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:

inc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE)
library(tidyverse)
library(knitr)
inc <- as_tibble(inc)

And lets preview this data:

head(inc) %>% kable()
Rank Name Growth_Rate Revenue Industry Employees City State
1 Fuhu 421.48 1.179e+08 Consumer Products & Services 104 El Segundo CA
2 FederalConference.com 248.31 4.960e+07 Government Services 51 Dumfries VA
3 The HCI Group 245.45 2.550e+07 Health 132 Jacksonville FL
4 Bridger 233.08 1.900e+09 Energy 50 Addison TX
5 DataXu 213.37 8.700e+07 Advertising & Marketing 220 Boston MA
6 MileStone Community Builders 179.38 4.570e+07 Real Estate 63 Austin TX 
summary(inc)
##       Rank                          Name       Growth_Rate     
##  Min.   :   1   (Add)ventures         :   1   Min.   :  0.340  
##  1st Qu.:1252   @Properties           :   1   1st Qu.:  0.770  
##  Median :2502   1-Stop Translation USA:   1   Median :  1.420  
##  Mean   :2502   110 Consulting        :   1   Mean   :  4.612  
##  3rd Qu.:3751   11thStreetCoffee.com  :   1   3rd Qu.:  3.290  
##  Max.   :5000   123 Exteriors         :   1   Max.   :421.480  
##                 (Other)               :4995                    
##     Revenue                                  Industry      Employees      
##  Min.   :2.000e+06   IT Services                 : 733   Min.   :    1.0  
##  1st Qu.:5.100e+06   Business Products & Services: 482   1st Qu.:   25.0  
##  Median :1.090e+07   Advertising & Marketing     : 471   Median :   53.0  
##  Mean   :4.822e+07   Health                      : 355   Mean   :  232.7  
##  3rd Qu.:2.860e+07   Software                    : 342   3rd Qu.:  132.0  
##  Max.   :1.010e+10   Financial Services          : 260   Max.   :66803.0  
##                      (Other)                     :2358   NA's   :12       
##             City          State     
##  New York     : 160   CA     : 701  
##  Chicago      :  90   TX     : 387  
##  Austin       :  88   NY     : 311  
##  Houston      :  76   VA     : 283  
##  San Francisco:  75   FL     : 282  
##  Atlanta      :  74   IL     : 273  
##  (Other)      :4438   (Other):2764

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:

stateCount <- inc$State %>% unique() %>% length()
stateCount # We can see this is higher than the expected. Lets see what is not in there.
## [1] 52
# Since I'm not American I don't know which abbreviations are not states
# I presume that they incldue Porto rico etc.
x <- inc %>% group_by(State) %>% 
  summarise(byMean = mean(Revenue))
max(x$byMean)
## [1] 231523529

Question 1

Create a graph that shows the distribution of companies in the dataset 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 (ie taller than wide), which should further guide your layout choices.

df <- inc %>% 
  group_by(State) %>% 
  summarise(bizCount = n()) %>% # States act as a proxy for unique business
  arrange(desc(bizCount))
library(ggthemes)

ggplot(data = df, aes(x = reorder(State, bizCount), y = bizCount)) +
  theme_tufte() + 
  geom_bar(fill="gray38", stat = "identity") +  theme(axis.title=element_blank()) +
  geom_hline(yintercept=seq(1, 800, 100), col="white", lwd=1) +
  coord_flip() + 
  labs(title = 'Number of Companies by State') +
  xlab('State') +
  ylab('Number of Companies')

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.

state3 <- toString(df$State[3])
df2 <- inc %>% 
  filter(State == state3) %>% 
  filter(complete.cases(.)) %>% 
  group_by(Industry) %>% 
  summarise(indMean = mean(Employees),
            indMed = median(Employees)) %>% 
  gather(statType, Amount, indMean, indMed)
ggplot(data = df2, aes(x = Industry, y = Amount)) +
  geom_bar(stat = 'identity', aes(fill = statType), position = 'dodge') +
  scale_fill_manual(values = c('grey80', 'grey33'))+
  geom_hline(yintercept=seq(1, 1500, 100), col="white", lwd=0.5) +
  theme_tufte() +
  coord_flip()

Question3

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.

df3 <- inc %>% 
  filter(State == state3) %>% 
  filter(complete.cases(.)) %>% 
  mutate(RevPerEmp = (Revenue / Employees)/1000) %>% 
  group_by(Industry) %>% 
  summarise(Mean = mean(RevPerEmp))
ggplot(data = df3, aes(x = Industry, y = Mean)) +
  geom_bar(stat = 'identity') +
  theme_tufte()+
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  geom_hline(yintercept=seq(1, 9000, 1000), col="white", lwd=0.5) +
  ylab('Revenue/Employee ,000 $')