Data608_HW1
Olga Shiligin
31/01/2019
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("ggplot2")
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, unionlibrary("scales")
data <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE)And lets preview this data:
head(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 TXsummary(data)## 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:
Rank was treated as integer instead of factor, that is why application of descriptive statistics is meaningless.
Max. and Min. look reasonable across all variables.
Now I want to check if data contains missing values or duplicates rows.
apply(data, 2, function(x) any(is.na(x)))## Rank Name Growth_Rate Revenue Industry Employees
## FALSE FALSE FALSE FALSE FALSE TRUE
## City State
## FALSE FALSEdata[duplicated(data),]## [1] Rank Name Growth_Rate Revenue Industry Employees
## [7] City State
## <0 rows> (or 0-length row.names)Variables “Employees” contains missing values.
Data does not have duplicates rows.
Exploratary information.
# growth rate by industry
gr_industry<-data %>%
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<-data %>%
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 56.2# growth rate by state
gr_state<-data %>%
select (State,Growth_Rate) %>%
group_by(State) %>%
summarise(avg_gr= mean(Growth_Rate)) %>%
arrange(desc(avg_gr))
head(gr_state)## # A tibble: 6 x 2
## State avg_gr
## <fct> <dbl>
## 1 WY 19.1
## 2 ME 16.2
## 3 RI 16.0
## 4 DC 8.30
## 5 HI 6.79
## 6 UT 6.31# number of employees by industry
num_empl<-data %>%
select (Industry,Employees) %>%
group_by (Industry) %>%
summarise (total= sum(Employees)) %>%
arrange(desc(total))
head(num_empl)## # A tibble: 6 x 2
## Industry total
## <fct> <int>
## 1 Human Resources 226980
## 2 Financial Services 47693
## 3 Consumer Products & Services 45464
## 4 Security 41059
## 5 Advertising & Marketing 39731
## 6 Retail 37068Question 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.
q1_data<-data %>%
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 = "steelblue") +
scale_fill_gradient2(low = "white", high = "steelblue") +
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.
q2_data<-data %>%
filter (State == "NY")
head(q2_data)## Rank Name Growth_Rate Revenue
## 1 26 BeenVerified 84.43 13700000
## 2 30 Sailthru 73.22 8100000
## 3 37 YellowHammer 67.40 18000000
## 4 38 Conductor 67.02 7100000
## 5 48 Cinium Financial Services 53.65 5900000
## 6 70 33Across 44.99 27900000
## Industry Employees City State
## 1 Consumer Products & Services 17 New York NY
## 2 Advertising & Marketing 79 New York NY
## 3 Advertising & Marketing 27 New York NY
## 4 Advertising & Marketing 89 New York NY
## 5 Financial Services 32 Rock Hill NY
## 6 Advertising & Marketing 75 New York NYq2_data <- q2_data[complete.cases(q2_data$Industry), ]
q2_data <- q2_data[complete.cases(q2_data$Employees), ]
ny_median<-median(q2_data$Employees)
lower <- min(q2_data$Employees)
upper <- max(q2_data$Employees)
q2_test<-ggplot(q2_data, aes(reorder(Industry, Employees, FUN=median), Employees)) +
geom_boxplot(outlier.shape = NA, color = "black", fill = "light blue", alpha = 0.5) +
scale_y_continuous(trans = log2_trans(), limits = c(lower, upper)) +
geom_hline(yintercept = ny_median, color="red") +
geom_text(aes(2.5,400,label = "NY: employees median number"), size = 3)+
coord_flip() +
ggtitle ("NY: Number Of Employess By Industry") + ylab("Number Of Employees")+
theme_bw()+
theme(axis.title.y=element_blank())+
theme(text = element_text(size = 9, color = "black"))
q2_test
Question 3
Now imagine you work for an investor and want to see which industries generate the most revenue per employee. Create a chart that makes this information clear. Once again, the distribution per industry should be shown.
q3_data<-data %>%
select (Revenue, Industry, Employees) %>%
group_by(Industry) %>%
summarise(total_revenue = sum(Revenue), total_employee = sum(Employees)) %>%
mutate(revenue_employee = total_revenue / total_employee/1000) %>%
arrange (revenue_employee)
q3_data <- q3_data[complete.cases(q3_data$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 = "steelblue") +
scale_fill_gradient2(low = "white", high = "steelblue") +
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