Data 608 Module 1
Aaron Zalki
09/06/2020
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:
#github path
inc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE)And lets preview this data:
## 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 TX## 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:
Correlation charts from the corrgram library help show if the variables are related to one another. It does not seem that they have a simple linear relationship. There seems to be a relationship between Employees and Revenue, which we can look at below.
# Insert your code here, create more chunks as necessary
corrgram(inc, order=TRUE, lower.panel=panel.ellipse,
upper.panel=panel.pts, text.panel=panel.txt,
diag.panel=panel.minmax)
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.
# Answer Question 1 here
state = inc %>%
#group data by state
group_by(State) %>%
#provide counts
count(State)%>%
#sort from highest to lowest
arrange(desc(n))
#top 6 states
head(state)## # A tibble: 6 x 2
## # Groups: State [6]
## State n
## <chr> <int>
## 1 CA 701
## 2 TX 387
## 3 NY 311
## 4 VA 283
## 5 FL 282
## 6 IL 273states_plot <- ggplot(state, aes(x=reorder(State, n), y=n, fill=n))
states_plot + geom_bar(stat="identity", width=0.4, position = position_dodge(width=0.5)) +
coord_flip() +
#label axis
labs(x = "State", y = "Number of Companies")
Question 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.
# Answer Question 2 here
#full data
inc <- inc[complete.cases(inc),]
#filter for New York, since this is the third highest
ny = inc %>%
filter(State == "NY")
#plot data to show the average employment by industry for companies in New York
ny_plot <- ggplot(ny, aes(reorder(Industry,Employees,mean), Employees))
ny_plot <- ny_plot + geom_boxplot() + coord_flip() +
labs(x = "Industry", y = "Number of Employees")
ny_plot
Remove Outliers
## Industry Employees
## 1 Business Products & Services 32000
## 2 Consumer Products & Services 10000
## 3 IT Services 3000
## 4 Travel & Hospitality 2280
## 5 Business Products & Services 2218
## 6 Human Resources 2081Let’s filter the data to only show employees less than or equal to 3000. We can see above there are high values (10000 and 32000)
ny_filtered = ny %>%
#our filter criteria
filter(Employees <= 3000)
#plot data without outliers
ny_plot2 <- ggplot(ny_filtered, aes(reorder(Industry,Employees,mean), Employees))
ny_plot2 <- ny_plot2 + geom_boxplot() + coord_flip() +
labs(x = "Industry", y = "Number of Employees")
ny_plot2
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.
# Answer Question 3 here
#full data
inc <- inc[complete.cases(inc),]
industry = inc %>%
#group data by industry
group_by(Industry) %>%
#aggregate revenue and employee data
summarise(Revenue=sum(Revenue), Employees=sum(Employees)) %>%
#divide values to find revenue per employee
mutate(per_employee = Revenue/Employees)
#plot data to show which industries generate the most revenue per employee
revenue_plot <- ggplot(industry, aes(x=reorder(Industry, per_employee),
y=per_employee, fill=per_employee))
revenue_plot + geom_bar(stat="identity") + coord_flip() +
labs(x = "Industry", y = "Revenue per Employee")
revenue_data = inc %>%
mutate(per_employee = Revenue/Employees)
mean_plot <- ggplot(revenue_data, aes(reorder(Industry,per_employee,mean), per_employee))
mean_plot <- mean_plot + geom_boxplot() + coord_flip() +
labs(x = "Industry", y = "Revenue per Employee")
mean_plot
