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:

library(Hmisc)
library(funModeling)
library(tidyverse)
library(dplyr)
library(psych)
library(plotly)
options(scipen=999)
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  117900000
## 2    2        FederalConference.com      248.31   49600000
## 3    3                The HCI Group      245.45   25500000
## 4    4                      Bridger      233.08 1900000000
## 5    5                       DataXu      213.37   87000000
## 6    6 MileStone Community Builders      179.38   45700000
##                       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    TX
summary(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   
##  Min.   :    2000000   IT Services                 : 733  
##  1st Qu.:    5100000   Business Products & Services: 482  
##  Median :   10900000   Advertising & Marketing     : 471  
##  Mean   :   48222535   Health                      : 355  
##  3rd Qu.:   28600000   Software                    : 342  
##  Max.   :10100000000   Financial Services          : 260  
##                        (Other)                     :2358  
##    Employees                  City          State     
##  Min.   :    1.0   New York     : 160   CA     : 701  
##  1st Qu.:   25.0   Chicago      :  90   TX     : 387  
##  Median :   53.0   Austin       :  88   NY     : 311  
##  Mean   :  232.7   Houston      :  76   VA     : 283  
##  3rd Qu.:  132.0   San Francisco:  75   FL     : 282  
##  Max.   :66803.0   Atlanta      :  74   IL     : 273  
##  NA's   :12        (Other)      :4438   (Other):2764

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:

# 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,...
## $ Name        <fct> Fuhu, FederalConference.com, The HCI Group, Bridge...
## $ Growth_Rate <dbl> 421.48, 248.31, 245.45, 233.08, 213.37, 179.38, 17...
## $ Revenue     <dbl> 117900000, 49600000, 25500000, 1900000000, 8700000...
## $ Industry    <fct> Consumer Products & Services, Government Services,...
## $ Employees   <int> 104, 51, 132, 50, 220, 63, 27, 75, 97, 15, 149, 16...
## $ City        <fct> El Segundo, Dumfries, Jacksonville, Addison, Bosto...
## $ State       <fct> CA, VA, FL, TX, MA, TX, TN, CA, UT, RI, VA, CA, FL...
describe(inc)
##             vars    n        mean           sd      median     trimmed
## Rank           1 5001     2501.64      1443.51     2502.00     2501.73
## Name*          2 5001     2501.00      1443.81     2501.00     2501.00
## Growth_Rate    3 5001        4.61        14.12        1.42        2.14
## Revenue        4 5001 48222535.49 240542281.14 10900000.00 17334966.26
## Industry*      5 5001       12.10         7.33       13.00       12.05
## Employees      6 4989      232.72      1353.13       53.00       81.78
## City*          7 5001      732.00       441.12      761.00      731.74
## State*         8 5001       24.80        15.64       23.00       24.44
##                     mad        min            max          range  skew
## Rank            1853.25       1.00        5000.00        4999.00  0.00
## Name*           1853.25       1.00        5001.00        5000.00  0.00
## Growth_Rate        1.22       0.34         421.48         421.14 12.55
## Revenue     10674720.00 2000000.00 10100000000.00 10098000000.00 22.17
## Industry*          8.90       1.00          25.00          24.00 -0.10
## Employees         53.37       1.00       66803.00       66802.00 29.81
## City*            604.90       1.00        1519.00        1518.00 -0.04
## State*            19.27       1.00          52.00          51.00  0.12
##             kurtosis         se
## Rank           -1.20      20.41
## Name*          -1.20      20.42
## Growth_Rate   242.34       0.20
## Revenue       722.66 3401441.44
## Industry*      -1.18       0.10
## Employees    1268.67      19.16
## City*          -1.26       6.24
## State*         -1.46       0.22
profiling_num(inc)
##      variable            mean         std_dev variation_coef       p_01
## 1        Rank     2501.640872      1443.50617      0.5770237      51.00
## 2 Growth_Rate        4.611826        14.12369      3.0624947       0.36
## 3     Revenue 48222535.492901 240542281.13587      4.9881716 2100000.00
## 4   Employees      232.717980      1353.12795      5.8144538       5.00
##         p_05       p_25        p_50        p_75         p_95         p_99
## 1     252.00    1252.00     2502.00     3751.00      4751.00      4951.00
## 2       0.43       0.77        1.42        3.29        17.16        52.54
## 3 2400000.00 5100000.00 10900000.00 28600000.00 155600000.00 573900000.00
## 4      10.00      25.00       53.00      132.00       688.00      3244.56
##        skewness    kurtosis         iqr              range_98
## 1 -0.0004897066    1.800288     2499.00            [51, 4951]
## 2 12.5532709896  245.434761        2.52         [0.36, 52.54]
## 3 22.1810979541  725.946609 23500000.00  [2100000, 573900000]
## 4 29.8193818091 1272.181074      107.00 [5, 3244.55999999999]
##              range_80
## 1         [502, 4501]
## 2         [0.5, 9.12]
## 3 [3000000, 76900000]
## 4         [14, 351.2]

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.

 question_one <- inc %>%
  group_by(State) %>%
  summarize(n=n()) %>%
  arrange(desc(n)) %>%
  ggplot(aes(x = reorder(State, n), y = n)) +
  geom_bar(stat = "identity", aes(fill = n), width = 0.8, position = position_dodge(width = 0.8)) +
  coord_flip() +
  ggtitle("Distribution of Companies by State") +
  xlab("State") +
  ylab("Count") +
  theme(legend.position="none")

question_one <- ggplotly(question_one)
question_one

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_filter <-inc %>%
  filter(complete.cases(.)) %>%
  group_by(State) %>%
  mutate(n=n()) %>%
  arrange(desc(n)) %>%
  ungroup() %>%
  mutate(ranks = dense_rank(desc(n))) %>%
  filter(ranks == 3) %>%
  group_by(Industry)

 
question_two <- ggplot(inc_filter, aes(Industry, Employees)) +
  geom_boxplot(fill='lightgrey') +
  scale_y_continuous(limits = quantile(inc_filter$Employees, c(0.1,0.9))) +
  coord_flip() +
  theme_gray()

question_two <- ggplotly(question_two)
## Warning: Removed 62 rows containing non-finite values (stat_boxplot).
question_two

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

question_three <- inc %>%
  filter(complete.cases(.)) %>%
  group_by(Industry) %>%
  summarise(n=n(), Revenue = sum(Revenue), Employees= sum(Employees)) %>%
  mutate(rev_per_emp = Revenue/Employees) %>%
  ggplot(aes(x=reorder(Industry, rev_per_emp), y=rev_per_emp)) +
  geom_bar(stat="identity", aes(fill=Employees)) +
  coord_flip() +
  ggtitle("Rev per Employee by Industry") +
  ylab("Rev Per Emp") +
  xlab("Industry") +
  theme(legend.position="none")

question_three <- ggplotly(question_three)
question_three