Data608-Module-01-HomeWork
Vinayak Kamath
02/14/2021
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:
And lets preview this data:
## 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 TX## 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:
=> Using the
strfunction we can see there are 50001 observations in the data set and there are 8 variables:## 'data.frame': 5001 obs. of 8 variables: ## $ Rank : int 1 2 3 4 5 6 7 8 9 10 ... ## $ Name : Factor w/ 5001 levels "(Add)ventures",..: 1770 1633 4423 690 1198 2839 4733 1468 1869 4968 ... ## $ Growth_Rate: num 421 248 245 233 213 ... ## $ Revenue : num 1.18e+08 4.96e+07 2.55e+07 1.90e+09 8.70e+07 ... ## $ Industry : Factor w/ 25 levels "Advertising & Marketing",..: 5 12 13 7 1 20 10 1 5 21 ... ## $ Employees : int 104 51 132 50 220 63 27 75 97 15 ... ## $ City : Factor w/ 1519 levels "Acton","Addison",..: 391 365 635 2 139 66 912 1179 131 1418 ... ## $ State : Factor w/ 52 levels "AK","AL","AR",..: 5 47 10 45 20 45 44 5 46 41 ...=> We can see that their are 52 unique values in the variable
Stateand this includes Puerto Rico (PR) and Washington D.C. (DC) in addition to the 50 states:## Factor w/ 52 levels "AK","AL","AR",..: 5 47 10 45 20 45 44 5 46 41 ...=> Not all observations have count of employees in it. There are 12 observations with NA values:
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 1.0 25.0 53.0 232.7 132.0 66803.0 12## Rank Name Growth_Rate Revenue ## 1 183 First Flight Solutions 22.32 2700000 ## 2 1064 Popchips 3.98 93300000 ## 3 1124 Vocalocity 3.72 42900000 ## 4 1653 Higher Logic 2.36 6000000 ## 5 1686 Global Communications Group 2.30 3600000 ## 6 2197 JeffreyM Consulting 1.68 12100000 ## 7 2743 Excalibur Exhibits 1.27 9900000 ## 8 3001 Heartland Business Systems 1.12 156300000 ## 9 3978 SSEC 0.68 80400000 ## 10 4112 Carolinas Home Medical Equipment 0.64 3300000 ## 11 4566 Oakbrook 0.48 8900000 ## 12 4968 Popcorn Palace 0.35 5500000 ## Industry Employees City State ## 1 Logistics & Transportation NA Emerald Isle NC ## 2 Food & Beverage NA San Francisco CA ## 3 Telecommunications NA Atlanta GA ## 4 Software NA Washington DC ## 5 Telecommunications NA Englewood CO ## 6 Business Products & Services NA Bellevue WA ## 7 Business Products & Services NA houston TX ## 8 IT Services NA Little Chute WI ## 9 Manufacturing NA Horsham PA ## 10 Health NA Matthews NC ## 11 Real Estate NA Madison WI ## 12 Food & Beverage NA Schiller Park IL=> The max value fro the variable
Rankshown is 5000; where as there are 5001 observations; telling the variableRankis not unique:## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 1 1252 2502 2502 3751 5000## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2 ## Rank n ## <int> <int> ## 1 3424 2 ## 2 5000 2
0.1 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.
## `summarise()` ungrouping output (override with `.groups` argument)# Plot the bar chart
barplot(data1$n,names.arg=data1$State,xlab="Count of Companies",ylab="State",col=data1$State, main="Distribution of Companies by State", horiz = T
, las=2 , cex.names=.3, space =0.2, font=2 , cex.axis = .7)
0.2 Question 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.
# R's `complete.cases()` function to get the observations with full data. 12 records that had NA will be discarded:
complete_data <- filter(inc, complete.cases(inc) )
str(complete_data)## 'data.frame': 4989 obs. of 8 variables:
## $ Rank : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Name : Factor w/ 5001 levels "(Add)ventures",..: 1770 1633 4423 690 1198 2839 4733 1468 1869 4968 ...
## $ Growth_Rate: num 421 248 245 233 213 ...
## $ Revenue : num 1.18e+08 4.96e+07 2.55e+07 1.90e+09 8.70e+07 ...
## $ Industry : Factor w/ 25 levels "Advertising & Marketing",..: 5 12 13 7 1 20 10 1 5 21 ...
## $ Employees : int 104 51 132 50 220 63 27 75 97 15 ...
## $ City : Factor w/ 1519 levels "Acton","Addison",..: 391 365 635 2 139 66 912 1179 131 1418 ...
## $ State : Factor w/ 52 levels "AK","AL","AR",..: 5 47 10 45 20 45 44 5 46 41 ...# Grouping by State to get the state with 3rd most companies in it:
complete_data_3rd_state <- complete_data %>%
group_by(State) %>%
summarise(n = n()) %>%
mutate(ranks = order(order(n, decreasing=T))) %>%
filter(ranks == 3)## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 1 x 3
## State n ranks
## <fct> <int> <int>
## 1 NY 311 3# Getting the data set to plot having total, mean and median of employees grouped by Industry:
data2 <- complete_data %>%
filter(State == complete_data_3rd_state$State)
# Ggplot2 plot that shows the average and/or median employment by industry for companies in this state:
ggplot(data2, aes(x = Industry, y = log(Employees) , fill= Industry) ) +
geom_boxplot(outlier.colour="seagreen", outlier.shape=16, outlier.size=2) +
theme(legend.position = "none") +
coord_flip() +
labs(title="Employment by Industry for Companies in New York State", y="Employees(log)", x="Industry")
0.3 Question 3
Now imagine you work for an investor and want to see which industries generate the most revenue per employee. Create a chart that makes this information clear. Once again, the distribution per industry should be shown.
# Grouping the data to the count by industries
data3 <- complete_data %>%
group_by(Industry) %>%
summarise(total_employees = sum(Employees), total_revenue = sum(Revenue)) %>%
mutate(revenue_per_employee = total_revenue/total_employees)## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 25 x 4
## Industry total_employees total_revenue revenue_per_employ~
## <fct> <int> <dbl> <dbl>
## 1 Advertising & Marketing 39731 7785000000 195943.
## 2 Business Products & Servic~ 117357 26345900000 224494.
## 3 Computer Hardware 9714 11885700000 1223564.
## 4 Construction 29099 13174300000 452741.
## 5 Consumer Products & Servic~ 45464 14956400000 328972.
## 6 Education 7685 1139300000 148250.
## 7 Energy 26437 13771600000 520921.
## 8 Engineering 20435 2532500000 123930.
## 9 Environmental Services 10155 2638800000 259852.
## 10 Financial Services 47693 13150900000 275741.
## # ... with 15 more rows# Plot the bar chart
barplot(data3$revenue_per_employee,names.arg=data3$Industry,col=data3$Industry, main="Distribution of Revenue Per Employee by Industry", horiz = F ,ylab="Revenue Per Employee", xlab="Industry" , las=2 , cex.names=.5, space =1 , cex.axis = .5 )