R Notebook
Claire Meyer
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(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.4 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()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:
Answer: I noticed a few things. First and foremost, the big difference between mean and median # of employees was interesting. Not sure how I might non-visually explore, but definitely made me think I’ll see some variance in that metric. I was curious about how many “Industry” options there were, so I did a count summary. I’m also guessting rank is one per company, but I did a count to confirm there were no shared ranks. Looks there are are in fact two!
# Insert your code here, create more chunks as necessary
inc %>% group_by(Industry) %>% summarise(n_companies = n()) ## # A tibble: 25 × 2
## Industry n_companies
## <chr> <int>
## 1 Advertising & Marketing 471
## 2 Business Products & Services 482
## 3 Computer Hardware 44
## 4 Construction 187
## 5 Consumer Products & Services 203
## 6 Education 83
## 7 Energy 109
## 8 Engineering 74
## 9 Environmental Services 51
## 10 Financial Services 260
## # … with 15 more rowsinc %>% group_by(Rank) %>% summarise(n_companies = n()) %>% filter(n_companies > 1)## # A tibble: 2 × 2
## Rank n_companies
## <int> <int>
## 1 3424 2
## 2 5000 2Question 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: To better accommodate a ‘portrait’ mode, I flipped the coordinates and ordered by the number of companies.
# Answer Question 1 here
st_cts <- inc %>% group_by(State) %>% summarize(n_companies = n())
ggplot(st_cts, aes(x = reorder(State,n_companies), y = n_companies)) +
geom_col(width = 0.75) + coord_flip()
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.
Answer: Based on the plot above, the state with the 3rd most is NY. Initially, I wanted to do a boxplot as I think those show variance well. It was definitely impacted by outliers, though. I followed the guidance in R Cookbook to make outlier dots smaller and less visually loud, but that didn’t change the data itself or how tricky it was to decipher because of the scale of some outliers. So I recreated the chart excluding those entirely. I also plotted just the averages, to see things a bit more cleanly.
# Answer Question 2 here
ny_data <- inc %>% filter(State == 'NY') %>% filter(complete.cases(.))
ggplot(ny_data,aes(Industry,Employees)) + geom_boxplot(outlier.size = 1.5, outlier.shape = 21) + coord_flip()
# Version without outliers
ny_data_ex_outliers <- ny_data %>% filter(Employees < 2000)
ggplot(ny_data_ex_outliers,aes(Industry,Employees)) + geom_boxplot(outlier.size = 1.5, outlier.shape = 21) + coord_flip()
industry_means <- ny_data %>% group_by(Industry) %>% summarise(avg_emp = mean(Employees))
ggplot(industry_means, aes(x = reorder(Industry,avg_emp), y = avg_emp)) +
geom_col(width = 0.75) + coord_flip() 
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.
Answer: To do this, I did a similar set of charts as above - boxplots as well as summarized industry means. This data had a ton of outliers. I cleaned out a few, but it’s still tricky to read the chart as is.
# Answer Question 3 here
inc_rev <- inc %>% mutate(rev_per_emp = Revenue/Employees) %>% filter(complete.cases(.))
ggplot(inc_rev,aes(Industry,rev_per_emp)) + geom_boxplot(outlier.size = 1.5, outlier.shape = 21) + coord_flip()
# filter outliers
inc_rev <- inc %>% mutate(rev_per_emp = Revenue/Employees) %>% filter(complete.cases(.)) %>% filter(rev_per_emp <10000000)
ggplot(inc_rev,aes(Industry,rev_per_emp)) + geom_boxplot(outlier.size = 1.5, outlier.shape = 21) + coord_flip()
industry_rev_per_emp <- inc %>% filter(complete.cases(.)) %>% group_by(Industry) %>% summarise(tot_rev = sum(Revenue),tot_emp = sum(Employees)) %>% mutate(rev_per_emp = tot_rev/tot_emp)
ggplot(industry_rev_per_emp, aes(x = reorder(Industry,rev_per_emp), y = rev_per_emp)) +
geom_col(width = 0.75) + coord_flip() 