R Notebook
Principles of Data Visualization and Introduction to ggplot2
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, unionI 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:
# Insert your code here, create more chunks as necessary
less_100_emp = subset(inc, Employees < 100)
btwn_100_1000_emp = subset(inc, (Employees <= 1000) & (Employees >= 100))
grtr_1000_emp = subset(inc, Employees > 1000)
na_emp = subset(inc, is.na(inc$Employees) )
summary(less_100_emp)## Rank Name Growth_Rate
## Min. : 2 (Add)ventures : 1 Min. : 0.340
## 1st Qu.:1134 @Properties : 1 1st Qu.: 0.810
## Median :2384 1-Stop Translation USA : 1 Median : 1.515
## Mean :2415 11thStreetCoffee.com : 1 Mean : 4.707
## 3rd Qu.:3674 1st American Systems and Services: 1 3rd Qu.: 3.700
## Max. :5000 1st Equity : 1 Max. :248.310
## (Other) :3418
## Revenue Industry Employees
## Min. :2.00e+06 IT Services : 493 Min. : 1.00
## 1st Qu.:4.10e+06 Advertising & Marketing : 396 1st Qu.:19.00
## Median :7.20e+06 Business Products & Services: 334 Median :32.00
## Mean :1.43e+07 Software : 216 Mean :39.14
## 3rd Qu.:1.38e+07 Health : 210 3rd Qu.:55.00
## Max. :1.90e+09 Manufacturing : 175 Max. :99.00
## (Other) :1600
## City State
## New York : 112 CA : 507
## Austin : 64 TX : 240
## Chicago : 63 NY : 227
## San Francisco: 54 FL : 195
## Atlanta : 53 VA : 187
## San Diego : 53 IL : 182
## (Other) :3025 (Other):1886summary(btwn_100_1000_emp)## Rank Name Growth_Rate
## Min. : 1 110 Consulting : 1 Min. : 0.350
## 1st Qu.:1491 123 Exteriors : 1 1st Qu.: 0.730
## Median :2704 2020 Exhibits : 1 Median : 1.300
## Mean :2646 21c Museum Hotels : 1 Mean : 4.523
## 3rd Qu.:3855 22nd Century Technologies: 1 3rd Qu.: 2.680
## Max. :4990 29 Prime : 1 Max. :421.480
## (Other) :1391
## Revenue Industry Employees
## Min. :2.10e+06 IT Services :227 Min. : 100.0
## 1st Qu.:1.72e+07 Business Products & Services:131 1st Qu.: 138.0
## Median :3.14e+07 Health :128 Median : 202.0
## Mean :6.99e+07 Software :118 Mean : 275.8
## 3rd Qu.:6.62e+07 Financial Services : 87 3rd Qu.: 345.0
## Max. :2.70e+09 Manufacturing : 74 Max. :1000.0
## (Other) :632
## City State
## New York : 42 CA :176
## Houston : 30 TX :129
## Austin : 24 VA : 92
## Chicago : 20 FL : 76
## San Francisco: 20 NY : 75
## Atlanta : 18 IL : 71
## (Other) :1243 (Other):778summary(grtr_1000_emp)## Rank Name Growth_Rate Revenue
## Min. : 15 ABC Supply : 1 Min. : 0.340 Min. :2.900e+06
## 1st Qu.:2090 Acadian Companies : 1 1st Qu.: 0.630 1st Qu.:8.358e+07
## Median :3404 Accurate Home Care: 1 Median : 0.915 Median :2.265e+08
## Mean :3050 Acro Service : 1 Mean : 3.488 Mean :5.603e+08
## 3rd Qu.:4128 Addison Group : 1 3rd Qu.: 1.785 3rd Qu.:5.091e+08
## Max. :4997 Advanced Disposal : 1 Max. :123.330 Max. :1.010e+10
## (Other) :162
## Industry Employees City
## Human Resources :21 Min. : 1001 Chicago : 7
## Health :16 1st Qu.: 1325 New York : 6
## Business Products & Services:15 Median : 1948 Dallas : 5
## Food & Beverage :15 Mean : 3820 Houston : 5
## Financial Services :12 3rd Qu.: 3899 Charlotte : 4
## IT Services :12 Max. :66803 Cincinnati: 3
## (Other) :77 (Other) :138
## State
## IL :19
## CA :17
## TX :17
## FL :11
## NY : 9
## MI : 7
## (Other):88summary(na_emp)## Rank Name Growth_Rate
## Min. : 183 Carolinas Home Medical Equipment:1 Min. : 0.350
## 1st Qu.:1521 Excalibur Exhibits :1 1st Qu.: 0.670
## Median :2470 First Flight Solutions :1 Median : 1.475
## Mean :2606 Global Communications Group :1 Mean : 3.408
## 3rd Qu.:4012 Heartland Business Systems :1 3rd Qu.: 2.700
## Max. :4968 Higher Logic :1 Max. :22.320
## (Other) :6
## Revenue Industry Employees
## Min. : 2700000 Business Products & Services:2 Min. : NA
## 1st Qu.: 5025000 Food & Beverage :2 1st Qu.: NA
## Median : 9400000 Telecommunications :2 Median : NA
## Mean : 35408333 Health :1 Mean :NaN
## 3rd Qu.: 52275000 IT Services :1 3rd Qu.: NA
## Max. :156300000 Logistics & Transportation :1 Max. : NA
## (Other) :3 NA's :12
## City State
## Atlanta :1 NC :2
## Bellevue :1 WI :2
## Emerald Isle:1 CA :1
## Englewood :1 CO :1
## Horsham :1 DC :1
## houston :1 GA :1
## (Other) :6 (Other):4Question 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
ggplot(inc, aes(x=State)) + geom_bar() + coord_flip()
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
counts = count(inc, vars=State, sort = TRUE)
ordered_states = counts %>% pull(vars)
third_most_state = ordered_states[3]
NY_state = subset(inc, State == third_most_state)
subset = NY_state[complete.cases(NY_state),]
ggplot(subset, aes(y = Employees, x = Industry)) + geom_boxplot() + scale_y_continuous(trans='log10') + stat_summary(fun.y=mean, geom="point", shape=20, size=2, color="red", fill="red") + ylab('Employees in log scale') + theme(axis.text.x = element_text(angle = 90, 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.
# Answer Question 3 here
# Assuming still for NY state
NY_state$Rev_per_emp = NY_state$Revenue/NY_state$Employees
subset = NY_state[complete.cases(NY_state),]
ggplot(subset, aes(y = Rev_per_emp, x = Industry)) + geom_boxplot() + scale_y_continuous(trans='log10') + stat_summary(fun.y=mean, geom="point", shape=20, size=2, color="red", fill="red") + ylab('Revenue per Employee in log scale') + theme(axis.text.x = element_text(angle = 90, hjust = 1))