Exercises: Session 2

suppressPackageStartupMessages(library(tidyverse))

Warning message:
“Installed Rcpp (0.12.12) different from Rcpp used to build dplyr (0.12.11).
Please reinstall dplyr to avoid random crashes or undefined behavior.”Warning message:
“package ‘dplyr’ was built under R version 3.4.1”

head(mtcars)

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.0	6	160	110	3.90	2.620	16.46	0	1	4	4
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108	93	3.85	2.320	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360	175	3.15	3.440	17.02	0	0	3	2
Valiant	18.1	6	225	105	2.76	3.460	20.22	1	0	3	1

1. Make a scatter plot with y=mpg and x=wt

2. Add a linear regression curve.

3. Add a title ‘Fuel efficiency decreases with weight’, and rename the x and y axis to ‘Weight’ and ‘Miles per gallon’.

4. Change the color of the scatter points to salmon.

5. Change the color of the scatter points to represent the horsepower hp.

6. Use color brewer to set the scale in Q4 with the Oranges sequential palette for the cyl variable.

7. Make a density plot of mpg and fill by the factor cyl, and set the transparency to 0.5.

8. Repeat Q7, but use 3 separate plots. Remove the legend.

9. Create a scatter plot -log(p value) on the y-axis and SNP location on the x-axis, coloring by chromosome number. This is known as a Manhattan plot. Use the code below to simulate data for the plot. Use the Set3 palette of qual type in scale_color_brewer for the color scheme.

n <- 10000 # number of genes
position <- 1:n
chromosome <- factor(rep(1:10, each=n/10))
p.value <- runif(n)
df <- data.frame(position=position, chromosome=chromosome, p.value=p.value)