Data_607_Tidyverse_Extended_Assignment
Mahmud Hasan Al Raji extended by Enid Roman
2022-11-14
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)## Warning: package 'tidyverse' was built under R version 4.2.2## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ 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 4898The 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
##
##
## $white
## type fixed.acidity volatile.acidity citric.acid
## Length:4898 Min. : 3.800 Min. :0.0800 Min. :0.0000
## Class :character 1st Qu.: 6.300 1st Qu.:0.2100 1st Qu.:0.2700
## Mode :character Median : 6.800 Median :0.2600 Median :0.3200
## Mean : 6.856 Mean :0.2783 Mean :0.3343
## 3rd Qu.: 7.300 3rd Qu.:0.3200 3rd Qu.:0.3900
## Max. :14.200 Max. :1.1000 Max. :1.6600
## NA's :8 NA's :7 NA's :2
## residual.sugar chlorides free.sulfur.dioxide total.sulfur.dioxide
## Min. : 0.600 Min. :0.00900 Min. : 2.00 Min. : 9.0
## 1st Qu.: 1.700 1st Qu.:0.03600 1st Qu.: 23.00 1st Qu.:108.0
## Median : 5.200 Median :0.04300 Median : 34.00 Median :134.0
## Mean : 6.393 Mean :0.04578 Mean : 35.31 Mean :138.4
## 3rd Qu.: 9.900 3rd Qu.:0.05000 3rd Qu.: 46.00 3rd Qu.:167.0
## Max. :65.800 Max. :0.34600 Max. :289.00 Max. :440.0
## NA's :2 NA's :2
## density pH sulphates alcohol
## Min. :0.9871 Min. :2.720 Min. :0.2200 Min. : 8.00
## 1st Qu.:0.9917 1st Qu.:3.090 1st Qu.:0.4100 1st Qu.: 9.50
## Median :0.9937 Median :3.180 Median :0.4700 Median :10.40
## Mean :0.9940 Mean :3.188 Mean :0.4898 Mean :10.51
## 3rd Qu.:0.9961 3rd Qu.:3.280 3rd Qu.:0.5500 3rd Qu.:11.40
## Max. :1.0390 Max. :3.820 Max. :1.0800 Max. :14.20
## NA's :7 NA's :2
## quality
## Min. :3.000
## 1st Qu.:5.000
## Median :6.000
## Mean :5.878
## 3rd Qu.:6.000
## Max. :9.000
## Tidyverse Extended by Enid Roman
Distribution of Single Variables
Wine Quality
Red wine sample is smaller. We know that number of observations for red and white are different in the dataset, but still we can see that for both colors it’s normal distribution with almost the same picks at 5 and 6 quality point.
table(df$quality)##
## 3 4 5 6 7 8 9
## 30 216 2138 2836 1079 193 5Here we use the function qplot() in ggplot2 part of Tidyverse Package is very similar to the basic plot() function from the R base package. It can be used to create and combine easily different types of plots.
library(ggplot2)
qplot(quality, data = df, fill = type, binwidth = 1) +
scale_x_continuous(breaks = seq(3,10,1), lim = c(3,10)) ## Warning: Removed 4 rows containing missing values (geom_bar).
### Level of alcohol
Alcohol level distribution looks skewed. Again, red wine sample is smaller but it gives the same pattern of alcohol level distribution as white wines. Most frequently wines have 9.5%, mean is 10.49% of alcohol.
summary(df$alcohol)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 8.00 9.50 10.30 10.49 11.30 14.90qplot(alcohol, data = df, fill = type, binwidth = 0.5) +
scale_x_continuous(breaks = seq(8,15,0.5), lim = c(8,15))## Warning: Removed 4 rows containing missing values (geom_bar).
### Wine Density
Looking at ‘table’ summary we see that there are two outliers: 1.0103 and 1.03898. To see the distribution of density clearer I used log10 and limited the data. Now we can see that density distribution of white wine is bimodal and of red wine is normal.
summary(df$density)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9871 0.9923 0.9949 0.9947 0.9970 1.0390qplot(density, data = df, fill = type, binwidth = 0.0002) +
scale_x_log10(lim = c(min(df$density), 1.00370),
breaks = seq(min(df$density), 1.00370, 0.002))## Warning: Removed 3 rows containing non-finite values (stat_bin).## Warning: Removed 4 rows containing missing values (geom_bar).
Distribution of Two and More Variables
Density of Quality by color
In out sample we have almost the same amount of red and white wines with quality ‘3’, ‘4’ and ‘9’, more red wines with quality ‘5’ and more white wines with quality “6”, “7” and “8”.
qplot(quality, data = df, binwidth = 1, color = type, geom = "density") +
scale_x_continuous(breaks = seq(3, 9, 1))## Warning: Ignoring unknown parameters: binwidth