DATA 608: Module 1

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

library(ggplot2)

# create bar chart
# re-order the states by company count, from highest to lowest
ggplot(inc, aes(x = reorder(State, rep(1, length(State)), FUN = sum))) + 
    geom_bar() + 
    # flip axes for portrait orientation
    coord_flip() +
    labs(title = "Distribution of 'Inc. 5000' Companies by State",
         subtitle = "50 US States, DC & Puerto Rico",
         #x = "State",
         y = "Number of Companies") + 
    # remove label for state axis
    theme(axis.title.y = element_blank())

Question 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

# 12 companies have incomplete data in the dataset
nrow(inc) - sum(complete.cases(inc))

## [1] 12

# subset data to include NY companies with full data
NYcos <- inc[complete.cases(inc) & inc$State == "NY", ]
str(NYcos)

## 'data.frame':    311 obs. of  8 variables:
##  $ Rank       : int  26 30 37 38 48 70 71 124 126 153 ...
##  $ Name       : Factor w/ 5001 levels "(Add)ventures",..: 529 3822 4972 1037 912 19 2608 3591 3684 3668 ...
##  $ Growth_Rate: num  84.4 73.2 67.4 67 53.6 ...
##  $ Revenue    : num  13700000 8100000 18000000 7100000 5900000 27900000 6900000 11500000 9800000 15400000 ...
##  $ Industry   : Factor w/ 25 levels "Advertising & Marketing",..: 5 1 1 1 10 1 1 24 21 25 ...
##  $ Employees  : int  17 79 27 89 32 75 42 28 17 42 ...
##  $ City       : Factor w/ 1519 levels "Acton","Addison",..: 929 929 929 929 1135 929 929 929 574 162 ...
##  $ State      : Factor w/ 52 levels "AK","AL","AR",..: 35 35 35 35 35 35 35 35 35 35 ...

summary(NYcos)

##       Rank                        Name      Growth_Rate    
##  Min.   :  26   1st Equity          :  1   Min.   : 0.350  
##  1st Qu.:1186   33Across            :  1   1st Qu.: 0.670  
##  Median :2702   5Linx Enterprises   :  1   Median : 1.310  
##  Mean   :2612   Access Display Group:  1   Mean   : 4.371  
##  3rd Qu.:4005   Adafruit            :  1   3rd Qu.: 3.580  
##  Max.   :4981   AdCorp Media Group  :  1   Max.   :84.430  
##                 (Other)             :305                   
##     Revenue                                  Industry     Employees      
##  Min.   :2.000e+06   Advertising & Marketing     : 57   Min.   :    1.0  
##  1st Qu.:4.300e+06   IT Services                 : 43   1st Qu.:   21.0  
##  Median :8.800e+06   Business Products & Services: 26   Median :   45.0  
##  Mean   :5.872e+07   Consumer Products & Services: 17   Mean   :  271.3  
##  3rd Qu.:2.570e+07   Telecommunications          : 17   3rd Qu.:  105.5  
##  Max.   :4.600e+09   Education                   : 14   Max.   :32000.0  
##                      (Other)                     :137                    
##         City         State    
##  New York :160   NY     :311  
##  Brooklyn : 15   AK     :  0  
##  Rochester:  9   AL     :  0  
##  Buffalo  :  5   AR     :  0  
##  Fairport :  5   AZ     :  0  
##  new york :  5   CA     :  0  
##  (Other)  :112   (Other):  0

# will cut-off employee count to remove outliers from chart
# but outliers will be included in mean/median/width, etc.
quantile(NYcos$Employees, probs = c(0, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99))

##     0%    25%    50%    75%    90%    95%    99% 
##    1.0   21.0   45.0  105.5  300.0  555.5 2273.8

# create box plot
ggplot(NYcos, aes(x = Industry, y = Employees)) + 
  geom_boxplot() + 
  # add means
  stat_summary(fun.y = "mean", geom = "point", shape = 23, size = 2, fill = "red") +
  # flip axes for portrait orientation
  # limit employee count to 600 which is >95%-ile
  coord_flip(ylim = c(0, 600)) +
  # reverse order of industries so alphabetical from top down
  scale_x_discrete(limits = rev(levels(NYcos$Industry))) + 
  labs(title = "Employment by Industry for 'Inc. 5000' Companies in NY State",
       subtitle = "Box Plot with Median (Solid Line) & Mean (Red Diamond)",
       y = "Employees per Company") + 
  # remove label for industry axis
  theme(axis.title.y = element_blank())

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.

# Answer Question 3 here

library(dplyr)

# compute revenue per employee in $K
revpem <- inc[complete.cases(inc), ] %>% mutate(rev = Revenue / Employees / 1000)

# will cut-off rev per employee to remove outliers from chart
# but outliers will be included in mean/median/width, etc.
quantile(revpem$rev, probs = c(0, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99))

##          0%         25%         50%         75%         90%         95% 
##    1.801242  125.000000  198.657718  375.000000  750.000000 1221.739130 
##         99% 
## 3053.000000

# create box plot
# re-order the industries by median rev per employee, from top to bottom
ggplot(revpem, aes(x = reorder(Industry, rev, FUN = median), y = rev)) + 
  geom_boxplot() + 
  # add means
  stat_summary(fun.y = "mean", geom = "point", shape = 23, size = 2, fill = "blue") +
  # flip axes for portrait orientation
  # limit rev per employee to 1.2MM which is ~95%-ile 
  coord_flip(ylim = c(0, 1200)) +
  labs(title = "Revenue per Employee by Industry for 'Inc. 5000' Companies", 
       subtitle = "Box Plot with Median (Solid Line) & Mean (Blue Diamond)",
       y = "Revenue per Employee ($K)") +
  theme(axis.title.y = element_blank())