Class 5: Data Viz with ggplot

Author

Cecilia Wang (PID:18625854)

Background

There are a lot of ways to make figures in R. These include so-called “base R” graphics. (e.g. plot()) and tones of add-on packages like ggplot2.

For example here we make a same plot with both:

head(cars)
  speed dist
1     4    2
2     4   10
3     7    4
4     7   22
5     8   16
6     9   10
plot(cars)

First I need to install the package with the command install.packages().

N.B. We never run an install cmd in a quarto code chunk or we will end up re-installing packages many many times- whcih is not we want!

Every time we want to use one of these “add-on” packages we need to load it up in R withlibrary() function:

library(ggplot2)
ggplot(cars)

Every ggplot need at least 3 things:

  • The data, the stuff you want to plotted
  • The aesthtics, how the data map to the plot
  • The geometry, they type of plot
p <- ggplot(cars)+
  aes(x=speed, y=dist)+
  geom_point()+
  geom_smooth(method = "lm", se = FALSE, color="purple")+
  labs(title = "Stopping distance of old cars", subtitle = "data from the `cars` object", x="Speed (mph)", y="Distance (ft)")

render it out

p
`geom_smooth()` using formula = 'y ~ x'

p+theme_bw()
`geom_smooth()` using formula = 'y ~ x'

Gene expression plot

We can read the input data data the class website

url <- "https://bioboot.github.io/bimm143_S20/class-material/up_down_expression.txt"
genes <- read.delim(url)
head(genes)
        Gene Condition1 Condition2      State
1      A4GNT -3.6808610 -3.4401355 unchanging
2       AAAS  4.5479580  4.3864126 unchanging
3      AASDH  3.7190695  3.4787276 unchanging
4       AATF  5.0784720  5.0151916 unchanging
5       AATK  0.4711421  0.5598642 unchanging
6 AB015752.4 -3.6808610 -3.5921390 unchanging
nrow(genes)
[1] 5196

A first version plot

ggplot(genes)+
  aes(Condition1, Condition2) +
  geom_point()

table(genes$State)

      down unchanging         up 
        72       4997        127

Version 2 plot, let’s color by State so we can see the “u”p and “down” significant genes compared to all the unchanging genes.

ggplot(genes)+
  aes(Condition1, Condition2, col=State) +
  geom_point()

Version 3 plot, let’s modify the colors to something we like:

ggplot(genes)+
  aes(Condition1, Condition2, col=State) +
  geom_point() +
  scale_color_manual(values = c("Blue","grey","red"))+
  labs(x="Control (no drug)", y="Drug",
       title = "Gene Expression Change upon GLP-1 Drug")+
  theme_linedraw()

Going Further

Let’s have a look at the famous gapminder

url <- "https://raw.githubusercontent.com/jennybc/gapminder/master/inst/extdata/gapminder.tsv"
gapminder <- read.delim(url)
head(gapminder,5)
      country continent year lifeExp      pop gdpPercap
1 Afghanistan      Asia 1952  28.801  8425333  779.4453
2 Afghanistan      Asia 1957  30.332  9240934  820.8530
3 Afghanistan      Asia 1962  31.997 10267083  853.1007
4 Afghanistan      Asia 1967  34.020 11537966  836.1971
5 Afghanistan      Asia 1972  36.088 13079460  739.9811

Version 1 Plot

ggplot(gapminder)+
  aes(x=gdpPercap, y=lifeExp)+
  geom_point(alpha=0.4)

Version 2 plot:

ggplot(gapminder)+
  aes(x=gdpPercap, y=lifeExp,col=continent)+
  geom_point(alpha=0.3)

Let’s “facet” by continent rather than big hot mess above

ggplot(gapminder)+
  aes(x=gdpPercap, y=lifeExp,col=continent)+
  geom_point(alpha=0.3)+
  facet_wrap(~continent)

Custom Plots

How big is this gapminder dataset?

nrow(gapminder)
[1] 1704

I want to “filter” down to a subset of this data. I will use dplyr package to help me.

First I need to install and load it up…

library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
gapminder_1977 <- gapminder %>% filter(year==1977)
head(gapminder_1977)
      country continent year lifeExp      pop  gdpPercap
1 Afghanistan      Asia 1977  38.438 14880372   786.1134
2     Albania    Europe 1977  68.930  2509048  3533.0039
3     Algeria    Africa 1977  58.014 17152804  4910.4168
4      Angola    Africa 1977  39.483  6162675  3008.6474
5   Argentina  Americas 1977  68.481 26983828 10079.0267
6   Australia   Oceania 1977  73.490 14074100 18334.1975
gapminder_2007 <- gapminder %>% filter(year==2007)
head(gapminder_2007)
      country continent year lifeExp      pop  gdpPercap
1 Afghanistan      Asia 2007  43.828 31889923   974.5803
2     Albania    Europe 2007  76.423  3600523  5937.0295
3     Algeria    Africa 2007  72.301 33333216  6223.3675
4      Angola    Africa 2007  42.731 12420476  4797.2313
5   Argentina  Americas 2007  75.320 40301927 12779.3796
6   Australia   Oceania 2007  81.235 20434176 34435.3674
filter(gapminder_2007, country=="United States")
        country continent year lifeExp       pop gdpPercap
1 United States  Americas 2007  78.242 301139947  42951.65

Q. Make a plot compring 1977 and 2007 for all countries.

gap_compare <- filter(gapminder,year %in% c(1977,2007))

ggplot(gap_compare)+
  aes(x=gdpPercap, y=lifeExp, col=continent)+
  geom_point(alpha=0.9)+
  facet_wrap(~year)