Data 608 - Assignment 1
2/14/2021
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:
library(dplyr)## Warning: package 'dplyr' was built under R version 3.6.2##
## 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(forcats)
library(funModeling)## Warning: package 'funModeling' was built under R version 3.6.2## Loading required package: Hmisc## Warning: package 'Hmisc' was built under R version 3.6.2## Loading required package: lattice## Loading required package: survival## Warning: package 'survival' was built under R version 3.6.2## Loading required package: Formula## Warning: package 'Formula' was built under R version 3.6.2## Loading required package: ggplot2##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, units## funModeling v.1.9.4 :)
## Examples and tutorials at livebook.datascienceheroes.com
## / Now in Spanish: librovivodecienciadedatos.ailibrary(ggplot2)
library(sqldf)## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Warning: package 'RSQLite' was built under R version 3.6.2library(tidyverse)## ── Attaching packages ───────────────────────────────────────── tidyverse 1.3.0 ──## ✔ tibble 2.1.3 ✔ purrr 0.3.4
## ✔ tidyr 1.0.0 ✔ stringr 1.4.0
## ✔ readr 1.3.1## Warning: package 'purrr' was built under R version 3.6.2## ── Conflicts ──────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ Hmisc::src() masks dplyr::src()
## ✖ Hmisc::summarize() masks dplyr::summarize()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
status(inc)## variable q_zeros p_zeros q_na p_na q_inf p_inf
## Rank Rank 0 0 0 0.00000000 0 0
## Name Name 0 0 0 0.00000000 0 0
## Growth_Rate Growth_Rate 0 0 0 0.00000000 0 0
## Revenue Revenue 0 0 0 0.00000000 0 0
## Industry Industry 0 0 0 0.00000000 0 0
## Employees Employees 0 0 12 0.00239952 0 0
## City City 0 0 0 0.00000000 0 0
## State State 0 0 0 0.00000000 0 0
## type unique
## Rank integer 4999
## Name factor 5001
## Growth_Rate numeric 1147
## Revenue numeric 1069
## Industry factor 25
## Employees integer 691
## City factor 1519
## State factor 52describe(inc)## inc
##
## 8 Variables 5001 Observations
## ---------------------------------------------------------------------------
## Rank
## n missing distinct Info Mean Gmd .05 .10
## 5001 0 4999 1 2502 1667 252 502
## .25 .50 .75 .90 .95
## 1252 2502 3751 4501 4751
##
## lowest : 1 2 3 4 5, highest: 4996 4997 4998 4999 5000
## ---------------------------------------------------------------------------
## Name
## n missing distinct
## 5001 0 5001
##
## lowest : (Add)ventures @Properties 1-Stop Translation USA 110 Consulting 11thStreetCoffee.com
## highest: Zoup! ZT Wealth and Altus Group of Companies Zumasys Zurple ZweigWhite
## ---------------------------------------------------------------------------
## Growth_Rate
## n missing distinct Info Mean Gmd .05 .10
## 5001 0 1147 1 4.612 6.493 0.43 0.50
## .25 .50 .75 .90 .95
## 0.77 1.42 3.29 9.12 17.16
##
## lowest : 0.34 0.35 0.36 0.37 0.38, highest: 213.37 233.08 245.45 248.31 421.48
## ---------------------------------------------------------------------------
## Revenue
## n missing distinct Info Mean Gmd .05
## 5001 0 1069 1 48222535 75111227 2400000
## .10 .25 .50 .75 .90 .95
## 3000000 5100000 10900000 28600000 76900000 155600000
##
## lowest : 2.00e+06 2.10e+06 2.20e+06 2.30e+06 2.40e+06
## highest: 3.80e+09 4.50e+09 4.60e+09 4.70e+09 1.01e+10
## ---------------------------------------------------------------------------
## Industry
## n missing distinct
## 5001 0 25
##
## lowest : Advertising & Marketing Business Products & Services Computer Hardware Construction Consumer Products & Services
## highest: Retail Security Software Telecommunications Travel & Hospitality
## ---------------------------------------------------------------------------
## Employees
## n missing distinct Info Mean Gmd .05 .10
## 4989 12 691 1 232.7 365.6 10.0 14.0
## .25 .50 .75 .90 .95
## 25.0 53.0 132.0 351.2 688.0
##
## lowest : 1 2 3 4 5, highest: 17057 18887 20000 32000 66803
## ---------------------------------------------------------------------------
## City
## n missing distinct
## 5001 0 1519
##
## lowest : Acton Addison Adrian Agoura Hills Aiea
## highest: Worthington Wyomissing Yonkers Youngsville Zumbrota
## ---------------------------------------------------------------------------
## State
## n missing distinct
## 5001 0 52
##
## lowest : AK AL AR AZ CA, highest: VT WA WI WV WY
## ---------------------------------------------------------------------------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
#Group by only States, where each data row represents a State
dist <- inc %>% group_by(State) %>% summarise(CompanyCount=n())
ggplot(dist,aes(x=reorder(State,CompanyCount, height=1),y=CompanyCount)) + geom_bar(stat='identity') +coord_flip()+labs(title='Distribution of Companies by State') +xlab('State')+ylab('Frequency')
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
dist <- sqldf("SELECT * FROM dist ORDER BY CompanyCount DESC")
thirdState <- dist$State[3]
thirdState## [1] NY
## 52 Levels: AK AL AR AZ CA CO CT DC DE FL GA HI IA ID IL IN KS KY LA ... WYemployment <- inc %>%
filter(State == thirdState) %>%
filter(complete.cases(.)) %>%
group_by(State, Industry) %>%
summarise(IndustryAvg = mean(Employees),
IndustryMed = median(Employees)) %>%
gather(Analysis, Count, IndustryAvg, IndustryMed)## `summarise()` has grouped output by 'State'. You can override using the `.groups` argument.ggplot(employment, aes(x=fct_reorder(Industry, Count), y=Count))+geom_bar(stat="identity")+scale_y_continuous(trans="log2")+labs(title="New York: Average Distribution of Employees by Industry", y="Average Count of Employees")+coord_flip()
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
dist_revenue_industry <- inc %>%
filter(complete.cases(.)) %>%
group_by(Industry) %>%
summarise(RevByEmployee=(sum(Revenue))/(sum(Employees)))
dist_revenue_industry <- sqldf("SELECT * FROM dist_revenue_industry ORDER BY RevByEmployee DESC")
ggplot(dist_revenue_industry, aes(x=fct_reorder(Industry, RevByEmployee), y=RevByEmployee)) +
geom_bar(stat = "identity") +
labs(title="Distribution of Revenue per Employee by Industry", x="Industry",
y="Revenue by Employee") + coord_flip()