Challenge 5 Akhilesh

challenge_5
railroads
cereal
air_bnb
pathogen_cost
australian_marriage
public_schools
usa_hh
Introduction to Visualization
Author

Akhilesh Kumar Meghwal

Published

August 28, 2022

library(tidyverse)
library(ggplot2)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Challenge Overview

Today’s challenge is to:

  1. read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
  2. tidy data (as needed, including sanity checks)
  3. mutate variables as needed (including sanity checks)
  4. create at least two univariate visualizations
  • try to make them “publication” ready
  • Explain why you choose the specific graph type
  1. Create at least one bivariate visualization
  • try to make them “publication” ready
  • Explain why you choose the specific graph type

R Graph Gallery is a good starting point for thinking about what information is conveyed in standard graph types, and includes example R code.

(be sure to only include the category tags for the data you use!)

Read in data

Read in one (or more) of the following datasets, using the correct R package and command.

  • cereal ⭐
  • pathogen cost ⭐
  • Australian Marriage ⭐⭐
  • AB_NYC_2019.csv ⭐⭐⭐
  • railroads ⭐⭐⭐
  • Public School Characteristics ⭐⭐⭐⭐
  • USA Households ⭐⭐⭐⭐⭐
cereal<-read_csv("_data/cereal.csv", show_col_types = FALSE)
names(cereal)
[1] "Cereal" "Sodium" "Sugar"  "Type"  
dim(cereal)
[1] 20  4
summary(cereal)
    Cereal              Sodium          Sugar           Type          
 Length:20          Min.   :  0.0   Min.   : 0.00   Length:20         
 Class :character   1st Qu.:137.5   1st Qu.: 4.00   Class :character  
 Mode  :character   Median :180.0   Median : 9.50   Mode  :character  
                    Mean   :167.0   Mean   : 8.75                     
                    3rd Qu.:202.5   3rd Qu.:12.50                     
                    Max.   :340.0   Max.   :18.00                     
View(cereal)

Briefly describe the data

print(summarytools::dfSummary(cereal,
                              varnumbers = FALSE,
                              plain.ascii  = FALSE,
                              style        = "grid",
                              graph.magnif = 0.50,
                              valid.col    = FALSE),
      method = 'render',
      table.classes = 'table-condensed')

Data Frame Summary

cereal

Dimensions: 20 x 4
Duplicates: 0
Variable Stats / Values Freqs (% of Valid) Graph Missing
Cereal [character]
1. All Bran
2. Apple Jacks
3. Captain Crunch
4. Cheerios
5. Cinnamon Toast Crunch
6. Corn Flakes
7. Crackling Oat Bran
8. Fiber One
9. Froot Loops
10. Frosted Flakes
[ 10 others ]
1(5.0%)
1(5.0%)
1(5.0%)
1(5.0%)
1(5.0%)
1(5.0%)
1(5.0%)
1(5.0%)
1(5.0%)
1(5.0%)
10(50.0%)
 0 (0.0%)
Sodium [numeric]
Mean (sd) : 167 (77.3)
min ≤ med ≤ max:
0 ≤ 180 ≤ 340
IQR (CV) : 65 (0.5)
 15 distinct values 0 (0.0%)
Sugar [numeric]
Mean (sd) : 8.8 (5.3)
min ≤ med ≤ max:
0 ≤ 9.5 ≤ 18
IQR (CV) : 8.5 (0.6)
 15 distinct values 0 (0.0%)
Type [character]
1. A
2. C
10(50.0%)
10(50.0%)
 0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.2.1)
2022-09-04

  • The dataset ‘cereal’ contains sodium, sugar value along with ‘type of cereal’ for each cereal type.
  • There are 20 different type of cereals
  • Type column variable has two values: ‘A’ and ‘C’, both have frequency of 10
  • ‘Sodium’ column variable has min, max, mean values of 0, 340, 167 respectively
  • ‘Sugar’ column variable has min, max, mean values of 0, 18, 8.75 respectively
  • For ‘A’ type cereals min, max, mean values of ‘Sodium’ is 0, 340, 149 respectively
  • For ‘A’ type cereals min, max, mean values of ‘Sugar’ is 0, 18, 9.22 respectively
  • For ‘C’ type cereals min, max, mean values of ‘Sodium’ is 130, 290, 185 respectively
  • For ‘C’ type cereals min, max, mean values of ‘Sugar’ is 1, 14, 9.2 respectively

Tidy Data (as needed)

Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.

cereal <- pivot_longer(cereal, cols= c('Sodium', 'Sugar'), names_to='Nutrient_type', values_to ='Value')

Sanity check

head(cereal)
# A tibble: 6 × 4
  Cereal              Type  Nutrient_type Value
  <chr>               <chr> <chr>         <dbl>
1 Frosted Mini Wheats A     Sodium            0
2 Frosted Mini Wheats A     Sugar            11
3 Raisin Bran         A     Sodium          340
4 Raisin Bran         A     Sugar            18
5 All Bran            A     Sodium           70
6 All Bran            A     Sugar             5
str(cereal)
tibble [40 × 4] (S3: tbl_df/tbl/data.frame)
 $ Cereal       : chr [1:40] "Frosted Mini Wheats" "Frosted Mini Wheats" "Raisin Bran" "Raisin Bran" ...
 $ Type         : chr [1:40] "A" "A" "A" "A" ...
 $ Nutrient_type: chr [1:40] "Sodium" "Sugar" "Sodium" "Sugar" ...
 $ Value        : num [1:40] 0 11 340 18 70 5 140 14 200 12 ...
summary(cereal)
    Cereal              Type           Nutrient_type          Value       
 Length:40          Length:40          Length:40          Min.   :  0.00  
 Class :character   Class :character   Class :character   1st Qu.:  8.50  
 Mode  :character   Mode  :character   Mode  :character   Median : 17.00  
                                                          Mean   : 87.88  
                                                          3rd Qu.:180.00  
                                                          Max.   :340.00  
View(cereal)

NA primary check

NA_SUM <- sum(is.na(cereal))
View(cereal)

NA_SUM is zero, so there is no NA value in the dataset

Mutate to change class of ‘Cereal’ column from character to factor

cereal <- cereal %>% 
  mutate_at(c('Cereal', 'Type', 'Nutrient_type'), factor)

  • ‘Cereal’, ‘Type’& ‘Nutrient_type’ columns are character class, need to be converted to factor and reorder for graphic visualization.

  • Remaining columns are numeric and don’t need mutation,

  • String values in the column, can be represented numerically by setting numeric labels

  • Factors are important for indicating subsets of dataset for categorical variables, also Reordering helps in alignment of graphs

Univariate Visualizations

b <- cereal %>% 
  group_by(Nutrient_type, Type) %>% 
  summarise(sum(Value),.groups = 'keep') %>% 
  rename(nutrient_value = `sum(Value)`)

ggplot(b, aes(nutrient_value, fill = Type)) + 
  geom_histogram(binwidth = 20) + 
  labs(title = "Sodium/Sugar Values") + 
  theme_bw() +
  facet_wrap(Nutrient_type~Type, scales = "free_x")

Bivariate Visualization(s)

ggplot(cereal, aes(x=Cereal, y=Value)) + 
  geom_bar(stat = "identity")+theme(axis.text.x=element_text(angle=90,hjust=1))+
  facet_wrap(~'Nutrient_type')+
  labs(title = "Bar Graph on Cereal wise Value")

Any additional comments?