R Notebook
Trishita Nath
9/5/2021
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 Revenue
## Min. : 1 Length:5001 Min. : 0.340 Min. :2.000e+06
## 1st Qu.:1252 Class :character 1st Qu.: 0.770 1st Qu.:5.100e+06
## Median :2502 Mode :character Median : 1.420 Median :1.090e+07
## Mean :2502 Mean : 4.612 Mean :4.822e+07
## 3rd Qu.:3751 3rd Qu.: 3.290 3rd Qu.:2.860e+07
## Max. :5000 Max. :421.480 Max. :1.010e+10
##
## Industry Employees City State
## Length:5001 Min. : 1.0 Length:5001 Length:5001
## Class :character 1st Qu.: 25.0 Class :character Class :character
## Mode :character Median : 53.0 Mode :character Mode :character
## Mean : 232.7
## 3rd Qu.: 132.0
## Max. :66803.0
## NA's :12Think 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:
Top 10 industries with the highest growth rates
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.0.6 v dplyr 1.0.4
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(scales)##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factortop_10_by_growth<-inc%>%
group_by(Industry)%>%
summarize(Avg_Growth=mean(Growth_Rate))%>%
top_n(10,Avg_Growth)%>%
arrange(desc(Avg_Growth))
top_10_by_growth## # A tibble: 10 x 2
## Industry Avg_Growth
## <chr> <dbl>
## 1 Energy 9.60
## 2 Consumer Products & Services 8.78
## 3 Real Estate 7.75
## 4 Government Services 7.24
## 5 Advertising & Marketing 6.23
## 6 Retail 6.18
## 7 Financial Services 5.44
## 8 Software 5.02
## 9 Health 4.86
## 10 Media 4.37Top 10 industries in terms of the number of employees
top_10_by_employees<-inc%>%
group_by(Industry)%>%
summarize(Total_Employees=sum(Employees))%>%
top_n(10,Total_Employees)%>%
arrange(desc(Total_Employees))
top_10_by_employees## # A tibble: 10 x 2
## Industry Total_Employees
## <chr> <int>
## 1 Human Resources 226980
## 2 Financial Services 47693
## 3 Consumer Products & Services 45464
## 4 Security 41059
## 5 Advertising & Marketing 39731
## 6 Retail 37068
## 7 Construction 29099
## 8 Energy 26437
## 9 Government Services 26185
## 10 Travel & Hospitality 23035Question 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.
# Dataframe to hold frequencies of companies in each state (grouping by States)
state <- inc %>%
group_by(State) %>%
summarise(companies_freq = n())
# Create visualization using ggplot
ggplot(state, aes(x=reorder(State, companies_freq), y=companies_freq)) +
geom_bar(stat= "identity", fill="#76448a")+labs(title="Distribution of Companies by State", x="States", y="Number of companies")+coord_flip()+geom_text(aes(label=companies_freq), vjust=0.6, hjust=1.2, size=3, color="black")
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.
# The state with the 3rd most companies in the data set is NY
plot_with_outliers<-inc%>%
filter(State=='NY')
plot_with_outliers<-plot_with_outliers[complete.cases(plot_with_outliers),]
plot_with_outliers<-plot_with_outliers%>%
group_by(Industry)
#A plot with outliers
ggplot(plot_with_outliers, aes(x=Industry, y=Employees)) +
geom_boxplot()+
scale_y_continuous("Average Employees", trans='log2')+
coord_flip()+
ggtitle("Employment by Industry in NY State")+
theme_classic()+
theme(panel.background = element_rect(fill = "#abebc6"))
#Removing the outliers
plot_without_outliers <- plot_with_outliers %>%
group_by(Industry) %>%
filter((Employees <= quantile(Employees,0.75)+1.5*IQR(Employees))
&Employees >= quantile(Employees,0.25)-1.5*IQR(Employees))%>%
mutate(avgemp=mean(Employees))
ggplot(plot_without_outliers, aes(x=reorder(Industry,avgemp), y=Employees)) +
geom_boxplot()+
scale_y_continuous("Average, Median and Distribution of Employees", trans='log2')+
stat_summary(fun.x=mean, geom="point", shape=20, size=2, color="purple", fill="purple")+
coord_flip()+
ggtitle("Employment by Industry in NY State")+
theme_classic()+
theme(panel.background = element_rect(fill = "#abebc6"))## Warning: Ignoring unknown parameters: fun.x## No summary function supplied, defaulting to `mean_se()`
# The purple circles represent the meanQuestion 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.
rev_per_emp <- inc%>%
filter(complete.cases(.))%>%
group_by(Industry)%>%
summarise(Revenue_total = sum(Revenue), Employees_Total= sum(Employees))%>%
mutate(Revenue_per_employee = Revenue_total/Employees_Total)
ggplot(rev_per_emp, aes(x=reorder(Industry, Revenue_per_employee), y=Revenue_per_employee))+
geom_bar(stat = "identity",fill="#f8c471")+
geom_hline(yintercept=seq(1,700000,100000), col="white", lwd=1)+
theme_classic() +
coord_flip() +
xlab("Industry") +
ggtitle("Industry Revenue per Employee")
# Computer hardware has the highest revenue per employee