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:
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:
# Insert your code here, create more chunks as necessary
#glimpse is similar to head, but it allows us to sample data on a different axis and see more sample 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, ...
## $ Name <chr> "Fuhu", "FederalConference.com", "The HCI Group", "Brid...
## $ Growth_Rate <dbl> 421.48, 248.31, 245.45, 233.08, 213.37, 179.38, 174.04,...
## $ Revenue <dbl> 1.179e+08, 4.960e+07, 2.550e+07, 1.900e+09, 8.700e+07, ...
## $ Industry <chr> "Consumer Products & Services", "Government Services", ...
## $ Employees <int> 104, 51, 132, 50, 220, 63, 27, 75, 97, 15, 149, 165, 25...
## $ City <chr> "El Segundo", "Dumfries", "Jacksonville", "Addison", "B...
## $ State <chr> "CA", "VA", "FL", "TX", "MA", "TX", "TN", "CA", "UT", "...#skim conveys more than the summary function - missing values, unique counts, and inline histograms
skim(inc)| Name | inc |
| Number of rows | 5001 |
| Number of columns | 8 |
| _______________________ | |
| Column type frequency: | |
| character | 4 |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Name | 0 | 1 | 2 | 51 | 0 | 5001 | 0 |
| Industry | 0 | 1 | 5 | 28 | 0 | 25 | 0 |
| City | 0 | 1 | 4 | 22 | 0 | 1519 | 0 |
| State | 0 | 1 | 2 | 2 | 0 | 52 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Rank | 0 | 1 | 2501.64 | 1443.51 | 1.0e+00 | 1.252e+03 | 2.502e+03 | 3.751e+03 | 5.0000e+03 | ▇▇▇▇▇ |
| Growth_Rate | 0 | 1 | 4.61 | 14.12 | 3.4e-01 | 7.700e-01 | 1.420e+00 | 3.290e+00 | 4.2148e+02 | ▇▁▁▁▁ |
| Revenue | 0 | 1 | 48222535.49 | 240542281.14 | 2.0e+06 | 5.100e+06 | 1.090e+07 | 2.860e+07 | 1.0100e+10 | ▇▁▁▁▁ |
| Employees | 12 | 1 | 232.72 | 1353.13 | 1.0e+00 | 2.500e+01 | 5.300e+01 | 1.320e+02 | 6.6803e+04 | ▇▁▁▁▁ |
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.
# Answer Question 1 here
inc_state <- inc %>%
group_by(State) %>%
tally(sort = TRUE)
ggplot(data = inc_state, aes(x = reorder(State,n), y = n)) + geom_bar(stat='identity',fill = "blue") + coord_flip() + ggtitle("Fastest 5,000 Growing Companies by State") + labs(x = "State", y = "# of Companies") + theme(plot.title = element_text(hjust = 0.5,size = 18))
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
inc_ny_ind <- inc %>%
filter(complete.cases(.)) %>%
filter(State == "NY") %>%
group_by(Industry)
ggplot(data = inc_ny_ind, aes(x = reorder(Industry,Employees,mean), y = Employees)) + geom_boxplot() + coord_flip() + labs(x = "State", y = "# of Companies") + theme(plot.title = element_text(hjust = 0.5,size = 18),plot.subtitle = element_text(hjust = 0.5,size = 12))+ ggtitle("Fastest Growing NY Companies by Industry: # of Employee Analysis",subtitle="Mean indicated by blue dot") + labs(x = "Industry", y = "Analysis: # of Employees") + stat_summary(fun=mean,geom="point",shape=20,size=4,color="blue",fill="blue")
The above graph is hard to interpret due to outliers. Let’s try log scaling the data.
ggplot(data = inc_ny_ind, aes(x = reorder(Industry,Employees,mean), y = Employees)) + geom_boxplot() + coord_flip() + labs(x = "State", y = "# of Companies") + theme(plot.title = element_text(hjust = 0.5,size = 18),plot.subtitle = element_text(hjust = 0.5,size = 12)) + ggtitle("Fastest Growing NY Companies: # of Employee Analysis",subtitle="Mean indicated by blue dot") + labs(x = "Industry", y = "Analysis: # of Employees (log scale)") + stat_summary(fun=mean,geom="point",shape=20,size=4,color="blue",fill="blue") + scale_y_continuous(trans='log10')
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
inc_ind <- inc %>%
group_by(Industry) %>%
summarize(across(
.cols = is.numeric,
.fns = list(Sum = sum), na.rm = TRUE,
.names = "{col}_{fn}",
.inform = F
))## Warning: Predicate functions must be wrapped in `where()`.
##
## # Bad
## data %>% select(is.numeric)
##
## # Good
## data %>% select(where(is.numeric))
##
## i Please update your code.
## This message is displayed once per session.ggplot(data = inc_ind, aes(x = reorder(Industry,Revenue_Sum/Employees_Sum), y = Revenue_Sum/Employees_Sum)) + geom_bar(stat='identity',fill = "blue") + coord_flip() + ggtitle("Revenue Per Employee by Industry",subtitle="Nation's 5,000 Fastest Growing Companies") + labs(x = "State", y = "Revenue Per Employee ($)") + theme(plot.title = element_text(hjust = 0.5,size = 18),plot.subtitle = element_text(hjust = 0.5,size = 12))