Class 05: Data Visualization with GGPLOT

Ethan 2025-12-05

• GGPLOT
• Plotting more relevant gene data
• Gap Minder Dataset Plotting

Today we are playing with plotting and graphics in R.

There are lots of ways to make cool figures in R. There is “base” R graphics (plot(), hist(), boxplot(), etc.)

There are also add-on packages, like ggplot.

cars

##    speed dist
## 1      4    2
## 2      4   10
## 3      7    4
## 4      7   22
## 5      8   16
## 6      9   10
## 7     10   18
## 8     10   26
## 9     10   34
## 10    11   17
## 11    11   28
## 12    12   14
## 13    12   20
## 14    12   24
## 15    12   28
## 16    13   26
## 17    13   34
## 18    13   34
## 19    13   46
## 20    14   26
## 21    14   36
## 22    14   60
## 23    14   80
## 24    15   20
## 25    15   26
## 26    15   54
## 27    16   32
## 28    16   40
## 29    17   32
## 30    17   40
## 31    17   50
## 32    18   42
## 33    18   56
## 34    18   76
## 35    18   84
## 36    19   36
## 37    19   46
## 38    19   68
## 39    20   32
## 40    20   48
## 41    20   52
## 42    20   56
## 43    20   64
## 44    22   66
## 45    23   54
## 46    24   70
## 47    24   92
## 48    24   93
## 49    24  120
## 50    25   85

Let;s plot this with “base” R:

plot(cars)

Plot of mpg vs. displacement

plot(mtcars$mpg, mtcars$disp)

GGPLOT

The main function in the GGPLOT2 package is ggplot. GGPLOT2 needs to be installed first. Packages can be installed with the function install.packages()

Note Do not install packages in individual files, install them in the global R console

Must call packages with a library() call in order to use their functions

#install.packages("ggplot2")
library(ggplot2)
ggplot(cars) + aes(x=speed, y=dist) + geom_point()

Every ggplot needs at least 3 things:

• The data (given with ggplot(cars))
• The aesthetic mapping (given with aes())
• The geometry (given with geom_...)

For simple canned graphs “base” R is nearly always faster

Adding more layers

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

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

Plotting more relevant gene data

Loading in the dataset

url <- "https://bioboot.github.io/bimm143_S20/class-material/up_down_expression.txt"
genes <- read.delim(url)

Interrogating the shape of the data

nrow(genes)

## [1] 5196

ncol(genes)

## [1] 4

table(genes$State == "up")

## 
## FALSE  TRUE 
##  5069   127

round(table(genes$State)/nrow(genes) * 100, 2)

## 
##       down unchanging         up 
##       1.39      96.17       2.44

There are 5196 genes in this dataset

There are 127 upregulated genes in this dataset

Plotting the data in the genes dataframe

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

Gap Minder Dataset Plotting

Importing the data

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

gapminder <- read.delim(url)

#install.packages("dplyr")

# install.packages("dplyr")  ## un-comment to install if needed
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

ggplot

## function (data = NULL, mapping = aes(), ..., environment = parent.frame()) 
## {
##     UseMethod("ggplot")
## }
## <bytecode: 0x000001df4a30bd58>
## <environment: namespace:ggplot2>

gapminder_2007 <- gapminder %>% filter(year==2007)

ggplot(gapminder_2007) + 
  aes(x=gdpPercap, y=lifeExp, color=continent) + 
  geom_point(alpha=0.5) + 
  facet_wrap(~continent) + 
  theme_bw()

The Patchwork package can be useful for assembling multi-panel figures