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)Packages Used
require(tidyverse)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 TXsummary(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):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:
# a table of counts of industry
inc %>% group_by(Industry) %>% tally() %>% arrange(desc(n))## # A tibble: 25 x 2
## Industry n
## <fct> <int>
## 1 IT Services 733
## 2 Business Products & Services 482
## 3 Advertising & Marketing 471
## 4 Health 355
## 5 Software 342
## 6 Financial Services 260
## 7 Manufacturing 256
## 8 Consumer Products & Services 203
## 9 Retail 203
## 10 Government Services 202
## # ... with 15 more rows# table of total revenue by industry
inc %>% group_by(Industry) %>% summarize(TotalRev=sum(Revenue)) %>% arrange(desc(TotalRev))## # A tibble: 25 x 2
## Industry TotalRev
## <fct> <dbl>
## 1 Business Products & Services 26367900000
## 2 IT Services 20681300000
## 3 Health 17863400000
## 4 Consumer Products & Services 14956400000
## 5 Logistics & Transportation 14840500000
## 6 Energy 13771600000
## 7 Construction 13174300000
## 8 Financial Services 13150900000
## 9 Food & Beverage 12911300000
## 10 Manufacturing 12684000000
## # ... with 15 more rowsQuestion 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.
inc %>%
group_by(State) %>%
tally(sort = T) %>%
filter(n>0) %>%
ggplot(aes(x=reorder(State,n),y=n))+
geom_segment(aes(xend=State,yend=0), color="grey50") +
geom_point(size=2,color="blue")+
geom_text(aes(label=State),size = 2, hjust=-.75, vjust=.4) +
guides(fill=F) +
ggtitle("Number of Fastest Growing Comapnies in US by State") +
labs(y='Number of Companies') +
coord_flip() +
theme_minimal()+
theme(axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=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.
# find the state with the third most companies
inc %>% group_by(State) %>% tally() %>% arrange(desc(n)) %>% slice(3)## # A tibble: 1 x 2
## State n
## <fct> <int>
## 1 NY 311# new dataset with only NY full cases
inc.NY <- inc %>% filter(complete.cases(.), State=='NY')
# get a list of top comapnies
inc.NY %>% arrange(desc(Employees)) %>% select(Name,Employees) %>% head()## Name Employees
## 1 Sutherland Global Services 32000
## 2 Coty 10000
## 3 Westcon Group 3000
## 4 Denihan Hospitality Group 2280
## 5 TransPerfect 2218
## 6 Sterling Infosystems 2081# Dotplot with outliers removed
# Blue dots represent median values - small black dots are observations
inc.NY %>%
filter(Employees<2000) %>% # removing outliers
group_by(Industry) %>% #
ggplot(aes(x=reorder(Industry, Employees,FUN=median), y=Employees)) +
geom_dotplot(dotsize = 20, binaxis="y", binwidth = .5, stackdir = "center") +
stat_summary(fun.y=median, geom="point", size=2, color="blue") +
labs(y='Number of Employees', title="Median Employment by Industry \n New York State") +
theme_minimal() +
theme(axis.title.y=element_blank()) +
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.
# calculate and display the metric rev/emp and plot
# one outlier was removed
inc.NY %>%
group_by(Industry) %>%
mutate(RevPerEmp=Revenue/Employees/1000) %>%
arrange(desc(RevPerEmp)) %>%
filter(RevPerEmp < 40000) %>%
ggplot(aes(x=reorder(Industry,RevPerEmp,FUN=median),y=RevPerEmp)) +
geom_dotplot(dotsize=100, binaxis="y", binwidth = .5, stackdir = "center") +
stat_summary(fun.y=median, geom="point", size=2, color="blue") +
labs(y='Revenue per Employee [thousands USD]', title="Median Revenue per Employee by Industry \n New York State") +
theme_minimal() +
theme(axis.title.y=element_blank())+
coord_flip()