Hantz_Angrand_Data608_project1
Hantz Angrand
February 9, 2019
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:
library(tidyverse)## -- Attaching packages ------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.1.0 v purrr 0.2.5
## v tibble 1.4.2 v dplyr 0.7.6
## v tidyr 0.8.1 v stringr 1.3.1
## v readr 1.1.1 v forcats 0.3.0## -- Conflicts ---------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()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)summary(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
names(inc)## [1] "Rank" "Name" "Growth_Rate" "Revenue" "Industry"
## [6] "Employees" "City" "State"#removing Na from the dataset
inc_na<-na.omit(inc)
head(inc_na)Aggregate to get the frequency of employee by industry
#indeed_skillaggr<-aggregate(read_indeed_url$Count,by=list(Category=read_indeed_url$Skills), FUN=sum)
#indeed_skillaggr
inc_na_aggr<-aggregate(inc_na$Employees, by=list(Categgory=inc_na$Industry), FUN=sum)
inc_na_aggrRevenue by state
#skills_count<-read_indeed_url %>%
# group_by(Skills) %>%
# summarise(Total=sum(Count)) %>%
# arrange(desc(Total))
#skills_count
revenue_by_state<-inc_na %>%
group_by(State) %>%
summarise(Total=sum(Revenue)) %>%
arrange(desc(Total))
revenue_by_state#skills_city<-read_indeed_url %>%
# group_by(Skills,City) %>%
# summarise(Total=sum(Count)) %>%
# arrange(desc(Total))
#skills_city
rev_by_ind_state<-inc_na %>%
group_by(Industry, State) %>%
summarise(Total=sum(Revenue)) %>%
arrange(desc(Total))
rev_by_ind_stateQuestion 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
inc_state<-inc %>%
group_by(State) %>%
summarise(Total=n()) %>%
arrange(desc(Total))
inc_stateGraph distribution of companies by state
#g<-ggplot(inc_state,aes(x=reorder(State,Total, height=1),y=Total))+ geom_bar(stat='identity')+
#coord_flip()+
# labs(title='Frequency of Companies by State') +
# xlab('State')+
# ylab('Total')
#g
ggplot(data=inc_state, aes(x=reorder(State, Total),y=Total, fill=State)) +
geom_bar(stat= "identity") +
#guides(fill=FALSE) +
xlab("State") + ylab("Total companies") +
ggtitle("Companies by State") +
theme(axis.text.x = element_text(angle=65, vjust=0.6))
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
inc_select<- inc%>%select(c(State, Industry, Employees))
inc_select<-inc_select[complete.cases(inc_select),]
inc_mean<-inc_select %>%
filter(State == 'NY') %>%
group_by(Industry) %>%
summarise(mean=mean(Employees), median=median(Employees))
ggplot(inc_mean, aes(x=Industry, y=mean)) +
geom_bar(stat="identity", width = 0.5) +
ggtitle("Average of employee by Industry")+
xlab("Industry")+
ylab("Mean Employees")+
theme(axis.text.x = element_text(angle=65, vjust=0.6))
ggplot(inc_mean, aes(x=Industry, y=median)) +
geom_bar(stat="identity", width = 0.5) +
ggtitle(" Industry by median employee")+
xlab("Industry")+
ylab("Median Employees")+
theme(axis.text.x = element_text(angle=65, vjust=0.6))
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.
# Answer Question 3 here
inc_investor<-inc %>%
select(c(Industry,Revenue, Employees))
inc_investor<-inc_investor[complete.cases(inc_investor),]
inc_rev_total<-inc_investor %>%
group_by(Industry) %>%
summarise(Revenue_Total=sum(Revenue),Employees_Total=sum(Employees))
inc_revenue_emp<-transform(inc_rev_total, rev_per_emp= Revenue_Total / Employees_Total)%>%
arrange(desc(rev_per_emp))
ggplot(inc_revenue_emp, aes(x=reorder(Industry,rev_per_emp), y=rev_per_emp))+
geom_bar(stat="identity")+
coord_flip()+
ggtitle("Industry with most Revenue per Employee")+
xlab("Industry")+
ylab("Revenue Per Employees") +
theme(axis.text.x = element_text(angle=65, vjust=0.6))