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)
summary(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   :12

Think 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:

Grouped by industry to determine what was the median revenue for each individual industry.

#load tidyverse 
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.1
## ✔ readr   2.1.2     ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
sum_by_ind_rev<-inc %>%
  group_by(Industry) %>%
  summarize(median_revenue = median(Revenue))
print(sum_by_ind_rev)
## # A tibble: 25 × 2
##    Industry                     median_revenue
##    <chr>                                 <dbl>
##  1 Advertising & Marketing             7900000
##  2 Business Products & Services        9850000
##  3 Computer Hardware                  22350000
##  4 Construction                       14000000
##  5 Consumer Products & Services        9400000
##  6 Education                           6800000
##  7 Energy                             29400000
##  8 Engineering                        12100000
##  9 Environmental Services             12500000
## 10 Financial Services                 15550000
## # … with 15 more rows
## # ℹ Use `print(n = ...)` to see more rows

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.

The graph used to create the distribution of companies in the dataset by State was a column based bar chart to address the ‘portrait’ oriented screen.

company_state <- inc %>% 
  group_by(State) %>% 
  summarise(n = n()) %>%
  arrange(desc(n))

ggplot(company_state, aes(x = n, y = State)) +
  geom_col(width = 0.5)+
  labs(x ="Number of Companies", y = "State",title = "Number of Companies in each State")+
  theme(panel.background = element_rect(fill = "transparent"), 
        plot.background = element_rect(fill = "transparent"))

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.

A box chart was median employment by industry for companies in the state with the 3rd most companies in the data set. Also, to show how variable the ranges are and dealing with outliers the graph was scaled logarithmically.

state_ny <- inc %>% 
  filter(State == "NY") %>%
  select(Industry, Employees)

ggplot(state_ny, aes(x = Industry, Employees)) +
  geom_boxplot()+
  theme(text = element_text(size=10),
        axis.text.x = element_text(angle=90)) +
  scale_y_log10()+
  labs(y = "", title = "Median Employment by Industry in NY (logarithmic scale)")+
  theme(panel.background = element_rect(fill = "transparent"), 
        plot.background = element_rect(fill = "transparent"))

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.

The graph used to create which industries generate the most revenue per employee was another bar graph.

industries_revenue_employee <- inc %>%
  drop_na() %>%
  select(Industry, Employees, Revenue)%>%
  group_by(Industry) %>%
  summarise_all(list(sum))%>%
  mutate(rev_per_employ = Revenue /Employees)

ggplot(industries_revenue_employee, aes(x = Industry, rev_per_employ)) +
  geom_col(width = 0.5)+
  theme(text = element_text(size=10),
        axis.text.x = element_text(angle=90))+
  labs(y = "", title = "Revenue Per Employee by Industry")+
  theme(panel.background = element_rect(fill = "transparent"), 
        plot.background = element_rect(fill = "transparent"))