Class 5: Data Viz with ggplot

Aadhya Tripathi (PID: A17878439)

• Background
• Gene Expression Plot
• Going further with gapminder
• First look at dplyr package

Background

There are lots 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 needlessly re-install every time we render the document.

Every time we want to use an add-on, 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 (aesthetics, that map the data to the plot)
3. the geom_ (geometry, i.e. the plot type like points, lines, etc.)

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 - starting simple

ggplot(genes) +
  aes(Condition1, Condition2) +
  geom_point(col="blue", alpha=0.3)

Let’s color by the State - up, down, or not changing. table() counts occurences of a value in a vector.

table(genes$State)

      down unchanging         up 
        72       4997        127 

ggplot(genes) +
  aes(x=Condition1, y=Condition2, col=State) +
  geom_point() +
  scale_color_manual(values = c("purple","gray","red")) +
  labs(x="Control (no drug)",
       y="GLP-1 Drug Treatment",
       title="Changes in Gene Expression in Response to GLP-1 Drug") +
  theme_light()

Going further with gapminder

Explore the famous gapminder dataset with custom plots.

# File location online
url <- "https://raw.githubusercontent.com/jennybc/gapminder/master/inst/extdata/gapminder.tsv"

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 the gapminder dataset have?

nrow(gapminder)

[1] 1704

Q. 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 for all rows

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

Construct a plot for each continent, separately. Inn ggplot, this is called “faceting”.

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

First look at 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=="India")

  country continent year lifeExp        pop gdpPercap
1   India      Asia 2007  64.698 1110396331   2452.21

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

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