Challenge 7

challenge_7
us_hh
Animesh Sengupta
Visualizing Multiple Dimensions
Author

Animesh Sengupta

Published

August 24, 2022

library(tidyverse)
library(ggplot2)
library(summarytools)
library(plotly)
library(stringr)
library(ggalluvial)
library(readxl)

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. Recreate at least two graphs from previous exercises, but introduce at least one additional dimension that you omitted before using ggplot functionality (color, shape, line, facet, etc) The goal is not to create unneeded chart ink (Tufte), but to concisely capture variation in additional dimensions that were collapsed in your earlier 2 or 3 dimensional graphs.
  • Explain why you choose the specific graph type
  1. If you haven’t tried in previous weeks, work this week to make your graphs “publication” ready with titles, captions, and pretty axis labels and other viewer-friendly features

R Graph Gallery is a good starting point for thinking about what information is conveyed in standard graph types, and includes example R code. And anyone not familiar with Edward Tufte should check out his fantastic books and courses on data visualizaton.

(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.

  • eggs ⭐
  • abc_poll ⭐⭐
  • australian_marriage ⭐⭐
  • hotel_bookings ⭐⭐⭐
  • air_bnb ⭐⭐⭐
  • us_hh ⭐⭐⭐⭐
  • faostat ⭐⭐⭐⭐⭐
#! label: Data loading
US_household_data <- read_excel("../posts/_data/USA Households by Total Money Income, Race, and Hispanic Origin of Householder 1967 to 2019.xlsx",skip = 5, n_max = 353, col_names = c( "Year", "Number","Total","pd_<15000","pd_15000-24999","pd_25000-34999","pd_35000-49999","pd_50000-74999","pd_75000-99999","pd_100000-149999","pd_150000-199999","pd_>200000","median_income_estimate","median_income_moe","mean_income_estimate","mean_income_moe"))

head(US_household_data,5)
# A tibble: 5 × 16
  Year      Number Total pd_<1…¹ pd_15…² pd_25…³ pd_35…⁴ pd_50…⁵ pd_75…⁶ pd_10…⁷
  <chr>     <chr>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
1 ALL RACES <NA>      NA    NA      NA      NA      NA      NA      NA      NA  
2 2019      128451   100     9.1     8       8.3    11.7    16.5    12.3    15.5
3 2018      128579   100    10.1     8.8     8.7    12      17      12.5    15  
4 2017 2    127669   100    10       9.1     9.2    12      16.4    12.4    14.7
5 2017      127586   100    10.1     9.1     9.2    11.9    16.3    12.6    14.8
# … with 6 more variables: `pd_150000-199999` <dbl>, `pd_>200000` <dbl>,
#   median_income_estimate <dbl>, median_income_moe <dbl>,
#   mean_income_estimate <chr>, mean_income_moe <chr>, and abbreviated variable
#   names ¹​`pd_<15000`, ²​`pd_15000-24999`, ³​`pd_25000-34999`,
#   ⁴​`pd_35000-49999`, ⁵​`pd_50000-74999`, ⁶​`pd_75000-99999`,
#   ⁷​`pd_100000-149999`

Briefly describe the data

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.

#! label: Data processing
US_processed_data <- US_household_data%>%
  rowwise()%>% #to ensure the following operation runs row wise
  mutate(Race=case_when(
    is.na(Number) ~ Year
  ))%>%
  ungroup()%>% # to stop rowwise operation
  fill(Race,.direction = "down")%>%
  subset(!is.na(Number))%>%
  rowwise()%>%
  mutate(
    Year=strsplit(Year,' ')[[1]][1],
    Race=ifelse(grepl("[0-9]", Race ,perl=TRUE)[1],strsplit(Race," \\s*(?=[^ ]+$)",perl=TRUE)[[1]][1],Race),
    mean_income_estimate=as.numeric(mean_income_estimate),
    Number=as.numeric(Number),
    Year=as.numeric(Year),
    CombinedRace=case_when(
      str_detect(Race,"ASIAN")~"ASIAN",
      str_detect(Race,"BLACK")~"BLACK",
      str_detect(Race,"WHITE")~"WHITE",
      TRUE ~ Race
    )
  )%>%
  pivot_longer(
    cols = starts_with("pd"),
    names_to = "income_range",
    values_to = "percent_distribution",
    names_prefix="pd_"
  )
view(dfSummary(US_processed_data))

Are there any variables that require mutation to be usable in your analysis stream? For example, do you need to calculate new values in order to graph them? Can string values be represented numerically? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?

Document your work here.

alluvial_viz<-ggplot(US_processed_data,aes(axis1=CombinedRace,axis2=income_range,y=percent_distribution))+
  geom_alluvium(aes(fill=CombinedRace))+
  geom_stratum(aes(fill = CombinedRace))+
  geom_text(stat = "stratum",size=3,
            aes(label = after_stat(stratum)))+
  theme_void()+
  labs(title="Income range distribution across different races")
alluvial_viz

Visualization with Multiple Dimensions