Data 608 HW1
Amit Kapoor
9/3/2020
Principles of Data Visualization and Introduction to ggplot2
library(dplyr)
library(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:
# Insert your code here, create more chunks as necessary
glimpse(inc)## Observations: 5,001
## Variables: 8
## $ Rank <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17…
## $ Name <fct> Fuhu, FederalConference.com, The HCI Group, Bridger, Data…
## $ Growth_Rate <dbl> 421.48, 248.31, 245.45, 233.08, 213.37, 179.38, 174.04, 1…
## $ Revenue <dbl> 1.179e+08, 4.960e+07, 2.550e+07, 1.900e+09, 8.700e+07, 4.…
## $ Industry <fct> Consumer Products & Services, Government Services, Health…
## $ Employees <int> 104, 51, 132, 50, 220, 63, 27, 75, 97, 15, 149, 165, 250,…
## $ City <fct> El Segundo, Dumfries, Jacksonville, Addison, Boston, Aust…
## $ State <fct> CA, VA, FL, TX, MA, TX, TN, CA, UT, RI, VA, CA, FL, SC, D…# Count by industries
industry <- inc %>%
group_by(Industry) %>%
count(Industry) %>%
arrange(desc(n))
industry## # A tibble: 25 x 2
## # Groups: Industry [25]
## Industry n
## <fct> <int>
## 1 IT Services 733
## 2 Business Products & Services 482
## 3 Advertising & Marketing 471
## 4 Health 355
## 5 Software 342
## 6 Financial Services 260
## 7 Manufacturing 256
## 8 Consumer Products & Services 203
## 9 Retail 203
## 10 Government Services 202
## # … with 15 more rows# select top 5 industries by revenue
ind_revenue <- inc %>%
group_by(Industry) %>%
summarise(tot_rev_ind = sum(Revenue)) %>%
mutate(total_revenue_billions = round((tot_rev_ind / 1e9), 1)) %>%
select(-tot_rev_ind) %>%
arrange(desc(total_revenue_billions)) %>%
top_n(n = 5)## Selecting by total_revenue_billionsind_revenue## # A tibble: 5 x 2
## Industry total_revenue_billions
## <fct> <dbl>
## 1 Business Products & Services 26.4
## 2 IT Services 20.7
## 3 Health 17.9
## 4 Consumer Products & Services 15
## 5 Logistics & Transportation 14.8# select top 5 industries that employs most
ind_employs <- inc %>%
group_by(Industry) %>%
summarise(tot_ind_emp = sum(Employees)) %>%
arrange(desc(tot_ind_emp)) %>%
top_n(n = 5)## Selecting by tot_ind_empind_employs## # A tibble: 5 x 2
## Industry tot_ind_emp
## <fct> <int>
## 1 Human Resources 226980
## 2 Financial Services 47693
## 3 Consumer Products & Services 45464
## 4 Security 41059
## 5 Advertising & Marketing 39731Question 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
# the distribution of companies by State
inc %>% count(State) %>%
ggplot(aes(x=reorder(State, n), y=n, fill=n)) +
geom_col() +
coord_flip() +
xlab("States") +
ylab("Number of Companies") +
ggtitle("Number of Companies by state")
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
# consider complete cases
inc_complete <- inc[complete.cases(inc),]
# for NY state
inc_complete %>%
filter(State=="NY") %>%
ggplot(aes(x=Industry, y=Employees)) +
geom_boxplot(width=.5, fill="grey", outlier.colour=NA) +
stat_summary(aes(colour = "mean"), fun.y = mean, geom="point", fill="black", colour="red", shape=21, size=2, show.legend=TRUE) +
stat_summary(aes(colour = "median"), fun.y = median, geom="point", fill="blue", colour="blue", shape=21, size=2, show.legend=TRUE) +
coord_flip(ylim = c(0, 1500), expand = TRUE) +
scale_y_continuous(labels = scales::comma, breaks = seq(0, 1500, by = 100)) +
xlab("Industry") +
ylab("Employees by industry for companies") +
ggtitle("Mean and Median Employment by Industry in NY State") +
theme(panel.background = element_blank(), legend.position = "top")
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
# group by industry and calculate revenue ind per employee
ind_rev_per_emp <- inc[complete.cases(inc),] %>%
group_by(Industry) %>%
summarise(Rev_ind_per_emp=sum(Revenue) / sum(Employees)) %>%
arrange(desc(Rev_ind_per_emp))
ind_rev_per_emp## # A tibble: 25 x 2
## Industry Rev_ind_per_emp
## <fct> <dbl>
## 1 Computer Hardware 1223564.
## 2 Energy 520921.
## 3 Construction 452741.
## 4 Logistics & Transportation 371001.
## 5 Consumer Products & Services 328972.
## 6 Insurance 318558.
## 7 Manufacturing 286824.
## 8 Retail 276718.
## 9 Financial Services 275741.
## 10 Environmental Services 259852.
## # … with 15 more rows# plot industries that generate the most revenue per employee
ggplot(ind_rev_per_emp, aes(x=reorder(Industry, Rev_ind_per_emp), y=Rev_ind_per_emp)) +
geom_bar(stat = 'Identity') +
coord_flip() +
xlab("Industries") +
ylab("Revenue per employee") +
ggtitle("Industries revenue per employee") +
scale_y_continuous(labels = scales::comma)