library(tidyverse)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:
The default summary function does not provide the standard deviation of the numeric variables. The standard deviation can be leveraged to understand the spread of the numeric data.
# Insert your code here, create more chunks as necessary
# Standard Deviations
inc %>% summarise(across(
.cols = is.numeric,
.fns = list(SD = sd), na.rm = TRUE,
.names = "{col}_{fn}"
))## Rank_SD Growth_Rate_SD Revenue_SD Employees_SD
## 1 1443.506 14.12369 240542281 1353.128The categorical variables of Name,
Industry, City, State were stored
as a character data type rather than a factor data type. The most
frequent factor levels of each variable are displayed below.
inc$Name <- as.factor(inc$Name)
inc$Industry <- as.factor(inc$Industry)
inc$City <- as.factor(inc$City)
inc$State <- as.factor(inc$State)get_most_least_freq <- function(variable){
top <- inc %>%
count({{variable}}) %>%
arrange(desc(n)) %>%
slice_head(n = 5)
bottom <- inc %>%
count({{variable}}) %>%
arrange(desc(n)) %>%
slice_tail(n = 5)
return(rbind(top, bottom))
}get_most_least_freq(Name)## Name n
## 1 (Add)ventures 1
## 2 @Properties 1
## 3 1-Stop Translation USA 1
## 4 110 Consulting 1
## 5 11thStreetCoffee.com 1
## 6 Zoup! 1
## 7 ZT Wealth and Altus Group of Companies 1
## 8 Zumasys 1
## 9 Zurple 1
## 10 ZweigWhite 1get_most_least_freq(Industry)## Industry n
## 1 IT Services 733
## 2 Business Products & Services 482
## 3 Advertising & Marketing 471
## 4 Health 355
## 5 Software 342
## 6 Travel & Hospitality 62
## 7 Media 54
## 8 Environmental Services 51
## 9 Insurance 50
## 10 Computer Hardware 44get_most_least_freq(City)## City n
## 1 New York 160
## 2 Chicago 90
## 3 Austin 88
## 4 Houston 76
## 5 San Francisco 75
## 6 Woodland Hills 1
## 7 Woodville 1
## 8 Wyomissing 1
## 9 Yonkers 1
## 10 Zumbrota 1get_most_least_freq(State)## State n
## 1 CA 701
## 2 TX 387
## 3 NY 311
## 4 VA 283
## 5 FL 282
## 6 SD 3
## 7 AK 2
## 8 WV 2
## 9 WY 2
## 10 PR 1After exploring the data summary information it is clear that the
Growth_Rate column is heavily skewed as the mean is 4.612,
median is 1.420, and the sd is 14.12369. There also appears to be
outliers as the 3rd quartile value is 3.290 and the max is 421.480.
The variable Employee also has a large amount of
variance. Most of the companies are relatively small in size 75% of the
companies had less than 132 employees with some just having a singular
worker. This data also contains much larger companies as the max value
is 66,803 employees. It would be interesting to investigate whether
employee size affects the growth rate of a company.
A large portion of the companies reside in large commercial cities and states. IT Services is the most popular industry by far in this dataset.
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
# Order the states in descending order
ordered.states <- inc %>% count(State)
# Create plot
ggplot(ordered.states, aes(x = reorder(State, n), y = n)) +
geom_bar(stat = "identity", width = 0.475, position = "dodge", fill = "#3B7696") +
ylim(0, 725) +
scale_y_continuous(breaks = (seq(0, 700, by = 100))) +
coord_flip() +
ylab("Number of Companies") +
xlab("State") +
ggtitle("Companies per State") +
theme_minimal()## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
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.
Using Barplots
# Answer Question 2 here
# Get data for the state with the 3r most companies (NY)
q2.data.barplot <- inc %>%
filter(State == "NY") %>%
filter(complete.cases(.)) %>%
group_by(Industry) %>%
summarise(Average = mean(Employees), Median = median(Employees)) %>%
gather("Measurement", "value", 2:3)
# Create Plot
q2.data.barplot %>% ggplot(aes(x = Industry, y = value)) +
geom_bar(stat = "identity", position = position_dodge(), aes(fill=Measurement)) +
coord_flip() +
xlab("Industry") +
ylab("Number of Employees") +
ggtitle("NY State: Employment by Industry") +
scale_fill_manual(values = c("#054C70","#05C3DE")) +
theme_minimal()
Using Boxplots
With all Outliers
q2.data <- inc %>%
filter(State == "NY") %>%
filter(complete.cases(.))
# Create plot
q2.data %>% ggplot(aes(x = Employees, y = Industry)) +
geom_boxplot() +
stat_summary(fun = "mean", size = 2, geom = "point", aes(color = "Mean")) +
stat_summary(fun = "median", size = 2, geom = "point", aes(color = "Median")) +
ggtitle("NY State: Employment by Industry") +
xlab("Number of Employees") +
theme_minimal()
Excluded Outliers
q2.data %>% ggplot(aes(x = Industry, y = Employees)) +
geom_boxplot(outlier.shape = NA) +
coord_flip(ylim = c(0, 1500)) +
stat_summary(fun = "mean", size = 2, geom = "point", aes(color = "Mean")) +
stat_summary(fun = "median", size = 2, geom = "point", aes(color = "Median")) +
ggtitle("NY State: Employment by Industry") +
ylab("Number of Employees") +
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.
# Answer Question 3 here
# remove scientific notation
options(scipen = 5)
# Generate the revenue per employee
revenue.data <- inc %>%
filter(complete.cases(.)) %>%
group_by(Industry) %>%
summarise(total_revenue = sum(Revenue), total_employees = sum(Employees), revenue_per_employee = (total_revenue / total_employees))
# Create plot
revenue.data %>% ggplot(aes(x = revenue_per_employee, y = reorder(Industry, revenue_per_employee))) +
geom_bar(stat = "identity", width = 0.475, position = "dodge", fill = "#04354F") +
xlab("Revenue per Employee (in $)") +
ylab("Industry") +
xlim(0, 1250000) +
ggtitle("Revenue per Employee by Industry") +
theme_minimal()