FZahir Assignment01
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
## 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:
Thoughts on the summary
I can see that the dataset is dominated by IT services, followed by Business Products and Services and Advertising and Marketing companies. The growth rate ranges from 34% to 421%, indicating these are companies at different stages in the business cycle.The mean growth rate is much higher than the median, so this is a positively skewed distrubition.The number of employees ranges from 1 to 66,803 so the distribution contains a mix of small to large sized companies. The distribution for the number of employees is also positively skewed. The IQR for employees is 107 so the dataset contains more companies with number of employees in this range. City of New York has the highest number of fastest growing numbers, and the state of California wins the trophy for being home to the largest number of fastest growing companies.
Relevant non visual exploratory information
Let us look at the top five industries with the highest growth rates.
library(tidyverse)
library(scales)inc1<-inc%>%
group_by(Industry)%>%
summarize(Avg_growth=mean(Growth_Rate))%>%
top_n(5,Avg_growth)%>%
arrange(desc(Avg_growth))
inc1## # A tibble: 5 x 2
## Industry Avg_growth
## <fct> <dbl>
## 1 Energy 9.60
## 2 Consumer Products & Services 8.78
## 3 Real Estate 7.75
## 4 Government Services 7.24
## 5 Advertising & Marketing 6.23Let us look at the top five industries with regards to the number of employees.The lists have Consumer products and services and advertising and marketing overlapping.
inc2<-inc%>%
group_by(Industry)%>%
summarize(Tot_employee=sum(Employees))%>%
top_n(5,Tot_employee)%>%
arrange(desc(Tot_employee))
inc2## # A tibble: 5 x 2
## Industry Tot_employee
## <fct> <int>
## 1 Human Resources 226980
## 2 Financial Services 47693
## 3 Consumer Products & Services 45464
## 4 Security 41059
## 5 Advertising & Marketing 39731Let us see which states have the highest average growth rate in the category of fastest growing companies. It is interesting to WY at the top even though the dataset has almost a forth of the companies from CA.
inc3<-inc%>%
group_by(State)%>%
summarize(Avg_growth=mean(Growth_Rate))%>%
top_n(5,Avg_growth)%>%
arrange(desc(Avg_growth))
inc3## # A tibble: 5 x 2
## State Avg_growth
## <fct> <dbl>
## 1 WY 19.1
## 2 ME 16.2
## 3 RI 16.0
## 4 DC 8.30
## 5 HI 6.79Question 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.
#Used the classic theme to remove gridlines and make the bars more prominent
#construct the plot
inct<-inc %>%
group_by(Industry) %>%
summarise(count=n())
x<-ggplot(inc, aes(x=State)) +
geom_bar()+
coord_flip() +
ggtitle("Distribution of Companies by State")+
theme_classic()
#readjust to make the state names readable
x+
theme(axis.text.y = element_text(size=8))+
scale_y_continuous("Number of companies", expand = c(0, 0))
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.
#From summary, I see state with third most companies is NY
inc4<-inc%>%
filter(State=='NY')
inc4<-inc4[complete.cases(inc4),]
inc4<-inc4%>%
group_by(Industry)
#plotting with outliers
ggplot(inc4, aes(x=Industry, y=Employees)) +
geom_boxplot()+
scale_y_continuous("Average Employees", trans='log2')+
coord_flip()+
ggtitle("Boxplot of Employment by Industry in NY State")+
theme_classic()+
theme(panel.background = element_rect(fill = "lightgrey"))
Removing the outliers and showing mean as the red circle
inc5 <- inc4 %>%
group_by(Industry) %>%
filter((Employees <= quantile(Employees,0.75)+1.5*IQR(Employees))
&Employees >= quantile(Employees,0.25)-1.5*IQR(Employees))%>%
mutate(avgemp=mean(Employees))
ggplot(inc5, aes(x=reorder(Industry,avgemp), y=Employees)) +
geom_boxplot()+
scale_y_continuous("Average, Median and Distribution of Employees", trans='log2')+
stat_summary(fun.x=mean, geom="point", shape=20, size=2, color="red", fill="red")+
coord_flip()+
ggtitle("Boxplot of Employment by Industry in NY State")+
theme_classic()+
theme(panel.background = element_rect(fill = "lightgrey"))
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$RevEmp<-inc$Revenue/inc$Employees
inc6<-inc[complete.cases(inc),]%>%
arrange(desc(RevEmp))
#Removing outliers
inc6 <- inc6 %>%
group_by(Industry) %>%
filter((RevEmp <= quantile(RevEmp,0.75)+1.5*IQR(RevEmp))&
(RevEmp>= quantile(RevEmp,0.25)-1.5*IQR(RevEmp)))%>%
mutate(avgrev=mean(RevEmp))
#Boxplot
ggplot(inc6, aes(x=reorder(Industry,avgrev), y=RevEmp)) +
geom_boxplot()+
scale_y_continuous("Revenue Per Employee",trans='log2', labels=comma)+
coord_flip()+
ggtitle("Boxplot of Revenue per Employee by Industry")+
theme_classic()+
theme(panel.background = element_rect(fill = "lightgrey"))+
stat_summary(fun.x=mean, geom="point", shape=20, size=2, color="red", fill="red")
Looks like Computer hardware has the highest revenue per employee