DATA 608 - Module 1
Gehad Gad
September 12, 2021
Principles of Data Visualization and Introduction to ggplot2
#Libraries Required
library(dplyr)
library(ggplot2)
library(tidyr)
library(pastecs)
library(knitr)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:
- There are 12 NA values in the Employees feature/column.
# Compute descriptive statistics for Revenue
Rev <- stat.desc(inc$Revenue)
round(Rev,2)## nbr.val nbr.null nbr.na min max range
## 5.001000e+03 0.000000e+00 0.000000e+00 2.000000e+06 1.010000e+10 1.009800e+10
## sum median mean SE.mean CI.mean.0.95 var
## 2.411609e+11 1.090000e+07 4.822254e+07 3.401441e+06 6.668317e+06 5.786059e+16
## std.dev coef.var
## 2.405423e+08 4.990000e+00# Compute descriptive statistics for Growth Rate
stat.desc(inc$Growth_Rate)## nbr.val nbr.null nbr.na min max range
## 5.001000e+03 0.000000e+00 0.000000e+00 3.400000e-01 4.214800e+02 4.211400e+02
## sum median mean SE.mean CI.mean.0.95 var
## 2.306374e+04 1.420000e+00 4.611826e+00 1.997192e-01 3.915372e-01 1.994787e+02
## std.dev coef.var
## 1.412369e+01 3.062495e+00# Compute descriptive statistics for Employees
stat.desc(inc$Employees)## nbr.val nbr.null nbr.na min max range
## 4.989000e+03 0.000000e+00 1.200000e+01 1.000000e+00 6.680300e+04 6.680200e+04
## sum median mean SE.mean CI.mean.0.95 var
## 1.161030e+06 5.300000e+01 2.327180e+02 1.915720e+01 3.755654e+01 1.830955e+06
## std.dev coef.var
## 1.353128e+03 5.814454e+00Question 1
Create a graph that shows the distribution of companies in the dataset by State (ie how many are in each state). There are a lot of States, so consider which axis you should use. This visualization is ultimately going to be consumed on a ‘portrait’ oriented screen (ie taller than wide), which should further guide your layout choices.
#Create a subset with just two features: State and Industry.
Ind_State <- aggregate(inc$Industry, by=list(inc$State), FUN=length)
Ind_State## Group.1 x
## 1 AK 2
## 2 AL 51
## 3 AR 9
## 4 AZ 100
## 5 CA 701
## 6 CO 134
## 7 CT 50
## 8 DC 43
## 9 DE 16
## 10 FL 282
## 11 GA 212
## 12 HI 7
## 13 IA 28
## 14 ID 17
## 15 IL 273
## 16 IN 69
## 17 KS 38
## 18 KY 40
## 19 LA 37
## 20 MA 182
## 21 MD 131
## 22 ME 13
## 23 MI 126
## 24 MN 88
## 25 MO 59
## 26 MS 12
## 27 MT 4
## 28 NC 137
## 29 ND 10
## 30 NE 27
## 31 NH 24
## 32 NJ 158
## 33 NM 5
## 34 NV 26
## 35 NY 311
## 36 OH 186
## 37 OK 46
## 38 OR 49
## 39 PA 164
## 40 PR 1
## 41 RI 16
## 42 SC 48
## 43 SD 3
## 44 TN 82
## 45 TX 387
## 46 UT 95
## 47 VA 283
## 48 VT 6
## 49 WA 130
## 50 WI 79
## 51 WV 2
## 52 WY 2ggplot(Ind_State, aes(x=Group.1, y=x))+
geom_bar(stat="identity")+
coord_flip() + labs(x = "State", y = "Number of Industries")+
theme_classic()
#re-order the states for better view
ggplot(Ind_State, aes(x=reorder(Group.1, x), y=x, fill=x))+
geom_bar(stat="identity",width=0.2, position = position_dodge(width=0.2))+
coord_flip() + labs(title= "Distribution of Companies", x = "State", y = "Number of Industris")+
theme_minimal()
California, Texas, and New York have the highest number of Industries.
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.
#NY is the thirst highest state with industries. (as per the graph above)
#Since we noted that Employees column has some NA values, complete.cases will list all rows in the data with completed values.
Ind_NY <- inc[complete.cases(inc),] %>%
filter(State=='NY') %>%
drop_na() %>%
group_by(Industry) ggplot(Ind_NY, aes(x=Industry, y=Employees))+
geom_boxplot()+
coord_flip() +
theme_classic()+
labs(x = "Industry", y = "Employees")
Detect and remove outliers
The interquartile range is the central 50% or the area between the 75th and the 25th percentile of a distribution. A point is an outlier if it is above the 75th or below the 25th percentile by a factor of 1.5 times the IQR.
#Detect Ouliers
Q <- quantile(Ind_NY$Employees, probs=c(.25, .75), na.rm = FALSE)
iqr <- IQR(Ind_NY$Employees)
up <- Q[2]+1.5*iqr # Upper Range
low<- Q[1]-1.5*iqr # Lower Range
#Removing outliers
eliminated<- subset(Ind_NY, Ind_NY$Employees > (Q[1] - 1.5*iqr) & Ind_NY$Employees < (Q[2]+1.5*iqr))#Plot after removing the outliers
ggplot(eliminated, aes(x=Industry, y=Employees))+
geom_boxplot()+
coord_flip() +
theme_classic()+
labs(x = "Industry", y = "Employees")
#Get the average employment for NY state
AVG_EMP <- eliminated %>%
group_by(Industry) %>%
summarise(Employees_AVG = round(mean(Employees),1))
#Plot a distribution of average employment for NY state
ggplot(AVG_EMP, aes(x=Industry, y=Employees_AVG))+
geom_bar(stat="identity")+
coord_flip() +
theme_classic()+
labs(x = "Industry", y = "Average Employees")
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.
inc %>%
#Select rows with companies based on NY
filter(State == "NY") %>%
#drop any rows with missing values.
drop_na() %>%
group_by(Industry) %>%
#Calculate the employees and total revenue and calculate revenue per employee
summarise(Employees = sum(Employees),
Revenue = sum(Revenue)) %>%
mutate(revenue_employee = Revenue/Employees) %>%
#Create a bar chart of all industries by most revenue per employee in a descending order.
ggplot(aes(x = reorder(Industry, revenue_employee), y = revenue_employee)) +
geom_bar(stat = "identity", fill = "#FF6666") +
coord_flip() +
labs(title = "Most Revenue per Employee in NY", x = "Industry", y = "Revenue per Employee") +
theme_minimal()