A subsection of a data frame is called a slice. We can take slices of character vectors as well:
animal <- c("m", "o", "n", "k", "e", "y")
# first three characters
animal[1:3]## [1] "m" "o" "n"# last three characters
animal[4:6]## [1] "k" "e" "y"If the first four characters are selected using the slice animal[1:4], how can we obtain the first four characters in reverse order?
What is animal[-1]? What is animal[-4]? Given those answers, explain what animal[-1:-4] does.
Use a slice of animal to create a new character vector that spells the word “eon”, i.e. c("e", "o", "n").
Create a plot showing the standard deviation of the inflammation data for each day across all patients.
Write a function called analyze that takes a filename as a argument and displays the three graphs produced in the previous lesson (average, min and max inflammation over time). analyze("data/inflammation-01.csv") should produce the graphs already shown, while analyze("data/inflammation-02.csv") should produce corresponding graphs for the second data set. Be sure to document your function with comments.
R has a built-in function called seq that creates a list of numbers:
seq(3)## [1] 1 2 3Using seq, write a function that prints the first N natural numbers, one per line:
print_N(3)## [1] 1
## [1] 2
## [1] 3Write a function called total that calculates the sum of the values in a vector. (R has a built-in function called sum that does this for you. Please don’t use it for this exercise.)
ex_vec <- c(4, 8, 15, 16, 23, 42)
total(ex_vec)## [1] 108Exponentiation is built into R:
2^4## [1] 16Write a function called expo that uses a loop to calculate the same result.
expo(2, 4)## [1] 16Write a function called analyze_all that takes a filename pattern as its sole argument and runs analyze for each file whose name matches the pattern.
Write a function plot_dist that plots a boxplot if the length of the vector is greater than a specified threshold and a stripchart otherwise. To do this you’ll use the R functions boxplot and stripchart.
dat <- read.csv("data/inflammation-01.csv", header = FALSE)
plot_dist(dat[, 10], threshold = 10) # day (column) 10
plot_dist(dat[1:5, 10], threshold = 10) # samples (rows) 1-5 on day (column) 10One of your collaborators asks if you can recreate the figures with lines instead of points. Find the relevant argument to plot by reading the documentation (?plot), update analyze, and then recreate all the figures with analyze_all.