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:
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:
# summary() function tells us about quantative variables.
# dim() function tells us there are 5001 companies.
dim(inc)## [1] 5001 8# str() function tells us about qualitative variables. There are 25 different industries in over 1519 cities in 52 states.
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 ...Question 1
Create a graph that shows the distribution of companies in the dataset by State (i.e. 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 (i.e. taller than wide), which should further guide your layout choices.
# First I will load tidyverse and ggplot libraries.
library(tidyverse)
library(ggplot2)
# Then, I count the state, reorder the states by descending order and flip the coordinates for better readability.
inc %>%
count(State) %>%
ggplot(aes(x = reorder(State, n), y = n)) +
geom_bar(stat = "identity" ) +
coord_flip() +
labs(title = "Number of Companies by State", x= "States", y = "Count") +
theme_minimal()
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.
# From the bar chart above, we know NY has the 3rd most companies in the dataset. I will filter all the rows based on that.
# Next, I use the drop_na() function from dplyr to remove any rows with missing values.
# Then, I graph the median exmployment per industry in NY using geom_boxplot().
# However, due to some extreme outliers, the graph is no readible. I will remove the outliers for better picture of the graph.
inc %>%
filter(State == "NY") %>%
drop_na() %>%
ggplot(aes(x = reorder(Industry, Employees), y = Employees)) +
geom_boxplot(fill = "#9bc9a7", colour = "#594d8f",
outlier.colour = "#d10235", outlier.shape = 2) +
coord_flip() +
#ylim(NA, 1000) +
labs(title = "Median Employment per Industry in New York", x = "Industry") +
theme_minimal()
# The boxplot below tells us several things about the employment in different industries in NY.
# The red dots show the outliers. There are 9 industries with more than 1000 employees.
# The rectangular boxs show the interquantile range. Travel & Hospitality has the most disparity. There are few companies with large workforce.
# The lines on either side of the box show the minimum and maximum range. Travel & Hospitality has the largest range.
# The blue point in the box show the average employment. Travel & Hospitality has the highest.
# Finally, the line inside the box show the median employment per industry. Retail has the lowest median and enviromental services has the highest median employment.
inc %>%
filter(State == "NY") %>%
drop_na() %>%
ggplot(aes(x = reorder(Industry, Employees), y = Employees)) +
geom_boxplot(fill = "#9bc9a7", colour = "#594d8f", outlier.colour = "#d10235", outlier.shape = 2) +
stat_summary(fun.y = mean, color = "#0000ff", geom = "point", shape = 20, size = 3) +
coord_flip() +
labs(title = "Median Employment per Industry in New York", x = "Industry") +
ylim(NA, 1000) +
theme_minimal() 
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.
# Continuing from the previous question, I first select rows with companies based on NY and drop any rows with missing values.
# Next, I sum up total employees and total revenue.
# Then, I calculate revenue per employee.
# Then, I create a bar chart of all industries by most revenue per employee in descending order.
# Lastly, I flip the coordinate for better readability.
inc %>%
filter(State == "NY") %>%
drop_na() %>%
group_by(Industry) %>%
summarise(Employees = sum(Employees),
Revenue = sum(Revenue)) %>%
mutate(revenue_employee = Revenue/Employees) %>%
ggplot(aes(x = reorder(Industry, revenue_employee), y = revenue_employee)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(title = "Most Revenue per Employee in New York", x = "Industry", y = "Revenue per Employee") +
theme_minimal()