Data 608 Assignment 1

#Load libraries

library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

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

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

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         Revenue         
##  Min.   :   1   Length:5001        Min.   :  0.340   Min.   :2.000e+06  
##  1st Qu.:1252   Class :character   1st Qu.:  0.770   1st Qu.:5.100e+06  
##  Median :2502   Mode  :character   Median :  1.420   Median :1.090e+07  
##  Mean   :2502                      Mean   :  4.612   Mean   :4.822e+07  
##  3rd Qu.:3751                      3rd Qu.:  3.290   3rd Qu.:2.860e+07  
##  Max.   :5000                      Max.   :421.480   Max.   :1.010e+10  
##                                                                         
##    Industry           Employees           City              State          
##  Length:5001        Min.   :    1.0   Length:5001        Length:5001       
##  Class :character   1st Qu.:   25.0   Class :character   Class :character  
##  Mode  :character   Median :   53.0   Mode  :character   Mode  :character  
##                     Mean   :  232.7                                        
##                     3rd Qu.:  132.0                                        
##                     Max.   :66803.0                                        
##                     NA's   :12

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

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

df <- inc %>% select(State, Name) %>% 
     group_by(State) %>% 
     mutate(State.count = n()) %>% 
     slice(1)


ggplot(df, aes(State, State.count)) + ggtitle("Companies in Each State") + geom_point()

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.

NY is the state with the 3rd most companies in the data set.

# Answer Question 2 here
attach(df)

new_df<-df[order(-State.count),]
head(new_df)

## # A tibble: 6 x 3
## # Groups:   State [6]
##   State Name                  State.count
##   <chr> <chr>                       <int>
## 1 CA    Fuhu                          701
## 2 TX    Bridger                       387
## 3 NY    BeenVerified                  311
## 4 VA    FederalConference.com         283
## 5 FL    The HCI Group                 282
## 6 IL    McAfee Institute              273

# Find out that NY is the state with the 3rd most companies in the data set.
complete_data <- inc[complete.cases(inc), ]
NY_df <-subset(complete_data, State=='NY')


ggplot(NY_df, aes(x=Industry, y = Employees)) + geom_boxplot(outlier.shape = NA)+ scale_y_continuous(limits = quantile(NY_df$Employees, c(0.05, 0.95))) + coord_flip() + ggtitle('Employment by Industry In NY')

## Warning: Removed 31 rows containing non-finite values (stat_boxplot).

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


inc <- inc[complete.cases(inc), ]
most_rev_industry<-inc %>% 
  group_by(Industry)%>% 
  select(Industry, Employees, Revenue)%>% 
  summarise(total_emply = sum(Employees), total_rev = sum(Revenue)) %>% 
  mutate(rev_emply = total_rev/total_emply) %>% 
  arrange(desc(rev_emply))

head(most_rev_industry)

## # A tibble: 6 x 4
##   Industry                     total_emply   total_rev rev_emply
##   <chr>                              <int>       <dbl>     <dbl>
## 1 Computer Hardware                   9714 11885700000  1223564.
## 2 Energy                             26437 13771600000   520921.
## 3 Construction                       29099 13174300000   452741.
## 4 Logistics & Transportation         39994 14837800000   371001.
## 5 Consumer Products & Services       45464 14956400000   328972.
## 6 Insurance                           7339  2337900000   318558.

# Get a bar graph for the data
ggplot(data=most_rev_industry, aes(Industry, rev_emply)) + geom_bar(stat='identity') + coord_flip() + ggtitle("Revenue per employee in each Industry")

Computer Hardware generate the most revenue per employee.