Introduction

Alice B. Popejoy and Stephanie M. Fullerton collected data to support that certain drugs may be less effective, or even unsafe, in some populations because of genetic differences. To determine ancestry, they analysed the sample descriptions included in the GWAS Catalog with an approach similar to that used in 2009. Data was collected again in 2016 as the 2009 data wasn’t diverse and to avoid genomic medicine being of benefit merely to “a privileged few”. In 2016, the proportion of individuals included in GWAS who are not of European descent has increased to nearly 20%.

Create data

Create vectors to contain the data and labels to make the pie graphs at the top of figures.

Each vector has 3 elements: European ancestry, Asian ancestry, and other non-European ancestry.

DO NOT name your vector for the labels “labels”, since this is the name of an existing R function.

Include new line characters in the text as needed to improve spacing.

ancestry <- c("European","Asian","Other")
euro_non_euro1 <- c(96,3,1)
euro_non_euro2 <- c(81,14,5)

Pie graphs

  1. Create a 1 x 2 grid using the command par(mfrow = c(1,2))
  2. Plot the 2009 data on the left and 2016 data on the right.
  3. This will require setting up the pie command twomce
  4. Use the argument main = … to add a title to above the plots
  5. Set the argument init.angle = … to -82. Experiment with how this affects the plot.
  6. Set the argument radius = … to 1. Experiment with how this affects the plot.
  7. Set the argument col = … to c(1,2,3), then experiment with different numbers. Try to make it ugly.
# set up par()
par(mfrow = c(1,2), mar = c(2,3,1,5))

#pie graphs 1
# add main, init.angle, radius, and col
pie(x = euro_non_euro1, main = 2009, init.angle = -82, radius = 1, col = c(1,2,3))

# pie graph 2
# add main, init.angle, radius, and col
pie(x = euro_non_euro2, main = 2016, init.angle = -82, radius = 1, col = c(1,2,3))

Bar graphs

If you want, you can examine this code below to see how stracked bar graphs are made

# data
dat2016 <- c(14, 3,1,0.54,0.28,0.08,0.05)
dat2016_rev <- rev(dat2016)
barplotdata2016 <- matrix(c(dat2016_rev))

# labels
labels_x <- rev(c("Asian","African","Mixed", "Hispanic &\nLatin American",
                        "Pacific Islander","Arab & Middle East","Native peoples"))

par(mfrow = c(1,1))

barplot(barplotdata2016,
        width = 0.01, 
        xlim = c(0,0.1),
         axes = F,
        col = c(1,2,3,4,5,6,7),
        legend.text = labels_x)