Data 608 HW 1

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:

library(knitr)
library(kableExtra)
library(ggplot2)
library(dplyr)
library(plyr)
library(treemapify)
library(papeR)
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) %>%
  kable() %>%
  kable_styling()

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

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   :2502   110 Consulting        :   1   Mean   :  4.612  
##  3rd Qu.:3751   11thStreetCoffee.com  :   1   3rd Qu.:  3.290  
##  Max.   :5000   123 Exteriors         :   1   Max.   :421.480  
##                 (Other)               :4995                    
##     Revenue                                  Industry      Employees      
##  Min.   :2.000e+06   IT Services                 : 733   Min.   :    1.0  
##  1st Qu.:5.100e+06   Business Products & Services: 482   1st Qu.:   25.0  
##  Median :1.090e+07   Advertising & Marketing     : 471   Median :   53.0  
##  Mean   :4.822e+07   Health                      : 355   Mean   :  232.7  
##  3rd Qu.:2.860e+07   Software                    : 342   3rd Qu.:  132.0  
##  Max.   :1.010e+10   Financial Services          : 260   Max.   :66803.0  
##                      (Other)                     :2358   NA's   :12       
##             City          State     
##  New York     : 160   CA     : 701  
##  Chicago      :  90   TX     : 387  
##  Austin       :  88   NY     : 311  
##  Houston      :  76   VA     : 283  
##  San Francisco:  75   FL     : 282  
##  Atlanta      :  74   IL     : 273  
##  (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: The summary provides an overall view of the list of attributes that the data set provides. In this particular case, there are 8 columns that describe this data. Summary of Growth Rate, Revenue, Number of Employees provides a glimpse of the kind of companies in this list. Also It provides a high level breakdown of number of companies by industry and by state. A fair and quick view of the content therein.

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.

dfinc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE)
dfresult <- count(dfinc, c('State'))

c1plot <- ggplot(dfresult, aes(x=State, y=freq))+
  geom_bar(stat="identity", fill="steelblue")+
  geom_text(aes(label=freq), vjust=-0.3, size=3.5)+
  theme_minimal()+coord_flip()
  c1plot <- c1plot + scale_y_log10()
ggsave("chart1.png", c1plot, width = 5, height = 9, limitsize = FALSE )

This chart has been set to log scale to ensure that states with a few companies are also seen in the graph. .

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
dfincclean = dfinc[complete.cases(dfinc),]
ThirdState =  dfresult[rev(order(dfresult$freq)),][3,]["State"]
dfSubset =  dfincclean[dfincclean$State == ThirdState[[1]], ]
dfSubset3 <- aggregate(dfSubset$Employees, list(Industry = dfSubset$Industry), median)

head(dfSubset3) %>%
  kable() %>%
  kable_styling()

Industry	x
Advertising & Marketing	38.0
Business Products & Services	70.5
Computer Hardware	44.0
Construction	24.5
Consumer Products & Services	25.0
Education	50.5

c2plot <- ggplot(dfSubset3, aes(x=Industry, y=x))+
  geom_bar(stat="identity", fill="red")+
  geom_text(aes(label=x), vjust=-0.3, size=3.5)+
  theme_minimal()+coord_flip()
  c2plot <- c2plot + scale_y_log10()
ggsave("chart2.png", c2plot, width = 5, height = 9, limitsize = FALSE )

Median Number of Employees by Industry in NY

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
dfinc_1 =   dfinc[dfinc$Employees > 0, ]
dfinc_1$Revperemp <- with(dfinc_1, Revenue/Employees)
dfSubset <- aggregate(dfinc_1$Revperemp, list(Industry = dfinc_1$Industry), mean)
dfSubset$Legend <-  paste(dfSubset$Industry, round(dfSubset$x/1000,0), 'K', sep=' ') 
ggplot(dfSubset, aes(area = x, fill = Industry, label = Legend)) +
  geom_treemap(show.legend = FALSE, na.rm = TRUE, stat="identity", layout="squarified") +
  geom_treemap_text(fontface = "italic", colour = "white", place = "centre",
                    grow = TRUE)

THe Tile plot above shows the average Revenue per employee segmented by Industry.