Files Corresponding to Short Course Programming in R
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 = ";")
##
## -- Column specification --------------------------------------------------------
## cols(
## `fixed acidity` = col_double(),
## `volatile acidity` = col_double(),
## `citric acid` = col_double(),
## `residual sugar` = col_double(),
## chlorides = col_double(),
## `free sulfur dioxide` = col_double(),
## `total sulfur dioxide` = col_double(),
## density = col_double(),
## pH = col_double(),
## sulphates = col_double(),
## alcohol = col_double(),
## quality = col_double()
## )
colMeans(wineData)
## fixed acidity volatile acidity citric acid
## 8.31963727 0.52782051 0.27097561
## residual sugar chlorides free sulfur dioxide
## 2.53880550 0.08746654 15.87492183
## total sulfur dioxide density pH
## 46.46779237 0.99674668 3.31111320
## sulphates alcohol quality
## 0.65814884 10.42298311 5.63602251
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.)matrixStats::colSds(as.matrix(wineData))
## [1] 1.741096318 0.179059704 0.194801137 1.409928060 0.047065302
## [6] 10.460156970 32.895324478 0.001887334 0.154386465 0.169506980
## [11] 1.065667582 0.807569440
#or
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
as.matrix(wineData) %>% matrixStats::colSds()
## [1] 1.741096318 0.179059704 0.194801137 1.409928060 0.047065302
## [6] 10.460156970 32.895324478 0.001887334 0.154386465 0.169506980
## [11] 1.065667582 0.807569440
#or
apply(wineData, MARGIN = 2, FUN = sd)
## fixed acidity volatile acidity citric acid
## 1.741096318 0.179059704 0.194801137
## residual sugar chlorides free sulfur dioxide
## 1.409928060 0.047065302 10.460156970
## total sulfur dioxide density pH
## 32.895324478 0.001887334 0.154386465
## sulphates alcohol quality
## 0.169506980 1.065667582 0.807569440
apply() function along with the summary() function to
find summaries about the columns.apply(wineData, MARGIN = 2, FUN = summary)
## fixed acidity volatile acidity citric acid residual sugar chlorides
## Min. 4.600000 0.1200000 0.0000000 0.900000 0.01200000
## 1st Qu. 7.100000 0.3900000 0.0900000 1.900000 0.07000000
## Median 7.900000 0.5200000 0.2600000 2.200000 0.07900000
## Mean 8.319637 0.5278205 0.2709756 2.538806 0.08746654
## 3rd Qu. 9.200000 0.6400000 0.4200000 2.600000 0.09000000
## Max. 15.900000 1.5800000 1.0000000 15.500000 0.61100000
## free sulfur dioxide total sulfur dioxide density pH sulphates
## Min. 1.00000 6.00000 0.9900700 2.740000 0.3300000
## 1st Qu. 7.00000 22.00000 0.9956000 3.210000 0.5500000
## Median 14.00000 38.00000 0.9967500 3.310000 0.6200000
## Mean 15.87492 46.46779 0.9967467 3.311113 0.6581488
## 3rd Qu. 21.00000 62.00000 0.9978350 3.400000 0.7300000
## Max. 72.00000 289.00000 1.0036900 4.010000 2.0000000
## alcohol quality
## Min. 8.40000 3.000000
## 1st Qu. 9.50000 5.000000
## Median 10.20000 6.000000
## Mean 10.42298 5.636023
## 3rd Qu. 11.10000 6.000000
## Max. 14.90000 8.000000
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.
lapply(slrFits, FUN = summary)
## $`fixed acidity`
##
## Call:
## lm(formula = wineData$quality ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8248 -0.6061 0.1925 0.4341 2.5550
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.15732 0.09789 52.684 < 2e-16 ***
## x 0.05754 0.01152 4.996 6.5e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8016 on 1597 degrees of freedom
## Multiple R-squared: 0.01539, Adjusted R-squared: 0.01477
## F-statistic: 24.96 on 1 and 1597 DF, p-value: 6.496e-07
##
##
## $`volatile acidity`
##
## Call:
## lm(formula = wineData$quality ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.79071 -0.54411 -0.00687 0.47350 2.93148
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.56575 0.05791 113.39 <2e-16 ***
## x -1.76144 0.10389 -16.95 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7437 on 1597 degrees of freedom
## Multiple R-squared: 0.1525, Adjusted R-squared: 0.152
## F-statistic: 287.4 on 1 and 1597 DF, p-value: < 2.2e-16
##
##
## $`citric acid`
##
## Call:
## lm(formula = wineData$quality ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0011 -0.5976 0.1021 0.5057 2.5901
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.38172 0.03372 159.610 <2e-16 ***
## x 0.93845 0.10104 9.288 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7869 on 1597 degrees of freedom
## Multiple R-squared: 0.05124, Adjusted R-squared: 0.05065
## F-statistic: 86.26 on 1 and 1597 DF, p-value: < 2.2e-16
##
##
## $`residual sugar`
##
## Call:
## lm(formula = wineData$quality ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6609 -0.6334 0.3580 0.3690 2.3729
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.616055 0.041616 134.950 <2e-16 ***
## x 0.007865 0.014331 0.549 0.583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8077 on 1597 degrees of freedom
## Multiple R-squared: 0.0001886, Adjusted R-squared: -0.0004375
## F-statistic: 0.3012 on 1 and 1597 DF, p-value: 0.5832
##
##
## $chlorides
##
## Call:
## lm(formula = wineData$quality ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6946 -0.6503 0.3010 0.3607 2.3607
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.82948 0.04229 137.852 < 2e-16 ***
## x -2.21184 0.42578 -5.195 2.31e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8011 on 1597 degrees of freedom
## Multiple R-squared: 0.01662, Adjusted R-squared: 0.016
## F-statistic: 26.99 on 1 and 1597 DF, p-value: 2.313e-07
##
##
## $`free sulfur dioxide`
##
## Call:
## lm(formula = wineData$quality ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6864 -0.6394 0.3215 0.3762 2.4661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.698107 0.036678 155.357 <2e-16 ***
## x -0.003911 0.001929 -2.027 0.0428 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8068 on 1597 degrees of freedom
## Multiple R-squared: 0.002566, Adjusted R-squared: 0.001941
## F-statistic: 4.109 on 1 and 1597 DF, p-value: 0.04283
##
##
## $`total sulfur dioxide`
##
## Call:
## lm(formula = wineData$quality ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8063 -0.6336 0.2164 0.3800 2.5527
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.8471792 0.0343670 170.140 < 2e-16 ***
## x -0.0045442 0.0006037 -7.527 8.62e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7939 on 1597 degrees of freedom
## Multiple R-squared: 0.03426, Adjusted R-squared: 0.03366
## F-statistic: 56.66 on 1 and 1597 DF, p-value: 8.622e-14
##
##
## $density
##
## Call:
## lm(formula = wineData$quality ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7885 -0.6216 0.1554 0.4271 2.5177
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 80.24 10.51 7.636 3.83e-14 ***
## x -74.85 10.54 -7.100 1.87e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7954 on 1597 degrees of freedom
## Multiple R-squared: 0.0306, Adjusted R-squared: 0.02999
## F-statistic: 50.41 on 1 and 1597 DF, p-value: 1.875e-12
##
##
## $pH
##
## Call:
## lm(formula = wineData$quality ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6817 -0.6394 0.3032 0.3878 2.4874
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.6359 0.4332 15.320 <2e-16 ***
## x -0.3020 0.1307 -2.311 0.021 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8065 on 1597 degrees of freedom
## Multiple R-squared: 0.003333, Adjusted R-squared: 0.002709
## F-statistic: 5.34 on 1 and 1597 DF, p-value: 0.02096
##
##
## $sulphates
##
## Call:
## lm(formula = wineData$quality ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2432 -0.5424 0.1102 0.4456 2.3977
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.84775 0.07842 61.82 <2e-16 ***
## x 1.19771 0.11539 10.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7819 on 1597 degrees of freedom
## Multiple R-squared: 0.0632, Adjusted R-squared: 0.06261
## F-statistic: 107.7 on 1 and 1597 DF, p-value: < 2.2e-16
##
##
## $alcohol
##
## Call:
## lm(formula = wineData$quality ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8442 -0.4112 -0.1690 0.5166 2.5888
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.87497 0.17471 10.73 <2e-16 ***
## x 0.36084 0.01668 21.64 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7104 on 1597 degrees of freedom
## Multiple R-squared: 0.2267, Adjusted R-squared: 0.2263
## F-statistic: 468.3 on 1 and 1597 DF, p-value: < 2.2e-16