R Notebook
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(psych)
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)##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
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## %+%, alphainc <- 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:
# Insert your code here, create more chunks as necessary
#Check structure of data
str(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 ...#look at the bottom of the data
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 PA#Check for missing variables across all columns
colSums(is.na(inc))## Rank Name Growth_Rate Revenue Industry Employees
## 0 0 0 0 0 12
## City State
## 0 0#Employees column has missing values 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 0.22#count for distinct values of state
#Top 36 states have 100 or more companies
count_state <- dplyr::count(inc,State)
#count for distinct values of City
#count_city <- dplyr::count(inc, City)
#Decided against using distinct count for cities as 1519 rows were calcultated, not a useful summarization
#count for distinct values of industry
count_industry <- dplyr::count(inc, Industry)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.
# Answer Question 1 here
desc_cs <- count_state %>% arrange(desc(n))
#the multiple colors helps distinguish the many states presented in the graph
ggplot(desc_cs, aes(x=reorder(State, n),y=n, color=State)) +
geom_bar(stat='identity', width = 0.5, color = 'black', fill=rainbow(52)) +
coord_flip() +
labs(title = 'Company Distribution By State', x='', y='')+
scale_y_continuous(breaks = seq(0, 700, 100))+
theme_classic()
#source: https://stackoverflow.com/questions/29587881/increase-plot-size-width-in-ggplot2
ggsave(file="Distribution By State.png", width=10, height=5, dpi=300)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.
Based on the graphic and data from above, the state with the 3rd most companies is NY. So we will be digging into the employment of different industries within the state of NY.
# Answer Question 2 here
inc_complete <- inc[complete.cases(inc),]
ny_industry <- inc_complete %>%filter(State == 'NY')
#Seperated Business products and services, they had an outsized number that distorted the rest of the visuals
ny_industry_business <- ny_industry %>% filter(Industry == 'Business Products & Services')
nyi_no_business <- ny_industry %>% filter(Industry != 'Business Products & Services')
#Going to utilize boxplots to illustrate the range/average/median employment by industry
# source: https://www.quora.com/What-is-the-best-graph-to-illustrate-ranges-in-a-data-series?share=1
ggplot(nyi_no_business, aes(x = Industry, y=Employees)) +
coord_flip() +
geom_boxplot(fill="seagreen", outlier.color = "red", outlier.size = 1) +
ylim(0,3000)## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
#The outlier in for the Business Products and Services created a very flat boxplot, I played with minimizing the outlier, but minimizing the size didn't change the overall shape
ggplot(ny_industry_business, aes(x = Industry, y=Employees)) +
geom_boxplot(fill="seagreen", outlier.color = "red", outlier.size = 1) 
ggplot(ny_industry, aes(reorder(x=Industry, Employees), y = Employees)) +
stat_summary(fun = "mean", geom = "bar") +
coord_flip() +
labs(title = "Avg. Employees per Industry", y = "Average")+
theme_classic()## Warning: Ignoring unknown parameters: fun## No summary function supplied, defaulting to `mean_se()
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.
The revenue per employee here is shown for the national dataset.
# Answer Question 3 here
#Let's calculate a new field, revenue per employee
rev_per_employee <- inc_complete %>% group_by(Industry) %>% summarise(revenue=sum(Revenue), employees=sum(Employees), revenue_per_employee=revenue/employees)
ggplot(rev_per_employee, aes(x=reorder(Industry, revenue_per_employee),y=revenue_per_employee)) +
geom_bar(stat='identity', width = 0.5, color = 'black', fill='skyblue') +
coord_flip() +
labs(title = 'Revenue per Employee', x='', y='')+
theme_classic()
Sources: https://www.tutorialgateway.org/r-ggplot2-boxplot/
https://www.quora.com/What-is-the-best-graph-to-illustrate-ranges-in-a-data-series?share=1
https://stackoverflow.com/questions/29587881/increase-plot-size-width-in-ggplot2