608_Homework_One
Raymond Fries
2/6/2023
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:
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(psych)glimpse(inc)## Rows: 5,001
## Columns: 8
## $ Rank <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…
## $ Name <chr> "Fuhu", "FederalConference.com", "The HCI Group", "Bridger…
## $ Growth_Rate <dbl> 421.48, 248.31, 245.45, 233.08, 213.37, 179.38, 174.04, 17…
## $ Revenue <dbl> 1.179e+08, 4.960e+07, 2.550e+07, 1.900e+09, 8.700e+07, 4.5…
## $ Industry <chr> "Consumer Products & Services", "Government Services", "He…
## $ Employees <int> 104, 51, 132, 50, 220, 63, 27, 75, 97, 15, 149, 165, 250, …
## $ City <chr> "El Segundo", "Dumfries", "Jacksonville", "Addison", "Bost…
## $ State <chr> "CA", "VA", "FL", "TX", "MA", "TX", "TN", "CA", "UT", "RI"…Glimpse is good for seeing the number of columns and their names. It also shows each column type. Plus, it give how many rows there are.
describe(inc)## vars n mean sd median trimmed
## Rank 1 5001 2501.64 1443.51 2.502e+03 2501.73
## Name* 2 5001 2501.00 1443.81 2.501e+03 2501.00
## Growth_Rate 3 5001 4.61 14.12 1.420e+00 2.14
## Revenue 4 5001 48222535.49 240542281.14 1.090e+07 17334966.26
## Industry* 5 5001 12.10 7.33 1.300e+01 12.05
## Employees 6 4989 232.72 1353.13 5.300e+01 81.78
## City* 7 5001 732.00 441.12 7.610e+02 731.74
## State* 8 5001 24.80 15.64 2.300e+01 24.44
## mad min max range skew kurtosis se
## Rank 1853.25 1.0e+00 5.0000e+03 4.9990e+03 0.00 -1.20 20.41
## Name* 1853.25 1.0e+00 5.0010e+03 5.0000e+03 0.00 -1.20 20.42
## Growth_Rate 1.22 3.4e-01 4.2148e+02 4.2114e+02 12.55 242.34 0.20
## Revenue 10674720.00 2.0e+06 1.0100e+10 1.0098e+10 22.17 722.66 3401441.44
## Industry* 8.90 1.0e+00 2.5000e+01 2.4000e+01 -0.10 -1.18 0.10
## Employees 53.37 1.0e+00 6.6803e+04 6.6802e+04 29.81 1268.67 19.16
## City* 604.90 1.0e+00 1.5190e+03 1.5180e+03 -0.04 -1.26 6.24
## State* 19.27 1.0e+00 5.2000e+01 5.1000e+01 0.12 -1.46 0.22Descibe is like summary, with the difference that it is in table form and the descriptive stats are a little deeper with mad(median absolute deviation), skew, kurtosis and se(standard error).
colSums(is.na(inc))## Rank Name Growth_Rate Revenue Industry Employees
## 0 0 0 0 0 12
## City State
## 0 0One of the most important non visual exploratory questions that needs to be answered in every data set, is how many na’s there are.
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.
library(ggplot2)##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphacompanies_per_state <- inc %>%
group_by(State) %>%
count(State)
head(companies_per_state,5)## # A tibble: 5 × 2
## # Groups: State [5]
## State n
## <chr> <int>
## 1 AK 2
## 2 AL 51
## 3 AR 9
## 4 AZ 100
## 5 CA 701companies_per_state %>%
ggplot(aes(y=reorder(State, n), x=n)) +
geom_bar(stat="identity",color='white',fill='blue') +
ggtitle('Companies per State') +
xlab("Number of Companies") +
ylab("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.
companies_ny_state <- inc %>%
filter(State=='NY') %>%
filter(complete.cases(.))mean_employees_per_industry <- companies_ny_state %>%
group_by(Industry)%>%
summarise(mean_employees = mean(Employees))
mean_max <- max(mean_employees_per_industry$mean_employees)
companies_max <- max(companies_ny_state$Employees)ggplot(companies_ny_state, aes(x= reorder(Industry, Employees, mean), y=Employees)) +
geom_boxplot(width = 0.5, fill = "blue", color="black", alpha=0.2) +
coord_flip() +
geom_point(data = mean_employees_per_industry, aes(x=Industry, y=mean_employees, fill = "Mean Employees"), color="red", size = 1, show.legend = TRUE) +
scale_y_continuous(expand = c(0,0), limits = c(0, mean_max + 50)) +
ggtitle("Distribution of Employees per Industry (New York State)") +
ylab("Employees") +
xlab("Industry") +
theme_bw() +
theme(plot.title = element_text(size=12, face="bold", hjust = 0.5, color = "black"),
axis.text=element_text(size=10, face = "bold"),
axis.title=element_text(size=10,face="bold", color = "black"),
panel.background = element_blank(),
panel.border = element_blank(),
axis.line = element_line(color = "black",
size = 0.5, linetype = "solid"),
axis.ticks.y = element_blank(),
axis.ticks.x = element_line(color="black"),
legend.position = "top") 
Most companies employ less than 250 people as the bar charts show, but big companies skew the mean number of employee’s, especially in the top industries.
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.
rev_per_employee <- inc %>%
filter(complete.cases(.)) %>%
group_by(Industry) %>%
summarise(rev_total = sum(Revenue), emp_total = sum(Employees)) %>%
mutate(rev_per_emp = rev_total / emp_total) %>%
arrange(desc(rev_per_emp))
head(rev_per_employee,5)## # A tibble: 5 × 4
## Industry rev_total emp_total rev_per_emp
## <chr> <dbl> <int> <dbl>
## 1 Computer Hardware 11885700000 9714 1223564.
## 2 Energy 13771600000 26437 520921.
## 3 Construction 13174300000 29099 452741.
## 4 Logistics & Transportation 14837800000 39994 371001.
## 5 Consumer Products & Services 14956400000 45464 328972.ggplot(rev_per_employee, aes(x = reorder(Industry, rev_per_emp), y = rev_per_emp)) +
geom_bar(stat = "identity", fill = "blue") +
coord_flip() +
scale_y_continuous(expand = c(0, 0), limits = c(0, 1500000),breaks = c(0, 500000, 1000000), labels = scales::comma) +
ggtitle("Revenue per Employee by Industry ") +
ylab("Revenue per Employee")+
xlab("Industry")+
geom_hline(yintercept=seq(0,1500000,250000), col="white", lwd=1) +
geom_text(aes(label = scales::comma(round(rev_per_emp, 0))), vjust = 0.25, hjust = -0.2, fontface='bold', color="black") +
theme(plot.title = element_text(size=12, face="bold", color = "black"),
axis.text=element_text(size=10, face = "bold"),
axis.title=element_text(size=10,face="bold", color = "black"),
panel.background = element_blank(),
axis.line = element_line(color = "black",
size = 0.75, linetype = "solid"),
axis.ticks.y = element_blank(),
axis.ticks.x = element_line(color="black"))## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
If I were to advise the investor, then the top 3 industries by revenue generated per employee are, Computer Hardware, Energy and Construction. Computer Hardware generates the most revenue per employee at $1.2 million per employee. It generates 2.35 times more revenue per employee than Energy does and 2.7 time more revenue per employee than Construction does.