Lab 5

Remember to change the author: field on this Rmd file to your own name.

Learning objectives

In today’s Lab you will gain practice with the following concepts from Lecture 5:

• Using apply and map as loop alternatives
• Using the various mutate commands to manipulate data
• Using the various summarize commands to produce simple tabular summaries, and interpreting the results

Problems

library(tidyverse)

## ── Attaching packages ─────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 3.2.1     ✔ purrr   0.3.3
## ✔ tibble  2.1.3     ✔ dplyr   0.8.3
## ✔ tidyr   1.0.0     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0

## ── Conflicts ────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

Cars93 <- as_tibble(MASS::Cars93)  # Pull Cars93 from MASS

1. map() practice

Note: This question previously (accidentally) appeared on Lab 4. Feel free to skip it if you already succeeded on this question in the previous week.

(a) The nlevels command tells you the number of levels in a factor variable. Use this function in combination with summarize_if() to produce an integer vector showing the number of levels for each factor variables in the Cars93 data.

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(b) levels() returns the possible levels of a factor variable. Use this function in combination with select and map to create a list of all the levels of the Manufacturer, AirBags, DriveTrain, and Man.trans.avail variables

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2. mutate() variants with Cars93

(a) Use the toupper() command in combination with mutate_if() to produce a new version of Cars93 where every factor variable has been converted to upper case.

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(b) Currently the price columns of the Cars93 reflect prices in $1000’s of dollars. Use mutate_at to create a version of Cars93 where all prices are in $’s. (e.g., what used to be a price of 12.9 should become 12900).

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(c) Use mutate_if to normalize all of the numeric variables in the Cars93 data to have variance 1. Save the resulting mutated data in a variable called Cars93.norm. (Hint: this is equivalent to dividing each of the columns by the standard deviation of the given column.)

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To check that you’ve succeeded, you can confirm that the following lines of code all return the answer 1.

var(Cars93.norm$Min.Price)
var(Cars93.norm$Horsepower)

3. summarize() variants

Use summarize_if to calculate the standard deviation of every numeric column in the original Cars93 data. You’ll want to further specify na.rm = TRUE to ensure that you get a non-NA output value even for variables that have some missing (NA) observations.

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