R Notebook
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 117900000
## 2 2 FederalConference.com 248 49600000
## 3 3 The HCI Group 245 25500000
## 4 4 Bridger 233 1900000000
## 5 5 DataXu 213 87000000
## 6 6 MileStone Community Builders 179 45700000
## 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 TXsummary(inc)## Rank Name Growth_Rate
## Min. : 1 (Add)ventures : 1 Min. : 0
## 1st Qu.:1252 @Properties : 1 1st Qu.: 1
## Median :2502 1-Stop Translation USA: 1 Median : 1
## Mean :2502 110 Consulting : 1 Mean : 5
## 3rd Qu.:3751 11thStreetCoffee.com : 1 3rd Qu.: 3
## Max. :5000 123 Exteriors : 1 Max. :421
## (Other) :4995
## Revenue Industry Employees
## Min. : 2000000 IT Services : 733 Min. : 1
## 1st Qu.: 5100000 Business Products & Services: 482 1st Qu.: 25
## Median : 10900000 Advertising & Marketing : 471 Median : 53
## Mean : 48222535 Health : 355 Mean : 233
## 3rd Qu.: 28600000 Software : 342 3rd Qu.: 132
## Max. :10100000000 Financial Services : 260 Max. :66803
## (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):2764Think 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:
library(psych)
describe(inc$Growth_Rate)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 5001 4.61 14.1 1.42 2.14 1.22 0.34 421 421 12.6 242
## se
## X1 0.2describe(inc$Revenue)## vars n mean sd median trimmed mad min
## X1 1 5001 48222535 240542281 10900000 17334966 10674720 2000000
## max range skew kurtosis se
## X1 10100000000 10098000000 22.2 723 3401441prop.table(table(inc$Industry))##
## Advertising & Marketing Business Products & Services
## 0.0942 0.0964
## Computer Hardware Construction
## 0.0088 0.0374
## Consumer Products & Services Education
## 0.0406 0.0166
## Energy Engineering
## 0.0218 0.0148
## Environmental Services Financial Services
## 0.0102 0.0520
## Food & Beverage Government Services
## 0.0262 0.0404
## Health Human Resources
## 0.0710 0.0392
## Insurance IT Services
## 0.0100 0.1466
## Logistics & Transportation Manufacturing
## 0.0310 0.0512
## Media Real Estate
## 0.0108 0.0192
## Retail Security
## 0.0406 0.0146
## Software Telecommunications
## 0.0684 0.0258
## Travel & Hospitality
## 0.0124describe(inc$Employees)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 4989 233 1353 53 81.8 53.4 1 66803 66802 29.8 1269
## se
## X1 19.2prop.table(table(inc$State))##
## AK AL AR AZ CA CO CT DC DE FL
## 0.0004 0.0102 0.0018 0.0200 0.1402 0.0268 0.0100 0.0086 0.0032 0.0564
## GA HI IA ID IL IN KS KY LA MA
## 0.0424 0.0014 0.0056 0.0034 0.0546 0.0138 0.0076 0.0080 0.0074 0.0364
## MD ME MI MN MO MS MT NC ND NE
## 0.0262 0.0026 0.0252 0.0176 0.0118 0.0024 0.0008 0.0274 0.0020 0.0054
## NH NJ NM NV NY OH OK OR PA PR
## 0.0048 0.0316 0.0010 0.0052 0.0622 0.0372 0.0092 0.0098 0.0328 0.0002
## RI SC SD TN TX UT VA VT WA WI
## 0.0032 0.0096 0.0006 0.0164 0.0774 0.0190 0.0566 0.0012 0.0260 0.0158
## WV WY
## 0.0004 0.0004Question 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.
# option 2 using forcats library
library(forcats)
ggplot(inc, aes(x=fct_infreq(State))) +
geom_bar(fill = "#58BFFF", stat="count") +
coord_flip() +
geom_text(aes(label=..count..), stat="count", size=3,
hjust=-0.2, color="darkgray") +
xlab("State Abbreviation") +
ylab("Number of Companies in State") +
ggtitle("5,000 Fastest Growing Companies in US") +
theme(panel.background = element_blank())
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.
NY <- subset(inc, State=="NY")
NY <- NY[complete.cases(NY), ]
ggplot(NY, aes(x=Industry, y=Employees)) +
#geom_violin(adjust=.5, coef = 0) +
geom_boxplot(width=.5, fill="#58BFFF", outlier.colour=NA) +
stat_summary(aes(colour = "mean"), fun.y = mean, geom="point", fill="red",
colour="red", shape=21, size=2, show.legend=TRUE) +
stat_summary(aes(colour = "median"), fun.y = median, geom="point", fill="blue",
colour="blue", shape=21, size=2, show.legend=TRUE) +
# I can't for the life of me figure out how to get a legend to show what the colored points represent :-(
coord_flip(ylim = c(0, 1500), expand = TRUE) +
scale_y_continuous(labels = scales::comma,
breaks = seq(0, 1500, by = 150)) +
xlab("Industry") +
ylab("") +
ggtitle("Mean and Median Employment by Industry for 311 Fastest Growing Companies in NY") +
theme(panel.background = element_blank(), legend.position = "top")
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.
library(dplyr)
revenue <-inc[complete.cases(inc),] %>%
group_by(Industry) %>%
summarise(sumR=sum(Revenue),sumE=sum(Employees)) %>%
mutate(rev_per_emp = sumR/sumE)
ggplot(revenue, aes(x=reorder(Industry, -rev_per_emp),y=rev_per_emp)) +
geom_bar(fill = "#58BFFF", stat="identity") +
coord_flip() +
xlab("Industry") +
ylab("Revenue Per Employee") +
ggtitle("Revenue Per Employee") +
theme(panel.background = element_blank(), legend.position = "top")