DATA608-Module1
IvanTikhonov
2023-01-28
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:
library(ggplot2)
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(corrgram)inc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA608/master/lecture1/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:
# Insert your code here, create more chunks as necessary
summary(lm(Employees ~ Revenue, data = inc))##
## Call:
## lm(formula = Employees ~ Revenue, data = inc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9130 -148 -128 -74 66211
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.574e+02 1.877e+01 8.384 <2e-16 ***
## Revenue 1.562e-06 7.643e-08 20.432 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1300 on 4987 degrees of freedom
## (12 observations deleted due to missingness)
## Multiple R-squared: 0.07725, Adjusted R-squared: 0.07706
## F-statistic: 417.5 on 1 and 4987 DF, p-value: < 2.2e-16Question 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_by(State) %>%
count(State)%>%
arrange(desc(n))
head(state)## # A tibble: 6 × 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 273f <- ggplot(state, aes(x=reorder(State, n), y=n, fill=n))
f + geom_bar(stat="identity", width=0.4, position = position_dodge(width=0.5)) + coord_flip() + labs(x = "State", y = "Number of Fastest Growing Companies")
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.
# Answer Question 2 here
inc <- inc[complete.cases(inc),]
ny = inc %>%
filter(State == "NY")
g <- ggplot(ny, aes(reorder(Industry,Employees,mean), Employees))
g <- g + geom_boxplot() + coord_flip() + labs(x = "Industry", y = "Employees")
g
g + scale_y_log10()
Removing Outliers
c <- ggplot(ny, aes(Employees))
c + geom_density(kernel = "gaussian") 
head(ny %>% arrange(desc(Employees)))## Rank Name Growth_Rate Revenue
## 1 4577 Sutherland Global Services 0.48 5.976e+08
## 2 4936 Coty 0.36 4.600e+09
## 3 4716 Westcon Group 0.44 3.800e+09
## 4 3899 Denihan Hospitality Group 0.71 2.808e+08
## 5 4363 TransPerfect 0.55 3.413e+08
## 6 1499 Sterling Infosystems 2.66 2.149e+08
## Industry Employees City State
## 1 Business Products & Services 32000 Pittsford NY
## 2 Consumer Products & Services 10000 New York NY
## 3 IT Services 3000 Tarrytown NY
## 4 Travel & Hospitality 2280 New York NY
## 5 Business Products & Services 2218 New York NY
## 6 Human Resources 2081 New York NYny_no_outliers = ny %>%
filter(Employees <= 3000)
g <- ggplot(ny_no_outliers, aes(reorder(Industry,Employees,mean), Employees))
g <- g + geom_boxplot() + coord_flip() + labs(x = "Industry", y = "Employees")
g
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
inc <- inc[complete.cases(inc),]
industry_emp = inc %>%
group_by(Industry) %>%
summarise(Revenue=sum(Revenue), Employees=sum(Employees)) %>%
mutate(Revenue_per_Employee = Revenue/Employees)
d <- ggplot(industry_emp, aes(x=reorder(Industry, Revenue_per_Employee), y=Revenue_per_Employee, fill=Revenue_per_Employee))
d + geom_bar(stat="identity") + coord_flip() + labs(x = "Industry", y = "Number of Employees")
rev_emp = inc %>%
mutate(Revenue_per_Employee = Revenue/Employees)
h <- ggplot(rev_emp, aes(reorder(Industry,Revenue_per_Employee,mean), Revenue_per_Employee))
h <- h + geom_boxplot() + coord_flip() + labs(x = "Industry", y = "Employees")
h
h + scale_y_log10()