R Notebook
Ashish Kumar
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:
# Insert your code here, create more chunks as necessary
dim(inc)## [1] 5001 8The Data has 5001 Rows (Including Labels) and 8 Columns
suppressMessages(suppressWarnings(library(tidyverse)))
suppressMessages(suppressWarnings(library(ggplot2)))
suppressMessages(suppressWarnings(library("RColorBrewer")))#Lets see the Standard Deviaition for Numberical fields Growth/Revenue and Employees
print(paste("SD for Growth Rate ",sd(inc$Growth_Rate)))## [1] "SD for Growth Rate 14.1236917640676"print(paste("SD for Revenue ",sd(inc$Revenue)))## [1] "SD for Revenue 240542281.135874"print(paste("SD for Employees",sd(inc$Employees, na.rm = TRUE))) # Some companies have missing employee counts## [1] "SD for Employees 1353.12794924661"plot(inc$Rank, inc$Growth_Rate,col="blue")
print(paste("Looks Like only Higher ranked Companies have good growth rates"))## [1] "Looks Like only Higher ranked Companies have good growth rates"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
# Create Summary of states dataframe and sort by companies
states <- inc %>%
group_by(State) %>%
count() %>%
rename(count = n) %>%
arrange(desc(count))
# plot
stplot <- ggplot(states, aes(x = reorder(State, count), y = count))
stplot <- stplot + geom_bar(stat = "identity", fill = 'Brown') + coord_flip()
stplot <- stplot + ggtitle("Distribution of Companies by States")
stplot <- stplot + labs(x = "State Name", y = "Number of Companies")
stplot <- stplot + theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))
stplot <- stplot + theme_bw()
stplot
print("California is the state with most distribution of companies")## [1] "California is the state with most distribution of companies"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
# find state with third most companies (3rd row b/c already ordered desc)
third_state <- states[3,"State"]
third_state## # A tibble: 1 x 1
## # Groups: State [1]
## State
## <fct>
## 1 NY# Get complete cases and 3rd ranked state only
third_state_data <- inc[complete.cases(inc),] %>%
inner_join(third_state, by = "State")
# Mean employment by industry
mean_emp <- aggregate(Employees ~ Industry, third_state_data, mean)
# Maximum average employee no.
maxmean <- max(mean_emp$Employees)
# plot data
emp_plot<- ggplot(third_state_data, aes(x = reorder(Industry,Employees,mean), y = Employees))
emp_plot<- emp_plot+ geom_boxplot(outlier.shape = NA, show.legend=F) + coord_flip()
emp_plot<- emp_plot+ labs(x = "industry", y = "employees", title="Mean Employment Size by Industry")
emp_plot<- emp_plot+ geom_point(data = mean_emp, aes(x=Industry, y=Employees), color='darkblue', size = 2)
emp_plot<- emp_plot+ theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))
emp_plot<- emp_plot+ scale_y_continuous(limits = c(0,maxmean), breaks = seq(0, maxmean, 500)) + theme_bw()
emp_plot <- emp_plot + theme_bw()
emp_plot
The average employee count in the Business Products & Services industry are many times greater than the other industries.
For Such a big skew and outliers we can use log scale
emp_plot <- emp_plot + scale_y_log10(limits = c(1, maxmean))## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.emp_plot <- emp_plot + labs(caption = "(grid line spacing on log scale)")
emp_plot <- emp_plot + theme(plot.caption = element_text(size = 8))
emp_plot <- emp_plot + theme_bw()
emp_plot