Update 29/06/2017: The approach outlined in this blogpost was taken under the assumption that gender is binary but that is incorrect. Do read on as it might inspire you to do better and if you do wish to contribute to the topic check out Forwards.

The representation of women in technology is gaining more and more attention. In the R community, R-Ladies has grown massively over the past year and been awarded funding by the R-Consortium. The useR! conference has offered diversity scholarships (for underrepesented minorities in general) for the conferences in 2016 and 2017. This is a good thing but it does make me wonder: how do we measure success? how will we know these actions have lead to a better situation? And by how much?

This blogpost by Maelle Salmon and this one by Colin Fay inspired me to explore this issue as follows: if we could somehow measure the representation of women currently in our community we can keep track of this. As Mango are organisers of the EARL conference it would also help us assess our own performance.

Before I jump to the code I should mention that this is a simplified approach to the problem. The issue of equal representation is much more complex and the subject of a separate branch of research. This blogpost is an attempt to approach the problem of data collection and measuring progress from a different angle.

So I set out to measure the proportion of women among the faces of #rstats. I used Colin’s code to first retrieve the profile pictures.

# Taken from http://colinfay.me/playing-with-rstats-and-microsoft-computer-vision-api/
library(dplyr)
library(rtweet)
token <- create_token(app = "rstats_faces", consumer_key = CONSUMER_KEY, 
                      consumer_secret = CONSUMER_SECRET)

users <- search_users(q= '#rstats', n = 1000, parse = TRUE) %>%
  unique() %>% 
  mutate(profile_image_url = gsub("_normal", "", profile_image_url))

Then I used the Microsoft Face API to tell me if the picture was of a man or a woman. Colin used lapply but I thought I’d give purrr::map a try.

library(httr)
library(purrr)
getGender <- function(myURLs){
  requestURL <- "https://westus.api.cognitive.microsoft.com/face/v1.0/detect"
  parameters <- list(returnFaceAttributes="gender")

  result <- map(myURLs, 
                function(myURL) {
                  Sys.sleep(3)
                  POST(requestURL, query=parameters, 
                       add_headers('Content-Type'="application/json",
                                   'Ocp-Apim-Subscription-Key'=FACE_API_KEY), 
                       body=sprintf('{"url":"%s"}', myURL)) %>% 
                    content("parsed")
                  } 
                ) %>% 
    # check if a face was detected and no error was returned
    map_chr(~ ifelse(length(.x)>0 && !("error"%in%names(.x)), .x[[1]]$faceAttribute$gender, NA))

  return(result)
}

usersWithGender <- users %>% 
  mutate(gender= getGender(profile_image_url)) %>% 
  select(user_id, gender)

From Maelle’s great collage I already knew not all profile pictures are of people and the Face API does not return any predictions in these cases. There are also some instances the Face API wrongfully didn’t detect a face (sorry Hillary Parker). Altogether the Face API detected 314 faces and the proportion of women among the #rstat faces is 20.4%.

This value is our first estimate and to assess its accuracy I want to compare it with other estimates. I have tried my best to find other sources but that has proven harder than I thought. Below I have plotted my estimate with estimates based on Dutch labour statistics¹, Google diversity statistics² and the StackOverflow Developer Survey³.

Although the percentage of women in technology at Google and in the Netherlands are based on an entirely different populations our estimate is close to these two. I have no specific knowledge about the #rstats community that would make it different from the wider tech community so I would expect the estimates to be close to each other. However there are some issues with the general estimates. The industry categorisation of the Netherlands means that we’re not capturing all tech jobs (for example, those in finance aren’t included). Also there has been quite the controversy on Google’s gender gap which may mean that estimate is too low as well. And finally in the discussion on the results of the StackOverflow Developer Survey it is stated their estimate is probably too low.

If I knew about these issues then why did I use them in the first place? Because it’s the best I could find and that is a problem. Without an accurate measure and without knowing how accurate my own measure is, it is clearly impossible to use it as an KPI ⁴. I will come back to this point later on but first I want to show how this methodology to measure the proportion of women can still be useful.

library(rvest)
earl2016URL<- "https://earlconf.com/2016/london/speakers"
# We used the SelectorGadget to determine the name of the xml node containing the image tage
speakers <- read_html(earl2016URL) %>% 
  html_nodes(".col-md-3 img") %>% 
  html_attr("src")

earl2016Genders <- getGender(paste0(earl2016URL, speakers))

library(jsonlite)
earlLondon2017 <- read_json("https://earlconf.com/london/londonSpeakers.json")
earlLondon2017Genders <- earlLondon2017 %>% 
  map("headshot") %>% 
  map(~paste0("https://earlconf.com/img/headshots/london/", .x)) %>% 
  map_chr(~getGender(.))

earlSF2017 <- read_json("https://earlconf.com/sanfrancisco/sanFranciscoSpeakers.json")
earlSF2017Genders <- earlSF2017 %>% 
  map("headshot") %>% 
  map(~paste0("https://earlconf.com/img/headshots/sanfrancisco/", .x)) %>% 
  map_chr(~getGender(.))

user2016URL <- "http://schedule.user2016.org/directory/speakers"
user2016Genders <- read_html(user2016URL) %>% 
  html_nodes("img") %>% 
  html_attr("src") %>% 
  map(~paste0("http:", .x)) %>% 
  map_chr(~getGender(.))