Module1
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
library(psych)
#to see the growth rate
describe(inc$Growth_Rate)## vars n mean sd median trimmed mad min max range skew
## X1 1 5001 4.61 14.12 1.42 2.14 1.22 0.34 421.48 421.14 12.55
## kurtosis se
## X1 242.34 0.2#to see the revenue
describe(inc$Revenue)## vars n mean sd median trimmed mad min max
## X1 1 5001 48222535 240542281 10900000 17334966 10674720 2e+06 1.01e+10
## range skew kurtosis se
## X1 1.0098e+10 22.17 722.66 3401441describe(inc$Employees)## vars n mean sd median trimmed mad min max range skew
## X1 1 4989 232.72 1353.13 53 81.78 53.37 1 66803 66802 29.81
## kurtosis se
## X1 1268.67 19.16Question 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.
ggplot(df2,aes(x=factor(name),y=depth)) + geom_bar(stat=‘identity’,data=subset(df2,df2\(Mut==2),fill='red') + geom_bar(stat='identity',data=subset(df2,df2\)Mut==1),fill=‘blue’) + coord_flip() + labs(y=‘depth’,x=‘species’)
# Answer Question 1 here
library(ggplot2)##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphalibrary(forcats)
library(ggthemes)
ggplot(inc, aes(x=fct_infreq(State))) +
geom_bar(fill="grey",stat="count") +
coord_flip() +
geom_text(aes(label=..count..), stat="count", size=2.5,hjust=-0.4,color="brown") +
labs(title = "5,000 fastest growing companies in the US", x = "State", y = "No. of Companies in State") +
theme_tufte()
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
NY <- subset(inc, State=="NY")
NY <- NY[complete.cases(NY),]
outliers <- NY[order(-NY$Employees),]
head(outliers)## Rank Name Growth_Rate Revenue
## 4577 4577 Sutherland Global Services 0.48 5.976e+08
## 4936 4936 Coty 0.36 4.600e+09
## 4716 4716 Westcon Group 0.44 3.800e+09
## 3899 3899 Denihan Hospitality Group 0.71 2.808e+08
## 4363 4363 TransPerfect 0.55 3.413e+08
## 1498 1499 Sterling Infosystems 2.66 2.149e+08
## Industry Employees City State
## 4577 Business Products & Services 32000 Pittsford NY
## 4936 Consumer Products & Services 10000 New York NY
## 4716 IT Services 3000 Tarrytown NY
## 3899 Travel & Hospitality 2280 New York NY
## 4363 Business Products & Services 2218 New York NY
## 1498 Human Resources 2081 New York NYOmmitted data
There are two outliers on this data. I have omitted both of them. The most people employed number are in Business Products & Services which is 32000 and in Consumer Products & Services which is 10,000 followed by IT Services and the least people emplyed are in Government Services. If I include these outliers then I cannot get anything from the graph so I have not included those industry in this graph. It will only distort the graph.
#ggplot for the
NY <- ggplot(NY, aes(x=Industry, y=Employees)) +
geom_boxplot(width=0.7, fill="light grey", outlier.size = 7,outlier.colour="red")+
coord_flip(ylim = c(0,3200)) +
stat_summary(aes(shape = "mean"), fun.y = mean, geom="point", fill="blue",colour="blue", size=3) +
stat_summary(aes(shape = "median"), fun.y =median, geom="point",fill="darkgreen",colour="darkgreen", size=3)+
scale_colour_manual(values=c("stat_summary", "outlier"))+
ggtitle('Mean and Median employment by Industry for fastest growing companies in NY') +
theme(legend.position = "right",plot.title = element_text(size=14, face="bold"))
NY
## Not able to change the color of the legend, the red circle is the outlier.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
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrevenue_data <- inc[complete.cases(inc),]
revenue_data <- revenue_data%>% group_by(Industry)%>%
summarise(Revenue=sum(Revenue),Employees=sum(Employees))%>%
mutate(revenue_per_employee = Revenue / Employees)
head(revenue_data)## # A tibble: 6 x 4
## Industry Revenue Employees revenue_per_employee
## <fct> <dbl> <int> <dbl>
## 1 Advertising & Marketing 7785000000 39731 195943.
## 2 Business Products & Services 26345900000 117357 224494.
## 3 Computer Hardware 11885700000 9714 1223564.
## 4 Construction 13174300000 29099 452741.
## 5 Consumer Products & Services 14956400000 45464 328972.
## 6 Education 1139300000 7685 148250.#let's plot the data into the graph
employee_revenue <- ggplot(revenue_data, aes(x=reorder(Industry, revenue_per_employee),y=revenue_per_employee))+
geom_bar(fill = "grey", stat="identity") +
coord_flip() +
xlab("Industry") +
ylab("Revenue Per Employee") +
ggtitle("Revenue Per Employee for Industry") +
theme_tufte()
employee_revenue
# highest revenue per employee
max_revenue_per_employee = max(revenue_data$revenue_per_employee)
max_revenue_per_employee## [1] 1223564##The most revenue generated is in the computer hardware industry.
# lowest revenue per employee
min_revenue_per_employee = min(revenue_data$revenue_per_employee)
min_revenue_per_employee## [1] 40735.31