Class 5: Data Viz with ggplot

Laura (A17844089)

Today we are exploring the ggplot package and how to make nice figures in R.

There are lots of ways to make figures and plot in R. Theses include:

• so called “base” R
• and add on package like ggplot2

Here is a simple “base” R plot.

head(cars)

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

We can simply pass to to the plot() function.

plot(cars)

Key-point: Base R is quick but not so nice and simple looking in some folks eyes

Let’s see how we can plot this with ggplot2…

1st I need to install this add-on package. For this we use the install.packages() function - WE DO THIS IN THE CONSOLE, NOT our report. This is a one time only deal.

2nd We need to load the package with the library() function every time we want to use it.

library(ggplot2)
ggplot(cars)

ggplot(cars)

Every ggplot is composed of at least 3 layers:

• data (i.e a data.frame with the things you want to plot),
• aesthitics aes() that map the columns of data to your plot features (i.e aesthitics)
• geoms like geom_point) that srt how the plot appears

library(ggplot2)
ggplot(cars) + 
  aes(x=speed, y=dist) + 
  geom_point()

hist(cars$speed)

Key point: For simple “canned” graphs base R is quicker but as things get more custom and elobrate then ggplot wins out…

Let’s add more layers to our ggplot

Add a line showing the relationship between x and y Add a title Add custom axis labels “Speed (MPH)” and “Distance (ft)” Change the theme…

ggplot(cars) + 
  aes(x=speed, y=dist) + 
  geom_point() + 
  geom_smooth(method="lm", se=FALSE) + 
 labs(title = "Silly plot of Speed vs Stoping distance", 
      x="Speed (MPH)", 
      y="Distance (ft)") + 
  theme_bw()

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

Going further

Read some gene expression data

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

Q1. How many genes are in this wee dataset?

nrow(genes) 

[1] 5196

Q2. How many “up” regulated genes are there?

sum( genes$State =="up" ) 

[1] 127

A useful function for counting up occurances of things in a vector is the table() function. Make a v1 figure

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

p + 
 scale_colour_manual( values= c("blue","gray","red") ) + 
  labs(title= "Expression changes upon drug treatment", 
       x="Control (no drug)",
       y= "Treatment (with drug)" ) + 
    theme_bw() 

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

gapminder <- read.delim(url) 

Lets have a wee peak

head( gapminder, 3) 

      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

Q4. How many different country values are in this dataset?

head( gapminder, 3) 

      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

length( table(gapminder$country) ) 

[1] 142

Q5. How many different continent values are in the dataset?

unique(gapminder$continenet) 

NULL

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

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

  geom_point()

geom_point: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity 

I can use the ggrepl package to make more sensible labels here.

I want a seperate panel per continent

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

library(ggrepel)
       
ggplot(gapminder) + 
  aes(gdpPercap, lifeExp, col=continent, label=country) + geom_point() 

The advantages of ggplot over base R plot: Layered system: ggplot builds plots by adding layers (data, aesthetics, geometry, themes, annotations) with the + operator, making it easy to iteratively refine and customize visualizations. Base R requires step-by-step specification, which can be more fiddly and time-consuming to polish D1 D2 D4 D5 D6 Q2 . Aesthetic mapping: ggplot allows mapping data columns to visual features like position, color, shape, size, and transparency, supporting rich, multi-dimensional plots. Base R is less flexible in this regard D1 D2 D3 D5 D6 Q1 Q3 . Themes and annotation: ggplot provides built-in themes and functions for adding titles, subtitles, captions, and custom axis labels, making it easier to standardize and polish figures D1 D2 D4 D5 D6 Q1 . Faceting: ggplot can split data into multiple panels by categories using facet_wrap, improving clarity for complex or grouped data D1 D2 D4 D5 D6 Q1 . Variety of plot types: ggplot supports over 40 core geometric types and many more via extensions, making advanced visualizations more accessible D1 D2 D6 Q3 . In summary, ggplot is more structured, modular, and user-friendly for creating publication-quality figures, while base R offers complete control but is less convenient for complex or polished visualizations D1 D2 D4 D5 D6 Q1 Q2 Q3 .