| title: “R Notebook - Module1: Exploratory Data Analysis” |
| author: “humbertohp” |
| date: “September 15, 2019” |
| output: |
| html_document: default |
| html_notebook: default |
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:
str(inc) # Data frame data types## 'data.frame': 5001 obs. of 8 variables:
## $ Rank : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Name : Factor w/ 5001 levels "(Add)ventures",..: 1770 1633 4423 690 1198 2839 4733 1468 1869 4968 ...
## $ 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 : Factor w/ 25 levels "Advertising & Marketing",..: 5 12 13 7 1 20 10 1 5 21 ...
## $ Employees : int 104 51 132 50 220 63 27 75 97 15 ...
## $ City : Factor w/ 1519 levels "Acton","Addison",..: 391 365 635 2 139 66 912 1179 131 1418 ...
## $ State : Factor w/ 52 levels "AK","AL","AR",..: 5 47 10 45 20 45 44 5 46 41 ...head(inc[order(-inc$Revenue),], 15) # Top Companies by Revenue## Rank Name Growth_Rate Revenue
## 4788 4788 CDW 0.41 1.01e+10
## 3853 3853 ABC Supply 0.73 4.70e+09
## 4936 4936 Coty 0.36 4.60e+09
## 4997 4997 Dot Foods 0.34 4.50e+09
## 4716 4716 Westcon Group 0.44 3.80e+09
## 4246 4246 American Tire Distributors 0.59 3.50e+09
## 4052 4052 Kum & Go 0.65 2.80e+09
## 4802 4802 Boise Cascade 0.41 2.80e+09
## 1396 1397 EnvisionRxOptions 2.88 2.70e+09
## 2521 2522 DLA Piper 1.41 2.40e+09
## 4629 4629 Prime Therapeutics 0.47 2.00e+09
## 4 4 Bridger 233.08 1.90e+09
## 1842 1843 Sun Coast Resources 2.08 1.90e+09
## 3844 3844 Atlas Oil Company 0.74 1.90e+09
## 4961 4961 Kirkland & Ellis 0.36 1.90e+09
## Industry Employees City State
## 4788 Computer Hardware 6800 Vernon Hills IL
## 3853 Construction 6549 Beloit WI
## 4936 Consumer Products & Services 10000 New York NY
## 4997 Food & Beverage 3919 Mt. Sterling IL
## 4716 IT Services 3000 Tarrytown NY
## 4246 Consumer Products & Services 3341 Huntersville NC
## 4052 Retail 4589 West Des Moines IA
## 4802 Construction 4470 Boise ID
## 1396 Health 625 Twinsburg OH
## 2521 Business Products & Services 4036 Chicago IL
## 4629 Health 2549 Eagan MN
## 4 Energy 50 Addison TX
## 1842 Energy 1640 Houston TX
## 3844 Logistics & Transportation 374 Taylor MI
## 4961 Business Products & Services 1517 Chicago ILtable(inc$Industry) # Number Companies by Industry##
## Advertising & Marketing Business Products & Services
## 471 482
## Computer Hardware Construction
## 44 187
## Consumer Products & Services Education
## 203 83
## Energy Engineering
## 109 74
## Environmental Services Financial Services
## 51 260
## Food & Beverage Government Services
## 131 202
## Health Human Resources
## 355 196
## Insurance IT Services
## 50 733
## Logistics & Transportation Manufacturing
## 155 256
## Media Real Estate
## 54 96
## Retail Security
## 203 73
## Software Telecommunications
## 342 129
## Travel & Hospitality
## 62Question 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.
library(ggplot2)
ggplot(inc, aes(x=State)) + geom_bar(fill="orange") + coord_flip()
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.
library(dplyr)
inc_state <- inc[complete.cases(inc),] %>% count(State) %>% arrange(desc(n))
head(inc_state,5)## # A tibble: 5 x 2
## State n
## <fct> <int>
## 1 CA 700
## 2 TX 386
## 3 NY 311
## 4 VA 283
## 5 FL 282## State with the 3rd most companies is NY
inc_ny_empl <- data.frame(filter(inc, State=="NY") %>% group_by(Industry) %>% summarise(avg = mean(Employees)))
ggplot(inc_ny_empl, aes(x=reorder(Industry, avg), y=avg)) + geom_bar(stat="identity", fill="blue") + coord_flip() +labs(y="Average Employment", x="Industry")
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.
inc_rev_empl <- inc[complete.cases(inc),] %>% mutate(Rev_Empl = Revenue/Employees)
inc_ind_rev_empl <- inc_rev_empl %>% group_by(Industry) %>% summarise(avg_rev_empl = mean(Rev_Empl))
ggplot(inc_ind_rev_empl, aes(x=reorder(Industry, avg_rev_empl), y=avg_rev_empl)) + geom_bar(stat="identity", fill="red") + coord_flip() +labs(y="Average Revenue per Employee", x="Industry")