DATA608: HW#1
Elina Azrilyan
February 11, 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) %>% kable(caption = "Preview") %>% kable_styling("striped", full_width = TRUE)| Rank | Name | Growth_Rate | Revenue | Industry | Employees | City | State |
|---|---|---|---|---|---|---|---|
| 1 | Fuhu | 421.48 | 1.179e+08 | Consumer Products & Services | 104 | El Segundo | CA |
| 2 | FederalConference.com | 248.31 | 4.960e+07 | Government Services | 51 | Dumfries | VA |
| 3 | The HCI Group | 245.45 | 2.550e+07 | Health | 132 | Jacksonville | FL |
| 4 | Bridger | 233.08 | 1.900e+09 | Energy | 50 | Addison | TX |
| 5 | DataXu | 213.37 | 8.700e+07 | Advertising & Marketing | 220 | Boston | MA |
| 6 | MileStone Community Builders | 179.38 | 4.570e+07 | Real Estate | 63 | Austin | TX |
summary(inc) %>% kable(caption = "Summary") %>% kable_styling("striped", "condensed", full_width = TRUE, font_size = 12)
|
| Growth_Rate |
|
| Employees |
|
| |
|---|---|---|---|---|---|---|---|---|
| Min. : 1 | (Add)ventures : 1 | Min. : 0.340 | Min. :2.000e+06 | IT Services : 733 | Min. : 1.0 | New York : 160 | CA : 701 | |
| 1st Qu.:1252 | @Properties : 1 | 1st Qu.: 0.770 | 1st Qu.:5.100e+06 | Business Products & Services: 482 | 1st Qu.: 25.0 | Chicago : 90 | TX : 387 | |
| Median :2502 | 1-Stop Translation USA: 1 | Median : 1.420 | Median :1.090e+07 | Advertising & Marketing : 471 | Median : 53.0 | Austin : 88 | NY : 311 | |
| Mean :2502 | 110 Consulting : 1 | Mean : 4.612 | Mean :4.822e+07 | Health : 355 | Mean : 232.7 | Houston : 76 | VA : 283 | |
| 3rd Qu.:3751 | 11thStreetCoffee.com : 1 | 3rd Qu.: 3.290 | 3rd Qu.:2.860e+07 | Software : 342 | 3rd Qu.: 132.0 | San Francisco: 75 | FL : 282 | |
| Max. :5000 | 123 Exteriors : 1 | Max. :421.480 | Max. :1.010e+10 | Financial Services : 260 | Max. :66803.0 | Atlanta : 74 | IL : 273 | |
| NA | (Other) :4995 | NA | NA | (Other) :2358 | NA’s :12 | (Other) :4438 | (Other):2764 |
Think 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:
#Identifying top 10 Industries by Average Growth Rate
inc1 <- inc %>%
group_by(Industry) %>%
summarize(Growth_Rate = mean(Growth_Rate))
arrange(inc1, desc(Growth_Rate)) %>% top_n(10) %>% kable(caption = "Top 10 Industries by Average Growth Rate") %>% kable_styling("striped", full_width = FALSE, position = "center")## Selecting by Growth_Rate| Industry | Growth_Rate |
|---|---|
| Energy | 9.603303 |
| Consumer Products & Services | 8.776108 |
| Real Estate | 7.746667 |
| Government Services | 7.238168 |
| Advertising & Marketing | 6.225478 |
| Retail | 6.184729 |
| Financial Services | 5.435308 |
| Software | 5.020643 |
| Health | 4.856394 |
| Media | 4.374074 |
#Identifying top 10 Industries by Average Revenue
inc2 <- inc %>%
group_by(Industry) %>%
summarize(Revenue = mean(Revenue))
arrange(inc2, desc(Revenue)) %>% top_n(10) %>% kable(caption = "Top 10 Industries by Average Revenue") %>% kable_styling("striped", full_width = FALSE, position = "center")## Selecting by Revenue| Industry | Revenue |
|---|---|
| Computer Hardware | 270129545 |
| Energy | 126344954 |
| Food & Beverage | 98559542 |
| Logistics & Transportation | 95745161 |
| Consumer Products & Services | 73676847 |
| Construction | 70450802 |
| Telecommunications | 56855814 |
| Business Products & Services | 54705187 |
| Security | 52230137 |
| Environmental Services | 51741176 |
#Identifying top 10 States by Average Revenue
inc3 <- inc %>%
group_by(State) %>%
summarize(Revenue = mean(Revenue))
arrange(inc3, desc(Revenue)) %>% top_n(10) %>% kable(caption = "Top 10 States by Average Revenue") %>% kable_styling("striped", full_width = FALSE, position = "center")## Selecting by Revenue| State | Revenue |
|---|---|
| ID | 231523529 |
| AK | 171500000 |
| IA | 123142857 |
| IL | 121773993 |
| HI | 99485714 |
| WI | 92362025 |
| DC | 76344186 |
| OH | 68745161 |
| NC | 67580292 |
| MI | 61950794 |
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.
inc4 <- inc %>% count(State)
ggplot(inc4, aes(x=reorder(State, n), y = n)) +
geom_bar(stat = "identity", fill = 'light blue')+
labs(title="Company Count by State", x = "", y = "") +
coord_flip() +
geom_text(aes(label=n), size = 2, color = "black") +
theme(axis.text=element_text(size=5, face="bold"))
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.
Based on the above plot - NY is the state with the 3rd most companies in our dataset.
inc5 <- inc[complete.cases(inc),]
inc_NY <- subset(inc5,State=="NY")
dim(inc_NY)## [1] 311 8ggplot(inc_NY, aes(Industry, Employees)) +
geom_boxplot(outlier.shape = NA) +
stat_summary(fun.y = mean, color = "blue", geom = "point", shape = 15, size = 2) +
coord_cartesian(ylim=c(0, 1200)) +
labs(title="Number of NY Employees Per industry", x="", y="") +
theme(axis.text.x = element_text(angle = 90, size = 8, hjust = 1)) 
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.
inc5_rev <- inc5 %>%
group_by(Industry) %>%
summarise(SumEmployees = sum(Employees), SumRevenue = sum(Revenue)) %>%
mutate(revperemp = SumRevenue / SumEmployees)
inc5_rev <- arrange(inc5_rev, desc(revperemp))
ggplot(inc5_rev, aes(x=reorder(Industry, revperemp), y=revperemp)) +
geom_bar(stat = "identity", fill = 'light blue')+
labs(title="Revenue per Employee", x="", y="") +
coord_flip() +
geom_text(aes(label=round(revperemp,1)), size = 2, color="black") +
theme(axis.text=element_text(size=6, face="bold"))