R Libraries:

Load necessary libraries -

library(kableExtra)
library(dplyr)
library(ggplot2)

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)
head(inc) %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")
Rank Name Growth_Rate Revenue Industry Employees City State
1 Fuhu 421.48 1.179e+08 Consumer Products & Services 104 El Segundo CA
2 FederalConference.com 248.31 4.960e+07 Government Services 51 Dumfries VA
3 The HCI Group 245.45 2.550e+07 Health 132 Jacksonville FL
4 Bridger 233.08 1.900e+09 Energy 50 Addison TX
5 DataXu 213.37 8.700e+07 Advertising & Marketing 220 Boston MA
6 MileStone Community Builders 179.38 4.570e+07 Real Estate 63 Austin TX
# Apply Complete.cases() function to exclude records with null values in any of the columns

inc <- inc[complete.cases(inc),]

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   :2501   110 Consulting        :   1   Mean   :  4.615  
##  3rd Qu.:3750   11thStreetCoffee.com  :   1   3rd Qu.:  3.290  
##  Max.   :5000   123 Exteriors         :   1   Max.   :421.480  
##                 (Other)               :4983                    
##     Revenue                                  Industry      Employees      
##  Min.   :2.000e+06   IT Services                 : 732   Min.   :    1.0  
##  1st Qu.:5.100e+06   Business Products & Services: 480   1st Qu.:   25.0  
##  Median :1.090e+07   Advertising & Marketing     : 471   Median :   53.0  
##  Mean   :4.825e+07   Health                      : 354   Mean   :  232.7  
##  3rd Qu.:2.860e+07   Software                    : 341   3rd Qu.:  132.0  
##  Max.   :1.010e+10   Financial Services          : 260   Max.   :66803.0  
##                      (Other)                     :2351                    
##             City          State     
##  New York     : 160   CA     : 700  
##  Chicago      :  90   TX     : 386  
##  Austin       :  88   NY     : 311  
##  Houston      :  76   VA     : 283  
##  San Francisco:  74   FL     : 282  
##  Atlanta      :  73   IL     : 272  
##  (Other)      :4428   (Other):2755

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:

High Level understanding from the statistical summary

A. Maximum no. of growing companies are located in state of CA in the West Coast most probably due to high concentration of start ups and tech firms

B. New York city in the East Coast has highest city level concentration of growing companies most likely due to being the major hub for banking and finanlcial industry

C. IT Services industry has highest no. of fast growing companies

D. The Employees count in the data set ranges from 1 to 67K and revenue from $2M to $10B+. So the data set includes growing companies of all sizes including start ups to much bigger corporate houses

# Insert your code here, create more chunks as necessary

# Ranking of States based on Mean/Avg. Company Growth Rate
topGrowthStates <- inc %>% group_by(State) %>% summarise(Growth_Rate.mean = mean(Growth_Rate)) %>% mutate(rank = rank(-Growth_Rate.mean)) %>% arrange(rank)


topGrowthStates %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")
State Growth_Rate.mean rank
WY 19.145000 1
ME 16.210000 2
RI 16.031250 3
DC 8.439524 4
HI 6.792857 5
UT 6.307790 6
SC 6.060625 7
TX 6.036503 8
CA 5.900229 9
FL 5.846099 10
MS 5.642500 11
MA 5.416648 12
CT 4.994600 13
MD 4.984809 14
CO 4.971955 15
TN 4.950366 16
VA 4.877350 17
AK 4.805000 18
IN 4.788261 19
AZ 4.616700 20
NJ 4.445380 21
NY 4.371158 22
WA 4.020698 23
MN 3.821477 24
IL 3.751213 25
KS 3.628684 26
OH 3.557527 27
GA 3.522607 28
NC 3.393630 29
OR 3.148367 30
OK 3.097174 31
WI 2.739351 32
ID 2.645294 33
PA 2.578159 34
MO 2.497288 35
DE 2.420000 36
AL 2.407451 37
NV 2.330769 38
MI 2.238571 39
NE 2.078889 40
KY 2.064000 41
LA 1.944595 42
IA 1.761071 43
PR 1.730000 44
AR 1.670000 45
NH 1.512917 46
SD 1.406667 47
NM 1.364000 48
VT 1.296667 49
ND 1.227000 50
MT 0.762500 51
WV 0.620000 52

From above ranking of states for mean company growth rate, we can see even though CA has maximum no. of growing companies, but CA ranks 9th interms of avg. growth rate. WY has the maximum avg. growth rate, but has only 2 companies included in the data set.

# Industry Revenue Share
industryRev <- inc %>% group_by(Industry) %>% summarise(TotalRevenue = sum(Revenue)) %>% mutate(share = TotalRevenue/sum(TotalRevenue)) %>% mutate(rank = rank(-share)) %>% arrange(rank)


industryRev %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")
Industry TotalRevenue share rank
Business Products & Services 26345900000 0.1094390 1
IT Services 20525000000 0.0852594 2
Health 17860100000 0.0741896 3
Consumer Products & Services 14956400000 0.0621278 4
Logistics & Transportation 14837800000 0.0616352 5
Energy 13771600000 0.0572062 6
Construction 13174300000 0.0547251 7
Financial Services 13150900000 0.0546279 8
Food & Beverage 12812500000 0.0532222 9
Manufacturing 12603600000 0.0523544 10
Computer Hardware 11885700000 0.0493723 11
Retail 10257400000 0.0426085 12
Human Resources 9246100000 0.0384076 13
Software 8134600000 0.0337905 14
Advertising & Marketing 7785000000 0.0323383 15
Telecommunications 7287900000 0.0302734 16
Government Services 6009100000 0.0249614 17
Security 3812800000 0.0158381 18
Real Estate 2956800000 0.0122823 19
Travel & Hospitality 2931600000 0.0121777 20
Environmental Services 2638800000 0.0109614 21
Engineering 2532500000 0.0105198 22
Insurance 2337900000 0.0097115 23
Media 1742400000 0.0072378 24
Education 1139300000 0.0047326 25

From the above table, we can see that Business Products & Services industry has the highest share of revenue of fastest growing companies followed by IT Services and Health sector.

# Industry Employment Share
industryEmployment <- inc %>% group_by(Industry) %>% summarise(TotalEmployees = sum(Employees)) %>% mutate(share = TotalEmployees/sum(TotalEmployees)) %>% mutate(rank = rank(-share)) %>% arrange(rank)


industryEmployment %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")
Industry TotalEmployees share rank
Human Resources 226980 0.1954988 1
Business Products & Services 117357 0.1010801 2
IT Services 102788 0.0885317 3
Health 82430 0.0709973 4
Food & Beverage 65911 0.0567694 5
Software 51262 0.0441522 6
Financial Services 47693 0.0410782 7
Consumer Products & Services 45464 0.0391583 8
Manufacturing 43942 0.0378474 9
Security 41059 0.0353643 10
Logistics & Transportation 39994 0.0344470 11
Advertising & Marketing 39731 0.0342205 12
Retail 37068 0.0319268 13
Telecommunications 30842 0.0265643 14
Construction 29099 0.0250631 15
Energy 26437 0.0227703 16
Government Services 26185 0.0225533 17
Travel & Hospitality 23035 0.0198401 18
Engineering 20435 0.0176008 19
Real Estate 18893 0.0162726 20
Environmental Services 10155 0.0087465 21
Computer Hardware 9714 0.0083667 22
Media 9532 0.0082100 23
Education 7685 0.0066191 24
Insurance 7339 0.0063211 25

From the above employees share table by industry, we can infer -

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
topStates <- inc %>% group_by(State) %>% summarise(compCount = n()) %>% mutate(rank = rank(-compCount)) %>% arrange(rank)

ggplot(topStates, aes(x = reorder(State,compCount), y = compCount)) +
  geom_bar(stat = "identity", position = "dodge", fill = "orange") +
  geom_text(aes(label=compCount), hjust=-0.5, color="black", position = position_dodge(0.9), size=3.5) +
  scale_fill_brewer(palette="Paired") +
  theme(axis.text.x=element_text(angle = 0, vjust = 0.5)) +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("Distribution of Fastest Growing Companies By State") +
  labs(x = "State", y = "No. of Companies") +
  coord_flip()

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
top3rdState <- inc %>% group_by(State) %>% summarise(compCount = n()) %>% mutate(rank = rank(-compCount)) %>% filter(rank == 3)

print(paste0("Top 3rd state in no. of companies: ",top3rdState$State, " with ", top3rdState$compCount," companies."))
## [1] "Top 3rd state in no. of companies: NY with 311 companies."
# Filter data for 3rd state in the rank for highest no. of companies
incTop3rdState <- inc %>% filter(State == toString(top3rdState$State)) %>% filter(Employees < 5000)

ggplot(incTop3rdState, aes(x = factor(Industry), y = Employees)) + 
  geom_boxplot(aes(colour = Industry), width = 0.7)+
  stat_boxplot(geom ='errorbar') +
  ggtitle("Distribution of Companies By Employee Count in NY") +
  ylab("No. of Employees") +
  xlab("Industry") +
  theme(legend.position="bottom") +
  coord_flip()

In order to deal with outliers, I have removed records from New York’s data set with employees > 5000.

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

# Derive Revenue Per Employee By individual company
revenueEmplRatio <- inc %>% mutate(revEmplRatio = round((Revenue/Employees)/1000000,0))

# Calculate mean of the ratio by industry
revenueEmplRatioMean <- revenueEmplRatio %>% group_by(Industry) %>% summarise(ratio.mean = round(mean(revEmplRatio),3))

ggplot(revenueEmplRatioMean, aes(x = reorder(Industry,ratio.mean), y = ratio.mean)) +
  geom_bar(stat = "identity", position = "dodge", fill = "blue") +
  geom_text(aes(label=paste("$",ratio.mean,"M")), hjust=-0.1, color="black", position = position_dodge(0.9), size=3.5) +
  scale_fill_brewer(palette="Paired") +
  theme(axis.text.x=element_text(angle = 0, vjust = 0.1)) +
  theme(plot.title = element_text(hjust = 0.1)) +
  ggtitle("National Ranking of Revenue Per Employee Ratio By Industry") +
  labs(x = "Industry", y = "Mean Revenue Per Employee Ratio (in Millions)") +
  coord_flip()

From the above plot, it can be inferred that Energy industry has by far the highest mean Revenue Per Employee numbers nationally. But from the below Histogram distribution plot including RED dotted line for the mean ratio, referring to the facet for ‘Energy’ industry, it can be observed that there are quite a few outliers.

# Distribution of the Ratio by Industry
ggplot(revenueEmplRatio, aes(x=revEmplRatio)) + geom_histogram(binwidth=.5, colour="black", fill="white") + 
   # facet_grid(Industry ~.,scales = "free") +
    facet_wrap(Industry ~., scales = "free", ncol = 3) +
    geom_vline(data=revenueEmplRatioMean, aes(xintercept=ratio.mean),
               linetype="dashed", size=1, colour="red")+
    ggtitle("Distribution of Revenue Per Employee Ratio By Industry") +
    xlab("Revenue Per Employee Ratio (in Millions)")