DATA608_Assignment_1
Charles Ugiagbe
Principles of Data Visualization and Introduction to ggplot2
# Loading libraries
library(tidyverse)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 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:
# glimpse help to see an overview of the summary of the data.
glimpse(inc)## Rows: 5,001
## Columns: 8
## $ Rank <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,~
## $ Name <chr> "Fuhu", "FederalConference.com", "The HCI Group", "Bridger~
## $ Growth_Rate <dbl> 421.48, 248.31, 245.45, 233.08, 213.37, 179.38, 174.04, 17~
## $ Revenue <dbl> 1.179e+08, 4.960e+07, 2.550e+07, 1.900e+09, 8.700e+07, 4.5~
## $ Industry <chr> "Consumer Products & Services", "Government Services", "He~
## $ Employees <int> 104, 51, 132, 50, 220, 63, 27, 75, 97, 15, 149, 165, 250, ~
## $ City <chr> "El Segundo", "Dumfries", "Jacksonville", "Addison", "Bost~
## $ State <chr> "CA", "VA", "FL", "TX", "MA", "TX", "TN", "CA", "UT", "RI"~This help to take a count of the different type of industry
count(inc, Industry, sort = TRUE)## Industry n
## 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
## 11 Human Resources 196
## 12 Construction 187
## 13 Logistics & Transportation 155
## 14 Food & Beverage 131
## 15 Telecommunications 129
## 16 Energy 109
## 17 Real Estate 96
## 18 Education 83
## 19 Engineering 74
## 20 Security 73
## 21 Travel & Hospitality 62
## 22 Media 54
## 23 Environmental Services 51
## 24 Insurance 50
## 25 Computer Hardware 44Question 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.
inc_state <- inc %>% group_by(State) %>% summarize(Count = n())
inc_state_plot <- inc_state %>% ggplot(aes(x = reorder(State, Count), y = Count)) +
geom_bar(stat = "identity", fill = "red") + coord_flip() +
labs(title = "Distribution of Companies by State", x = "State", y = "Number of Companies") +
theme_bw() + theme(panel.grid.major = element_line(size = 0.4),
plot.title = element_text(hjust = 0.5),
panel.background = element_rect(fill = "gray",
colour = "cornsilk"))
inc_state_plot
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.
# Bar plot to show the distribution of Employee by Industry in New York
# plotting NY state
ny_state <- filter(inc, State == 'NY')
ny_industry <- ny_state %>%
filter(complete.cases(.)) %>% # complete cases only
group_by(Industry) %>%
select(Industry, Employees)
# box plot showing NY by industry
ggplot(ny_industry, mapping = aes(x = Industry, y = Employees)) +
geom_boxplot() +
theme_classic() +
labs(title = 'Distribution of Employment by Industry in NY', x = 'Industry', y = 'Number of Employees') +
coord_cartesian(ylim = c(0, 1500)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
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.
revenue_info <- inc %>%
group_by(Industry) %>%
summarize(TotalRev = sum(Revenue), TotalEmp = sum(Employees), RevPerEmp = TotalRev/TotalEmp) %>%
arrange(desc(RevPerEmp)) %>%
na.omit()
revenue_info## # A tibble: 16 x 4
## Industry TotalRev TotalEmp RevPerEmp
## <chr> <dbl> <int> <dbl>
## 1 Computer Hardware 11885700000 9714 1223564.
## 2 Energy 13771600000 26437 520921.
## 3 Construction 13174300000 29099 452741.
## 4 Consumer Products & Services 14956400000 45464 328972.
## 5 Insurance 2337900000 7339 318558.
## 6 Retail 10257400000 37068 276718.
## 7 Financial Services 13150900000 47693 275741.
## 8 Environmental Services 2638800000 10155 259852.
## 9 Government Services 6009100000 26185 229486.
## 10 Advertising & Marketing 7785000000 39731 195943.
## 11 Media 1742400000 9532 182795.
## 12 Education 1139300000 7685 148250.
## 13 Travel & Hospitality 2931600000 23035 127267.
## 14 Engineering 2532500000 20435 123930.
## 15 Security 3812800000 41059 92861.
## 16 Human Resources 9246100000 226980 40735.rev_plot <- revenue_info %>% ggplot(aes(x = reorder(Industry, RevPerEmp), y = RevPerEmp)) +
geom_bar(stat = "identity", fill ="Red") +
labs(title = "Revenue per Employee by Industry", x = "Industy", y = "Revenue per Employee") +
coord_flip() + theme_bw() +
theme(panel.grid.major = element_line(size = 0.4), plot.title = element_text(hjust = 0.5),
panel.background = element_rect(fill = "gray", colour = "gray"))
rev_plot