Principles of Data Visualization and Introduction to ggplot2

I have provided you with data about the 5,000 fastest growing companies in the US, as compiled by Inc. magazine. lets read this in:

library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(scales)
library(tidyverse)
## ── Attaching packages ──────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ tibble  3.0.3     ✓ purrr   0.3.4
## ✓ tidyr   1.1.1     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ─────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x readr::col_factor() masks scales::col_factor()
## x purrr::discard()    masks scales::discard()
## x dplyr::filter()     masks stats::filter()
## x dplyr::lag()        masks stats::lag()
library(formattable)
## 
## Attaching package: 'formattable'
## The following objects are masked from 'package:scales':
## 
##     comma, percent, scientific
inc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE)

Explaratory

And lets preview this data:

head(inc)
##   Rank                         Name Growth_Rate   Revenue
## 1    1                         Fuhu      421.48 1.179e+08
## 2    2        FederalConference.com      248.31 4.960e+07
## 3    3                The HCI Group      245.45 2.550e+07
## 4    4                      Bridger      233.08 1.900e+09
## 5    5                       DataXu      213.37 8.700e+07
## 6    6 MileStone Community Builders      179.38 4.570e+07
##                       Industry Employees         City State
## 1 Consumer Products & Services       104   El Segundo    CA
## 2          Government Services        51     Dumfries    VA
## 3                       Health       132 Jacksonville    FL
## 4                       Energy        50      Addison    TX
## 5      Advertising & Marketing       220       Boston    MA
## 6                  Real Estate        63       Austin    TX
summary(inc)
##       Rank          Name            Growth_Rate         Revenue         
##  Min.   :   1   Length:5001        Min.   :  0.340   Min.   :2.000e+06  
##  1st Qu.:1252   Class :character   1st Qu.:  0.770   1st Qu.:5.100e+06  
##  Median :2502   Mode  :character   Median :  1.420   Median :1.090e+07  
##  Mean   :2502                      Mean   :  4.612   Mean   :4.822e+07  
##  3rd Qu.:3751                      3rd Qu.:  3.290   3rd Qu.:2.860e+07  
##  Max.   :5000                      Max.   :421.480   Max.   :1.010e+10  
##                                                                         
##    Industry           Employees           City              State          
##  Length:5001        Min.   :    1.0   Length:5001        Length:5001       
##  Class :character   1st Qu.:   25.0   Class :character   Class :character  
##  Mode  :character   Median :   53.0   Mode  :character   Mode  :character  
##                     Mean   :  232.7                                        
##                     3rd Qu.:  132.0                                        
##                     Max.   :66803.0                                        
##                     NA's   :12

Think a bit on what these summaries mean. Use the space below to add some more relevant non-visual exploratory information you think helps you understand this data:

Companies by Industry

df <- as.data.frame(inc)
cbi <- df %>% 
  count(Industry) %>% 
  group_by(Industry) %>%
  arrange(desc(n))

formattable(cbi)
Industry n
IT Services 733
Business Products & Services 482
Advertising & Marketing 471
Health 355
Software 342
Financial Services 260
Manufacturing 256
Consumer Products & Services 203
Retail 203
Government Services 202
Human Resources 196
Construction 187
Logistics & Transportation 155
Food & Beverage 131
Telecommunications 129
Energy 109
Real Estate 96
Education 83
Engineering 74
Security 73
Travel & Hospitality 62
Media 54
Environmental Services 51
Insurance 50
Computer Hardware 44

Relationship between Rank and Growth_Rate?

cor(inc$Rank, inc$Growth_Rate)
## [1] -0.3976698

This confirms that generally lower Rank companies have lower Growth Rates (as expected)

Relationship between Rank and Revenue?

cor(inc$Rank, inc$Revenue)
## [1] 0.08210681

Interestingly, a companies Rank (based on Growth) has only a small correlation with the company’s Revenue. Maybe faster growing companies are in a start-up phase and relying on investors rather than revenue? Broadly, having more revenue offers little explanatory value towards a companies rank.

Question 1

Create a graph that shows the distribution of companies in the data set by State (ie how many are in each state). There are a lot of States, so consider which axis you should use. This visualization is ultimately going to be consumed on a ‘portrait’ oriented screen (i.e. taller than wide), which should further guide your layout choices.

plot_data <- df %>% 
  count(State) %>% 
  group_by(State)

p <- ggplot(plot_data, aes(x=reorder(State, n), y=n)) +
    geom_bar(stat="identity", fill="steelblue") +
    coord_flip() +
    ylab('Number of Companies') +
    xlab('State') + 
    ggtitle('Count of Fastest Growing Companyies by State') +
    theme_minimal()

p

Quesiton 2

Lets dig in on the state with the 3rd most companies in the data set. Imagine you work for the state and are interested in how many people are employed by companies in different industries. Create a plot that shows the average and/or median employment by industry for companies in this state (only use cases with full data, use R’s complete.cases() function.) In addition to this, your graph should show how variable the ranges are, and you should deal with outliers.

state_3 <- df %>% 
  filter(State == 'NY') %>%
  drop_na %>%
  filter(Employees < mean(Employees) + 3 * sd(Employees))

p <- ggplot(state_3, aes(x=reorder(Industry, Employees), y=Employees)) +
  geom_boxplot() + 
  coord_flip() +
  theme_minimal()

p

Question 3

Now imagine you work for an investor and want to see which industries generate the most revenue per employee. Create a chart that makes this information clear. Once again, the distribution per industry should be shown.

rev_empl <- df %>%
  drop_na %>%
  mutate(rpe = Revenue / Employees) %>%
  filter(rpe < mean(rpe) + 3 * sd(rpe))


p <- ggplot(rev_empl, aes(x=reorder(Industry, rpe), y=rpe)) +
  geom_boxplot() + 
  coord_flip() +
  xlab('Industry') +
  ylab('Revenue per Employee') + 
  ggtitle('Revenue/Employee by Industry (outliers removed)') +
  theme_minimal()
p