Module 1 By Brian Weinfeld
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. : 1 110 Consulting : 1 Min. : 0.340
## 1st Qu.:1252 11thStreetCoffee.com : 1 1st Qu.: 0.770
## Median :2502 123 Exteriors : 1 Median : 1.420
## Mean :2502 1st American Systems and Services: 1 Mean : 4.612
## 3rd Qu.:3751 1st Equity : 1 3rd Qu.: 3.290
## Max. :5000 1-Stop Translation USA : 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:
library(tidyverse)## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.0.0 ✔ purrr 0.2.5
## ✔ tibble 1.4.2 ✔ dplyr 0.7.6
## ✔ tidyr 0.8.1 ✔ stringr 1.3.1
## ✔ readr 1.1.1 ✔ forcats 0.3.0## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()#Summary of Employees by Industry
inc %>%
filter(complete.cases(.)) %>%
group_by(Industry) %>%
summarise(count = n(),
min = min(Employees),
avg = mean(Employees),
med = median(Employees),
max = max(Employees)) %>%
arrange(desc(med))## # A tibble: 25 x 6
## Industry count min avg med max
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Security 73 7 562. 77 20000
## 2 Human Resources 196 4 1158. 71.5 66803
## 3 Government Services 202 6 130. 71 1352
## 4 Health 354 2 233. 71 4390
## 5 Energy 109 2 243. 70 2501
## 6 Financial Services 260 5 183. 70 1829
## 7 Software 341 1 150. 69 3000
## 8 Environmental Services 51 4 199. 67 5347
## 9 Food & Beverage 129 3 511. 65 7681
## 10 Telecommunications 127 6 243. 65 10000
## # ... with 15 more rows#Number of Unique Cities Appears by State
inc %>%
group_by(State) %>%
summarise(Cities = n_distinct(City)) %>%
arrange(desc(Cities))## # A tibble: 52 x 2
## State Cities
## <fct> <int>
## 1 CA 204
## 2 IL 104
## 3 FL 102
## 4 NJ 97
## 5 NY 90
## 6 PA 80
## 7 OH 79
## 8 MA 72
## 9 TX 67
## 10 VA 56
## # ... with 42 more rows#Summary of Growth Rate by Industry
inc %>%
filter(complete.cases(.)) %>%
group_by(Industry) %>%
summarise(count = n(),
min = min(Growth_Rate),
avg = mean(Growth_Rate),
med = median(Growth_Rate),
max = max(Growth_Rate)) %>%
arrange(desc(med))## # A tibble: 25 x 6
## Industry count min avg med max
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Government Services 202 0.35 7.24 2.11 248.
## 2 Energy 109 0.35 9.60 2.08 233.
## 3 Real Estate 95 0.35 7.82 2.08 179.
## 4 Media 54 0.41 4.37 1.94 23.0
## 5 Consumer Products & Services 203 0.35 8.78 1.82 421.
## 6 Retail 203 0.34 6.18 1.76 167.
## 7 Software 341 0.35 5.03 1.7 129.
## 8 Advertising & Marketing 471 0.35 6.23 1.61 213.
## 9 Health 354 0.35 4.87 1.57 245.
## 10 Security 73 0.37 3.39 1.54 31.2
## # ... with 15 more rows#Correlation between Number of Employees and Growth Rate
temp <- inc %>%
filter(complete.cases(.))
cor(temp$Employees, temp$Growth_Rate)## [1] -0.0178689Question 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.
inc %>%
group_by(State) %>%
count() %>%
ggplot(aes(reorder(State, n), n)) +
geom_bar(aes(fill=n), stat='identity', show.legend = FALSE) +
geom_text(aes(y = n + 15, label=n)) +
coord_flip() +
scale_fill_continuous(low='blue', high='red') +
scale_y_continuous(expand = c(0, 0, .02, .02), labels=seq(0, 800, 50), breaks = seq(0, 800, 50)) +
theme_bw() +
theme(panel.grid.major.y = element_blank(),
axis.text.y = element_text(size=17, family='mono'),
axis.text.x = element_text(size=14),
axis.title = element_text(size=20),
plot.title = element_text(size=25, face='bold'),
plot.subtitle = element_text(size=12)) +
labs(title = 'Fastest Growing Companies By State',
subtitle = 'By Brian Weinfeld',
y = 'Number of Companies',
x = 'State')
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.
inc %>%
filter(complete.cases(.),
State == 'NY') %>%
ggplot(aes(reorder(Industry, Employees, FUN=median), Employees)) +
geom_boxplot() +
scale_y_log10(labels = scales::comma) +
scale_x_discrete(expand = c(0.05, 0.05, 0.05, 0.05)) +
annotation_logticks(sides = 'l') +
theme_bw() +
theme(axis.text.x = element_text(angle = -30, vjust = 1, hjust = 0),
panel.grid.minor.y = element_blank(),
plot.margin = margin(0, 75, 0, 0)) +
labs(title = 'Distribution of Employees by Industry in New York',
subtitle = 'By Brian Weinfeld',
x = 'Industry')
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.
inc %>%
filter(!is.na(Revenue),
!is.na(Employees)) %>%
transform(Per = Revenue / Employees) %>%
ggplot(aes(reorder(Industry, Per, FUN=median), Per)) +
geom_boxplot() +
scale_y_log10(labels = scales::comma) +
scale_x_discrete(expand = c(.05, .05, .05, .05)) +
annotation_logticks(sides='l') +
theme_bw() +
theme(axis.text.x = element_text(angle = -30, vjust = 1, hjust = 0),
panel.grid.minor.y = element_blank(),
plot.margin = margin(0, 100, 0, 0)) +
labs(title = 'Revenue Per Employee By Industry',
subtitle = 'By Brian Weinfeld',
x = 'Industry',
y = 'Revenue / Employee')