One of R`s cool features is functional programming. It makes development much easier and the code you write shorter and less prone to errors. There are few tool kits for functional programming in R (with famous apply functions family among them). In this set of exercises, you will familiarize yourself with basic functions from the purrr package, which has a great advantage of being consistent both internally and with the rest of the tidyverse packages.
Answers to the exercises are available here.
Please do all exercises using the purrr package. If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.
Exercise 1
Load the purrr package. For each column in the mtcars data set, calculate the mean. Results should be a list.
Exercise 2
Do the same thing as above, but returning the named vector as a result.
Exercise 3
Calculate the mean once again, but with max/min 10% values trimmed.
Exercise 4
Split mtcars by cyl into a list and calculate the number of rows for each element of the list.
R Programming: Advanced Analytics In R For Data Science. this course you will learn how to:
- Work extensively with R’s inbuild functions of the apply family,
- Learn how to incorporate your own functions into the apply family,
- And much more
Exercise 5
For each element of lists from Exercise 4, calculate the mean of each column. Return results as a data.frame with one record for each input element (3 rows in total).
Exercise 6
For each element of the list from Exercise 4, fit a linear model between 1/4 mile time and gross horsepower.
Exercise 7
For each model from the previous exercise, extract the co-efficient table.
Exercise 8
Fit each of the models to all mtcars.
Exercise 9
For each model, plot separate histograms of the predicted value with the model indicated in the title.
Exercise 10
For each car, calculate the average prediction of the models.
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