Data 608-01 Spring 2021: Homework #1

Additional Summaries

inc <- readr::read_csv("inc5000_data.csv")

# Number of unique industries represented
length(unique(inc$Industry))
## [1] 25

# Unique States
# 50 + DC + PR
length(unique(inc$State))
## [1] 52
table(inc$State)
## 
##  AK  AL  AR  AZ  CA  CO  CT  DC  DE  FL  GA  HI  IA  ID  IL  IN  KS  KY  LA  MA 
##   2  51   9 100 701 134  50  43  16 282 212   7  28  17 273  69  38  40  37 182 
##  MD  ME  MI  MN  MO  MS  MT  NC  ND  NE  NH  NJ  NM  NV  NY  OH  OK  OR  PA  PR 
## 131  13 126  88  59  12   4 137  10  27  24 158   5  26 311 186  46  49 164   1 
##  RI  SC  SD  TN  TX  UT  VA  VT  WA  WI  WV  WY 
##  16  48   3  82 387  95 283   6 130  79   2   2

# Top 5 industries by count
inc %>% 
  group_by(Industry) %>% 
  summarize(Count = n()) %>% 
  arrange(desc(Count)) %>% 
  head(5)
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 5 x 2
##   Industry                     Count
##   <chr>                        <int>
## 1 IT Services                    733
## 2 Business Products & Services   482
## 3 Advertising & Marketing        471
## 4 Health                         355
## 5 Software                       342

# Top 5 States by count
inc %>% 
  group_by(State) %>% 
  summarize(Count = n()) %>% 
  arrange(desc(Count)) %>% 
  head(5)
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 5 x 2
##   State Count
##   <chr> <int>
## 1 CA      701
## 2 TX      387
## 3 NY      311
## 4 VA      283
## 5 FL      282

# Top 5 companies by revenue
inc %>% 
  mutate(RevenueBillions = Revenue / 1e9) %>% 
  select(Name, RevenueBillions) %>% 
  arrange(desc(RevenueBillions)) %>% 
  head(5)
## # A tibble: 5 x 2
##   Name          RevenueBillions
##   <chr>                   <dbl>
## 1 CDW                      10.1
## 2 ABC Supply                4.7
## 3 Coty                      4.6
## 4 Dot Foods                 4.5
## 5 Westcon Group             3.8

Question 1

• Show this two different ways
• By count – makes it easier to compare similar counts over so many levels
• But it makes it hard to find, so having an option to show it alphabetically would also be good

# Sorted by count for easier comparison
inc %>%
  mutate(State = factor(
    inc$State,
    levels = names(sort(table(inc$State), decreasing = FALSE)))) %>%
  group_by(State) %>%
  summarize(Count = n()) %>%
  ggplot() +
  aes(x = State, y = Count) +
  geom_col() +
  coord_flip()
## `summarise()` ungrouping output (override with `.groups` argument)

# Sorted alphabetically for easier finding
inc %>% 
  mutate(State = factor(
    inc$State,
    levels = sort(unique(inc$State), decreasing = TRUE))) %>%
  group_by(State) %>% 
  summarize(Count = n()) %>% 
  ggplot() +
  aes(x = State, y = Count) + 
  geom_col() +
  coord_flip()
## `summarise()` ungrouping output (override with `.groups` argument)

Question 2

• Sort industries alphabetically
• Use a boxplot to show variability
• Outliers are shown as individual points
• Show x axis on a log-scale because there are some very large companies
• Add the company count per industry to the label

# New York is the 3rd most represented State
table(inc$State) %>% sort(decreasing = TRUE) %>% head()
## 
##  CA  TX  NY  VA  FL  IL 
## 701 387 311 283 282 273

inc_NY <- inc %>% 
  filter(State == 'NY', complete.cases(.))

# Add company counts to labels
counts_by_industry <- inc_NY %>% 
  group_by(Industry) %>% 
  summarize(Count = n())
## `summarise()` ungrouping output (override with `.groups` argument)

counts_by_industry$`Industry:N` <- 
  apply(
    X = counts_by_industry[, 1:2], 
    MARGIN = 1, 
    FUN = function(r) {paste0(r, collapse = ':')})

# Show employee counts on LOG scale because outliers are HUGE
inc_NY %>% 
  inner_join(counts_by_industry) %>% 
  mutate(`Industry:N` = factor(
      x = `Industry:N`, 
      levels = sort(unique(`Industry:N`), decreasing = TRUE))) %>% 
  ggplot() +
  aes(x = `Industry:N`, y = Employees) +
  geom_boxplot() +
  scale_y_log10() +
  coord_flip()
## Joining, by = "Industry"

Question 3

• Use a boxplot to show variability
• Sort industries by overall revenue per employee
• Boxplot shows the variability of the individual revenue per employee values

ranked <- inc %>% 
  filter(complete.cases(.)) %>% 
  group_by(Industry) %>% 
  summarize(RevPerEE_IndustryWide = sum(Revenue) / sum(Employees)) %>% 
  arrange(RevPerEE_IndustryWide)
## `summarise()` ungrouping output (override with `.groups` argument)

# Assuming I should go back to full dataset
inc %>% 
  filter(complete.cases(.)) %>% 
  mutate(
    RevPerEE = Revenue / Employees,
    Industry = factor(Industry, ranked$Industry)) %>% 
  ggplot() +
  aes(x = Industry, y = RevPerEE) +
  geom_boxplot() +
  scale_y_log10() +
  coord_flip()