DATA608 HW 1
Peter Goodridge
September 9, 2018
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)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:
We look to get more information on the factor variables and indentify the numer of extreme outliers in two of the numeric variables we’ll be plotting.
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 ...sum(inc$Employees[!is.na(inc$Employees)]>10000)## [1] 11sum(inc$Revenue>10^8)## [1] 381Question 1
Create a graph that shows the distribution of companies in the dataset by State (ie how many are in each state). There are a lot of States, so consider which axis you should use. This visualization is ultimately going to be consumed on a ‘portrait’ oriented screen (ie taller than wide), which should further guide your layout choices.
library(tidyverse)
library(forcats)
library(tidyr)
library(scales)
library(extrafont)
#data for plot1
state_counts <- inc %>%
group_by(State) %>%
summarize(Companies = n()) %>%
ungroup %>%
mutate(State = fct_reorder(State, Companies))Because there are 52 different categories for state, it can be easy to get crosseyed when figuring out the number of employees in individual states. We solve this by putting the state labels right next to each dot.
ggplot(state_counts) +
geom_text(aes(x = State, y = Companies, label = State), hjust = -.2, size = 3) +
geom_point(aes(x = State, y = Companies), size = 1) +
labs(title = 'Count Of Companies by State', ylab = '# of Companies') +
coord_flip() +
theme_minimal() +
theme(
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.text = element_text(color = 'Grey25'),
axis.title = element_text(color = 'Grey50')
)
Quesiton 2
Lets dig in on the state with the 3rd most companies in the data set. Imagine you work for the state and are interested in how many people are employed by companies in different industries. Create a plot that shows the average and/or median employment by industry for companies in this state (only use cases with full data, use R’s complete.cases() function.) In addition to this, your graph should show how variable the ranges are, and you should deal with outliers.
To get an idea of the typical company in each industry, we remove the extreme outliers. 4 standard deviations should suffice as an outlier cutoff.
thrid_st <- nth(levels(state_counts$State),-3)
state_3 <- inc %>%
filter(State == thrid_st) %>%
drop_na
state_3_test <- state_3[complete.cases(state_3), ] #double check it's the same number of rows
min(state_3 == state_3_test)## [1] 1median_emps <- state_3 %>%
filter(Employees <= sd(state_3$Employees) + mean(state_3$Employees)) %>%
group_by(Industry) %>%
summarize(Median = median(Employees),
Mean = mean(Employees),
fifth = quantile(Employees, .1),
nine_five = quantile(Employees, .9),
most_empls = max(Employees)
) %>%
mutate(Industry = fct_reorder(Industry, Mean)) %>%
gather(Stat, value, c(Mean, Median))ggplot(median_emps) +
geom_point(aes(x = Industry, y = value, shape = Stat)) +
geom_errorbar(aes(x = Industry, ymin = fifth, ymax = nine_five)) +
labs(y = 'Employees',
title = 'Headcount Distribution by Industry',
subtitle = '10th percentile and 90th percentile at ends') +
coord_flip() +
scale_shape_manual(values = c(3,5), name = NULL) +
theme_minimal() +
theme(
axis.title = element_text(color = 'Grey50'),
axis.ticks.y = element_blank(),
#legend.box.spacing = unit(2, 'mm'),
legend.position = 'bottom'
)
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.
It would also be valuable to know the scale of these industries in terms of employees.
rev_ind <- inc %>%
select(Revenue, Employees, Industry) %>%
drop_na %>%
group_by(Industry) %>%
summarize(
rev_per = sum(Revenue/1000)/sum(Employees),
scaler = sum(Employees)
) %>%
mutate(Industry = fct_reorder(Industry, rev_per))ggplot(rev_ind, aes(x = Industry, y = rev_per)) +
geom_segment(aes(xend = Industry), yend = 0, color = 'grey50') +
geom_point(aes(size = scaler)) +
scale_y_continuous(labels = dollar) +
scale_size_continuous(name = 'Tot Employees') +
labs(title = 'Most Profitable Industries', y = 'Revenue Per Employee (000s)') +
coord_flip() +
theme_minimal() +
theme(
axis.title = element_text(color = 'Grey50'),
axis.ticks.y = element_blank(),
legend.position = "bottom",
legend.text = element_text(color = 'Grey25')
)
Sources:
Segment plots: R Graphics Cookbook (Winston Chang) pp 45-46