Challenge 5 Instructions

Introduction to Visualization

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

5. 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 ⭐⭐⭐⭐⭐

data <- read_csv('_data/AB_NYC_2019.csv')
data

# A tibble: 48,895 × 16
      id name      host_id host_…¹ neigh…² neigh…³ latit…⁴ longi…⁵ room_…⁶ price
   <dbl> <chr>       <dbl> <chr>   <chr>   <chr>     <dbl>   <dbl> <chr>   <dbl>
 1  2539 Clean & …    2787 John    Brookl… Kensin…    40.6   -74.0 Privat…   149
 2  2595 Skylit M…    2845 Jennif… Manhat… Midtown    40.8   -74.0 Entire…   225
 3  3647 THE VILL…    4632 Elisab… Manhat… Harlem     40.8   -73.9 Privat…   150
 4  3831 Cozy Ent…    4869 LisaRo… Brookl… Clinto…    40.7   -74.0 Entire…    89
 5  5022 Entire A…    7192 Laura   Manhat… East H…    40.8   -73.9 Entire…    80
 6  5099 Large Co…    7322 Chris   Manhat… Murray…    40.7   -74.0 Entire…   200
 7  5121 BlissArt…    7356 Garon   Brookl… Bedfor…    40.7   -74.0 Privat…    60
 8  5178 Large Fu…    8967 Shunic… Manhat… Hell's…    40.8   -74.0 Privat…    79
 9  5203 Cozy Cle…    7490 MaryEl… Manhat… Upper …    40.8   -74.0 Privat…    79
10  5238 Cute & C…    7549 Ben     Manhat… Chinat…    40.7   -74.0 Entire…   150
# … with 48,885 more rows, 6 more variables: minimum_nights <dbl>,
#   number_of_reviews <dbl>, last_review <date>, reviews_per_month <dbl>,
#   calculated_host_listings_count <dbl>, availability_365 <dbl>, and
#   abbreviated variable names ¹​host_name, ²​neighbourhood_group,
#   ³​neighbourhood, ⁴​latitude, ⁵​longitude, ⁶​room_type

Briefly describe the data

The AB_NYC dataset shows the reviews of the place in New york, along with the host details and the reviews and information about the places. It has a lot of information about these places and can be used to see a lot of statistics. ## 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.

library(dplyr)
library(scales)
plotdata <- data %>%
  count(neighbourhood_group) %>%
  mutate(pct = n / sum(n),
         pctlabel = paste0(round(pct*100), "%"))
# plot the bars as percentages, 
# in decending order with bar labels
ggplot(plotdata, 
       aes(x = reorder(neighbourhood_group, -pct),
           y = pct)) + 
  geom_bar(stat = "identity", 
           fill = "indianred3", 
           color = "black") +
  geom_text(aes(label = pctlabel), 
            vjust = -0.25) +
  scale_y_continuous(labels = percent) +
  labs(x = "neighbourhood_group", 
       y = "Percent", 
       title  = "Review in NY by neighbourhood_group")

plotdata <- data %>%
  count(room_type) %>%
  arrange(desc(room_type)) %>%
  mutate(prop = round(n*100/sum(n), 1),
         lab.ypos = cumsum(prop) - 0.5*prop)

plotdata$label <- paste0(plotdata$room_type, "\n",
                         round(plotdata$prop), "%")

ggplot(plotdata, 
       aes(x = "", 
           y = prop, 
           fill = room_type)) +
  geom_bar(width = 1, 
           stat = "identity", 
           color = "black") +
  geom_text(aes(y = lab.ypos, label = label), 
            color = "black") +
  coord_polar("y", 
              start = 0, 
              direction = -1) +
  theme_void() +
  theme(legend.position = "FALSE") +
  labs(title = "Reviews by room_type")

library(ggplot2)
ggplot(data, 
       aes(x = neighbourhood_group, 
           fill = room_type)) + 
  geom_bar(position = "fill") +
  labs(y = "Proportion")

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.

Univariate Visualizations

Bivariate Visualization(s)

Any additional comments?