Pie Graph

Introduction

In partnership with the European Bioinformatics Institute, the US National Human Genome Research Institute manage the GWAS Catalog, where curators compile information from human genetic association research. This information comes from GWAS that are reported on PubMed and then go through two rounds of data extraction and validation. Alice B. Popejoy and Stephanie M. Fullerton analyzed data from GWAS in 2009 to examine the demographic divisions of variants associated with disease traits. In 2016, they decided to update this analysis to keep up with the information that has been added to GWAS since.

Create data

euro_asian_other2009 <- c(96,3,1)
labels2009 <- c("European\nAncestry", "\n\nAsian\nAncestry", "Other")
pie(x = euro_asian_other2009,
    labels = labels2009)

euro_asian_other2016 <- c(81,14,5)
labels2016 <- c("European\nAncestry", "\nAsian\nAncestry", "Other")
pie(x = euro_asian_other2016,
    labels = labels2016)

Pie graphs

# 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_asian_other2009,
    labels = labels2009,
    main = "2009",
    init.angle = -82,
    radius = 0.7,
    col = c(7,5,2))

# pie graph 2
# add main, init.angle, radius, and col
pie(x = euro_asian_other2016,
    labels = labels2016,
    main = "2016",
    init.angle = -82,
    radius = 0.7,
    col = c(7,5,2))

Bar graphs

# 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)