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)

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     
##  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:

# Insert your code here, create more chunks as necessary
dim(inc)
## [1] 5001    8

The Data has 5001 Rows (Including Labels) and 8 Columns

suppressMessages(suppressWarnings(library(tidyverse)))
suppressMessages(suppressWarnings(library(ggplot2)))
suppressMessages(suppressWarnings(library("RColorBrewer")))
#Lets see the Standard Deviaition for Numberical fields Growth/Revenue and Employees

print(paste("SD for Growth Rate ",sd(inc$Growth_Rate)))
## [1] "SD for Growth Rate  14.1236917640676"
print(paste("SD for Revenue ",sd(inc$Revenue)))
## [1] "SD for Revenue  240542281.135874"
print(paste("SD for Employees",sd(inc$Employees, na.rm = TRUE))) # Some companies have missing employee counts
## [1] "SD for Employees 1353.12794924661"
plot(inc$Rank, inc$Growth_Rate,col="blue")

print(paste("Looks Like only Higher ranked Companies have good growth rates"))
## [1] "Looks Like only Higher ranked Companies have good growth rates"

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.

# Answer Question 1 here
# Create Summary of states dataframe and sort by companies
states <- inc %>%
  group_by(State) %>%
  count() %>%
  rename(count = n) %>%
  arrange(desc(count))
# plot 
stplot <- ggplot(states, aes(x = reorder(State, count), y = count)) 
stplot <- stplot + geom_bar(stat = "identity", fill = 'Brown') + coord_flip() 
stplot <- stplot + ggtitle("Distribution of Companies by States")
stplot <- stplot + labs(x = "State Name", y = "Number of Companies")
stplot <- stplot + theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))
stplot <- stplot + theme_bw()
stplot

print("California is the state with most distribution of companies")
## [1] "California is the state with most distribution 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.

# Answer Question 2 here
# find state with third most companies (3rd row b/c already ordered desc)
third_state <- states[3,"State"]
third_state
## # A tibble: 1 x 1
## # Groups:   State [1]
##   State
##   <fct>
## 1 NY
# Get complete cases and 3rd ranked state only   
third_state_data <- inc[complete.cases(inc),] %>%
  inner_join(third_state, by = "State")
# Mean employment by industry
mean_emp <- aggregate(Employees ~ Industry, third_state_data, mean)
# Maximum average employee no.
maxmean <- max(mean_emp$Employees)
# plot data
emp_plot<- ggplot(third_state_data, aes(x = reorder(Industry,Employees,mean), y = Employees))
emp_plot<- emp_plot+ geom_boxplot(outlier.shape = NA, show.legend=F) + coord_flip()
emp_plot<- emp_plot+ labs(x = "industry", y = "employees", title="Mean Employment Size by Industry")
emp_plot<- emp_plot+ geom_point(data = mean_emp, aes(x=Industry, y=Employees), color='darkblue', size = 2)
emp_plot<- emp_plot+ theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))

emp_plot<- emp_plot+  scale_y_continuous(limits = c(0,maxmean), breaks = seq(0, maxmean, 500)) + theme_bw()
emp_plot <- emp_plot  + theme_bw()
emp_plot

The average employee count in the Business Products & Services industry are many times greater than the other industries.

For Such a big skew and outliers we can use log scale

emp_plot <- emp_plot + scale_y_log10(limits = c(1, maxmean))
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
emp_plot <- emp_plot + labs(caption = "(grid line spacing on log scale)")
emp_plot <- emp_plot + theme(plot.caption = element_text(size = 8))
emp_plot <- emp_plot   + theme_bw()
emp_plot