DATA 608 - Homework 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 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:
# Insert your code here, create more chunks as necessary
tail(inc)## Rank Name Growth_Rate Revenue Industry
## 4996 4996 cSubs 0.34 1.34e+07 Business Products & Services
## 4997 4997 Dot Foods 0.34 4.50e+09 Food & Beverage
## 4998 4998 Lethal Performance 0.34 6.80e+06 Retail
## 4999 4999 ArcaTech Systems 0.34 3.26e+07 Financial Services
## 5000 5000 INE 0.34 6.80e+06 IT Services
## 5001 5000 ALL4 0.34 4.70e+06 Environmental Services
## Employees City State
## 4996 19 Montvale NJ
## 4997 3919 Mt. Sterling IL
## 4998 8 Wellington FL
## 4999 63 Mebane NC
## 5000 35 Bellevue WA
## 5001 34 Kimberton PAlibrary(tidyverse)## Warning: package 'tidyverse' was built under R version 4.0.5## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.1 v dplyr 1.0.5
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.0.5## Warning: package 'tidyr' was built under R version 4.0.5## Warning: package 'readr' was built under R version 4.0.5## Warning: package 'purrr' was built under R version 4.0.5## Warning: package 'dplyr' was built under R version 4.0.5## Warning: package 'forcats' was built under R version 4.0.5## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()glimpse(inc)## Rows: 5,001
## Columns: 8
## $ Rank <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,~
## $ Name <chr> "Fuhu", "FederalConference.com", "The HCI Group", "Bridger~
## $ Growth_Rate <dbl> 421.48, 248.31, 245.45, 233.08, 213.37, 179.38, 174.04, 17~
## $ Revenue <dbl> 1.179e+08, 4.960e+07, 2.550e+07, 1.900e+09, 8.700e+07, 4.5~
## $ Industry <chr> "Consumer Products & Services", "Government Services", "He~
## $ Employees <int> 104, 51, 132, 50, 220, 63, 27, 75, 97, 15, 149, 165, 250, ~
## $ City <chr> "El Segundo", "Dumfries", "Jacksonville", "Addison", "Bost~
## $ State <chr> "CA", "VA", "FL", "TX", "MA", "TX", "TN", "CA", "UT", "RI"~inc %>%
mutate(across(where(is_character), factor)) %>%
select(where(is.factor)) %>%
summary()## Name Industry
## (Add)ventures : 1 IT Services : 733
## @Properties : 1 Business Products & Services: 482
## 1-Stop Translation USA: 1 Advertising & Marketing : 471
## 110 Consulting : 1 Health : 355
## 11thStreetCoffee.com : 1 Software : 342
## 123 Exteriors : 1 Financial Services : 260
## (Other) :4995 (Other) :2358
## 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):2764Question 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.
by_state <- inc %>%
group_by(State) %>%
summarize(Count = n())
ggplot(data = by_state, aes(x = reorder(State, Count), y = Count)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
labs(title = "Number of Companies by State", x = "State", y = "No. of Companies")
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.
#NY is the third most company as per our earlier analysis
employment <- inc %>%
filter(State == "NY") %>%
filter(complete.cases(.))
ggplot(employment, aes(x = Industry, y = Employees)) +
geom_boxplot() +
coord_flip() +
labs(title = "Employments by Industry in NY", x = "Industries", y = "No. of Employees")
ggplot(employment, aes(x = Industry, y = Employees)) +
geom_boxplot() +
labs(title = "Employments by Industry in NY", x = "Industries", y = "No. of Employees") +
coord_cartesian(ylim = c(0, 1500)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
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.
revenue_per_employee <- inc %>%
group_by(Industry) %>%
summarize(TotalRev = sum(Revenue), TotalEmp = sum(Employees), RevPerEmp = TotalRev/TotalEmp) %>%
arrange(desc(RevPerEmp)) %>%
na.omit()
ggplot(data = revenue_per_employee, aes(x = reorder(Industry, RevPerEmp), y = RevPerEmp)) +
geom_bar(stat = "identity") +
labs(title = "Industry wise - Revenue per Employee", x = "Industries", y = "Revenue per Employee") +
coord_flip()