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:

##Answer tried to understand by summarizing to understand he data.

#install.packages("kableExtra")

library(kableExtra)
## Warning: package 'kableExtra' was built under R version 4.1.3
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
#install.packages("ggthemes")
library(ggthemes)
## Warning: package 'ggthemes' was built under R version 4.1.3
str(inc)
## 'data.frame':    5001 obs. of  8 variables:
##  $ Rank       : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Name       : chr  "Fuhu" "FederalConference.com" "The HCI Group" "Bridger" ...
##  $ 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   : chr  "Consumer Products & Services" "Government Services" "Health" "Energy" ...
##  $ Employees  : int  104 51 132 50 220 63 27 75 97 15 ...
##  $ City       : chr  "El Segundo" "Dumfries" "Jacksonville" "Addison" ...
##  $ State      : chr  "CA" "VA" "FL" "TX" ...
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.1.2
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.5     v stringr 1.4.0
## v tidyr   1.1.4     v forcats 0.5.1
## v readr   2.1.1
## Warning: package 'readr' was built under R version 4.1.2
## Warning: package 'forcats' was built under R version 4.1.2
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter()     masks stats::filter()
## x dplyr::group_rows() masks kableExtra::group_rows()
## x dplyr::lag()        masks stats::lag()
# Tufe theme. Quick graph showing industries with greater than 1000 employees along with revenue
ggplot(filter(inc,Employees > 1000 ), aes(x = Industry, y = Revenue)) +
  geom_point() +
  labs(x = "Industry", y = "Revenue") +
   theme_tufte() + coord_flip()

# Tufe theme. Quick graph showing industries with greater than 100 employees along with revenue
ggplot(filter(inc,Employees < 100 ), aes(x = Industry, y = Revenue)) +
  geom_point(fill = "indianred") +
  labs(x = "Industry", y = "Revenue") +
   theme_tufte() + coord_flip()

#Top 50 records by revenue
revenuetop50 = inc %>% arrange(desc(Revenue)) %>% head(50)
#revenuetop50
revenuetop50$Revenue1 = sapply(revenuetop50$Revenue, function(x) paste(round((x / 1e9), 1), " Billion"))
revenuetop50 %>% kable() %>% kable_styling()
Rank Name Growth_Rate Revenue Industry Employees City State Revenue1
4788 CDW 0.41 1.010e+10 Computer Hardware 6800 Vernon Hills IL 10.1 Billion
3853 ABC Supply 0.73 4.700e+09 Construction 6549 Beloit WI 4.7 Billion
4936 Coty 0.36 4.600e+09 Consumer Products & Services 10000 New York NY 4.6 Billion
4997 Dot Foods 0.34 4.500e+09 Food & Beverage 3919 Mt. Sterling IL 4.5 Billion
4716 Westcon Group 0.44 3.800e+09 IT Services 3000 Tarrytown NY 3.8 Billion
4246 American Tire Distributors 0.59 3.500e+09 Consumer Products & Services 3341 Huntersville NC 3.5 Billion
4052 Kum & Go 0.65 2.800e+09 Retail 4589 West Des Moines IA 2.8 Billion
4802 Boise Cascade 0.41 2.800e+09 Construction 4470 Boise ID 2.8 Billion
1397 EnvisionRxOptions 2.88 2.700e+09 Health 625 Twinsburg OH 2.7 Billion
2522 DLA Piper 1.41 2.400e+09 Business Products & Services 4036 Chicago IL 2.4 Billion
4629 Prime Therapeutics 0.47 2.000e+09 Health 2549 Eagan MN 2 Billion
4 Bridger 233.08 1.900e+09 Energy 50 Addison TX 1.9 Billion
1843 Sun Coast Resources 2.08 1.900e+09 Energy 1640 Houston TX 1.9 Billion
3844 Atlas Oil Company 0.74 1.900e+09 Logistics & Transportation 374 Taylor MI 1.9 Billion
4961 Kirkland & Ellis 0.36 1.900e+09 Business Products & Services 1517 Chicago IL 1.9 Billion
1488 Sprouts Farmers Market 2.68 1.800e+09 Consumer Products & Services 13200 Phoenix AZ 1.8 Billion
4689 Global Brass and Copper Holdings 0.45 1.700e+09 Manufacturing 1986 Schaumburg IL 1.7 Billion
3463 Hogan Lovells 0.89 1.600e+09 Business Products & Services 2280 Washington DC 1.6 Billion
2145 AdvancePierre Foods 1.73 1.500e+09 Food & Beverage 4000 Cincinnati OH 1.5 Billion
3650 Genco 0.82 1.500e+09 Logistics & Transportation 10800 Pittsburgh PA 1.5 Billion
960 Advanced Disposal 4.51 1.400e+09 Environmental Services 5347 Ponte Vedra FL 1.4 Billion
2236 Total Quality Logistics 1.65 1.400e+09 Logistics & Transportation 2116 Cincinnati OH 1.4 Billion
2496 Carahsoft Technology 1.42 1.400e+09 Government Services 365 Reston VA 1.4 Billion
3414 Restoration Hardware 0.91 1.200e+09 Retail 2900 Corte Madera CA 1.2 Billion
1908 Diplomat Specialty Pharmacy 1.99 1.100e+09 Health 761 Flint MI 1.1 Billion
3734 KSS 0.78 1.000e+09 Manufacturing 8500 Sterling Heights MI 1 Billion
3425 Blackhawk Network Holdings 0.90 9.591e+08 Financial Services 725 Pleasanton CA 1 Billion
1996 Ambit Energy 1.88 9.303e+08 Energy 492 Dallas TX 0.9 Billion
4532 GoDaddy.com 0.49 9.109e+08 IT Services 3369 Scottsdale AZ 0.9 Billion
2958 ImmixGroup 1.15 8.836e+08 IT Services 252 McLean VA 0.9 Billion
4793 LORD Corporation 0.41 8.615e+08 Manufacturing 2959 Cary NC 0.9 Billion
3163 Quinn Emanuel 1.03 8.525e+08 Business Products & Services 697 Los Angeles CA 0.9 Billion
2961 Genesis-ATC 1.15 8.462e+08 Telecommunications 347 San Antonio TX 0.8 Billion
1284 Hearthside Food Solutions 3.17 8.396e+08 Food & Beverage 5000 Downers Grove IL 0.8 Billion
1480 Coyote Logistics 2.70 7.864e+08 Logistics & Transportation 1219 Chicago IL 0.8 Billion
4765 Squire Sanders 0.42 7.745e+08 Business Products & Services 1257 Cleveland OH 0.8 Billion
3921 Granite Telecommunications 0.70 7.362e+08 Telecommunications 1000 Quincy MA 0.7 Billion
4854 Arnold & Porter 0.40 7.310e+08 Business Products & Services 748 Washington DC 0.7 Billion
1869 Universal Services of America 2.04 7.181e+08 Security 20000 Santa Ana CA 0.7 Billion
2274 Liberty Power 1.61 7.108e+08 Energy 277 Fort Lauderdale FL 0.7 Billion
4427 Sunshine Minting 0.53 7.061e+08 Manufacturing 280 Coeur D’ Alene ID 0.7 Billion
4459 Belcan 0.52 6.887e+08 Engineering 10000 Cincinnati OH 0.7 Billion
2806 Goodman Networks 1.23 6.509e+08 Telecommunications 1693 Plano TX 0.7 Billion
4009 Schumacher Group 0.67 6.130e+08 Health 1945 Lafayette LA 0.6 Billion
4815 Perkins Coie 0.40 6.080e+08 Business Products & Services 823 Seattle WA 0.6 Billion
1768 The Cellular Connection 2.17 6.065e+08 Telecommunications 1428 Marion IN 0.6 Billion
2537 Wayfair.com 1.40 6.020e+08 Retail 1300 Boston MA 0.6 Billion
4577 Sutherland Global Services 0.48 5.976e+08 Business Products & Services 32000 Pittsford NY 0.6 Billion
4093 Advanced BioEnergy 0.64 5.848e+08 Energy 75 Bloomington MN 0.6 Billion
4857 AVI-SPL 0.39 5.807e+08 Business Products & Services 1800 Tampa FL 0.6 Billion 
#print 
sample <- revenuetop50 %>% arrange(desc(Revenue)) %>% head(10) 

#describe unique value to find unique industries in the sample data
sample
##    Rank                       Name Growth_Rate  Revenue
## 1  4788                        CDW        0.41 1.01e+10
## 2  3853                 ABC Supply        0.73 4.70e+09
## 3  4936                       Coty        0.36 4.60e+09
## 4  4997                  Dot Foods        0.34 4.50e+09
## 5  4716              Westcon Group        0.44 3.80e+09
## 6  4246 American Tire Distributors        0.59 3.50e+09
## 7  4052                   Kum & Go        0.65 2.80e+09
## 8  4802              Boise Cascade        0.41 2.80e+09
## 9  1397          EnvisionRxOptions        2.88 2.70e+09
## 10 2522                  DLA Piper        1.41 2.40e+09
##                        Industry Employees            City State      Revenue1
## 1             Computer Hardware      6800    Vernon Hills    IL 10.1  Billion
## 2                  Construction      6549          Beloit    WI  4.7  Billion
## 3  Consumer Products & Services     10000        New York    NY  4.6  Billion
## 4               Food & Beverage      3919    Mt. Sterling    IL  4.5  Billion
## 5                   IT Services      3000       Tarrytown    NY  3.8  Billion
## 6  Consumer Products & Services      3341    Huntersville    NC  3.5  Billion
## 7                        Retail      4589 West Des Moines    IA  2.8  Billion
## 8                  Construction      4470           Boise    ID  2.8  Billion
## 9                        Health       625       Twinsburg    OH  2.7  Billion
## 10 Business Products & Services      4036         Chicago    IL  2.4  Billion
unique(inc$Industry)
##  [1] "Consumer Products & Services" "Government Services"         
##  [3] "Health"                       "Energy"                      
##  [5] "Advertising & Marketing"      "Real Estate"                 
##  [7] "Financial Services"           "Retail"                      
##  [9] "Software"                     "Computer Hardware"           
## [11] "Logistics & Transportation"   "Food & Beverage"             
## [13] "IT Services"                  "Business Products & Services"
## [15] "Education"                    "Construction"                
## [17] "Manufacturing"                "Telecommunications"          
## [19] "Security"                     "Human Resources"             
## [21] "Travel & Hospitality"         "Media"                       
## [23] "Environmental Services"       "Engineering"                 
## [25] "Insurance"
unique(sample$Industry)
## [1] "Computer Hardware"            "Construction"                
## [3] "Consumer Products & Services" "Food & Beverage"             
## [5] "IT Services"                  "Retail"                      
## [7] "Health"                       "Business Products & Services"
#Plot to show by State, industries with the highest revenue where employees > 1000 from Sample of 50
ggplot(filter(sample, Employees > 1000),aes(x = State, y = Revenue, color = Industry)) +
  geom_point() +
  geom_label(aes(label = Industry)) +
  labs(x = "State", y = "Revenue") +
   theme(legend.position = "none")

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 1

I used ggplot below to show a portrait graph by using coord_flip().Then using Theme_Tufte. Additionally then I tried to plot the numbers on the bar graph and adjusted to size 3 so I can show the numbers on the graph.

# Answer Question 1 here

library(ggplot2)
library(tidyverse)
library(dplyr)
library(scales)
## 
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
## 
##     discard
## The following object is masked from 'package:readr':
## 
##     col_factor
#graph1
inc %>% count(State) %>%
  ggplot(aes(x=reorder(State, n), y=n)) +
  geom_bar(stat = 'identity',width = 0.5, position = position_dodge(0.8)) +
  geom_text(aes(label=n), vjust=1.6, color="white", size=3.5) + 
  coord_flip() +
  theme_tufte() +
  xlab("State") +
  ylab("Count by State") +
  ggtitle("Distribution of companies by state")

#FINAL GRAPH FOR QUESTION1 : 2graph2 with numbers, and make the color a light blue with blank count of companies by state
inc %>% count(State) %>%
  ggplot(aes(x=reorder(State, n), y=n)) +
  geom_bar(stat = 'identity',fill = "light blue", position = "dodge") +
  geom_text(aes(label=n), position = position_stack(vjust= 0.5),
            colour = "black", size = 3) + 
 
    coord_flip() +
  theme_tufte() +
  xlab("State") +
  ylab("Count by State") +
  ggtitle("Distribution of companies by state") + scale_color_brewer(palette = "Pastel1")

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 2 :

I tried to create a few plots to understand how to show the outlier data using axis transformation for log2 and log10. Also showed triangular on outlier data. User reorder method The “default” method treats its first argument as a categorical variable (Industry), and reorders its levels based on the values of a second variable (Employees), usually numeric

# Answer Question 2 here

#1 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.)

# Subset only NY records
NyState <- inc[ which(inc$State == 'NY'), ]
# Remove incomplete records
NyState  <- NyState[complete.cases(NyState ), ] 
# show data
head(NyState )
##    Rank                      Name Growth_Rate  Revenue
## 26   26              BeenVerified       84.43 13700000
## 30   30                  Sailthru       73.22  8100000
## 37   37              YellowHammer       67.40 18000000
## 38   38                 Conductor       67.02  7100000
## 48   48 Cinium Financial Services       53.65  5900000
## 70   70                  33Across       44.99 27900000
##                        Industry Employees      City State
## 26 Consumer Products & Services        17  New York    NY
## 30      Advertising & Marketing        79  New York    NY
## 37      Advertising & Marketing        27  New York    NY
## 38      Advertising & Marketing        89  New York    NY
## 48           Financial Services        32 Rock Hill    NY
## 70      Advertising & Marketing        75  New York    NY
#Find statistics
summary(NyState)
##       Rank          Name            Growth_Rate        Revenue         
##  Min.   :  26   Length:311         Min.   : 0.350   Min.   :2.000e+06  
##  1st Qu.:1186   Class :character   1st Qu.: 0.670   1st Qu.:4.300e+06  
##  Median :2702   Mode  :character   Median : 1.310   Median :8.800e+06  
##  Mean   :2612                      Mean   : 4.371   Mean   :5.872e+07  
##  3rd Qu.:4005                      3rd Qu.: 3.580   3rd Qu.:2.570e+07  
##  Max.   :4981                      Max.   :84.430   Max.   :4.600e+09  
##    Industry           Employees           City              State          
##  Length:311         Min.   :    1.0   Length:311         Length:311        
##  Class :character   1st Qu.:   21.0   Class :character   Class :character  
##  Mode  :character   Median :   45.0   Mode  :character   Mode  :character  
##                     Mean   :  271.3                                        
##                     3rd Qu.:  105.5                                        
##                     Max.   :32000.0
NyState %>% arrange(desc(Employees)) %>% head(50) %>% kable() %>% kable_styling()
Rank Name Growth_Rate Revenue Industry Employees City State
4577 Sutherland Global Services 0.48 5.976e+08 Business Products & Services 32000 Pittsford NY
4936 Coty 0.36 4.600e+09 Consumer Products & Services 10000 New York NY
4716 Westcon Group 0.44 3.800e+09 IT Services 3000 Tarrytown NY
3899 Denihan Hospitality Group 0.71 2.808e+08 Travel & Hospitality 2280 New York NY
4363 TransPerfect 0.55 3.413e+08 Business Products & Services 2218 New York NY
1499 Sterling Infosystems 2.66 2.149e+08 Human Resources 2081 New York NY
4465 OpenLink 0.52 3.043e+08 Software 1271 Uniondale NY
3136 FSO Onsite Outsourcing 1.05 5.530e+07 Human Resources 1134 New York NY
2830 ReSource Pro 1.22 2.430e+07 Business Products & Services 1124 New York NY
2995 Pride Technologies 1.13 2.310e+08 Human Resources 1000 New York NY
3387 Mitchell/Martin 0.92 1.453e+08 IT Services 1000 New York NY
4747 TravelClick 0.43 2.571e+08 Travel & Hospitality 906 New York NY
2964 DataArt 1.14 3.070e+07 Software 800 New York NY
4913 Jackson Lewis 0.37 3.520e+08 Business Products & Services 690 White Plains NY
4646 Mimeo.com 0.46 8.770e+07 Business Products & Services 632 New York NY
4153 Everyday Health 0.62 1.464e+08 Media 602 New York NY
4003 Ovation Travel Group 0.67 5.860e+07 Travel & Hospitality 509 New York NY
1640 BlueWolf 2.38 9.040e+07 IT Services 500 New York NY
2971 ConServe 1.14 5.270e+07 Financial Services 483 Fairport NY
4727 Arrow Security 0.44 1.400e+07 Security 450 New York NY
3584 Infusion 0.84 6.790e+07 IT Services 432 New York NY
4535 Capital Access Network 0.49 1.516e+08 Financial Services 425 New York NY
4224 Fragomen 0.60 3.465e+08 Business Products & Services 404 New York NY
3500 Magnolia Bakery 0.88 2.360e+07 Food & Beverage 383 New York NY
1069 Systems Made Simple 3.94 1.671e+08 IT Services 382 Syracuse NY
4039 Empire Office 0.66 3.496e+08 Business Products & Services 345 New York NY
3704 Infinity Consulting Solutions 0.79 3.570e+07 Human Resources 325 New York NY
3021 Linium 1.11 4.590e+07 IT Services 322 New York NY
2218 Globo Mobile 1.67 4.500e+06 Software 320 New York NY
2896 Telx 1.18 2.143e+08 Telecommunications 316 New York NY
4820 Aluf Plastics 0.40 1.029e+08 Manufacturing 307 Orangeburg NY
2436 Shinetech Software 1.48 1.200e+07 IT Services 300 New York NY
3871 eTransMedia Technology 0.72 2.590e+07 Health 298 Troy NY
4981 SmartSource Computer & Audio Visual Rentals 0.35 5.730e+07 Business Products & Services 296 Hauppauge NY
1190 Usablenet 3.52 5.900e+07 IT Services 295 New York NY
2556 Precision Pipeline Solutions 1.39 3.120e+07 Energy 294 New Windsor NY
4944 McElroy Deutsch 0.36 1.165e+08 Business Products & Services 288 New York NY
4732 Mycroft 0.43 2.640e+07 IT Services 275 New York NY
4552 Paradysz 0.49 4.100e+07 Advertising & Marketing 270 New York NY
4738 McMurry/TMG 0.43 9.140e+07 Advertising & Marketing 269 New York City NY
3009 5Linx Enterprises 1.12 1.036e+08 Telecommunications 268 Rochester NY
1565 Droga5 2.54 6.730e+07 Advertising & Marketing 250 New York NY
2997 Fibertech Networks 1.13 1.423e+08 Telecommunications 250 ROCHESTER NY
3661 Environmental Products & Services of Vermont 0.81 4.510e+07 Environmental Services 250 Syracuse NY
1895 nfrastructure 2.00 5.320e+07 IT Services 237 Clifton Park NY
1371 Impelsys 2.94 1.290e+07 Software 220 New York NY
3054 DDS Companies 1.09 5.250e+07 Construction 219 Rochester NY
275 Technical Solutions 15.85 1.630e+07 Telecommunications 213 Walden NY
3262 Sriven Systems 0.98 2.530e+07 IT Services 210 Melville NY
890 Payoneer 5.00 4.040e+07 Financial Services 207 New YOrk NY 
#quick graph of NY state
ggplot(NyState, aes(x = Industry, y = Employees)) +
  geom_boxplot(fill = "indianred") +
  labs(x = "Industry", y = "Employees") +
   theme_tufte() + coord_flip()

# Graph to show boxplot with  log10 transformation. 
ggplot(NyState, aes(x = Industry, y = Employees)) +
  geom_boxplot(outlier.colour = "Red", 
               outlier.shape = 2) + theme_tufte() + coord_flip()+
  
 scale_y_continuous(trans = "log10")

#to show boxplot with  log2 transformation. 
ggplot(NyState, aes(x = Industry, y = Employees)) +
  geom_boxplot(outlier.colour = "Red", 
               outlier.shape = 2) + theme_tufte() + coord_flip() +
scale_y_continuous(trans = "log2")

#FINAL GRAPH FOR QUESTION2 : Graph to show boxplot with  log2 transformation. Use re-order. Shows Business Products and Services with max outlier 
ggplot(NyState, aes(reorder(Industry, Employees, FUN = median), Employees)) + 
  geom_boxplot(outlier.colour = "Red", 
               outlier.shape = 2) +
 scale_y_continuous(trans = "log2", limits = c(min(NyState$Employees), max(NyState$Employees))) +
  coord_flip() +
  geom_hline(yintercept = median(NyState$Employees), 
             color="Red", 
             linetype="dashed") +
  geom_text(aes(x=2, 
            label="Median for Employees", y = median(NyState$Employees)+100 ), 
            size = 3, 
            colour="blue") +
  
  xlab("Industry") +
  ylab("Number of Employees") +
  theme_tufte() + 
  ggtitle("Number of Employees by Industry in New York")
## Warning: Use of `NyState$Employees` is discouraged. Use `Employees` instead.

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 3 :

I checked the summary by using table and then colored the graph light blue to keep it more easy to view. Highest revenue is Computer Hardware, which generates revenue and employeers fewer total employees within this industry.

inc_nonmissing  <- inc[complete.cases(inc), ] 
revemployee <- group_by(inc_nonmissing, Industry) %>% summarise(sum(Revenue),sum(Employees), revperemp = sum(Revenue) / sum(Employees))


revemployee %>% arrange(desc(revperemp)) %>% head(50) %>% kable() %>% kable_styling()
Industry sum(Revenue) sum(Employees) revperemp
Computer Hardware 11885700000 9714 1223563.93
Energy 13771600000 26437 520921.44
Construction 13174300000 29099 452740.64
Logistics & Transportation 14837800000 39994 371000.65
Consumer Products & Services 14956400000 45464 328972.37
Insurance 2337900000 7339 318558.39
Manufacturing 12603600000 43942 286823.54
Retail 10257400000 37068 276718.46
Financial Services 13150900000 47693 275740.67
Environmental Services 2638800000 10155 259852.29
Telecommunications 7287900000 30842 236297.91
Government Services 6009100000 26185 229486.35
Business Products & Services 26345900000 117357 224493.64
Health 17860100000 82430 216669.90
IT Services 20525000000 102788 199682.84
Advertising & Marketing 7785000000 39731 195942.71
Food & Beverage 12812500000 65911 194390.92
Media 1742400000 9532 182794.80
Software 8134600000 51262 158686.75
Real Estate 2956800000 18893 156502.41
Education 1139300000 7685 148249.84
Travel & Hospitality 2931600000 23035 127267.20
Engineering 2532500000 20435 123929.53
Security 3812800000 41059 92861.49
Human Resources 9246100000 226980 40735.31 
ggplot(revemployee, aes(x=reorder(Industry, revperemp), y=revperemp)) + 
  geom_bar(stat = 'Identity',fill = "light blue", position = "dodge") +
   coord_flip() +
  xlab("Industry") +
  ylab("Revenue per employee ($)") +
  ggtitle("Revenue per employee for industry") +
  scale_y_continuous(labels = scales::comma)+
  theme_tufte()