In jr-packages/jrIntroBio: Jumping Rivers: Introduction to R for Biologists

Data manipulation

This section will hopefully help you get more comfortable with some of the dplyr functionality for "wrangling" your data. We will do some data wrangling and the use that to summarise the data. Make sure you load in the dplyr package and the yeast data set.

library(dplyr)
data("yeast", package = "jrIntroBio")

Question 1

(1) Make sure you are comfortable with what the data looks like using head() and colnames(). r head(yeast) colnames(yeast)

(1) Create a subset of extracellular proteins, i.e class == EXC called exc. r exc = filter(yeast, class == "EXC")

(1) What is the mean value for McGeoch's signal sequence method for extracellular proteins? r summarise(exc, mean(mcg)) # or # mean(exc$mcg)

(1) Are there more nuclear or mitochondrial proteins in the dataset? Hint: you can use the function count() to count tally up the categories within a column of a tibble. r count(yeast, class) ## more nuclear

(1) How many proteins had a von Heijne's score of less than 0.2? r yeast %>% filter(gvh < 0.2) %>% nrow()

Question 2

(1) We want to look at the average value across of all scores aggregated for each localisation class. We can start with the following code that will calculate the average value for the mitochondrial measure. Lets use the %>% pipe operator to do this. r yeast %>% summarise(mean = mean(mit))

(2) Now adapt this code to use the group_by() function to produce a mean for each localisation class. Remember you can simply add another line into the pipeline. r yeast %>% group_by(class) %>% summarise(mean = mean(mit))

(3) Now create a another additional summaries by adding to your summarise function. Calculate the sample size and standard deviation of the mit measure as well as its mean. See the examples in ?summarise if you get stuck. r yeast %>% group_by(class) %>% summarise(n = n(), mean = mean(mit), sd = sd(mit))

(4) Bonus Question: We want to calculate the mean for all of the amino acid measures across all of the localisations. You can use the summarise_all() function for this but you will need to remove a collumn first... r yeast %>% select(-seq) %>% group_by(class) %>% summarise_all(mean)

jr-packages/jrIntroBio documentation built on Dec. 24, 2019, 8:03 a.m.