DATA 608 Assignment 1
Salma Elshahawy
August 28, 2020
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:
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# list types for each attribute
sapply(inc, class)## Rank Name Growth_Rate Revenue Industry Employees
## "integer" "factor" "numeric" "numeric" "factor" "integer"
## City State
## "factor" "factor"# Insert your code here, create more chunks as necessary
sd(inc$Growth_Rate)## [1] 14.12369sd(inc$Revenue)## [1] 240542281sd(inc$Employees, na.rm = TRUE)#A few companies have missing employee counts## [1] 1353.128#We can also do IQR in case the data is skewed
IQR(inc$Growth_Rate)## [1] 2.52IQR(inc$Revenue)## [1] 23500000IQR(inc$Employees, na.rm = TRUE)## [1] 107#Revenue has quite a large range. I used a base 10 logrithm to compress that scale.
#I used mutate from tidyr to make these new calculations part of the dataframe for later use.
inc <- inc %>% mutate(log_Rev = log10(Revenue))
inc$log_Rev %>% summary()## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.301 6.708 7.037 7.132 7.456 10.004# I also like doing ratios, in this case normalizing businesses of different sizes to see how much revenue or growth is generated per employee.
inc <- inc %>%
mutate(rev_per_empl = Revenue/Employees)
inc$rev_per_empl %>%
summary() ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1801 125000 198658 393613 375000 40740000 12inc <- inc %>%
mutate(grw_per_empl = Growth_Rate/Employees)
inc$grw_per_empl %>%
summary()## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000015 0.009022 0.027857 0.168649 0.093446 13.342500 12inc <- inc %>%
mutate(log_rev_per_grw = log10(Revenue/Growth_Rate))
inc$log_rev_per_grw %>%
summary()## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.015 6.376 6.873 6.876 7.366 10.392# growth rate by industry
gr_industry<-inc %>%
select (Industry,Growth_Rate) %>%
group_by(Industry) %>%
summarise(avg_gr= mean(Growth_Rate)) %>%
arrange(desc(avg_gr))
head(gr_industry)## # A tibble: 6 x 2
## Industry avg_gr
## <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 6.23
## 6 Retail 6.18# growth rate by city
gr_city<-inc %>%
select (City,Growth_Rate) %>%
group_by(City) %>%
summarise(avg_gr= mean(Growth_Rate)) %>%
arrange(desc(avg_gr))
head(gr_city)## # A tibble: 6 x 2
## City avg_gr
## <fct> <dbl>
## 1 Dumfries 248.
## 2 Chino 111.
## 3 columbus 100.
## 4 Cupertino 92.4
## 5 Bluffdale 59.9
## 6 El Segundo 56.2Question 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.
library(ggplot2)
q1_data<-inc %>%
select (Name,State) %>%
group_by(State) %>%
dplyr::summarise(company_count = n_distinct(Name)) %>%
arrange(desc(company_count))
q1<-ggplot(q1_data, aes(x=reorder(State,company_count), y=company_count)) +
geom_bar(stat="identity")+
geom_col(aes(fill = company_count)) +
geom_point(size=0.5, colour = "red") +
scale_fill_gradient2(low = "white", high = "red") +
theme_bw()+
coord_flip() +
theme(text = element_text(size = 9, color = "black")) +
ggtitle ("Number Of Fastest Growing Companies By State") + ylab("Number of Companies") +
theme(axis.title.y=element_blank()) +
theme(legend.position="none")
q1
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
ny_data <- inc %>%
filter(State == 'NY', complete.cases(.)) %>%
arrange(Industry) %>% select(Industry, Employees)
ny_data <- ny_data %>% group_by(Industry) %>%
filter(!(abs(Employees - median(Employees)) > 1.5*IQR(Employees)))# Using 1.5xIQR as the outlier limit
ny_data## # A tibble: 262 x 2
## # Groups: Industry [25]
## Industry Employees
## <fct> <int>
## 1 Advertising & Marketing 79
## 2 Advertising & Marketing 27
## 3 Advertising & Marketing 89
## 4 Advertising & Marketing 75
## 5 Advertising & Marketing 42
## 6 Advertising & Marketing 15
## 7 Advertising & Marketing 46
## 8 Advertising & Marketing 19
## 9 Advertising & Marketing 45
## 10 Advertising & Marketing 12
## # … with 252 more rows#The 1.5xIQR rule reduced the number of negative error bars better than the 2xstd dev rule.
ind_means <- ny_data %>%
group_by(Industry) %>%
summarise(mean_emp = mean(Employees), emp_sd = sd(Employees))
ind_means$emp_sd[is.na(ind_means$emp_sd)] <- 0
ind_means## # A tibble: 25 x 3
## Industry mean_emp emp_sd
## <fct> <dbl> <dbl>
## 1 Advertising & Marketing 38.2 24.2
## 2 Business Products & Services 102. 122.
## 3 Computer Hardware 44 0
## 4 Construction 29.4 22.4
## 5 Consumer Products & Services 36.5 28.1
## 6 Education 49.1 28.2
## 7 Energy 116. 23.8
## 8 Engineering 53.5 39.8
## 9 Environmental Services 155 134.
## 10 Financial Services 88 68.9
## # … with 15 more rowsggplot(ind_means, aes(x=reorder(Industry, mean_emp),y=mean_emp)) +
geom_bar(stat='identity', color = 'black', fill='lightgray') +
geom_errorbar(aes(ymin = mean_emp - emp_sd, ymax = mean_emp + emp_sd), width=0.2) +
theme(legend.position="none") +
ylab('Mean Employees')+ xlab('Industry')+
coord_flip() +
theme_classic()
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
q3_data<-inc %>%
select (Revenue, Industry, Employees) %>%
group_by(Industry) %>%
summarise(total_revenue = sum(Revenue), total_employee = sum(Employees)) %>%
mutate(revenue_employee = total_revenue / total_employee/1000) %>%
arrange (revenue_employee)
q3_data <- q3_data[complete.cases(q3_data$Industry), ]
q3_data <- q3_data[complete.cases(q3_data$total_employee), ]
q3<-ggplot(q3_data, aes(x=reorder(Industry, revenue_employee), y=revenue_employee)) +
geom_bar(stat="identity")+
theme_bw()+
geom_col(aes(fill = revenue_employee)) +
geom_point(size=0.5, colour = "red") +
scale_fill_gradient2(low = "white", high = "red") +
coord_flip() +
ggtitle ("Revenue Generated Per Employee By Industry") + ylab("Revenue Per Employee, in thousands") +
theme(legend.position="none") +
theme(axis.title.y=element_blank())+
theme(text = element_text(size = 8, color = "black"))
q3