R Notebook
Dennis Pong
2021-02-14
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:
# install.packages("tidyr")
# loading libraries
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(tidyr)
library(ggplot2)
library(scales)
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:
# Insert your code here, create more chunks as necessary
inc %>%
select(Industry, Revenue, Growth_Rate) %>%
group_by(Industry) %>%
summarize(
Count = n(),
Percent = round(n()/nrow(.)*100,2),
Tot_Rev = sum(Revenue)
) %>%
arrange(desc(Tot_Rev)) ## # A tibble: 25 x 4
## Industry Count Percent Tot_Rev
## <chr> <int> <dbl> <dbl>
## 1 Business Products & Services 482 9.64 26367900000
## 2 IT Services 733 14.7 20681300000
## 3 Health 355 7.1 17863400000
## 4 Consumer Products & Services 203 4.06 14956400000
## 5 Logistics & Transportation 155 3.1 14840500000
## 6 Energy 109 2.18 13771600000
## 7 Construction 187 3.74 13174300000
## 8 Financial Services 260 5.2 13150900000
## 9 Food & Beverage 131 2.62 12911300000
## 10 Manufacturing 256 5.12 12684000000
## # … with 15 more rowsinc %>%
select(State) %>%
group_by(State) %>%
nrow(.)## [1] 5001Question 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
inc %>%
select(State) %>%
group_by(State) %>%
tally() %>%
ggplot() +
aes(x = reorder(State, n) , y = n, fill = n) +
labs(title = 'Distribution of 5K Fastest Growing Companies (Inc. 5000) by State') +
xlab('') +
ylab('') +
geom_col(position = 'dodge') +
geom_text( size = 3.1, hjust= - 0.08, aes(label = n)) +
coord_flip() +
theme(
legend.position = "none",
panel.background = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text.y = element_text(size = 6.8)
)
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.
sum(complete.cases(inc %>% filter(State == 'CA') ))## [1] 700# complete.cases is not very useful. So I decided not to use it.
inc %>%
filter(State == 'CA') %>%
drop_na() %>%
tally()## n
## 1 700Step 1:
- Removed outliers that has an absolute z-score that is over 0.18
Step 2:
- Visually hiding a few outliers remained that is above 390 to make the visuals more pleasing and less noisy.
Point: Preserved the full ranges of box-plots for Environmental Services
# Answer Question 2 here
# Step 1
CA_list <- inc %>%
filter(State == 'CA') %>%
filter(abs(scale(Employees)) <= 0.18)
# Step 2:
CA_list %>%
ggplot() +
ylim(0,390) +
aes(x = reorder(Industry, Employees, median, na.rm = T), y = Employees) + # na.rm = T removed missing values
geom_boxplot(color = 'NavyBlue', outlier.color = 'red', outlier.shape = 19, outlier.alpha = 0.51) +
labs(title = "Number of Empolyees by Industry", caption = "*Outliers above 390 employees were excluded") +
xlab('') +
ylab('') +
coord_flip() +
theme_minimal() +
theme(
panel.grid.major.x = element_line(color = "gray80", linetype = "dashed"),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
axis.ticks.x = element_line(color = "grey")
) ## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
Note that Environmental Services followed by Logistics and Transportation have the 2 highest median number of employees out of all industries in California.
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.
Step 1:
- Removed outliers that has an absolute z-score that is over 0.24
Step 2:
- Used ggplot2::geom_boxplot to display the distribution of Revenue per employee by industry and did some fine adjustments to the plot margins.
# Answer Question 3 here
# Step 1
CA_list1 <- inc %>%
filter(State == 'CA') %>%
filter(abs(scale(Revenue / Employees)) <= 0.24)
# Step 2
CA_list1 %>%
mutate(rev_per_emp = Revenue / Employees) %>%
ggplot() +
aes(x = reorder(Industry, rev_per_emp, median, na.rm = T), y = rev_per_emp) +
geom_boxplot(color = 'NavyBlue', outlier.color = 'red', outlier.shape = 19, outlier.alpha = 0.51) +
labs(title = "Revenue Per Employee by Industry") +
xlab('') +
ylab('') +
coord_flip() +
scale_y_continuous(labels=dollar_format()) +
theme_minimal() +
theme(
panel.grid.major.x = element_line(color = "gray80", linetype = "dashed"),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
axis.ticks.x = element_line(color = "grey"),
plot.margin=unit(c(.51,.85,.2,.2),"cm") # margin(t = 0, r = 0, b = 0, l = 0, unit = "pt")
) 