Exercises 3 - apply

Files Corresponding to Short Course Programming in R

Exercises 3 - apply

We will use a dataset from the UCI Machine Learning Repository. This data set is about wine quality (we’ll consider the red wine data). You can learn more about the data here.

The data description describes the following variables:

Input variables (based on physicochemical tests)
1 - fixed acidity
2 - volatile acidity
3 - citric acid
4 - residual sugar
5 - chlorides
6 - free sulfur dioxide
7 - total sulfur dioxide
8 - density
9 - pH
10 - sulphates
11 - alcohol

Output variable (based on sensory data):
12 - quality (score between 0 and 10)

Read in the data using the code below.

wineData <- readr::read_delim("https://raw.githubusercontent.com/jbpost2/Improving-R-Programs/master/datasets/winequality-red.csv", delim = ";")

  1. Find the column means.

  2. Find the column standard deviations. Note that you will need to coerce the tibble to a matrix (use as.matrix()) in order to use the colSds function from the matrixStats package. (Alternatively, you can use the apply() function.)

  3. Use the apply() function along with the summary() function to find summaries about the columns.

  4. Run the code below to fit a simple linear regression model for quality using each of the variables (columns in the data set). We’ll learn more about writing functions in a bit!

slrFits <- apply(wineData[,-ncol(wineData)], MARGIN = 2, function(x){lm(wineData$quality ~ x)})

slrFits is a list object where each element has the results of a call to lm() (which itself is a list of length 12).

str(slrFits, max.level = 1)

## List of 11
##  $ fixed acidity       :List of 12
##   ..- attr(*, "class")= chr "lm"
##  $ volatile acidity    :List of 12
##   ..- attr(*, "class")= chr "lm"
##  $ citric acid         :List of 12
##   ..- attr(*, "class")= chr "lm"
##  $ residual sugar      :List of 12
##   ..- attr(*, "class")= chr "lm"
##  $ chlorides           :List of 12
##   ..- attr(*, "class")= chr "lm"
##  $ free sulfur dioxide :List of 12
##   ..- attr(*, "class")= chr "lm"
##  $ total sulfur dioxide:List of 12
##   ..- attr(*, "class")= chr "lm"
##  $ density             :List of 12
##   ..- attr(*, "class")= chr "lm"
##  $ pH                  :List of 12
##   ..- attr(*, "class")= chr "lm"
##  $ sulphates           :List of 12
##   ..- attr(*, "class")= chr "lm"
##  $ alcohol             :List of 12
##   ..- attr(*, "class")= chr "lm"

Use lapply() to run the summary() function on each SLR fit. This should output p-values and info about each model fit.