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:

Industries having most fast growing companies (in desc order)

# Insert your code here, create more chunks as necessary

describe(inc$Employees)
##    vars    n   mean      sd median trimmed   mad min   max range  skew kurtosis
## X1    1 4989 232.72 1353.13     53   81.78 53.37   1 66803 66802 29.81  1268.67
##       se
## X1 19.16
I <- data.frame(inc %>% group_by(Industry) %>% tally()) %>% arrange(desc(n))

kable(I) %>% scroll_box(height = "300px")
Industry n
IT Services 733
Business Products & Services 482
Advertising & Marketing 471
Health 355
Software 342
Financial Services 260
Manufacturing 256
Consumer Products & Services 203
Retail 203
Government Services 202
Human Resources 196
Construction 187
Logistics & Transportation 155
Food & Beverage 131
Telecommunications 129
Energy 109
Real Estate 96
Education 83
Engineering 74
Security 73
Travel & Hospitality 62
Media 54
Environmental Services 51
Insurance 50
Computer Hardware 44 
prop.table(table(inc$Industry))
## 
##      Advertising & Marketing Business Products & Services 
##                   0.09418116                   0.09638072 
##            Computer Hardware                 Construction 
##                   0.00879824                   0.03739252 
## Consumer Products & Services                    Education 
##                   0.04059188                   0.01659668 
##                       Energy                  Engineering 
##                   0.02179564                   0.01479704 
##       Environmental Services           Financial Services 
##                   0.01019796                   0.05198960 
##              Food & Beverage          Government Services 
##                   0.02619476                   0.04039192 
##                       Health              Human Resources 
##                   0.07098580                   0.03919216 
##                    Insurance                  IT Services 
##                   0.00999800                   0.14657069 
##   Logistics & Transportation                Manufacturing 
##                   0.03099380                   0.05118976 
##                        Media                  Real Estate 
##                   0.01079784                   0.01919616 
##                       Retail                     Security 
##                   0.04059188                   0.01459708 
##                     Software           Telecommunications 
##                   0.06838632                   0.02579484 
##         Travel & Hospitality 
##                   0.01239752

States having most fast growing companies (in desc order)

S <- data.frame(inc %>% group_by(State) %>% tally()) %>% arrange(desc(n))

kable(S) %>% scroll_box(height = "300px")
State n
CA 701
TX 387
NY 311
VA 283
FL 282
IL 273
GA 212
OH 186
MA 182
PA 164
NJ 158
NC 137
CO 134
MD 131
WA 130
MI 126
AZ 100
UT 95
MN 88
TN 82
WI 79
IN 69
MO 59
AL 51
CT 50
OR 49
SC 48
OK 46
DC 43
KY 40
KS 38
LA 37
IA 28
NE 27
NV 26
NH 24
ID 17
DE 16
RI 16
ME 13
MS 12
ND 10
AR 9
HI 7
VT 6
NM 5
MT 4
SD 3
AK 2
WV 2
WY 2
PR 1 
prop.table(table(inc$State))
## 
##         AK         AL         AR         AZ         CA         CO         CT 
## 0.00039992 0.01019796 0.00179964 0.01999600 0.14017197 0.02679464 0.00999800 
##         DC         DE         FL         GA         HI         IA         ID 
## 0.00859828 0.00319936 0.05638872 0.04239152 0.00139972 0.00559888 0.00339932 
##         IL         IN         KS         KY         LA         MA         MD 
## 0.05458908 0.01379724 0.00759848 0.00799840 0.00739852 0.03639272 0.02619476 
##         ME         MI         MN         MO         MS         MT         NC 
## 0.00259948 0.02519496 0.01759648 0.01179764 0.00239952 0.00079984 0.02739452 
##         ND         NE         NH         NJ         NM         NV         NY 
## 0.00199960 0.00539892 0.00479904 0.03159368 0.00099980 0.00519896 0.06218756 
##         OH         OK         OR         PA         PR         RI         SC 
## 0.03719256 0.00919816 0.00979804 0.03279344 0.00019996 0.00319936 0.00959808 
##         SD         TN         TX         UT         VA         VT         WA 
## 0.00059988 0.01639672 0.07738452 0.01899620 0.05658868 0.00119976 0.02599480 
##         WI         WV         WY 
## 0.01579684 0.00039992 0.00039992

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

X <- inc %>% group_by(State) %>% summarise(counts = n()) %>% arrange(counts)

ggplot(X, aes(x = reorder(State, counts), y = counts)) + geom_bar(stat = "identity", fill='light blue',color='blue') + coord_flip() + geom_text(aes(label = counts), vjust = 0.5, hjust = -0.1, size=2.75) + ggtitle("Distribution of companies in the dataset by State") + xlab("State") + ylab("Company Count")

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

NY <- inc %>% filter(State == 'NY') %>% filter(complete.cases(.)) %>% group_by(Industry) %>% summarize(A=mean(Employees), M=median(Employees)) 

kable(NY) %>% scroll_box(height = "300px")
Industry A M
Advertising & Marketing 58.43860 38.0
Business Products & Services 1492.46154 70.5
Computer Hardware 44.00000 44.0
Construction 61.00000 24.5
Consumer Products & Services 626.29412 25.0
Education 59.85714 50.5
Energy 129.20000 120.0
Engineering 53.50000 54.5
Environmental Services 155.00000 155.0
Financial Services 144.30769 81.0
Food & Beverage 76.44444 41.0
Government Services 17.00000 17.0
Health 81.84615 45.0
Human Resources 437.54545 56.0
Insurance 32.50000 32.5
IT Services 204.09302 54.0
Logistics & Transportation 29.50000 23.5
Manufacturing 73.30769 30.0
Media 108.00000 45.0
Real Estate 18.25000 18.0
Retail 24.78571 13.5
Security 135.00000 32.5
Software 245.92308 80.0
Telecommunications 95.35294 31.0
Travel & Hospitality 547.71429 61.0 
ggplot(melt(NY, id.vars='Industry'), aes(x=Industry, y=value, fill=variable)) + geom_bar(stat='identity') + coord_flip() + ylab("Employees") + ggtitle("New York Employees per Industry")

To see the outliers properly boxplot can be used.

NY_X <- inc %>% filter(State == 'NY') %>% filter(complete.cases(.)) %>% group_by(Industry)

ggplot(NY_X, aes(x=Industry, y=Employees)) + geom_boxplot(width = 0.5,fill='blue',outlier.colour="red") + coord_flip(ylim = c(0, 1500), expand = TRUE)

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

NY_R <- NY_X %>%  mutate(R = Revenue/Employees) %>%  group_by(Industry) %>% summarize(A = mean(R), M = median(R))

kable(NY_R) %>% scroll_box(height = "300px")
Industry A M
Advertising & Marketing 373403.5 255555.6
Business Products & Services 527816.9 203148.1
Computer Hardware 520454.5 520454.5
Construction 238694.5 238613.0
Consumer Products & Services 382942.6 222222.2
Education 112060.6 114848.5
Energy 8472533.5 283211.7
Engineering 215744.7 202998.1
Environmental Services 134366.7 134366.7
Financial Services 400174.4 219607.8
Food & Beverage 174630.9 120238.1
Government Services 158823.5 158823.5
Health 532491.0 155000.0
Human Resources 337366.3 175000.0
Insurance 371000.0 371000.0
IT Services 228816.1 164285.7
Logistics & Transportation 1245870.1 996285.7
Manufacturing 665818.6 217500.0
Media 333549.6 262500.0
Real Estate 383809.5 330952.4
Retail 520790.3 305000.0
Security 153277.8 149000.0
Software 143749.0 133333.3
Telecommunications 408143.4 410714.3
Travel & Hospitality 282089.8 223333.3 
ggplot(melt(NY_R, id.vars='Industry'), aes(x=Industry, y=value, fill=variable)) + geom_bar(stat='identity') + coord_flip() + ylab("Revenue") + ggtitle("New York Revenue per Employees per Industry")