DATA 608. Module1. Principles of Data Visualization and Introduction to ggplot2
Uliana Plotnikova
February 14, 2021
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## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## intersect, setdiff, setequal, union##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
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## group_rowsI have provided you with data about the 5,000 fastest growing companies in the US, as compiled by Inc. magazine. lets read this in:
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 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):2764Exploratory Analysis
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:
# Let's check on the structure and show sample data for each variable.
str(inc)## 'data.frame': 5001 obs. of 8 variables:
## $ Rank : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Name : Factor w/ 5001 levels "(Add)ventures",..: 1770 1633 4423 690 1198 2839 4733 1468 1869 4968 ...
## $ Growth_Rate: num 421 248 245 233 213 ...
## $ Revenue : num 1.18e+08 4.96e+07 2.55e+07 1.90e+09 8.70e+07 ...
## $ Industry : Factor w/ 25 levels "Advertising & Marketing",..: 5 12 13 7 1 20 10 1 5 21 ...
## $ Employees : int 104 51 132 50 220 63 27 75 97 15 ...
## $ City : Factor w/ 1519 levels "Acton","Addison",..: 391 365 635 2 139 66 912 1179 131 1418 ...
## $ State : Factor w/ 52 levels "AK","AL","AR",..: 5 47 10 45 20 45 44 5 46 41 ...## [1] "Number of Rows in Dataframe: 5,001"## [1] "Number of Columns in Dataframe: 8"# Number of Distinct values for the variable "Industry".
with(inc, table(Industry))## Industry
## Advertising & Marketing Business Products & Services
## 471 482
## Computer Hardware Construction
## 44 187
## Consumer Products & Services Education
## 203 83
## Energy Engineering
## 109 74
## Environmental Services Financial Services
## 51 260
## Food & Beverage Government Services
## 131 202
## Health Human Resources
## 355 196
## Insurance IT Services
## 50 733
## Logistics & Transportation Manufacturing
## 155 256
## Media Real Estate
## 54 96
## Retail Security
## 203 73
## Software Telecommunications
## 342 129
## Travel & Hospitality
## 62# Maximum Revenue
max<-inc %>% slice(which.max(Revenue))
max## Rank Name Growth_Rate Revenue Industry Employees City State
## 1 4788 CDW 0.41 1.01e+10 Computer Hardware 6800 Vernon Hills IL# Minimum Revenue
min<-inc %>% slice(which.min(Revenue))
min## Rank Name Growth_Rate Revenue Industry
## 1 246 Cardinal Point Captains 17.65 2e+06 Government Services
## Employees City State
## 1 30 Carlsbad CA# Standard deviation in Revenue
sd(inc$Revenue)## [1] 240542281# Standard deviation in Growth_Rate
sd(inc$Growth_Rate)## [1] 14.12369Question 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.
head(inc1)## Warning: `...` is not empty.
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## We detected these problematic arguments:
## * `needs_dots`
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## These dots only exist to allow future extensions and should be empty.
## Did you misspecify an argument?## # A tibble: 6 x 2
## State n
## <fct> <int>
## 1 CA 701
## 2 TX 387
## 3 NY 311
## 4 VA 283
## 5 FL 282
## 6 IL 273dist
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.
NY <-inc[complete.cases(inc),]%>%
filter(State == "NY")
head(NY)## 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 NYggplot(NY, aes(x= Employees, y=Industry)) + geom_boxplot(fill="slateblue", alpha=0.9, outlier.size = -1) + xlim(0,1000)## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
ggplot(
Mean, aes(x = reorder(Industry, -avg), y = avg)) +
geom_bar(stat="identity", width=0.5, fill="#1F3552") +
ggtitle("Average Employment by Industry in NY")+
labs(x="Industry",y="Mean")+ theme(axis.text.x = element_text(angle = 60, hjust = 1))
Quesiton 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.
ggplot(
b, aes(x = reorder(Industry, -AvgRev), y = AvgRev)) +
geom_bar(stat="identity", width=0.5, fill="#1F3552") +
ggtitle("Revenue Per Employee in NY")+
labs(x="Industry",y="Mean")+ theme(axis.text.x = element_text(angle = 60, hjust = 1))