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Thursday, 5 May 2016

Overview of gender breakdown across types of staff

I have spent a large part of the last month grappling with Athena SWAN data for the School of Medicine at Cardiff University. To apply for this equality award, the Athena SWAN Self Assessment Team needed to look at the gender breakdown of students and staff in the School.

Here is a bar chart I generated with the data to give an overview of the School:

Here's is the script I used to generate this data and some other graphs that explore using ggplot to make bar charts.

# overview of the School in terms of gender for our Athena SWAN application

# the data is here
link <- "https://raw.githubusercontent.com/brennanpincardiff/AthenaSWANBenchmarkData/master/asStaffOverview_comparedtype_20160505.xlsx"

# the download.file() function downloads and saves the file with the name given
download.file(url=link, destfile="file.xlsx", mode="wb")

# read in the data...
data <- read_excel("file.xlsx")

# calculate Female and Male as a percentage
data$tot <- data$f + data$m
data$Female <- (data$f / data$tot) *100 
data$Male <- (data$m / data$tot) *100

# first a simple plot of the Cardiff School of Medicine Data
# plot the number of females of each type of member of the School
# pull out the Cardiff data
data.c <- data[data$uni == "Cardiff",2:5]

# make the object with the Cardiff subset  
g <- ggplot(data.c,
       aes(x = type,
           y = f)) +
     theme(axis.text.x = element_text(size = 12))

# show the graph

# The order of the bars is not what I want.
# take the order from the data file
types <- data.c$type

# apply this order to the graph
# http://stackoverflow.com/questions/5208679/order-bars-in-ggplot2-bar-graph
g + scale_x_discrete(limits = types)

# I want to put female and male on the same graph so...
# pull out and melt the Cardiff data so that have f and m for each type in long format
data.melt <- melt(data[data$uni == "Cardiff",2:5], 
                  id.var = c("type", "year"))

# put the data in an object
g <- ggplot(data.melt, 
       aes(x = type, 
           y = value,
           fill = variable)) + 
      scale_x_discrete(limits = types)

# show with geom_bar
# this is a stacked bar chart showing both female and male numbers
g + geom_bar(stat="identity")

# show female and male in separate bars using position arguement
g + geom_bar(stat="identity", position="dodge")

# have as stacked bar chart but normalised to one. 
g +  geom_bar(stat="identity", position="fill")

# I really want a percentage of each stacked 
# to allow different parts of the School to be compared
# remove the raw numbers
data.s <- data[, -c(3:6)]
data.melt.s <- melt(data.s[data.s$uni == "Cardiff",2:4], 
                  id.var = c("type"))  # id = or id.var are same
colnames(data.melt.s) <- c("type", "Gender", "Percent")

# make a new graph
p <- ggplot(data.melt.s, 
           aes(x = type, 
               y = Percent,
               fill = Gender)) +
      geom_bar(stat="identity") + 
      scale_x_discrete(limits = types) +
      ylab("Percentage of staff") +
      xlab("") +
      theme_bw() +
      theme(axis.text.x = element_text(size = 14))
p # show the graph

# adjust the colours using scale_fill_brewer()
p + scale_fill_brewer(palette = 16, direction=-1) +
  # and add a title
    ggtitle("Gender breakdown of different members of the School of Medicine") +
    theme(axis.title.y = element_text(size = 14 ))

# use data from Imperial & Leeds as comparison
# use the facet_wrap function to make all looks the same. 
# remove Prof & Support as we only have for Cardiff
data.s <- data.s[-5,]
types <- types[-5]

# melt the data into the format for the bar chart. 
data.melt.s <- melt(data.s, 
                    id.var = c("uni", "type"))
colnames(data.melt.s) <- c("uni", "type", "Gender", "Percent")

# this is a final plot with three Medical Schools (it's a bit big). 
p <- ggplot(data.melt.s, 
            aes(x = type, 
                y = Percent,
                group = uni,
                fill = Gender)) +
  geom_bar(stat="identity") +
  facet_wrap(~uni) +    # this function gives three plots
  scale_x_discrete(limits = types) +
  ylab("Percentage of staff") +
  xlab("") +
  theme_bw() +
  scale_fill_brewer(palette = 16, direction=-1) +
  ggtitle("Gender breakdown of three Schools of Medicine") +
  theme(strip.text.x = element_text(size = 14, colour = "black")) +
  theme(axis.text.x = element_text(size = 11))

p # show the plot


I looked up these pages for help:

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