Data 608 - Assignment 1
Karim Hammoud
09/12/2021
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:
Here I want to use the disctibe function to get little more data about the inc data
library(psych)
# I will use the describe function here to create more calculation on the variables.
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 0.22Question 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(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.4 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x ggplot2::%+%() masks psych::%+%()
## x ggplot2::alpha() masks psych::alpha()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()#vigualizing the count per states
plot1 <- ggplot(inc, aes(x=State)) +geom_bar(color = 'skyblue') +
labs(title="Distribution of 5000 Fastest Growing Companies by State")+
theme(axis.text.x = element_text(angle = 90, size = 6))
plot1
#Reorder the states
d1 <- inc %>%
group_by(State) %>%
tally()
# here we create a plot where the states are sorted from top to bottom
ggplot(d1, aes(x=reorder(State, n), y=n)) +
geom_point(size=1) +
geom_segment(aes(x=State, xend=State, y=0, yend=n)) +
labs(title="Distribution of 5000 Fastest Growing Companies by State" , x = 'States', y = 'Count') +
theme(axis.text.x = element_text(angle = 90, size = 6))
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.
# First we sort the data to see the third state.
inc %>% count(State, sort = TRUE)## 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 82
## 21 WI 79
## 22 IN 69
## 23 MO 59
## 24 AL 51
## 25 CT 50
## 26 OR 49
## 27 SC 48
## 28 OK 46
## 29 DC 43
## 30 KY 40
## 31 KS 38
## 32 LA 37
## 33 IA 28
## 34 NE 27
## 35 NV 26
## 36 NH 24
## 37 ID 17
## 38 DE 16
## 39 RI 16
## 40 ME 13
## 41 MS 12
## 42 ND 10
## 43 AR 9
## 44 HI 7
## 45 VT 6
## 46 NM 5
## 47 MT 4
## 48 SD 3
## 49 AK 2
## 50 WV 2
## 51 WY 2
## 52 PR 1# After checking NY as the third state we start our analysis
NY <- inc %>% filter(State == "NY") %>%
arrange(desc(Employees))
# Get the total employees per industry
NY1 <- NY %>% count(Industry, wt=Employees, sort = TRUE)
NY1## Industry n
## 1 Business Products & Services 38804
## 2 Consumer Products & Services 10647
## 3 IT Services 8776
## 4 Human Resources 4813
## 5 Travel & Hospitality 3834
## 6 Advertising & Marketing 3331
## 7 Software 3197
## 8 Financial Services 1876
## 9 Telecommunications 1621
## 10 Media 1188
## 11 Health 1064
## 12 Manufacturing 953
## 13 Education 838
## 14 Food & Beverage 688
## 15 Energy 646
## 16 Security 540
## 17 Construction 366
## 18 Retail 347
## 19 Environmental Services 310
## 20 Engineering 214
## 21 Logistics & Transportation 118
## 22 Real Estate 73
## 23 Insurance 65
## 24 Computer Hardware 44
## 25 Government Services 17# get a plot with the median of all industries in NY
ggplot(NY1, aes(x=reorder(Industry, n, FUN=median), y=n)) +
geom_boxplot(outlier.shape = NA) +
labs(title="Median distribution of top industries in NY" , x = 'Industry', y = 'Median') +
theme_classic() +
coord_flip() #filp the variables to read more easly.
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.
# group by industry and get the sum of revenue and empoyees with the return or revenue per employee
data3 <- inc %>%
group_by(Industry) %>%
summarise(Total_revenue = sum(Revenue, na.rm = T), Total_employees = sum(Employees, na.rm = T)) %>%
mutate(Avg_rev_emp = (Total_revenue/Total_employees)/1000)
# plot the data
ggplot(data3, aes(x=reorder(Industry, Avg_rev_emp), y=Avg_rev_emp)) +
geom_bar(stat="identity", width=.5, fill="skyblue")+
labs(title="Revenue Per Employee",
subtitle="5000 Fastest Growing Companies by Industry",
y="Revenue per thousand",
x="Industry") +
theme_classic() +
coord_flip()
Conclusion:
Here we can see the top industry in the top 5000 growing companies in terms of revenue return per employee is Computer hardware while the least revenue per employee is HR.