Challenge 5

challenge_5

railroads

Introduction to Visualization

Author

Pooja Shah

Published

April 27, 2023

library(tidyverse)
library(ggplot2)
library(readxl)

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

Challenge Overview

Today’s challenge is to:

read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
tidy data (as needed, including sanity checks)
mutate variables as needed (including sanity checks)
create at least two univariate visualizations

try to make them “publication” ready
Explain why you choose the specific graph type

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.csv ⭐
Total_cost_for_top_15_pathogens_2018.xlsx ⭐
Australian Marriage ⭐⭐
AB_NYC_2019.csv ⭐⭐⭐
StateCounty2012.xls ⭐⭐⭐
Public School Characteristics ⭐⭐⭐⭐
USA Households ⭐⭐⭐⭐⭐

employ <- read_excel("_data/StateCounty2012.xls", skip=4, col_names = c("State", "B1", "County", "B2", "Total"))

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.

employment <- employ %>%
  select("State", "County", "Total")

employment

# A tibble: 2,990 × 3
   State     County               Total
   <chr>     <chr>                <dbl>
 1 AE        APO                      2
 2 AE Total1 <NA>                     2
 3 AK        ANCHORAGE                7
 4 AK        FAIRBANKS NORTH STAR     2
 5 AK        JUNEAU                   3
 6 AK        MATANUSKA-SUSITNA        2
 7 AK        SITKA                    1
 8 AK        SKAGWAY MUNICIPALITY    88
 9 AK Total  <NA>                   103
10 AL        AUTAUGA                102
# ℹ 2,980 more rows

employment <- employment %>%
  drop_na()

employment

# A tibble: 2,930 × 3
   State County               Total
   <chr> <chr>                <dbl>
 1 AE    APO                      2
 2 AK    ANCHORAGE                7
 3 AK    FAIRBANKS NORTH STAR     2
 4 AK    JUNEAU                   3
 5 AK    MATANUSKA-SUSITNA        2
 6 AK    SITKA                    1
 7 AK    SKAGWAY MUNICIPALITY    88
 8 AL    AUTAUGA                102
 9 AL    BALDWIN                143
10 AL    BARBOUR                  1
# ℹ 2,920 more rows

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.

#State and its total employees
total <- employment %>%
  group_by(State) %>%
  summarise(Employees = sum(Total)) %>%
  select(State, Employees)
total

# A tibble: 53 × 2
   State Employees
   <chr>     <dbl>
 1 AE            2
 2 AK          103
 3 AL         4257
 4 AP            1
 5 AR         3871
 6 AZ         3153
 7 CA        13137
 8 CO         3650
 9 CT         2592
10 DC          279
# ℹ 43 more rows

state_count <- employment %>%
  count(State)

state_count

# A tibble: 53 × 2
   State     n
   <chr> <int>
 1 AE        1
 2 AK        6
 3 AL       67
 4 AP        1
 5 AR       72
 6 AZ       15
 7 CA       55
 8 CO       57
 9 CT        8
10 DC        1
# ℹ 43 more rows

Univariate Visualizations

ggplot(employment, aes(Total)) + geom_histogram()

Bivariate Visualization(s)

#Graph for County vs Employees
ggplot(employment, aes(County, Total)) +
  geom_boxplot() +
  labs(title = "County vs Employess", y = "Employees", x = "County")

#Graph for State vs Employees
ggplot(total, aes(State, Employees)) + 
  geom_boxplot() +
  labs(title = "State vs Employess", y = "Employees", x = "State")

#Graph for No. of Counties per State
ggplot(state_count, aes(State, n)) + 
  geom_boxplot() +
  labs(title = "Counties per State", y = "Count", x = "State")

Any additional comments?