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:

path <- 
"https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv"
inc <- read.csv(path,header= TRUE)

Let’s preview this data:

options(digits=7,scipen=999,width=120)
library(kableExtra)
head(inc) %>% 
  kable(format.args = list(big.mark = ",")) %>% 
  kable_styling(c("bordered","striped"))
Rank Name Growth_Rate Revenue Industry Employees City State
1 Fuhu 421.48 117,900,000 Consumer Products & Services 104 El Segundo CA
2 FederalConference.com 248.31 49,600,000 Government Services 51 Dumfries VA
3 The HCI Group 245.45 25,500,000 Health 132 Jacksonville FL
4 Bridger 233.08 1,900,000,000 Energy 50 Addison TX
5 DataXu 213.37 87,000,000 Advertising & Marketing 220 Boston MA
6 MileStone Community Builders 179.38 45,700,000 Real Estate 63 Austin TX 
summary(inc)
##       Rank                          Name       Growth_Rate         Revenue           
##  Min.   :   1   (Add)ventures         :   1   Min.   :  0.340   Min.   :    2000000  
##  1st Qu.:1252   @Properties           :   1   1st Qu.:  0.770   1st Qu.:    5100000  
##  Median :2502   1-Stop Translation USA:   1   Median :  1.420   Median :   10900000  
##  Mean   :2502   110 Consulting        :   1   Mean   :  4.612   Mean   :   48222535  
##  3rd Qu.:3751   11thStreetCoffee.com  :   1   3rd Qu.:  3.290   3rd Qu.:   28600000  
##  Max.   :5000   123 Exteriors         :   1   Max.   :421.480   Max.   :10100000000  
##                 (Other)               :4995                                          
##                          Industry      Employees                  City          State     
##  IT Services                 : 733   Min.   :    1.0   New York     : 160   CA     : 701  
##  Business Products & Services: 482   1st Qu.:   25.0   Chicago      :  90   TX     : 387  
##  Advertising & Marketing     : 471   Median :   53.0   Austin       :  88   NY     : 311  
##  Health                      : 355   Mean   :  232.7   Houston      :  76   VA     : 283  
##  Software                    : 342   3rd Qu.:  132.0   San Francisco:  75   FL     : 282  
##  Financial Services          : 260   Max.   :66803.0   Atlanta      :  74   IL     : 273  
##  (Other)                     :2358   NA's   :12        (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
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
## <U+2713> ggplot2 3.2.1     <U+2713> purrr   0.3.3
## <U+2713> tibble  2.1.3     <U+2713> dplyr   0.8.3
## <U+2713> tidyr   1.0.0     <U+2713> stringr 1.4.0
## <U+2713> readr   1.3.1     <U+2713> forcats 0.4.0
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter()     masks stats::filter()
## x dplyr::group_rows() masks kableExtra::group_rows()
## x dplyr::lag()        masks stats::lag()
# Add a column for revenue per employee at each company
inc2 <- inc %>% 
  mutate(RevenuePerEmployee = Revenue / Employees)

### Group by Industry
inc2 %>%
  group_by(Industry) -> inc_byIndustry

### Aggregate by Industry
inc_byIndustry %>% summarise(
  N         = n(),
  AvgGrowth = mean(Growth_Rate,na.rm=T),
  AvgRev    = mean(Revenue,na.rm=T),
  TotalRev  = sum(Revenue,na.rm=T),
  AvgEmpl   = mean(Employees,na.rm=T),
  TotalEmpl = sum(Employees,na.rm=T),
  AvgRevPerEmpl = mean(RevenuePerEmployee,na.rm=T),
  MedRevPerEmpl = median(RevenuePerEmployee,na.rm=T)
) -> Summary_byIndustry

### Group by state
inc2 %>%
  group_by(State) -> inc_byState

### Aggregate by state
inc_byState %>% summarise(
  Num_Companies         = n(),
  AvgGrowth = mean(Growth_Rate,na.rm=T),
  AvgRev    = mean(Revenue,na.rm=T),
  TotalRev  = sum(Revenue,na.rm=T),
  AvgEmpl  = mean(Employees,na.rm=T),
  TotalEmpl = sum(Employees,na.rm=T),
  AvgRevPerEmpl = mean(RevenuePerEmployee,na.rm=T),
  MedRevPerEmpl = median(RevenuePerEmployee,na.rm=T)
) -> Summary_byState

Display summary by industry

options(digits = 10)
Summary_byIndustry %>% 
  kable(format.args = list(big.mark = ","),digits=2) %>% 
  kable_styling(c("bordered","striped"))
Industry N AvgGrowth AvgRev TotalRev AvgEmpl TotalEmpl AvgRevPerEmpl MedRevPerEmpl
Advertising & Marketing 471 6.23 16,528,662.42 7,785,000,000 84.35 39,731 306,036.31 202,061.86
Business Products & Services 482 3.52 54,705,186.72 26,367,900,000 244.49 117,357 359,096.93 200,000.00
Computer Hardware 44 4.09 270,129,545.45 11,885,700,000 220.77 9,714 817,702.22 516,477.27
Construction 187 3.37 70,450,802.14 13,174,300,000 155.61 29,099 465,682.41 280,000.00
Consumer Products & Services 203 8.78 73,676,847.29 14,956,400,000 223.96 45,464 466,068.10 313,043.48
Education 83 3.64 13,726,506.02 1,139,300,000 92.59 7,685 296,453.51 166,666.67
Energy 109 9.60 126,344,954.13 13,771,600,000 242.54 26,437 1,554,655.82 283,211.68
Engineering 74 1.98 34,222,972.97 2,532,500,000 276.15 20,435 201,119.95 164,840.86
Environmental Services 51 2.07 51,741,176.47 2,638,800,000 199.12 10,155 283,607.34 179,723.50
Financial Services 260 5.44 50,580,384.62 13,150,900,000 183.43 47,693 394,230.87 214,858.48
Food & Beverage 131 3.64 98,559,541.98 12,911,300,000 510.94 65,911 618,382.85 231,372.55
Government Services 202 7.24 29,748,019.80 6,009,100,000 129.63 26,185 243,596.00 165,323.89
Health 355 4.86 50,319,436.62 17,863,400,000 232.85 82,430 325,198.89 174,166.67
Human Resources 196 3.30 47,173,979.59 9,246,100,000 1,158.06 226,980 395,972.25 149,547.51
Insurance 50 2.01 46,758,000.00 2,337,900,000 146.78 7,339 474,966.36 225,622.97
IT Services 733 3.33 28,214,597.54 20,681,300,000 140.42 102,788 270,494.21 163,914.89
Logistics & Transportation 155 4.34 95,745,161.29 14,840,500,000 259.70 39,994 794,810.91 425,024.15
Manufacturing 256 2.30 49,546,875.00 12,684,000,000 172.32 43,942 453,524.01 231,250.00
Media 54 4.37 32,266,666.67 1,742,400,000 176.52 9,532 307,143.79 261,458.33
Real Estate 96 7.75 30,892,708.33 2,965,700,000 198.87 18,893 434,515.57 253,571.43
Retail 203 6.18 50,529,064.04 10,257,400,000 182.60 37,068 412,554.86 312,755.10
Security 73 3.39 52,230,136.99 3,812,800,000 562.45 41,059 283,391.38 158,744.39
Software 342 5.02 23,802,923.98 8,140,600,000 150.33 51,262 225,989.25 155,319.15
Telecommunications 129 2.88 56,855,813.95 7,334,400,000 242.85 30,842 449,259.60 284,000.00
Travel & Hospitality 62 2.35 47,283,870.97 2,931,600,000 371.53 23,035 414,788.11 224,404.76

Display summary by state

Summary_byState[1:26,] %>% 
  kable(format.args = list(big.mark = ","),digits=2) %>% 
  kable_styling(c("bordered","striped"))
State Num_Companies AvgGrowth AvgRev TotalRev AvgEmpl TotalEmpl AvgRevPerEmpl MedRevPerEmpl
AK 2 4.80 171,500,000.00 343,000,000 1,264.00 2,528 154,669.98 154,669.98
AL 51 2.41 25,907,843.14 1,321,300,000 125.35 6,393 249,273.67 194,059.41
AR 9 1.67 8,333,333.33 75,000,000 55.11 496 180,752.59 154,761.90
AZ 100 4.62 55,015,000.00 5,501,500,000 342.81 34,281 314,132.28 190,814.85
CA 701 5.90 33,463,480.74 23,457,900,000 230.31 161,219 407,764.93 222,967.03
CO 134 4.95 31,270,149.25 4,190,200,000 198.78 26,438 418,053.48 176,086.96
CT 50 4.99 49,486,000.00 2,474,300,000 139.78 6,989 595,983.46 218,750.00
DC 43 8.30 76,344,186.05 3,282,800,000 219.55 9,221 309,673.37 195,652.17
DE 16 2.42 42,300,000.00 676,800,000 4,284.00 68,544 261,811.20 98,250.08
FL 282 5.85 37,625,177.30 10,610,300,000 217.10 61,221 409,142.84 191,153.85
GA 212 3.52 30,510,849.06 6,468,300,000 163.73 34,546 417,074.41 216,666.67
HI 7 6.79 99,485,714.29 696,400,000 88.71 621 1,001,869.05 366,428.57
IA 28 1.76 123,142,857.14 3,448,000,000 405.14 11,344 337,214.61 218,436.29
ID 17 2.65 231,523,529.41 3,935,900,000 342.18 5,817 675,024.18 271,428.57
IL 273 3.74 121,773,992.67 33,244,300,000 379.65 103,266 462,003.12 216,447.09
IN 69 4.79 50,105,797.10 3,457,300,000 184.01 12,697 281,487.24 164,516.13
KS 38 3.63 40,752,631.58 1,548,600,000 229.61 8,725 480,676.68 167,715.73
KY 40 2.06 33,232,500.00 1,329,300,000 138.60 5,544 435,194.31 198,015.87
LA 37 1.94 56,648,648.65 2,096,000,000 288.35 10,669 271,525.50 166,666.67
MA 182 5.42 33,174,725.27 6,037,800,000 135.62 24,682 336,959.11 200,000.00
MD 131 4.98 25,193,893.13 3,300,400,000 308.69 40,439 269,938.18 183,428.57
ME 13 16.21 12,476,923.08 162,200,000 67.62 879 293,313.56 157,777.78
MI 126 2.24 61,950,793.65 7,805,800,000 292.90 36,905 294,574.44 153,887.46
MN 88 3.82 57,256,818.18 5,038,600,000 210.61 18,534 510,810.49 186,144.07
MO 59 2.50 45,164,406.78 2,664,700,000 293.15 17,296 408,161.70 265,217.39
MS 12 5.64 43,766,666.67 525,200,000 460.92 5,531 611,781.75 288,038.28 
Summary_byState[27:52,] %>% 
  kable(format.args = list(big.mark = ","),digits=2) %>% 
  kable_styling(c("bordered","striped"))
State Num_Companies AvgGrowth AvgRev TotalRev AvgEmpl TotalEmpl AvgRevPerEmpl MedRevPerEmpl
MT 4 0.76 6,150,000.00 24,600,000 418.25 1,673 264,333.47 246,187.36
NC 137 3.51 67,580,291.97 9,258,500,000 271.74 36,685 307,454.39 181,382.98
ND 10 1.23 18,240,000.00 182,400,000 96.30 963 230,156.51 245,285.17
NE 27 2.08 40,696,296.30 1,098,800,000 141.59 3,823 382,766.77 203,076.92
NH 24 1.51 41,691,666.67 1,000,600,000 120.42 2,890 427,787.67 310,344.83
NJ 158 4.45 29,574,683.54 4,672,800,000 190.90 30,162 357,740.83 179,261.36
NM 5 1.36 9,640,000.00 48,200,000 123.40 617 133,085.46 135,000.00
NV 26 2.33 19,915,384.62 517,800,000 66.35 1,725 418,628.12 213,116.88
NY 311 4.37 58,715,112.54 18,260,400,000 271.29 84,370 495,851.34 200,000.00
OH 186 3.56 68,745,161.29 12,786,600,000 204.31 38,002 490,292.92 230,958.39
OK 46 3.10 41,015,217.39 1,886,700,000 151.65 6,976 321,872.32 256,220.10
OR 49 3.15 28,589,795.92 1,400,900,000 89.78 4,399 355,951.88 192,307.69
PA 164 2.57 34,568,902.44 5,669,300,000 186.45 30,392 285,522.91 195,918.37
PR 1 1.73 2,300,000.00 2,300,000 29.00 29 79,310.34 79,310.34
RI 16 16.03 46,981,250.00 751,700,000 185.25 2,964 337,965.65 183,571.43
SC 48 6.06 30,993,750.00 1,487,700,000 111.42 5,348 317,050.73 222,162.16
SD 3 1.41 5,900,000.00 17,700,000 253.67 761 72,534.37 67,647.06
TN 82 4.95 35,870,731.71 2,941,400,000 177.88 14,586 458,637.47 190,109.88
TX 387 6.02 57,271,834.63 22,164,200,000 235.14 90,765 499,383.92 196,168.41
UT 95 6.31 36,038,947.37 3,423,700,000 200.29 19,028 356,986.20 172,195.12
VA 283 4.88 30,627,915.19 8,667,700,000 126.03 35,667 298,791.42 174,545.45
VT 6 1.30 46,200,000.00 277,200,000 178.17 1,069 275,514.23 237,422.71
WA 130 4.00 27,156,923.08 3,530,400,000 135.01 17,416 255,218.89 181,374.72
WI 79 2.69 92,362,025.32 7,296,600,000 201.92 15,548 390,960.27 202,127.66
WV 2 0.62 15,650,000.00 31,300,000 120.00 240 109,674.55 109,674.55
WY 2 19.14 34,750,000.00 69,500,000 53.50 107 568,315.02 568,315.02

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

## Select just the State name and the number of companies
tempgrid <- Summary_byState  %>% select (State,Num_Companies)

## Reverse the grid so the results will display alphabetically (rather than backwards)
tempgrid <- tempgrid[rev(tempgrid$State),]


ggplot(tempgrid, aes(x=State, y=Num_Companies, fill=State))+
geom_col(position=position_dodge(+.5),color="black", alpha=0.3) +
geom_text( aes(label=Num_Companies), hjust=-0.4, vjust=+0.3, color="blue",
   size=2) +
ggtitle(label="INC 5000: Number of companies in each state",
        subtitle="Formatted to display in portrait mode") +
scale_x_discrete(limits = rev(levels(tempgrid$State)))+
theme(axis.text.y = element_text(angle = 0, 
                                 hjust = +0.1, vjust=+0.1, 
                                 size=7,color="blue"))+  
theme(plot.title = element_text(hjust = 0.5))+
theme(plot.subtitle = element_text(hjust = 0.5))+
theme(legend.position="none")+
coord_flip()

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 Question 2 here

### Determine which state has the 3rd most companies
whichRow <- order(Summary_byState$Num_Companies,decreasing = T)[3]
whichState <- as.character(Summary_byState$State)[whichRow]
numCompanies  <- as.integer(Summary_byState[whichRow,"Num_Companies"])
print(paste0("The state with the 3rd most companies is ", 
             whichState, " with ", numCompanies, " companies."))
## [1] "The state with the 3rd most companies is NY with 311 companies."
### Subset the dataset with companies just from that state
inc2 %>% filter(State==whichState) -> inc3
### Filter out any cases with NA values  [ for this example, there are no such cases]
inc3 <- inc3[complete.cases(inc3),]
ggplot(inc3, aes(x = reorder(x=Industry,
                             X=Employees,
                             FUN = median), 
                 y = Employees,
                 fill=Industry)) + 
  geom_boxplot(color="blue", alpha=0.3) +
  scale_y_log10(label=scales::comma_format(accuracy = 1)) +
  labs(x="Industry",
       y="Boxplot: Number of Employees (log scale)",
       title = "Number of employees in each industry",
       subtitle = "NY-based companies in the INC 5000")+
  theme(plot.title = element_text(hjust = 0.5))+
  theme(plot.subtitle = element_text(hjust = 0.5))+
  theme(legend.position="none")+
  coord_flip()

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

# Add a column for revenue per employee at each company
inc4 <- inc %>% mutate(RevenuePerEmployee = Revenue / Employees)

# There are 12 companies for which the number of employees is unknown.
# Drop them.
inc4 <- inc4[complete.cases(inc4),]



ggplot(inc4, aes(x = reorder(x=Industry,
                             X=RevenuePerEmployee,
                             FUN = median), 
                 y = RevenuePerEmployee,
                 fill=Industry)) + 
  geom_boxplot(color="blue", alpha=0.3) +
  scale_y_log10(label=scales::dollar_format(accuracy = 1)) +
  labs(x="Industry",
       y="Boxplot: Revenue per Employee (log scale)",
       title = "Revenue per employee, by industry",
       subtitle = "All companies in the INC 5000")+
  theme(plot.title = element_text(hjust = 0.5))+
  theme(plot.subtitle = element_text(hjust = 0.5))+
  theme(legend.position="none")+
  coord_flip()