DATA608 HW1
Juanelle Marks
8/28/2019
library(tidyverse)## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.0.0 ✔ purrr 0.2.5
## ✔ tibble 1.4.2 ✔ dplyr 0.7.8
## ✔ tidyr 0.8.1 ✔ stringr 1.3.1
## ✔ readr 1.1.1 ✔ forcats 0.3.0## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(dplyr)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:
inc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE)Preview of 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:
Non-visual exploratory information using skim()
library(skimr)## Warning: package 'skimr' was built under R version 3.5.2##
## Attaching package: 'skimr'## The following object is masked from 'package:stats':
##
## filterskim(inc)## Skim summary statistics
## n obs: 5001
## n variables: 8
##
## ── Variable type:factor ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## variable missing complete n n_unique
## City 0 5001 5001 1519
## Industry 0 5001 5001 25
## Name 0 5001 5001 5001
## State 0 5001 5001 52
## top_counts ordered
## New: 160, Chi: 90, Aus: 88, Hou: 76 FALSE
## IT : 733, Bus: 482, Adv: 471, Hea: 355 FALSE
## (Ad: 1, @Pr: 1, 1-S: 1, 110: 1 FALSE
## CA: 701, TX: 387, NY: 311, VA: 283 FALSE
##
## ── Variable type:integer ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## variable missing complete n mean sd p0 p25 p50 p75 p100
## Employees 12 4989 5001 232.72 1353.13 1 25 53 132 66803
## Rank 0 5001 5001 2501.64 1443.51 1 1252 2502 3751 5000
## hist
## ▇▁▁▁▁▁▁▁
## ▇▇▇▇▇▇▇▇
##
## ── Variable type:numeric ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## variable missing complete n mean sd p0 p25
## Growth_Rate 0 5001 5001 4.61 14.12 0.34 0.77
## Revenue 0 5001 5001 4.8e+07 2.4e+08 2e+06 5100000
## p50 p75 p100 hist
## 1.42 3.29 421.48 ▇▁▁▁▁▁▁▁
## 1.1e+07 2.9e+07 1e+10 ▇▁▁▁▁▁▁▁Some interesting information is revealed by the skim() function. The inc data set contains 5001 observations and 8 variables. Four of these variables are categorical and four are numeric. The Employees variable has 12 missing peices of data. In this dataset there are 1519 unique City names, 25 unique industry names and 52 unique state names.The average number of employees across all companies is 232.72.
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.
#str(inc)
stateCount <- dplyr::group_by(inc, State) %>% dplyr::summarize(Count=n())%>%dplyr::arrange(desc(Count))
stateCount## # A tibble: 52 x 2
## State Count
## <fct> <int>
## 1 CA 701
## 2 TX 387
## 3 NY 311
## 4 VA 283
## 5 FL 282
## 6 IL 273
## 7 GA 212
## 8 OH 186
## 9 MA 182
## 10 PA 164
## # ... with 42 more rowsggplot(stateCount, aes(x=reorder(State,Count),y=Count))+
geom_bar(stat="identity", fill="skyblue1")+
labs(title="Distribution of Unique Companies by State",
x="State",y="Count of Unique companies")+
geom_text(aes(label=Count), vjust=0.5, size=2, position=position_dodge(width = 1), hjust=1.5)+
theme_bw(base_size=5)+
theme(axis.text.y=element_text(size=6, vjust=0.5))+
theme(plot.title = element_text(size=12))+
theme(plot.title = element_text(hjust = 0.5))+
coord_flip()
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.
comp3 = inc%>%dplyr::filter(State=='NY')
comp3 = comp3[complete.cases(comp3), ]
comp3 = comp3%>%dplyr::group_by(Industry)%>%dplyr::summarise(average_emp = mean(Employees, na.rm=TRUE))
ggplot(comp3, aes(x=reorder(Industry, average_emp), y=average_emp)) +
geom_bar(stat='identity', fill='#E69F00') +
labs(title="NY-Total Number of Employees per Industry",
x='Industry', y='Employee Count') +
geom_text(aes(y=average_emp-40, label=round(average_emp,0)), color='black', size=3) +
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.
rev_emp = inc[complete.cases(inc),]
rev_emp = inc%>%dplyr::group_by(Industry)%>%dplyr::summarise(TotEmp = sum(Employees, na.rm = TRUE), TotRev = sum(Revenue, na.rm = TRUE))
rev_emp$rev_per_emp = rev_emp$TotRev/rev_emp$TotEmp
ggplot(rev_emp, aes(x=reorder(Industry, rev_per_emp), y=rev_per_emp)) +
geom_bar(stat='identity', fill="pink" , width = 0.5) +
labs(title="Revenue per Employee by Industry",
x='Industry', y='Revenue per Employee')+
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
geom_text(aes(y=rev_per_emp-50000, label=round(rev_per_emp,0)), color='black', size=2) 