R Notebook

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
str(inc)

## 'data.frame':    5001 obs. of  8 variables:
##  $ Rank       : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Name       : Factor w/ 5001 levels "(Add)ventures",..: 1770 1633 4423 690 1198 2839 4733 1468 1869 4968 ...
##  $ Growth_Rate: num  421 248 245 233 213 ...
##  $ Revenue    : num  1.18e+08 4.96e+07 2.55e+07 1.90e+09 8.70e+07 ...
##  $ Industry   : Factor w/ 25 levels "Advertising & Marketing",..: 5 12 13 7 1 20 10 1 5 21 ...
##  $ Employees  : int  104 51 132 50 220 63 27 75 97 15 ...
##  $ City       : Factor w/ 1519 levels "Acton","Addison",..: 391 365 635 2 139 66 912 1179 131 1418 ...
##  $ State      : Factor w/ 52 levels "AK","AL","AR",..: 5 47 10 45 20 45 44 5 46 41 ...

library(kableExtra)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:kableExtra':
## 
##     group_rows

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

head(inc) %>% kable() %>% kable_styling()

### Top 10 companies by revenue from this big list
top10_by_revenue = inc %>% arrange(desc(Revenue)) %>% head(10)
top10_by_revenue$Revenue = sapply(top10_by_revenue$Revenue, function(x) paste(round((x / 1e9), 1), " Billion"))
top10_by_revenue %>% kable() %>% kable_styling()

### Top 10 states in this list by frequency or the number of companies in these states
table(inc$State) %>% sort(decreasing = TRUE) %>% head(10) %>% kable() %>% kable_styling()

library(ggplot2)
# Answer Question 1 here

inc %>% count(State) %>%
  ggplot(aes(x=reorder(State, n), y=n)) +
  geom_bar(stat = 'identity') +
  coord_flip() +
  theme_classic(base_size = 11) +
  xlab("state wise counts") +
  ylab("state") +
  ggtitle("Top growing companies in the U.S. - state wise counts")

# Answer Question 2 here
## Finding the state with the 3rd largest number of companies
inc_complete = inc[complete.cases(inc), ]

third_most_companies_state <- toString(as.data.frame(list(sort(table(inc_complete$State), decreasing = TRUE)))[3,1])

ny_df <- inc_complete[inc_complete$State == third_most_companies_state,]

ggplot(ny_df, aes(x=factor(Industry), y=Employees)) +
  geom_boxplot() +
  xlab("Industry") +
  ylab("Employees counts") +
  ggtitle("NY - industry wise counts") +
  stat_summary(fun.y=mean, colour="darkred", geom="point", 
               shape=18, size=3) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

### Removing the outliers
max_value <- vector()

for (i in levels(ny_df$Industry))
  {
  upper.limit <- quantile(ny_df[ny_df$Industry == i, ]$Employees)[4] +
    1.5*IQR(ny_df[ny_df$Industry == i, ]$Employees)
    
    max_value <- c(max_value, upper.limit)
    
  }

#Now we have the max value for each category for the data notn outlier. We will use the minimum value as 0
## Plotting without the outliers

ggplot(ny_df, aes(x=factor(Industry), y=Employees)) +
  geom_boxplot(outlier.colour = NA) +
  xlab("Industry") +
  ylab("Employees counts") +
  ggtitle("NY - industry wise counts") +
  stat_summary(fun.y=mean, colour="darkred", geom="point", 
               shape=18, size=3) +
  coord_cartesian(ylim = c(0, max(max_value))) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

# Answer Question 3 here
inc_revenue_per_employee_df <- group_by(inc_complete, Industry) %>% summarise(Revenue_per_employee = sum(Revenue) / sum(Employees))

ggplot(inc_revenue_per_employee_df, aes(x=reorder(Industry, Revenue_per_employee), y=Revenue_per_employee)) + 
  geom_bar(stat = 'Identity') +
  coord_flip() +
  xlab("Industry") +
  ylab("Revenue per employee (in dollars)") +
  ggtitle("Revenue per employee - industry wise") +
  scale_y_continuous(labels = scales::comma)