Class 5: Data Viz with ggplot

Author

Pooja Parthasarathy (PID:A17817456)

Quarto

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

Background

There are lots of ways to make visualization and plots in R. These include, so-called “base R” (like the function plot()) and ggplot2

Let’s make the same plot with these two graphics systems. We can use 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
plot(cars)

first you need to install ggplot using the command install.packages("ggplot2")

N.B We never run an install of packages in a code chunk otherwise we will reinstall needlesely every time we render our document

Everytime you want to use an add on packaged 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. struff to plot as a dataframe
  2. Aesthetics that map the data to the plot
  3. geom_ or geometry of the plot
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_dark()
`geom_smooth()` using formula = 'y ~ x'

Gene Expression Plot

Read some fata 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 some ink on the page!

table(genes$State)

      down unchanging         up 
        72       4997        127 
ggplot(genes)+
  aes(Condition1,Condition2, col= State)+
  geom_point() + 
  scale_color_manual(values = c("purple","gray","orange")) +
  labs(x="No Drug", y= "Drug Treatment", title = "Gene Expression Response to GLP-1 drug") +
  theme_classic()

Going further with gapminder

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"

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 datasethave

nrow(gapminder)
[1] 1704

Q. how many different countries are in this dataset

table(gapminder$continent)

  Africa Americas     Asia   Europe  Oceania 
     624      300      396      360       24

Version 1 plot GDP Vs Life Expectancy for all rows

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

We want to see plots for each of the continents in ggplot lingo this is called “faceting”

ggplot(gapminder) +
  aes(x=gdpPercap, y=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
input <- filter(gapminder, year == 2007 | year == 1977 )

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