Class 05: Data Viz with ggplot

Karolina (PID: A19106745)

• Background
• Gene Expression
• Going Further
• Custom plots

Background

There are lots 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 the 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 - which is not what we want!

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

library(ggplot2)

ggplot(cars)

Every ggplot needs at least 3 things:

• The data, the stuff you want plotted
• The aesthitics, how the data map to the plot
• The geometry, the type of plot

p <- ggplot(cars) +
  aes(x=speed, y=dist) +
  geom_point() +
  geom_smooth(method="lm", se=FALSE) + labs(title="Stopping distance of old cars", subtitle= "Data from the `cars` object", x="Speed (mph)", y="Distance (ft)")

p 

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

p + theme_bw()

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

Gene Expression

We can read the input data from 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

A first version plot

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

table( genes$State ) 

      down unchanging         up 
        72       4997        127 

Version 2 let’s color by State so we can see the “up” 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 default colors to something we like

ggplot(genes) +
  aes(Condition1, Condition2, col=State) +
  geom_point() + 
  scale_colour_manual(values=c("pink", "gray", "brown")) + theme_bw()

  labs(x="Control (no drug)", y="Drug", title="Gene Expression Changes upon GLP-1 drug") 

<ggplot2::labels> List of 3
 $ x    : chr "Control (no drug)"
 $ y    : chr "Drug"
 $ title: chr "Gene Expression Changes upon GLP-1 drug"

Going Further

Let’s have a look at the famous Gapminder

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

gapminder <- read.delim(url)

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

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

Let’s “facet” (i.e make a separate plot) by continent rather than the 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 the dplyr package to help me.

First I need to install it and then load it up… install.packages("dplyr") and then library(dplyr)

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_2007 <- filter(gapminder, 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=="Ireland")

  country continent year lifeExp     pop gdpPercap
1 Ireland    Europe 2007  78.885 4109086     40676

filter(gapminder, year==2007, country=="Ireland")

  country continent year lifeExp     pop gdpPercap
1 Ireland    Europe 2007  78.885 4109086     40676

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

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

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

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

ggplot(input) + 
  geom_point(aes(x = gdpPercap, y = lifeExp, color=continent,
                 size = pop), alpha=0.7) + 
  scale_size_area(max_size = 10) +
  facet_wrap(~year)