Data 608 Homework 1
Rathish Sasidharan
knitr::opts_chunk$set(warning = FALSE) 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(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, unionlibrary(ggplot2)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:
Dimension of the data
dim(inc)## [1] 5001 8Analysis of companies per state
inc %>% count(State,sort =TRUE,name='NoOfCompanies' )## State NoOfCompanies
## 1 CA 701
## 2 TX 387
## 3 NY 311
## 4 VA 283
## 5 FL 282
## 6 IL 273
## 7 GA 212
## 8 OH 186
## 9 MA 182
## 10 PA 164
## 11 NJ 158
## 12 NC 137
## 13 CO 134
## 14 MD 131
## 15 WA 130
## 16 MI 126
## 17 AZ 100
## 18 UT 95
## 19 MN 88
## 20 TN 82
## 21 WI 79
## 22 IN 69
## 23 MO 59
## 24 AL 51
## 25 CT 50
## 26 OR 49
## 27 SC 48
## 28 OK 46
## 29 DC 43
## 30 KY 40
## 31 KS 38
## 32 LA 37
## 33 IA 28
## 34 NE 27
## 35 NV 26
## 36 NH 24
## 37 ID 17
## 38 DE 16
## 39 RI 16
## 40 ME 13
## 41 MS 12
## 42 ND 10
## 43 AR 9
## 44 HI 7
## 45 VT 6
## 46 NM 5
## 47 MT 4
## 48 SD 3
## 49 AK 2
## 50 WV 2
## 51 WY 2
## 52 PR 1We could see that California state has more companies , followed by Texas and New York
Analyze the number of companies per industry
inc %>% count(Industry,sort =TRUE,name='CompaniesPerIndustry' )## Industry CompaniesPerIndustry
## 1 IT Services 733
## 2 Business Products & Services 482
## 3 Advertising & Marketing 471
## 4 Health 355
## 5 Software 342
## 6 Financial Services 260
## 7 Manufacturing 256
## 8 Consumer Products & Services 203
## 9 Retail 203
## 10 Government Services 202
## 11 Human Resources 196
## 12 Construction 187
## 13 Logistics & Transportation 155
## 14 Food & Beverage 131
## 15 Telecommunications 129
## 16 Energy 109
## 17 Real Estate 96
## 18 Education 83
## 19 Engineering 74
## 20 Security 73
## 21 Travel & Hospitality 62
## 22 Media 54
## 23 Environmental Services 51
## 24 Insurance 50
## 25 Computer Hardware 44We could see more industries are on IT Services
Top 5 Companies with Highest Growth Rate
inc %>% arrange(desc(Growth_Rate)) %>% head(n = 5)%>%select(Rank,Name,Growth_Rate,Industry)## Rank Name Growth_Rate Industry
## 1 1 Fuhu 421.48 Consumer Products & Services
## 2 2 FederalConference.com 248.31 Government Services
## 3 3 The HCI Group 245.45 Health
## 4 4 Bridger 233.08 Energy
## 5 5 DataXu 213.37 Advertising & MarketingTop 5 Companies with Lowest Growth Rate
inc %>% arrange(desc(Growth_Rate)) %>% tail(n = 5)%>%select(Rank,Name,Growth_Rate,Industry)## Rank Name Growth_Rate Industry
## 4997 4997 Dot Foods 0.34 Food & Beverage
## 4998 4998 Lethal Performance 0.34 Retail
## 4999 4999 ArcaTech Systems 0.34 Financial Services
## 5000 5000 INE 0.34 IT Services
## 5001 5000 ALL4 0.34 Environmental ServicesQuestion 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 %>% count(State,sort=TRUE) %>% ggplot(aes(x = n,y= reorder(State,n), fill = n)) + geom_col(width = .9)+
labs(x= "Number of companies", y = "States")
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
#clean up the data using complete.cases
#then find and filter the Rank 3 state
inc_rank3 <-inc %>%filter(complete.cases(.)) %>% add_count(State,name='TotalCompanies')%>%mutate(StateRank=dense_rank(desc(TotalCompanies)))%>% filter(StateRank == 3)
#use box plot ,color the outliers
ggplot(inc_rank3, aes(x=Employees, y=reorder(Industry, Employees, median, order = TRUE)))+
geom_boxplot(fill="yellow", alpha=0.2,outlier.color = "red", outlier.fill = "red", outlier.size = 2)+
scale_x_log10()+
labs(title = "Employees by Industry", x= "Employees", y = "Industries") 
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
# Which Industries generate the most revenue per employee?
#clean up the data
#find revenue per employee and then plot the data
inc %>%
filter(complete.cases(.))%>%
group_by(Industry) %>%
summarise(TotEmployees = sum(Employees), TotRevenue = sum(Revenue)) %>%
mutate( revPerEmp = TotRevenue / TotEmployees) %>%
ggplot(aes(x = reorder(Industry, revPerEmp), y = revPerEmp)) +
geom_bar(stat = "identity", fill = "steelblue") +
coord_flip() +
labs(title = "Revenue per Employee by Industry", x= "Industry", y = "Revenue Per Employee") ## `summarise()` ungrouping output (override with `.groups` argument)
From the chart we could see that “Computer Hardware” generate more revenue per employee