Class 5: Data Viz with ggplot

Background

There are lot’s of ways to make plots in R. These include so-called “base R” (like the plot()) and add on packages like ggplot2.

Let’s make the same plot with these two graphics systems. We can use the inbuilt cars dataset:

head( cars ) 

  speed dist
1     4    2
2     4   10
3     7    4
4     7   22
5     8   16
6     9   10

With “base R” we can simply:

plot(cars)

Now let’s try ggplot. First I need to install the package using install.packages("ggplot2")

N.B. We never run an install.packages() in a code chunk otherwise we will re-install needlessly every time we render our document.

Every time we want to use an add-on package we need to load it up with a call to library()

library(ggplot2)
ggplot(cars)

Every ggplot needs at least 3 things:

1. The data i.e. stuff to plot as a data.frame
2. The aes or ‘aesthetics’ that map the data to the plot
3. The geom_ or ‘geometry’ i.e. the plot type such as points, lines, etc.

ggplot(cars) + 
  aes(x=speed, y = dist) +
  geom_point() +
  geom_line()

ggplot(cars) +
  aes(x = speed, y = dist) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(
    x = "Speed (MPH)",
    y = "Distance (ft)",
    title = "Stopping Distance of old Cars"
  ) +
  theme_bw()

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

Gene Expression Plot

Read some data on the effects of GLP-1 inhibitor (drug) on gene expression values:

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

Version 1 plot - start simple by getting soem ink the page.

p <- ggplot(genes) +
  aes(x = Condition1, y = Condition2, col = State) +
  geom_point(col = "blue", alpha = 0.2) 

Let’s color by State up, down, or no change.

p <- ggplot(genes) +
  aes(x = Condition1, y = Condition2, col = State) +
  geom_point()
p + scale_colour_manual(values = c("blue","gray","red")) +
  labs(title = "Gene Expression Changes Upon Drug Treatment",
       x = "Control (no drug)",
       y = "Drug Treatment")

Going further with gapmider

Here we explore the famous gapminder dataset with some custom plots.

# File location online
url <- "https://raw.githubusercontent.com/jennybc/gapminder/master/inst/extdata/gapminder.tsv"
library(gapminder)
gapminder <- read.delim(url)
head(gapminder)

      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
6 Afghanistan      Asia 1977  38.438 14880372  786.1134

Q. How many rows does this dataset have?

nrow(gapminder)

[1] 1704

How many different continents are in this dataset?

table(gapminder$continent)

  Africa Americas     Asia   Europe  Oceania 
     624      300      396      360       24 

Version 1 plot gdpPercap vs LifeExp

ggplot(gapminder) +
  aes(gdpPercap, lifeExp, col=continent) +
  geom_point()

I want to see a plot for each continent - in ggplot lingo this is called “faceting”

ggplot(gapminder) +
  aes(gdpPercap, lifeExp, col=continent) +
  geom_point() +
  facet_wrap(~continent)

First look at the dplyr package

Another add-on package with a function called filter() that we want to use.

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

filter(gapminder, year==2007, country =="United States")

        country continent year lifeExp       pop gdpPercap
1 United States  Americas 2007  78.242 301139947  42951.65

input <- filter(gapminder, year== 2007 | year == 1977)

gapminder_1957 <- gapminder %>% filter(year==1957 | year==2007)
ggplot(gapminder_1957) +
  aes(gdpPercap, lifeExp, col=continent,) +
  geom_point() +
  scale_size_area(max_size = 10) +
  facet_wrap(~year)