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 421.48 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:
# Maximum and Minimum Growth Rate
(Growth_max <- inc[which.max(inc$Growth_Rate),])## Rank Name Growth_Rate Revenue Industry Employees
## 1 1 Fuhu 421.48 117900000 Consumer Products & Services 104
## City State
## 1 El Segundo CA(Growth_min <- inc[which.min(inc$Growth_Rate),])## Rank Name Growth_Rate Revenue Industry
## 4996 4996 cSubs 0.34 13400000 Business Products & Services
## Employees City State
## 4996 19 Montvale NJ# Maximum and Minimum Revenue
(Revenue_max <- inc[which.max(inc$Revenue),])## Rank Name Growth_Rate Revenue Industry Employees
## 4788 4788 CDW 0.41 1.01e+10 Computer Hardware 6800
## City State
## 4788 Vernon Hills IL(Revenue_min <- inc[which.min(inc$Revenue),])## Rank Name Growth_Rate Revenue Industry
## 245 246 Cardinal Point Captains 17.65 2e+06 Government Services
## Employees City State
## 245 30 Carlsbad CA# Maximum and Minimum Employees
(Employees_max <- inc[which.max(inc$Employees),])## Rank Name Growth_Rate Revenue
## 2344 2345 Integrity staffing Solutions 1.55 278200000
## Industry Employees City State
## 2344 Human Resources 66803 Wilmington DE(Employees_min <- inc[which.min(inc$Employees),])## Rank Name Growth_Rate Revenue Industry
## 413 414 Merch Makers 10.85 2100000 Consumer Products & Services
## Employees City State
## 413 1 Ames IALoading libraries
suppressMessages(if (!require('dplyr')) install.packages('dplyr'))
suppressMessages(if (!require('ggplot2')) install.packages('ggplot2'))
suppressMessages(if (!require('outliers')) install.packages('outliers'))
suppressMessages(if (!require('sqldf')) install.packages('sqldf'))## Warning: package 'sqldf' was built under R version 3.6.2## Warning: package 'gsubfn' was built under R version 3.6.2## Warning: package 'proto' was built under R version 3.6.2Question 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.
# Here we are using sqldf for writing the Sql code for data manipulation
q1 <- sqldf("select
State, count(distinct Name) as number_companies
from inc
group by State")
ggplot(q1, aes(x=reorder(State,number_companies),number_companies))+
geom_bar(stat="identity", fill="LightGreen")+
geom_text(aes(label=round(number_companies, digits=2)), vjust=0.2, size=2.5, position=position_dodge(width = 1), hjust=1.5)+
theme_minimal()+
theme(axis.text.x=element_text(size=12, vjust=0.5))+
theme(axis.text.y=element_text(size=8, vjust=0.5))+
labs( x="State", y="Number of Unique Companies")+
coord_flip()+
labs(caption="Inc Data")+
ggtitle("Distribution of Unique Companies by State")
ggsave('Q1.png')## Saving 7 x 5 in imageQuesiton 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.
q2 <- inc[complete.cases(inc), ]
q2 <- subset(inc, State == "NY")
q2 <- group_by(q2, Industry) %>% summarize(m = mean(Employees), max= max(Employees), min = min(Employees)) %>%
na.omit()
upper <- q2$max
lower <- q2$min
ggplot(q2, aes(x = Industry, y =m, ymax=max, ymin = min, lower = lower, upper= upper)) + geom_boxplot(outlier.shape = NA) + coord_flip()+
labs(title="Number of Employees By Industry in NY State", y = "Mean")
ggsave('Q2.png')## Saving 7 x 5 in imageQuestion 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.
q3 <- inc[complete.cases(inc), ]
q3 <- q3[, c("Industry", "Revenue", "Employees")] %>% group_by(Industry) %>% summarise_each(funs(sum))## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.q3$RevPerEmp <- q3$Revenue / q3$Employees
ggplot(q3, aes(x = Industry, y = RevPerEmp)) +
geom_point(aes(size = RevPerEmp), color = "Orange") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
ggsave('Q3.png')## Saving 7 x 5 in image