Data 608 - Module 1 Assignment
Zhouxin Shi
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:
str(inc)## 'data.frame': 5001 obs. of 8 variables:
## $ Rank : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Name : chr "Fuhu" "FederalConference.com" "The HCI Group" "Bridger" ...
## $ Growth_Rate: num 421 248 245 233 213 ...
## $ Revenue : num 1.18e+08 4.96e+07 2.55e+07 1.90e+09 8.70e+07 ...
## $ Industry : chr "Consumer Products & Services" "Government Services" "Health" "Energy" ...
## $ Employees : int 104 51 132 50 220 63 27 75 97 15 ...
## $ City : chr "El Segundo" "Dumfries" "Jacksonville" "Addison" ...
## $ State : chr "CA" "VA" "FL" "TX" ...library(sqldf)## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLitelibrary(ggplot2)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.
q1<-sqldf("select State
,count(distinct Name) as cnt_names
from inc
group by State")
ggplot(data = q1, aes(x=reorder(State,cnt_names),y = cnt_names))+
geom_bar(stat="identity", fill="#33CCFF")+
geom_text(aes(label=cnt_names), vjust=0.5, size=3, position=position_dodge(width = 2), hjust=0)+
theme_minimal()+
theme(axis.text.y=element_text(size=9, vjust=0.5))+
theme(axis.text.x=element_text(size=12, vjust=0.5))+
labs( x="State", y="Number of Companies")+
coord_flip()+
ggtitle("Distribution of Companies by State")## Warning: position_dodge requires non-overlapping x intervals
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.
inc_comp<- inc[complete.cases(inc), ]
q2 <- sqldf("select *from inc_comp where State = 'NY'")
ggplot(q2, aes(x=Industry, y=Employees)) +
geom_boxplot(width=.5, fill="#33CCFF", outlier.colour=NA) +
stat_summary(aes(colour = "mean"), fun.y = mean, geom="point", fill="red",
colour="red", shape=21, size=2, show.legend=TRUE) +
stat_summary(aes(colour = "median"), fun.y = median, geom="point", fill="blue",
colour="blue", shape=21, size=2, show.legend=TRUE) +
coord_flip(ylim = c(0, 1200), expand = TRUE) +
scale_y_continuous(labels = scales::comma,
breaks = seq(0, 1500, by = 150)) +
xlab("Industry") +
ylab("Number of Employees per company") +
ggtitle("Mean and Median Employment by Industry for NY State") +
theme(panel.background = element_blank(), legend.position = "top")## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.y` is deprecated. Use `fun` instead.
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.
q3 <- sqldf("select Industry
, round(sum(Revenue)/sum(Employees),0) as revenue_per_employee
from inc
group by Industry
order by sum(Revenue)/sum(Employees) desc")
ggplot(data = q3, aes(x=reorder(Industry,revenue_per_employee),y = revenue_per_employee))+
geom_bar(stat="identity", fill="steelblue")+
geom_text(aes(label=round(revenue_per_employee, digits=0)), vjust=0.5, size=3, position=position_dodge(width = 1), hjust=0)+
theme_minimal()+
theme(axis.text.y=element_text(size=12, vjust=0.5))+
theme(axis.text.x=element_text(size=12, vjust=0.5))+
labs( x="Industry", y="Revenue per employee")+
coord_flip()+
ggtitle("Distribution of Revenue per Employee by Industry")