R Notebook
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(scales)
library(tidyverse)## ── Attaching packages ──────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ tibble 3.0.3 ✓ purrr 0.3.4
## ✓ tidyr 1.1.1 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ─────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x readr::col_factor() masks scales::col_factor()
## x purrr::discard() masks scales::discard()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(formattable)##
## Attaching package: 'formattable'## The following objects are masked from 'package:scales':
##
## comma, percent, scientificinc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE)Explaratory
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:
Companies by Industry
df <- as.data.frame(inc)
cbi <- df %>%
count(Industry) %>%
group_by(Industry) %>%
arrange(desc(n))
formattable(cbi)| Industry | n |
|---|---|
| IT Services | 733 |
| Business Products & Services | 482 |
| Advertising & Marketing | 471 |
| Health | 355 |
| Software | 342 |
| Financial Services | 260 |
| Manufacturing | 256 |
| Consumer Products & Services | 203 |
| Retail | 203 |
| Government Services | 202 |
| Human Resources | 196 |
| Construction | 187 |
| Logistics & Transportation | 155 |
| Food & Beverage | 131 |
| Telecommunications | 129 |
| Energy | 109 |
| Real Estate | 96 |
| Education | 83 |
| Engineering | 74 |
| Security | 73 |
| Travel & Hospitality | 62 |
| Media | 54 |
| Environmental Services | 51 |
| Insurance | 50 |
| Computer Hardware | 44 |
Relationship between Rank and Growth_Rate?
cor(inc$Rank, inc$Growth_Rate)## [1] -0.3976698This confirms that generally lower Rank companies have lower Growth Rates (as expected)
Relationship between Rank and Revenue?
cor(inc$Rank, inc$Revenue)## [1] 0.08210681Interestingly, a companies Rank (based on Growth) has only a small correlation with the company’s Revenue. Maybe faster growing companies are in a start-up phase and relying on investors rather than revenue? Broadly, having more revenue offers little explanatory value towards a companies rank.
Question 1
Create a graph that shows the distribution of companies in the data set 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 (i.e. taller than wide), which should further guide your layout choices.
plot_data <- df %>%
count(State) %>%
group_by(State)
p <- ggplot(plot_data, aes(x=reorder(State, n), y=n)) +
geom_bar(stat="identity", fill="steelblue") +
coord_flip() +
ylab('Number of Companies') +
xlab('State') +
ggtitle('Count of Fastest Growing Companyies by State') +
theme_minimal()
p
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.
state_3 <- df %>%
filter(State == 'NY') %>%
drop_na %>%
filter(Employees < mean(Employees) + 3 * sd(Employees))
p <- ggplot(state_3, aes(x=reorder(Industry, Employees), y=Employees)) +
geom_boxplot() +
coord_flip() +
theme_minimal()
p
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_empl <- df %>%
drop_na %>%
mutate(rpe = Revenue / Employees) %>%
filter(rpe < mean(rpe) + 3 * sd(rpe))
p <- ggplot(rev_empl, aes(x=reorder(Industry, rpe), y=rpe)) +
geom_boxplot() +
coord_flip() +
xlab('Industry') +
ylab('Revenue per Employee') +
ggtitle('Revenue/Employee by Industry (outliers removed)') +
theme_minimal()
p
As investors, we might be more interested with identifying outliers as areas of opportunity.
rpe_outliers <- df %>%
drop_na %>%
mutate(rpe = Revenue / Employees) %>%
filter(rpe >= mean(rpe) + 3 * sd(rpe)) %>%
select(Rank, Name, Growth_Rate, Revenue, Employees, rpe)
formattable(rpe_outliers)| Rank | Name | Growth_Rate | Revenue | Employees | rpe |
|---|---|---|---|---|---|
| 4 | Bridger | 233.08 | 1.900e+09 | 50 | 38000000 |
| 17 | Intelligent Audit | 105.73 | 1.450e+08 | 15 | 9666667 |
| 34 | US Logistics | 70.87 | 4.830e+07 | 10 | 4830000 |
| 49 | Solar Alliance of America | 53.37 | 1.730e+07 | 4 | 4325000 |
| 73 | iSend | 44.41 | 1.058e+08 | 15 | 7053333 |
| 143 | Apex Resources | 26.25 | 2.280e+07 | 3 | 7600000 |
| 331 | Pivot Employment Platforms | 13.05 | 1.002e+08 | 13 | 7707692 |
| 380 | Integrated Prescription Management | 11.67 | 5.310e+07 | 15 | 3540000 |
| 543 | Precious Metal Refining Services | 8.36 | 1.222e+08 | 27 | 4525926 |
| 564 | Fast Fusion | 7.98 | 1.280e+07 | 1 | 12800000 |
| 642 | FastSpring | 7.15 | 9.540e+07 | 20 | 4770000 |
| 777 | Tevet | 5.88 | 6.530e+07 | 12 | 5441667 |
| 1017 | Sundia | 4.23 | 3.080e+07 | 8 | 3850000 |
| 1025 | Nature’s Earthly Choice | 4.18 | 2.220e+07 | 5 | 4440000 |
| 1040 | American Global Logistics | 4.08 | 1.993e+08 | 35 | 5694286 |
| 1214 | ALAC International | 3.42 | 5.650e+07 | 11 | 5136364 |
| 1306 | BrightStar Care | 3.10 | 2.119e+08 | 31 | 6835484 |
| 1395 | Go2 Communications | 2.88 | 4.890e+07 | 7 | 6985714 |
| 1397 | EnvisionRxOptions | 2.88 | 2.700e+09 | 625 | 4320000 |
| 1428 | Fleet Advantage | 2.81 | 1.130e+08 | 27 | 4185185 |
| 1444 | Royal Buying Group | 2.78 | 3.568e+08 | 63 | 5663492 |
| 1978 | Hightowers Petroleum Company | 1.90 | 3.030e+08 | 19 | 15947368 |
| 2195 | ThunderCat Technology | 1.68 | 2.164e+08 | 31 | 6980645 |
| 2496 | Carahsoft Technology | 1.42 | 1.400e+09 | 365 | 3835616 |
| 2622 | Leviathan | 1.34 | 4.840e+07 | 11 | 4400000 |
| 2675 | Cedar Petrochemcials | 1.31 | 2.037e+08 | 5 | 40740000 |
| 2728 | P-Fleet | 1.28 | 1.089e+08 | 14 | 7778571 |
| 2754 | Greenland (America) | 1.27 | 1.055e+08 | 21 | 5023810 |
| 2818 | The Invisible Close | 1.22 | 5.100e+06 | 1 | 5100000 |
| 3121 | Apogee Jets | 1.06 | 3.600e+06 | 1 | 3600000 |
| 3407 | Heron Lake BioEnergy | 0.91 | 1.687e+08 | 33 | 5112121 |
| 3599 | ProService Hawaii | 0.84 | 5.634e+08 | 108 | 5216667 |
| 3779 | SDB Trade International | 0.77 | 5.210e+07 | 10 | 5210000 |
| 3844 | Atlas Oil Company | 0.74 | 1.900e+09 | 374 | 5080214 |
| 4093 | Advanced BioEnergy | 0.64 | 5.848e+08 | 75 | 7797333 |
| 4227 | TTS | 0.60 | 3.965e+08 | 97 | 4087629 |
| 4371 | NeoGov | 0.55 | 1.260e+07 | 1 | 12600000 |
| 4437 | AmeriQuest Business Services | 0.53 | 5.376e+08 | 150 | 3584000 |
| 4673 | Resource Management | 0.45 | 4.083e+08 | 65 | 6281538 |
| 4739 | G&A Partners | 0.43 | 5.377e+08 | 120 | 4480833 |
| 4751 | Edible Arrangements International | 0.43 | 4.661e+08 | 89 | 5237079 |
| 4892 | Tropical Smoothie Cafe | 0.38 | 1.496e+08 | 35 | 4274286 |