Data 608 Homework 1
Anthony Pagan
September 15, 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:
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:
Some of the summary information below include number of rows, type of data and list of factors
nrow(inc)## [1] 5001ncol(inc)## [1] 8str(inc)## '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 ...levels(inc$Industry)## [1] "Advertising & Marketing" "Business Products & Services"
## [3] "Computer Hardware" "Construction"
## [5] "Consumer Products & Services" "Education"
## [7] "Energy" "Engineering"
## [9] "Environmental Services" "Financial Services"
## [11] "Food & Beverage" "Government Services"
## [13] "Health" "Human Resources"
## [15] "Insurance" "IT Services"
## [17] "Logistics & Transportation" "Manufacturing"
## [19] "Media" "Real Estate"
## [21] "Retail" "Security"
## [23] "Software" "Telecommunications"
## [25] "Travel & Hospitality"levels(inc$State)## [1] "AK" "AL" "AR" "AZ" "CA" "CO" "CT" "DC" "DE" "FL" "GA" "HI" "IA" "ID"
## [15] "IL" "IN" "KS" "KY" "LA" "MA" "MD" "ME" "MI" "MN" "MO" "MS" "MT" "NC"
## [29] "ND" "NE" "NH" "NJ" "NM" "NV" "NY" "OH" "OK" "OR" "PA" "PR" "RI" "SC"
## [43] "SD" "TN" "TX" "UT" "VA" "VT" "WA" "WI" "WV" "WY"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.
The Chart below shows that CA has the maximum number businesses and PR has the minimum number businesses.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 15.25 48.50 96.17 131.75 701.00Quesiton 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.
The state with the 3rd highest number of busineses is New York. The inital blox plots helps easily point out industries that may be outliers in the data for New York. “Business Products and Services” appear the be an outlier for industries
This next box plot highlights the outliers and confirms the assumption that “Business Products and Services” is the largest outlier. The summary information follows the chart.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 17.00 53.50 81.85 198.05 155.00 1492.46The last chart removes the outliers. The summary information shows how removing the outliers change 1st QTR/mean/median/3rd QTR and max data points.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 17.00 44.00 73.31 87.97 129.20 245.92Question 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.
The barchart below orders the data by Revenue per Employee by Industry. The barchart shows Computer Hardware as the highest Industry for Revenue per Employee. The box plot shows Computer Hardware and Energy Industries are outliers. This tell us that the summarized data is higer then the Industry average. However it could be the result of higher revenue for the industry.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 40735 158687 224494 273715 286824 1223564