In IDPT7810/practical-data-analysis: Practical Biological Data Analysis in R/RStudio

knitr::opts_chunk$set(echo = TRUE)

Overview

Here is a link to the text of these exercises.

Question 1

Tidy the mtcars data set. Note that car names are rownames in the built-in data, so they need to be moved to their own column prior to tibble conversion.

The tidied data should look like:

# A tibble: 352 x 3
                name   var value
               <chr> <chr> <dbl>
 1         Mazda RX4   mpg  21.0
 2     Mazda RX4 Wag   mpg  21.0
 3        Datsun 710   mpg  22.8
 4    Hornet 4 Drive   mpg  21.4
 5 Hornet Sportabout   mpg  18.7
 6           Valiant   mpg  18.1
 7        Duster 360   mpg  14.3
 8         Merc 240D   mpg  24.4
 9          Merc 230   mpg  22.8
10          Merc 280   mpg  19.2
# ... with 342 more rows

Strategy

Interpretation

Question 2

For each car in the tidy mtcars data set, calculate the mean (mean()) and variance (var()) for each variable. Try using summarize() and summarize_at() and summarize_all().

Strategy

Interpretation

Question 3

Plot mpg vs cyl for the mtcars data set. Which format should you use? The original data set, or the tidied one? Why?

Strategy

Interpretation

Question 4

Using the provided qpcr data, plot the changes in gene expression over time. Use colors to represent genotypes and facets for the different genes. If that's too easy, add error bars (geom_errorbar()) to the plot and connect each point with a line (geom_line()).

Strategy

Interpretation

IDPT7810/practical-data-analysis documentation built on July 8, 2022, 9:32 p.m.