DATA 608 Assignment 1
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:
Load Libraries
library(psych)
library(dplyr)
library(ggplot2)
library(dplyr)describe(inc)## vars n mean sd median trimmed
## Rank 1 5001 2501.64 1443.51 2.502e+03 2501.73
## Name* 2 5001 2501.00 1443.81 2.501e+03 2501.00
## Growth_Rate 3 5001 4.61 14.12 1.420e+00 2.14
## Revenue 4 5001 48222535.49 240542281.14 1.090e+07 17334966.26
## Industry* 5 5001 12.10 7.33 1.300e+01 12.05
## Employees 6 4989 232.72 1353.13 5.300e+01 81.78
## City* 7 5001 732.00 441.12 7.610e+02 731.74
## State* 8 5001 24.80 15.64 2.300e+01 24.44
## mad min max range skew kurtosis se
## Rank 1853.25 1.0e+00 5.0000e+03 4.9990e+03 0.00 -1.20 20.41
## Name* 1853.25 1.0e+00 5.0010e+03 5.0000e+03 0.00 -1.20 20.42
## Growth_Rate 1.22 3.4e-01 4.2148e+02 4.2114e+02 12.55 242.34 0.20
## Revenue 10674720.00 2.0e+06 1.0100e+10 1.0098e+10 22.17 722.66 3401441.44
## Industry* 8.90 1.0e+00 2.5000e+01 2.4000e+01 -0.10 -1.18 0.10
## Employees 53.37 1.0e+00 6.6803e+04 6.6802e+04 29.81 1268.67 19.16
## City* 604.90 1.0e+00 1.5190e+03 1.5180e+03 -0.04 -1.26 6.24
## State* 19.27 1.0e+00 5.2000e+01 5.1000e+01 0.12 -1.46 0.22Based on the summary and describe results, it appears that Growth Rate, Revenue, and Employees are highly skewed. Thus, this dataset includes a variety of different companies from startups to large corporations.
#Get value counts of each column
inc %>% summarise_all(n_distinct)## Rank Name Growth_Rate Revenue Industry Employees City State
## 1 4999 5001 1147 1069 25 692 1519 52Looking at the number of distinct values in each column, we see that there are 4999 Rank values but 5001 Names. This tells us that there are 2 cases where there are 2 Companies with the same rank. In addition, there are 52 unique State values, which means there are other areas included in the data besides the 50 U.S. states.
#Get value counts of Rank
count(inc, Rank, sort = TRUE)## # A tibble: 4,999 x 2
## Rank n
## <int> <int>
## 1 3424 2
## 2 5000 2
## 3 1 1
## 4 2 1
## 5 3 1
## 6 4 1
## 7 5 1
## 8 6 1
## 9 7 1
## 10 8 1
## # … with 4,989 more rows#subset data based on Rank value
inc[inc$Rank == '3424',]## Rank Name Growth_Rate Revenue Industry
## 3423 3424 Stemp Systems Group 19.37 6800000 IT Services
## 3424 3424 Total Beverage Solution 0.90 41500000 Food & Beverage
## Employees City State
## 3423 39 Long Island City NY
## 3424 35 Mt. Pleasant SCinc[inc$Rank == '5000',]## Rank Name Growth_Rate Revenue Industry Employees City
## 5000 5000 INE 0.34 6800000 IT Services 35 Bellevue
## 5001 5000 ALL4 0.34 4700000 Environmental Services 34 Kimberton
## State
## 5000 WA
## 5001 PA#Get unique State values
levels(inc$State)## [1] "AK" "AL" "AR" "AZ" "CA" "CO" "CT" "DC" "DE" "FL" "GA" "HI" "IA" "ID" "IL"
## [16] "IN" "KS" "KY" "LA" "MA" "MD" "ME" "MI" "MN" "MO" "MS" "MT" "NC" "ND" "NE"
## [31] "NH" "NJ" "NM" "NV" "NY" "OH" "OK" "OR" "PA" "PR" "RI" "SC" "SD" "TN" "TX"
## [46] "UT" "VA" "VT" "WA" "WI" "WV" "WY"Upon investigation, 2 companies were ranked 3424 and 2 companies were ranked 5000. The additional 2 “states” are DC (District of Columbia) and PR (Puerto Rico).
Question 1
Create a graph that shows the distribution of companies in the dataset by State (ie how many are in each state). There are a lot of States, so consider which axis you should use. This visualization is ultimately going to be consumed on a ‘portrait’ oriented screen (ie taller than wide), which should further guide your layout choices.
#Get number of Names in each State
company_by_state <- inc %>%
group_by(State) %>%
summarize(N_Companies=n_distinct(Name))
#Create barplot
ggplot(company_by_state, aes(x = reorder(State, N_Companies), y = N_Companies), fill=as.factor(N_Companies))+
geom_bar(stat = "identity") +
coord_flip() +
labs(title = "Companies by State", x = "State", y = "Number of Companies")
The 3 states with the largest number of companies are California, Texas, and New York.
Question 2
Lets dig in on the state with the 3rd most companies in the data set. Imagine you work for the state and are interested in how many people are employed by companies in different industries. Create a plot that shows the average and/or median employment by industry for companies in this state (only use cases with full data, use R’s complete.cases() function.) In addition to this, your graph should show how variable the ranges are, and you should deal with outliers.
#get complete cases
inc_complete <- inc[complete.cases(inc), ]
#filter for state with 3rd most companies (NY)
ny <- filter(inc_complete, State == "NY")
# Identify outliers
outliers <- boxplot(ny$Employees, plot = FALSE)$out
# Remove outliers
ny_no_ex <- ny[!(ny$Employees %in% outliers), ]
#Create boxplots
ggplot(ny_no_ex, aes(x=Industry, y=Employees)) +
geom_boxplot() +
labs(title = "Employees by Industry in New York", x = "Number of Employees", y = "Industry") +
coord_flip()
Solely looking at median, the Energy industry has the highest number of employees. Financial Services has the largest IQR while Government Services, Environmental Services, and Computer Hardware have the smallest IQR. Many of the box plots are positively skewed indicating that the data is right skewed.
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.
#Get revenue per employee by industry using complete NY data created from Question 2
revenue <- ny %>%
group_by(Industry) %>%
summarize(total_rev = sum(Revenue), total_emp = sum(Employees), rev_per_emp = total_rev/total_emp)
#Create barplot
ggplot(data = revenue, aes(x = reorder(Industry, rev_per_emp), y = rev_per_emp)) +
geom_bar(stat = "identity") +
labs(title = "Revenue per Employee by Industry In New York", x = "Industy", y = "Revenue per Employee") +
coord_flip()
The Energy industry generates the largest revenue per employee of $650,000 while the Security industry generates the smallest of about $57,000.