class05

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’ data set:

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 simply:

plot(cars)

Now let’s try ggplot. First I need to install the package using install.packages("ggplot2")

N.I.K. We never run on `install.packages() in a code chunk otherwise we will re-install needlessly every time we open the document

Every time we want to use an add we must load it up using library()

library(ggplot2)
ggplot(cars)

Every ggplot needs at least three things:

1. The Data i.e. stuff to plot as data frame
2. the aes are aesthetics that map the data to the plot
3. the geom or geometry i.e. the plot type such as scatter, linear, etc

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

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 - start simple by getting some ink on the page

ggplot(genes) + 
  aes(x=Condition1, y=Condition2) + 
  geom_point(col="blue", alpha=0.2)

Let’s color by State up, down or no change

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("red",
                              "gray",
                              "blue") ) + 
  labs(x="Control (no drug)",
     y= "Drug",
     title= "Expression changes with GLP-1 Drug") 

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

I want to see a plot for each continent - 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

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

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

input <- filter(gapminder, year == 2007 | year == 1997)

ggplot(gapminder) +
  aes(gdpPercap, lifeExp, color = continent) +
  geom_point() +
  filter(gapminder, year ==2007 | year == 1997) + 
  facet_wrap(~year)