporting/" "" "https://www.fourseasons.com/towerbridge/" "" ...
##  $ ResponsiblePerson        : chr  "Dylan Jones (dej20@aber.ac.uk)" "Osmond Ramsay (Managing Director)" "Linda Stigter (Director of People and Culture)" "David Maccormack (Director)" ...
##  $ EmployerSize             : chr  "1000 to 4999" "250 to 499" "250 to 499" "1000 to 4999" ...
##  $ CurrentName              : chr  "'PRIFYSGOL ABERYSTWYTH' AND 'ABERYSTWYTH UNIVERSITY'" "\"RED BAND\" CHEMICAL COMPANY, LIMITED" "10 TRINITY SQUARE HOTEL LIMITED" "10442565 LTD" ...
##  $ SubmittedAfterTheDeadline: chr  "False" "True" "False" "False" ...
##  $ DueDate                  : chr  "2025/04/05 00:00:00" "2025/04/05 00:00:00" "2025/04/05 00:00:00" "2025/04/05 00:00:00" ...
##  $ DateSubmitted            : chr  "2025/03/28 09:02:53" "2025/04/11 07:57:32" "2025/01/21 14:19:52" "2025/04/03 12:14:38" ...

# There are some differences that will be key later on

Data Dictionary

Data are from https://gender-pay-gap.service.gov.uk/viewing/download

“The gender pay gap is the difference between the average earnings of men and women, expressed relative to men’s earnings. For example, ‘men earn 15% more than women per hour’.”

Details are available at https://www.gov.uk/guidance/gender-pay-gap-reporting-overview and descriptions of the data fields can be found under ““About CSV Data” on https://gender-pay-gap.service.gov.uk/viewing/download.

Variable	Description
EmployerName	Employer name
EmployerId
Address	Address
PostCode
CompanyNumber	Number from Companies House
SicCodes	Standard industrial classification of economic activities
DiffMeanHourlyPercent	As described at the above link: \(\frac{male~mean - female~mean}{male~mean} \times 100\%\)
DiffMedianHourlyPercent
DiffMeanBonusPercent
DiffMedianBonusPercent
MaleBonusPercent	Percentage of males receiving a bonus
FemaleBonusPercent
MaleLowerQuartile	Percentage of lower quartile earners that are male
FemaleLowerQuartile	For each quartile, male and female percentages sum to 100
MaleLowerMiddleQuartile
FemaleLowerMiddleQuartile
MaleUpperMiddleQuartile
FemaleUpperMiddleQuartile
MaleTopQuartile
FemaleTopQuartile
CompanyLinkToGPGInfo	URL to information on individual company web sites
ResponsiblePerson	Person responsible, often the Managing Director
EmployerSize	Character string with range of number of employees e.g., “500 to 999”
CurrentName	Matches EmployerName for all but 2%
SubmittedAfterTheDeadline	Boolean; 6% of companies submitted after the deadline
DueDate	Date by which the information shoudl have been submitted
DateSubmitted	Date and time submitted; no missing data

Mean hourly pay gap

pay.gap %>% 
  summarise(mean(DiffMeanHourlyPercent))

## # A tibble: 1 × 1
##   `mean(DiffMeanHourlyPercent)`
##                           <dbl>
## 1                          12.0

#non-piped version
summarise(pay.gap, mean(DiffMeanHourlyPercent)) #uses tidyverse functions

## # A tibble: 1 × 1
##   `mean(DiffMeanHourlyPercent)`
##                           <dbl>
## 1                          12.0

#single variable version not using summarise() 
mean(pay.gap$DiffMeanHourlyPercent)

## [1] 11.99673

# benefits of the summarise() funciton
summary_table <- summarise(pay.gap,
                           mean_pay_gap = mean(DiffMeanHourlyPercent),
                           std_pay_gap = sd(DiffMeanHourlyPercent),
                           n = n(),
                           sum_diffs = sum(DiffMeanHourlyPercent))

summary_table

## # A tibble: 1 × 4
##   mean_pay_gap std_pay_gap     n sum_diffs
##          <dbl>       <dbl> <int>     <dbl>
## 1         12.0        14.6 11240   134843.

#Note that this can look different if outputting to console or inline. Even with different numbers of decimal places / significant figures.

The mean hourly pay for males is 12% higher than for females

Vodafone pay gap

First I search for all rows with Vodafone in them. grepl() is great for searching text fields, and is very, very powerful. Then, I created a temporary datatable so that I can easily see the number of employees for each vodafone entry and select the largest. I also use select() to keep only the columns that I think will be useful to work out which “Vodafone” I am interested in. Then I extract only the relevant row. Note using print() as a trick to print the whole tibble

(vodafone.pay.gap <- filter(pay.gap, grepl("VODAFONE", EmployerName)))

## # A tibble: 2 × 27
##   EmployerName                EmployerId Address PostCode CompanyNumber SicCodes
##   <chr>                            <dbl> <chr>   <chr>    <chr>         <chr>   
## 1 VODAFONE GROUP SERVICES LI…      13374 Vodafo… RG14 2FN 03802001      "61900" 
## 2 VODAFONE LIMITED                 13375 Vodafo… RG14 2FN 01471587      "33200,…
## # ℹ 21 more variables: DiffMeanHourlyPercent <dbl>,
## #   DiffMedianHourlyPercent <dbl>, DiffMeanBonusPercent <dbl>,
## #   DiffMedianBonusPercent <dbl>, MaleBonusPercent <dbl>,
## #   FemaleBonusPercent <dbl>, MaleLowerQuartile <dbl>,
## #   FemaleLowerQuartile <dbl>, MaleLowerMiddleQuartile <dbl>,
## #   FemaleLowerMiddleQuartile <dbl>, MaleUpperMiddleQuartile <dbl>,
## #   FemaleUpperMiddleQuartile <dbl>, MaleTopQuartile <dbl>, …

grepl("VODAFONE", pay.gap$EmployerName)

##     [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##    [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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pay.gap %>% 
  filter(grepl("VODAFONE", EmployerName)) %>% 
  select(EmployerName, Address, EmployerSize, CurrentName)

## # A tibble: 2 × 4
##   EmployerName                    Address               EmployerSize CurrentName
##   <chr>                           <chr>                 <chr>        <chr>      
## 1 VODAFONE GROUP SERVICES LIMITED Vodafone House, The … 1000 to 4999 VODAFONE G…
## 2 VODAFONE LIMITED                Vodafone House, The … 5000 to 19,… VODAFONE L…

pay.gap %>% 
  filter(EmployerName=="VODAFONE") %>% 
  print(width=1000)

## # A tibble: 0 × 27
## # ℹ 27 variables: EmployerName <chr>, EmployerId <dbl>, Address <chr>, PostCode <chr>, CompanyNumber <chr>, SicCodes <chr>, DiffMeanHourlyPercent <dbl>, DiffMedianHourlyPercent <dbl>, DiffMeanBonusPercent <dbl>, DiffMedianBonusPercent <dbl>, MaleBonusPercent <dbl>, FemaleBonusPercent <dbl>, MaleLowerQuartile <dbl>, FemaleLowerQuartile <dbl>, MaleLowerMiddleQuartile <dbl>, FemaleLowerMiddleQuartile <dbl>, MaleUpperMiddleQuartile <dbl>, FemaleUpperMiddleQuartile <dbl>, MaleTopQuartile <dbl>, FemaleTopQuartile <dbl>, CompanyLinkToGPGInfo <chr>, ResponsiblePerson <chr>, EmployerSize <chr>, CurrentName <chr>, SubmittedAfterTheDeadline <lgl>, DueDate <chr>, DateSubmitted <chr>

# Important to look at the documentation/help files when using a new function
?grepl

# Shows us we can use the ignore.case argument
pay.gap %>% 
  filter(grepl("Vodafone", EmployerName)) %>% 
  select(EmployerName, Address, EmployerSize, CurrentName)

## # A tibble: 0 × 4
## # ℹ 4 variables: EmployerName <chr>, Address <chr>, EmployerSize <chr>,
## #   CurrentName <chr>

pay.gap %>% 
  filter(grepl("Vodafone", EmployerName, ignore.case = TRUE)) %>% 
  select(EmployerName, Address, EmployerSize, CurrentName)

## # A tibble: 2 × 4
##   EmployerName                    Address               EmployerSize CurrentName
##   <chr>                           <chr>                 <chr>        <chr>      
## 1 VODAFONE GROUP SERVICES LIMITED Vodafone House, The … 1000 to 4999 VODAFONE G…
## 2 VODAFONE LIMITED                Vodafone House, The … 5000 to 19,… VODAFONE L…

# Shows us it will find any row that has the string _within_ it
pay.gap %>% 
  filter(grepl("one", EmployerName, ignore.case = TRUE)) %>% 
  select(EmployerName, Address, EmployerSize, CurrentName)

## # A tibble: 141 × 4
##    EmployerName                          Address        EmployerSize CurrentName
##    <chr>                                 <chr>          <chr>        <chr>      
##  1 AUTONEUM GREAT BRITAIN LIMITED        Stanley Matth… 250 to 499   AUTONEUM G…
##  2 BARKER & STONEHOUSE LIMITED           Barker And St… 250 to 499   BARKER & S…
##  3 BLACKSTONE EUROPE LLP                 40 Berkeley S… 500 to 999   BLACKSTONE…
##  4 BLUESTONE RESORTS LIMITED             The Grange, C… 500 to 999   BLUESTONE …
##  5 BLUESTONES STAFFING N.I. LIMITED      Unit A Telfor… 250 to 499   BLUESTONES…
##  6 BROADSTONE CORPORATE BENEFITS LIMITED 100 Wood Stre… 250 to 499   BROADSTONE…
##  7 CAPITAL ONE (EUROPE) PLC              Trent House, … 1000 to 4999 CAPITAL ON…
##  8 CARDZONE LIMITED                      Hexgreave Hal… 500 to 999   CARDZONE L…
##  9 COMBINED PRECISION COMPONENTS LIMITED 150 Armley Ro… 250 to 499   COMBINED P…
## 10 CONEXIA LIMITED                       Golden Cross … 500 to 999   CONEXIA LI…
## # ℹ 131 more rows

## benefits of using filter over $ and []

pay.gap[1, c(1:4)]

## # A tibble: 1 × 4
##   EmployerName                                       EmployerId Address PostCode
##   <chr>                                                   <dbl> <chr>   <chr>   
## 1 'PRIFYSGOL ABERYSTWYTH' AND 'ABERYSTWYTH UNIVERSI…      19070 Aberys… SY23 3FL

pay.gap[1, c("EmployerName", "Address", "EmployerSize", "CurrentName")]

## # A tibble: 1 × 4
##   EmployerName                                  Address EmployerSize CurrentName
##   <chr>                                         <chr>   <chr>        <chr>      
## 1 'PRIFYSGOL ABERYSTWYTH' AND 'ABERYSTWYTH UNI… Aberys… 1000 to 4999 'PRIFYSGOL…

pay.gap[grepl("VODAFONE", pay.gap$EmployerName), c("EmployerName", "Address", "EmployerSize", "CurrentName")]

## # A tibble: 2 × 4
##   EmployerName                    Address               EmployerSize CurrentName
##   <chr>                           <chr>                 <chr>        <chr>      
## 1 VODAFONE GROUP SERVICES LIMITED Vodafone House, The … 1000 to 4999 VODAFONE G…
## 2 VODAFONE LIMITED                Vodafone House, The … 5000 to 19,… VODAFONE L…

# More prone to mistakes than filter()
# Output is a data.frame, not specifically a tibble


# Note the lack of flexibility in manually finding the rows and hardcoding the indexing

which(grepl("VODAFONE", pay.gap$EmployerName))

## [1] 10676 10677

grep("VODAFONE", pay.gap$EmployerName)

## [1] 10676 10677

pay.gap[c(10676, 10677), c("EmployerName", "Address", "EmployerSize", "CurrentName")]

## # A tibble: 2 × 4
##   EmployerName                    Address               EmployerSize CurrentName
##   <chr>                           <chr>                 <chr>        <chr>      
## 1 VODAFONE GROUP SERVICES LIMITED Vodafone House, The … 1000 to 4999 VODAFONE G…
## 2 VODAFONE LIMITED                Vodafone House, The … 5000 to 19,… VODAFONE L…

pay.gap.old[c(10676, 10677), c("EmployerName", "Address", "EmployerSize", "CurrentName")]

## # A tibble: 2 × 4
##   EmployerName                        Address           EmployerSize CurrentName
##   <chr>                               <chr>             <chr>        <chr>      
## 1 WEIGHTMANS LLP                      100 Old Hall Str… 1000 to 4999 WEIGHTMANS…
## 2 WEIL, GOTSHAL & MANGES (LONDON) LLP 110 Fetter Lane,… 250 to 499   WEIL, GOTS…

(vodafone.pay.gap.old <- filter(pay.gap.old, grepl("VODAFONE", EmployerName)))

## # A tibble: 3 × 27
##   EmployerName                EmployerId Address PostCode CompanyNumber SicCodes
##   <chr>                            <dbl> <chr>   <chr>    <chr>         <chr>   
## 1 VODAFONE GLOBAL ENTERPRISE…      13372 Vodafo… RG14 2FN 02844851      "61900" 
## 2 VODAFONE GROUP SERVICES LI…      13374 Vodafo… RG14 2FN 03802001      "61900" 
## 3 VODAFONE LIMITED                 13375 Vodafo… RG14 2FN 01471587      "33200,…
## # ℹ 21 more variables: DiffMeanHourlyPercent <dbl>,
## #   DiffMedianHourlyPercent <dbl>, DiffMeanBonusPercent <dbl>,
## #   DiffMedianBonusPercent <dbl>, MaleBonusPercent <dbl>,
## #   FemaleBonusPercent <dbl>, MaleLowerQuartile <dbl>,
## #   FemaleLowerQuartile <dbl>, MaleLowerMiddleQuartile <dbl>,
## #   FemaleLowerMiddleQuartile <dbl>, MaleUpperMiddleQuartile <dbl>,
## #   FemaleUpperMiddleQuartile <dbl>, MaleTopQuartile <dbl>, …

# Even in this dataset there have been changes in the last week!
pay.gap_updated <- read_csv("UK Gender Pay Gap Data - 2024 to 2025 UPDATED.csv")

## Rows: 11243 Columns: 27
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): EmployerName, Address, PostCode, CompanyNumber, SicCodes, CompanyL...
## dbl (15): EmployerId, DiffMeanHourlyPercent, DiffMedianHourlyPercent, DiffMe...
## lgl  (1): SubmittedAfterTheDeadline
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

changes <- anti_join(pay.gap_updated, pay.gap)

## Joining with `by = join_by(EmployerName, EmployerId, Address, PostCode,
## CompanyNumber, SicCodes, DiffMeanHourlyPercent, DiffMedianHourlyPercent,
## DiffMeanBonusPercent, DiffMedianBonusPercent, MaleBonusPercent,
## FemaleBonusPercent, MaleLowerQuartile, FemaleLowerQuartile,
## MaleLowerMiddleQuartile, FemaleLowerMiddleQuartile, MaleUpperMiddleQuartile,
## FemaleUpperMiddleQuartile, MaleTopQuartile, FemaleTopQuartile,
## CompanyLinkToGPGInfo, ResponsiblePerson, EmployerSize, CurrentName,
## SubmittedAfterTheDeadline, DueDate, DateSubmitted)`

changes

## # A tibble: 27 × 27
##    EmployerName               EmployerId Address PostCode CompanyNumber SicCodes
##    <chr>                           <dbl> <chr>   <chr>    <chr>         <chr>   
##  1 ASSA ABLOY GLOBAL SOLUTIO…      21575 Imperi… RG2 0TD  02590364      "80200" 
##  2 CITY RESPONSE LIMITED            3299 7th Fl… NW1 3AX  04471280      "43210,…
##  3 CROWN AGENTS BANK LIMITED       22627 3 Lond… SE1 9SG  02334687      "64999,…
##  4 DHL ECOMMERCE UK LIMITED          610 Capito… LS27 0WH 00965783      "53201,…
##  5 DLA PIPER UK LLP                 4161 160 Al… EC1A 4HT OC307847       <NA>   
##  6 GRANITE RESTAURANTS LTD         14833 Brook … EX5 1GD  08475040      "56102" 
##  7 HALEWOOD ARTISANAL SPIRIT…       5873 3 Spir… NN10 0FN 03699814      "70100" 
##  8 L'ARCHE                          7458 L'Arch… SE1 7JB  01055041      "87200,…
##  9 LINK FINANCIAL OUTSOURCIN…       7680 11 Bel… SW1V 1RB 07059696      "82990" 
## 10 LONDON & PARTNERS LIMITED       21980 169 (L… SE1 0LL  07493460      "70210,…
## # ℹ 17 more rows
## # ℹ 21 more variables: DiffMeanHourlyPercent <dbl>,
## #   DiffMedianHourlyPercent <dbl>, DiffMeanBonusPercent <dbl>,
## #   DiffMedianBonusPercent <dbl>, MaleBonusPercent <dbl>,
## #   FemaleBonusPercent <dbl>, MaleLowerQuartile <dbl>,
## #   FemaleLowerQuartile <dbl>, MaleLowerMiddleQuartile <dbl>,
## #   FemaleLowerMiddleQuartile <dbl>, MaleUpperMiddleQuartile <dbl>, …

pay.gap_updated[c(10676, 10677), c("EmployerName", "Address", "EmployerSize", "CurrentName")]

## # A tibble: 2 × 4
##   EmployerName                   Address                EmployerSize CurrentName
##   <chr>                          <chr>                  <chr>        <chr>      
## 1 VOCALINK INTERNATIONAL LIMITED 1 Angel Lane, London,… 500 to 999   VOCALINK I…
## 2 VOCALINK LIMITED               1 Angel Lane, London,… 500 to 999   VOCALINK L…

# How can we find the largest of the 2 Vodafone companies?
pay.gap %>% 
  filter(grepl("VODAFONE", EmployerName), EmployerSize == max(vodafone.pay.gap$EmployerSize))

## # A tibble: 1 × 27
##   EmployerName     EmployerId Address            PostCode CompanyNumber SicCodes
##   <chr>                 <dbl> <chr>              <chr>    <chr>         <chr>   
## 1 VODAFONE LIMITED      13375 Vodafone House, T… RG14 2FN 01471587      "33200,…
## # ℹ 21 more variables: DiffMeanHourlyPercent <dbl>,
## #   DiffMedianHourlyPercent <dbl>, DiffMeanBonusPercent <dbl>,
## #   DiffMedianBonusPercent <dbl>, MaleBonusPercent <dbl>,
## #   FemaleBonusPercent <dbl>, MaleLowerQuartile <dbl>,
## #   FemaleLowerQuartile <dbl>, MaleLowerMiddleQuartile <dbl>,
## #   FemaleLowerMiddleQuartile <dbl>, MaleUpperMiddleQuartile <dbl>,
## #   FemaleUpperMiddleQuartile <dbl>, MaleTopQuartile <dbl>, …

vodafone.pay.gap$EmployerSize

## [1] "1000 to 4999"   "5000 to 19,999"

pay.gap <- mutate(pay.gap, 
       EmployerSize = factor(EmployerSize, c("Not Provided", "Less than 250", "250 to 499", "500 to 999", "1000 to 4999", "5000 to 19,999",  "20,000 or more")), ordered = is.ordered(c("Not Provided", "Less than 250", "250 to 499", "500 to 999", "1000 to 4999", "5000 to 19,999",  "20,000 or more")))

# Example using a different (wrong) ordering for the factor levels
# pay.gap <- mutate(pay.gap,
# EmployerSize = factor(EmployerSize, c("Not Provided", "5000 to 19,999",  "Less than 250", "250 to 499", "500 to 999", "1000 to 4999",  "20,000 or more")), ordered = is.ordered(c("Not Provided", "5000 to 19,999",  "Less than 250", "250 to 499", "500 to 999", "1000 to 4999",  "20,000 or more")))

vodafone.pay.gap <- filter(pay.gap, grepl("VODAFONE", EmployerName)) %>% 
  arrange(EmployerSize) %>% #sort by EmployerSize
  slice_tail()  #slices tail one row

vodafone.pay.gap

## # A tibble: 1 × 28
##   EmployerName     EmployerId Address            PostCode CompanyNumber SicCodes
##   <chr>                 <dbl> <chr>              <chr>    <chr>         <chr>   
## 1 VODAFONE LIMITED      13375 Vodafone House, T… RG14 2FN 01471587      "33200,…
## # ℹ 22 more variables: DiffMeanHourlyPercent <dbl>,
## #   DiffMedianHourlyPercent <dbl>, DiffMeanBonusPercent <dbl>,
## #   DiffMedianBonusPercent <dbl>, MaleBonusPercent <dbl>,
## #   FemaleBonusPercent <dbl>, MaleLowerQuartile <dbl>,
## #   FemaleLowerQuartile <dbl>, MaleLowerMiddleQuartile <dbl>,
## #   FemaleLowerMiddleQuartile <dbl>, MaleUpperMiddleQuartile <dbl>,
## #   FemaleUpperMiddleQuartile <dbl>, MaleTopQuartile <dbl>, …

# But what I am really doing here is finding the main Vodafone company. Since there are only 2 companies, and I anticipate I will only need to do this once... I can just look up the name myself

vodafone.pay.gap <- filter(pay.gap, grepl("VODAFONE LIMITED", EmployerName))
vodafone.pay.gap

## # A tibble: 1 × 28
##   EmployerName     EmployerId Address            PostCode CompanyNumber SicCodes
##   <chr>                 <dbl> <chr>              <chr>    <chr>         <chr>   
## 1 VODAFONE LIMITED      13375 Vodafone House, T… RG14 2FN 01471587      "33200,…
## # ℹ 22 more variables: DiffMeanHourlyPercent <dbl>,
## #   DiffMedianHourlyPercent <dbl>, DiffMeanBonusPercent <dbl>,
## #   DiffMedianBonusPercent <dbl>, MaleBonusPercent <dbl>,
## #   FemaleBonusPercent <dbl>, MaleLowerQuartile <dbl>,
## #   FemaleLowerQuartile <dbl>, MaleLowerMiddleQuartile <dbl>,
## #   FemaleLowerMiddleQuartile <dbl>, MaleUpperMiddleQuartile <dbl>,
## #   FemaleUpperMiddleQuartile <dbl>, MaleTopQuartile <dbl>, …

# An easy way of seeing just the columns with the quartile information
select(vodafone.pay.gap, contains("quartile"))

## # A tibble: 1 × 8
##   MaleLowerQuartile FemaleLowerQuartile MaleLowerMiddleQuartile
##               <dbl>               <dbl>                   <dbl>
## 1                53                  47                    63.8
## # ℹ 5 more variables: FemaleLowerMiddleQuartile <dbl>,
## #   MaleUpperMiddleQuartile <dbl>, FemaleUpperMiddleQuartile <dbl>,
## #   MaleTopQuartile <dbl>, FemaleTopQuartile <dbl>

The gender pay gap for Vodafone Limited is 9.6%

Looking at the quartile gender splits shows us that for Vodafone Limited there is a relatively equal gender split amongst the lowest paid 25% of employees (53% male, 47% female), but a notable imbalance amongst the highest paid 25% of employees (68.0% male, 31.1% female)

Is the gap higher/lower for public sector employers than private organisations?

First I add an extra column called Sector indicating whether the employer is public sector or private using mutate(). Then I use group_by() to split the data by Sector and summarise() to produce counts and means for each sector

pay.gap <- mutate(pay.gap, 
                  Sector=ifelse(is.na(CompanyNumber), "Public Sector", "Business / Charity"))


# Why the read.csv() function isn't always as robust as read_csv()
summarise_all(pay.gap, ~ sum(is.na(.x)))

## # A tibble: 1 × 29
##   EmployerName EmployerId Address PostCode CompanyNumber SicCodes
##          <int>      <int>   <int>    <int>         <int>    <int>
## 1            0          0      34       58          1483      910
## # ℹ 23 more variables: DiffMeanHourlyPercent <int>,
## #   DiffMedianHourlyPercent <int>, DiffMeanBonusPercent <int>,
## #   DiffMedianBonusPercent <int>, MaleBonusPercent <int>,
## #   FemaleBonusPercent <int>, MaleLowerQuartile <int>,
## #   FemaleLowerQuartile <int>, MaleLowerMiddleQuartile <int>,
## #   FemaleLowerMiddleQuartile <int>, MaleUpperMiddleQuartile <int>,
## #   FemaleUpperMiddleQuartile <int>, MaleTopQuartile <int>, …

summarise_all(pay.gap_alt, ~ sum(is.na(.x)))

##   EmployerName EmployerId Address PostCode CompanyNumber SicCodes
## 1            0          0       0        0             0        0
##   DiffMeanHourlyPercent DiffMedianHourlyPercent DiffMeanBonusPercent
## 1                     0                       0                 2719
##   DiffMedianBonusPercent MaleBonusPercent FemaleBonusPercent MaleLowerQuartile
## 1                   2719                0                  0                 0
##   FemaleLowerQuartile MaleLowerMiddleQuartile FemaleLowerMiddleQuartile
## 1                   0                       0                         0
##   MaleUpperMiddleQuartile FemaleUpperMiddleQuartile MaleTopQuartile
## 1                       0                         0               0
##   FemaleTopQuartile CompanyLinkToGPGInfo ResponsiblePerson EmployerSize
## 1                 0                    0                 0            0
##   CurrentName SubmittedAfterTheDeadline DueDate DateSubmitted
## 1           0                         0       0             0

# Where CompanyNumber is empty, read_csv() identifies this as an NA. But read.csv() leaves it as a string.


# Why you can't use == NA
( public_sample <- filter(pay.gap, Sector == "Public Sector")[1:10,] )

## # A tibble: 10 × 29
##    EmployerName               EmployerId Address PostCode CompanyNumber SicCodes
##    <chr>                           <dbl> <chr>   <chr>    <chr>         <chr>   
##  1 A.W. Jenkinson Forest Pro…      23553 Clifto… CA10 2EY <NA>           <NA>   
##  2 Aberdein Considine              16349 5-9 Bo… AB11 6DN <NA>          "68310,…
##  3 Abingdon & Witney College         799 Hollow… OX28 6NE <NA>          "1,\n85…
##  4 Activate Learning               17170 Oxpens… OX1 1SA  <NA>          "1,\n85…
##  5 Activate Learning Educati…      18548 Oxpens… OX1 1SA  <NA>          "1,\n85…
##  6 Addington School                21963 Adding… RG5 3EU  <NA>           <NA>   
##  7 Adrian Flux Insurance Ser…      17234 East W… PE32 1HN <NA>          "66220" 
##  8 Advanced Forwarding Limit…      20576 606 Ho… BD4 6SG  <NA>           <NA>   
##  9 Advanced Supply Chain Gro…      20575 606 Ho… BD4 6SG  <NA>           <NA>   
## 10 Advisory, Conciliation an…        496 8th Fl… SW1H 0TL <NA>          "1,\n84…
## # ℹ 23 more variables: DiffMeanHourlyPercent <dbl>,
## #   DiffMedianHourlyPercent <dbl>, DiffMeanBonusPercent <dbl>,
## #   DiffMedianBonusPercent <dbl>, MaleBonusPercent <dbl>,
## #   FemaleBonusPercent <dbl>, MaleLowerQuartile <dbl>,
## #   FemaleLowerQuartile <dbl>, MaleLowerMiddleQuartile <dbl>,
## #   FemaleLowerMiddleQuartile <dbl>, MaleUpperMiddleQuartile <dbl>,
## #   FemaleUpperMiddleQuartile <dbl>, MaleTopQuartile <dbl>, …

is.na(public_sample$CompanyNumber)  #correct approach

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

public_sample$CompanyNumber == NA

##  [1] NA NA NA NA NA NA NA NA NA NA

is.na(public_sample$SicCodes) #correct

##  [1]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE

public_sample$SicCodes == NA

##  [1] NA NA NA NA NA NA NA NA NA NA

public_sample$SicCodes == "66220"  #correct

##  [1]    NA FALSE FALSE FALSE FALSE    NA  TRUE    NA    NA FALSE

public_sample$SicCodes == public_sample$CompanyNumber

##  [1] NA NA NA NA NA NA NA NA NA NA

# Some people used separate variables for the different sectors

public_sector_gap <- pay.gap$DiffMeanHourlyPercent[pay.gap$Sector == "Public Sector"]
private_sector_gap <- pay.gap$DiffMeanHourlyPercent[pay.gap$Sector == "Business / Charity"]

mean(public_sector_gap)

## [1] 11.38299

mean(private_sector_gap)

## [1] 12.09001

# But it is usually better to avoid creating new variables for intermediate steps as this will clutter the environment

pay.gap %>% 
  group_by(Sector) %>% 
  summarise(frequency=n(), mean=mean(DiffMeanHourlyPercent))

## # A tibble: 2 × 3
##   Sector             frequency  mean
##   <chr>                  <int> <dbl>
## 1 Business / Charity      9757  12.1
## 2 Public Sector           1483  11.4

The gender pay gap is smaller for the 1,483 companies in the public sector, at 11.4%, than for businesses or charities, where the gap is 12.1%

Does submitting late predict pay gap?

Again, here I use group_by() and summarise() together

pay.gap %>% 
  group_by(SubmittedAfterTheDeadline) %>% 
  summarise(frequency=n(), mean=mean(DiffMeanHourlyPercent))

## # A tibble: 2 × 3
##   SubmittedAfterTheDeadline frequency  mean
##   <lgl>                         <int> <dbl>
## 1 FALSE                         10628  12.0
## 2 TRUE                            612  11.2

Those 10628 employers submitting on time have a pay gap of 12%, and the 612 employers submitting late have a pay gap of 11.2%

But some of you noticed a discrepancy in the data…

pay.gap <- mutate(pay.gap,
                  late_check = DateSubmitted > DueDate)

count(pay.gap, SubmittedAfterTheDeadline, late_check)

## # A tibble: 3 × 3
##   SubmittedAfterTheDeadline late_check     n
##   <lgl>                     <lgl>      <int>
## 1 FALSE                     FALSE      10284
## 2 FALSE                     TRUE         344
## 3 TRUE                      TRUE         612

# This shows that there are 344 employers whose submission date is later than their deadline date, but have "FALSE" in the variable for `SubmittedAfterTheDeadline`
# It seems from the dataset documentation that these are employers who initially submitted on time, and then submitted an update afterwards.

# Note that the "DateSubmitted > DueDate" approach happens to work here because the date-time character string is ordered in a way where time is perfectly matched to "alphabetical" ordering of the numbers- year, month, date

# The more robust approach is to turn these into proper date-time variable types

pay.gap <- mutate(pay.gap,
                  DateSubmitted_d = ymd_hms(DateSubmitted),
                  DueDate_d = ymd_hms(DueDate),
                  late_check_d = DateSubmitted_d > DueDate_d)

count(pay.gap, SubmittedAfterTheDeadline, late_check_d)

## # A tibble: 3 × 3
##   SubmittedAfterTheDeadline late_check_d     n
##   <lgl>                     <lgl>        <int>
## 1 FALSE                     FALSE        10284
## 2 FALSE                     TRUE           344
## 3 TRUE                      TRUE           612

Does a change of employer name predict a worse pay gap?

pay.gap <- mutate(pay.gap, NameChange=EmployerName!=CurrentName)

pay.gap %>% 
  group_by(NameChange) %>% 
  summarise(frequency=n(), mean=mean(DiffMeanHourlyPercent))

## # A tibble: 2 × 3
##   NameChange frequency  mean
##   <lgl>          <int> <dbl>
## 1 FALSE          11138  12.0
## 2 TRUE             102  13.1

The 102 employers that change their name have a pay gap of 13.1% compared to 12% for those employers who do not change their name

Case study: Gender Pay Gap

Tim Mullett