DATA608_Homework1
LeTicia Cancel
2/13/2022
#libraries
library(dplyr)
library(ggplot2)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:
tail(inc)## Rank Name Growth_Rate Revenue Industry
## 4996 4996 cSubs 0.34 1.34e+07 Business Products & Services
## 4997 4997 Dot Foods 0.34 4.50e+09 Food & Beverage
## 4998 4998 Lethal Performance 0.34 6.80e+06 Retail
## 4999 4999 ArcaTech Systems 0.34 3.26e+07 Financial Services
## 5000 5000 INE 0.34 6.80e+06 IT Services
## 5001 5000 ALL4 0.34 4.70e+06 Environmental Services
## Employees City State
## 4996 19 Montvale NJ
## 4997 3919 Mt. Sterling IL
## 4998 8 Wellington FL
## 4999 63 Mebane NC
## 5000 35 Bellevue WA
## 5001 34 Kimberton PAsummary(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:
After looking over the summary data, I now want to dig into some max values and other data around max values. The easiest to explore is max number of Employees by Industry and the company within that industry. The top 3 industries based on the number of employees within 1 company is Human Resources, Business Products & Services, and Security. The top companies within these industries are Integrity Staffing Solutions, Sutherland Global Services, and Universal Services of America, respectively.
inc %>%
select(Name, Industry, Employees) %>%
filter(!is.na(Employees)) %>%
group_by(Industry) %>%
arrange(desc(Employees)) %>%
top_n(1)## Selecting by Employees## # A tibble: 25 x 3
## # Groups: Industry [25]
## Name Industry Employees
## <chr> <chr> <int>
## 1 Integrity staffing Solutions Human Resources 66803
## 2 Sutherland Global Services Business Products & Services 32000
## 3 Universal Services of America Security 20000
## 4 Sprouts Farmers Market Consumer Products & Services 13200
## 5 Genco Logistics & Transportation 10800
## 6 VXI Global Solutions Telecommunications 10000
## 7 Belcan Engineering 10000
## 8 KSS Manufacturing 8500
## 9 Bojangles' Famous Chicken 'n Biscuits Food & Beverage 7681
## 10 Collabera IT Services 7000
## # ... with 15 more rowsQuestion 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.
Since there are so many states to view, my approach to viewing this data was to split it by states with the highest number of companies and states with the least companies. I also
#get top 20 states
state_count_top <- inc %>%
count(State) %>%
arrange(desc(n)) %>%
slice(1:20)
state_count_top## State n
## 1 CA 701
## 2 TX 387
## 3 NY 311
## 4 VA 283
## 5 FL 282
## 6 IL 273
## 7 GA 212
## 8 OH 186
## 9 MA 182
## 10 PA 164
## 11 NJ 158
## 12 NC 137
## 13 CO 134
## 14 MD 131
## 15 WA 130
## 16 MI 126
## 17 AZ 100
## 18 UT 95
## 19 MN 88
## 20 TN 82ggplot(state_count_top, aes(x = reorder(State, n), y = n)) +
geom_col() +
labs(x = "State", y = "Company Count", title = "Top 20 States with Most Companies") +
theme_bw()
20 states with the least number of companies:
#get bottom 20 states
state_count_low <- inc %>%
count(State) %>%
arrange(n) %>%
slice(1:20)
state_count_low## State n
## 1 PR 1
## 2 AK 2
## 3 WV 2
## 4 WY 2
## 5 SD 3
## 6 MT 4
## 7 NM 5
## 8 VT 6
## 9 HI 7
## 10 AR 9
## 11 ND 10
## 12 MS 12
## 13 ME 13
## 14 DE 16
## 15 RI 16
## 16 ID 17
## 17 NH 24
## 18 NV 26
## 19 NE 27
## 20 IA 28ggplot(state_count_low, aes(x = reorder(State, n), y = n)) +
geom_col() +
labs(x = "State", y = "Company Count", title = "20 States with Least Companies") +
theme_bw()
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 %>%
count(State) %>%
arrange(desc(n)) %>%
slice(1:3)## State n
## 1 CA 701
## 2 TX 387
## 3 NY 311ny_inc <- inc %>%
filter(State == 'NY')
head(ny_inc)## Rank Name Growth_Rate Revenue
## 1 26 BeenVerified 84.43 13700000
## 2 30 Sailthru 73.22 8100000
## 3 37 YellowHammer 67.40 18000000
## 4 38 Conductor 67.02 7100000
## 5 48 Cinium Financial Services 53.65 5900000
## 6 70 33Across 44.99 27900000
## Industry Employees City State
## 1 Consumer Products & Services 17 New York NY
## 2 Advertising & Marketing 79 New York NY
## 3 Advertising & Marketing 27 New York NY
## 4 Advertising & Marketing 89 New York NY
## 5 Financial Services 32 Rock Hill NY
## 6 Advertising & Marketing 75 New York NYny_avg <- ny_inc %>%
group_by(Industry) %>%
summarise_at(vars(Employees), list(Avg = mean))
ny_med <- ny_inc %>%
group_by(Industry) %>%
summarise_at(vars(Employees), list(Median = median))
ny_summary <- cbind(ny_avg, ny_med$Median)
ggplot(ny_summary, aes(x = Industry, y = ny_med$Median)) +
geom_col() +
coord_flip() +
labs(y = "Median Employees", title = "Median Number of Employees by Industry in New York")
ggplot(ny_summary, aes(x = Industry, y = Avg)) +
geom_col() +
coord_flip() +
labs(y = "Average Employees", title = "Average Number of Employees by Industry in New York")
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_emp <- inc %>%
select(Industry, Employees, Revenue) %>%
filter(!is.na(Employees)) %>%
group_by(Industry) %>%
summarise(Emp = sum(Employees), Rev = sum(Revenue))
ggplot(rev_per_emp, aes(x = Emp, y = Rev, colour = Industry)) +
geom_point() +
labs(y = "Revenue", x = "Employee", title = "Revenue per Employee by Industry")