The main task here is to Create an example by using one or more
TidyVerse packages, and any dataset from fivethirtyeight.com or Kaggle,
create a programming sample “vignette” that demonstrates how to use one
or more of the capabilities of the selected TidyVerse package with the
selected dataset.Here, I have selected a data set from kaggle.com and
put that data set on my github. The data set reflects the different
properties of two types of wine.
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
df<-read.csv("https://raw.githubusercontent.com/Raji030/data607_tidyverse_create_assignment/main/winequalityN.csv")
glimpse(df)
## Rows: 6,497
## Columns: 13
## $ type <chr> "white", "white", "white", "white", "white", "whi…
## $ fixed.acidity <dbl> 7.0, 6.3, 8.1, 7.2, 7.2, 8.1, 6.2, 7.0, 6.3, 8.1,…
## $ volatile.acidity <dbl> 0.27, 0.30, 0.28, 0.23, 0.23, 0.28, 0.32, 0.27, 0…
## $ citric.acid <dbl> 0.36, 0.34, 0.40, 0.32, 0.32, 0.40, 0.16, 0.36, 0…
## $ residual.sugar <dbl> 20.70, 1.60, 6.90, 8.50, 8.50, 6.90, 7.00, 20.70,…
## $ chlorides <dbl> 0.045, 0.049, 0.050, 0.058, 0.058, 0.050, 0.045, …
## $ free.sulfur.dioxide <dbl> 45, 14, 30, 47, 47, 30, 30, 45, 14, 28, 11, 17, 1…
## $ total.sulfur.dioxide <dbl> 170, 132, 97, 186, 186, 97, 136, 170, 132, 129, 6…
## $ density <dbl> 1.0010, 0.9940, 0.9951, 0.9956, 0.9956, 0.9951, 0…
## $ pH <dbl> 3.00, 3.30, 3.26, 3.19, 3.19, 3.26, 3.18, 3.00, 3…
## $ sulphates <dbl> 0.45, 0.49, 0.44, 0.40, 0.40, 0.44, 0.47, 0.45, 0…
## $ alcohol <dbl> 8.8, 9.5, 10.1, 9.9, 9.9, 10.1, 9.6, 8.8, 9.5, 11…
## $ quality <int> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 5, 5, 7, 5, 7, 6…
The dplyr package in tidyverse can be used to subset a data frame by
subsetting rows using specific column value:
read_wine_data<-df %>% filter(type=="red")
glimpse(read_wine_data)
## Rows: 1,599
## Columns: 13
## $ type <chr> "red", "red", "red", "red", "red", "red", "red", …
## $ fixed.acidity <dbl> 7.4, 7.8, 7.8, 11.2, 7.4, 7.4, 7.9, 7.3, 7.8, 7.5…
## $ volatile.acidity <dbl> 0.700, 0.880, 0.760, 0.280, 0.700, 0.660, 0.600, …
## $ citric.acid <dbl> 0.00, 0.00, 0.04, 0.56, 0.00, 0.00, 0.06, 0.00, 0…
## $ residual.sugar <dbl> 1.9, 2.6, 2.3, 1.9, 1.9, 1.8, 1.6, 1.2, 2.0, 6.1,…
## $ chlorides <dbl> 0.076, 0.098, 0.092, 0.075, 0.076, 0.075, 0.069, …
## $ free.sulfur.dioxide <dbl> 11, 25, 15, 17, 11, 13, 15, 15, 9, 17, 15, 17, 16…
## $ total.sulfur.dioxide <dbl> 34, 67, 54, 60, 34, 40, 59, 21, 18, 102, 65, 102,…
## $ density <dbl> 0.9978, 0.9968, 0.9970, 0.9980, 0.9978, 0.9978, 0…
## $ pH <dbl> 3.51, 3.20, 3.26, 3.16, 3.51, 3.51, 3.30, 3.39, 3…
## $ sulphates <dbl> 0.56, 0.68, 0.65, 0.58, 0.56, 0.56, 0.46, 0.47, 0…
## $ alcohol <dbl> 9.4, 9.8, 9.8, 9.8, 9.4, 9.4, 9.4, 10.0, 9.5, 10.…
## $ quality <int> 5, 5, 5, 6, 5, 5, 5, 7, 7, 5, 5, 5, 5, 5, 5, 5, 7…
The dplyr package in tidyverse package can also be used to count the
number of times a column value occurs:
count_wine_type<-df %>% count(type)
count_wine_type
## type n
## 1 red 1599
## 2 white 4898
The ggplot2 package in tidyverse can be used to visualize
relationship between variables of interest
# Creating horizontal plot to visualize the count by wine types
ggplot(data=count_wine_type, aes(x=type, y=n)) +
geom_bar(stat="identity") +
labs(
x = "Wine Type",
y = "Count",
title = "The count of wine types"
) +
coord_flip()
The purrr package is used to compute the summary of different
variables
df %>% split(.$type) %>% # from base R
map(summary)
## $red
## type fixed.acidity volatile.acidity citric.acid
## Length:1599 Min. : 4.600 Min. :0.1200 Min. :0.0000
## Class :character 1st Qu.: 7.100 1st Qu.:0.3900 1st Qu.:0.0900
## Mode :character Median : 7.900 Median :0.5200 Median :0.2600
## Mean : 8.322 Mean :0.5277 Mean :0.2711
## 3rd Qu.: 9.200 3rd Qu.:0.6400 3rd Qu.:0.4200
## Max. :15.900 Max. :1.5800 Max. :1.0000
## NA's :2 NA's :1 NA's :1
## residual.sugar chlorides free.sulfur.dioxide total.sulfur.dioxide
## Min. : 0.900 Min. :0.01200 Min. : 1.00 Min. : 6.00
## 1st Qu.: 1.900 1st Qu.:0.07000 1st Qu.: 7.00 1st Qu.: 22.00
## Median : 2.200 Median :0.07900 Median :14.00 Median : 38.00
## Mean : 2.539 Mean :0.08747 Mean :15.87 Mean : 46.47
## 3rd Qu.: 2.600 3rd Qu.:0.09000 3rd Qu.:21.00 3rd Qu.: 62.00
## Max. :15.500 Max. :0.61100 Max. :72.00 Max. :289.00
##
## density pH sulphates alcohol
## Min. :0.9901 Min. :2.740 Min. :0.3300 Min. : 8.40
## 1st Qu.:0.9956 1st Qu.:3.210 1st Qu.:0.5500 1st Qu.: 9.50
## Median :0.9968 Median :3.310 Median :0.6200 Median :10.20
## Mean :0.9967 Mean :3.311 Mean :0.6581 Mean :10.42
## 3rd Qu.:0.9978 3rd Qu.:3.400 3rd Qu.:0.7300 3rd Qu.:11.10
## Max. :1.0037 Max. :4.010 Max. :2.0000 Max. :14.90
## NA's :2 NA's :2
## quality
## Min. :3.000
## 1st Qu.:5.000
## Median :6.000
## Mean :5.636
## 3rd Qu.:6.000
## Max. :8.000
##
##
## $white
## type fixed.acidity volatile.acidity citric.acid
## Length:4898 Min. : 3.800 Min. :0.0800 Min. :0.0000
## Class :character 1st Qu.: 6.300 1st Qu.:0.2100 1st Qu.:0.2700
## Mode :character Median : 6.800 Median :0.2600 Median :0.3200
## Mean : 6.856 Mean :0.2783 Mean :0.3343
## 3rd Qu.: 7.300 3rd Qu.:0.3200 3rd Qu.:0.3900
## Max. :14.200 Max. :1.1000 Max. :1.6600
## NA's :8 NA's :7 NA's :2
## residual.sugar chlorides free.sulfur.dioxide total.sulfur.dioxide
## Min. : 0.600 Min. :0.00900 Min. : 2.00 Min. : 9.0
## 1st Qu.: 1.700 1st Qu.:0.03600 1st Qu.: 23.00 1st Qu.:108.0
## Median : 5.200 Median :0.04300 Median : 34.00 Median :134.0
## Mean : 6.393 Mean :0.04578 Mean : 35.31 Mean :138.4
## 3rd Qu.: 9.900 3rd Qu.:0.05000 3rd Qu.: 46.00 3rd Qu.:167.0
## Max. :65.800 Max. :0.34600 Max. :289.00 Max. :440.0
## NA's :2 NA's :2
## density pH sulphates alcohol
## Min. :0.9871 Min. :2.720 Min. :0.2200 Min. : 8.00
## 1st Qu.:0.9917 1st Qu.:3.090 1st Qu.:0.4100 1st Qu.: 9.50
## Median :0.9937 Median :3.180 Median :0.4700 Median :10.40
## Mean :0.9940 Mean :3.188 Mean :0.4898 Mean :10.51
## 3rd Qu.:0.9961 3rd Qu.:3.280 3rd Qu.:0.5500 3rd Qu.:11.40
## Max. :1.0390 Max. :3.820 Max. :1.0800 Max. :14.20
## NA's :7 NA's :2
## quality
## Min. :3.000
## 1st Qu.:5.000
## Median :6.000
## Mean :5.878
## 3rd Qu.:6.000
## Max. :9.000
##