R Notebook
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, unioninc <- 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:
A little bit of extra exploration is make easier to read names of the columns and understand the number of rows in the dataset
# Insert your code here, create more chunks as necessary
names(inc)## [1] "Rank" "Name" "Growth_Rate" "Revenue" "Industry"
## [6] "Employees" "City" "State"nrow(inc)## [1] 5001Another important is understand where missing values are located since they might affect or skew our visualizations
colSums(is.na(inc))## Rank Name Growth_Rate Revenue Industry Employees
## 0 0 0 0 0 12
## City State
## 0 0sum(is.na(inc$Employees))## [1] 12Question 1
Create a graph that shows the distribution of companies in the dataset by State (ie how many are in each state). There are a lot of States, so consider which axis you should use. This visualization is ultimately going to be consumed on a ‘portrait’ oriented screen (ie taller than wide), which should further guide your layout choices.
library(Hmisc)## Warning: package 'Hmisc' was built under R version 3.5.3## Loading required package: lattice## Loading required package: survival## Warning: package 'survival' was built under R version 3.5.2## Loading required package: Formula##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
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## src, summarize## The following objects are masked from 'package:base':
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## format.pval, units# Answer Question 1 here
qrtile <- inc %>% count(State) %>% arrange(desc(n))## Warning: The `printer` argument is deprecated as of rlang 0.3.0.
## This warning is displayed once per session.qrtile <- qrtile %>% mutate(quant = cut2(qrtile$n,quantile(qrtile$n, include.lowest=TRUE)))
# https://stackoverflow.com/questions/11728419/using-cut-and-quartile-to-generate-breaks-in-r-function
ggplot(qrtile, aes(x = reorder(State, n), y = n)) +
geom_bar(aes(fill = quant), color="black", stat = "identity") +
coord_flip() +
ggtitle("States with the Fastest Growing Companies") +
labs(y= NULL, x = NULL) +
scale_fill_discrete(name = "Quantile Groups") +
theme(legend.position="bottom") 
Question 2
Lets dig in on the state with the 3rd most companies in the data set. Imagine you work for the state and are interested in how many people are employed by companies in different industries. Create a plot that shows the average and/or median employment by industry for companies in this state (only use cases with full data, use R’s complete.cases() function.) In addition to this, your graph should show how variable the ranges are, and you should deal with outliers.
# Answer Question 2 here
#Wjo is the third state?
qrtile$State[3]## [1] NY
## 52 Levels: AK AL AR AZ CA CO CT DC DE FL GA HI IA ID IL IN KS KY LA ... WYny <- inc %>%
mutate(cases = complete.cases(inc)) %>%
filter(cases=="TRUE") %>%
filter(State == "NY") %>%
#looks to see if values are more than 2 standard deviations from the mean.I take care of outliers
filter(!(abs(Employees - mean(Employees)) > 2*sd(Employees))) %>%
group_by(Industry)%>%
#Find the mean and standard error
summarise(mean = mean(Employees),
n = length(Industry),
se = sd(Employees)/sqrt(n))
# Take a look at the outliers we eliminated
test <- inc %>%
mutate(cases = complete.cases(inc)) %>%
filter(cases=="TRUE") %>%
filter(State == "NY") %>%
arrange(desc(Employees))
ggplot(ny, aes(x = reorder(Industry, mean), y = mean)) +
geom_bar(fill = "Lightblue", color="black", stat = "identity") +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.6) +
ggtitle("Average # of Employees per Company by Industry") +
labs(y= NULL, x = NULL) +
guides(fill=FALSE) +
coord_flip()## Warning: Removed 2 rows containing missing values (geom_errorbar).
#####################################################
library(scales)
qtile2<-inc %>%
filter (State == "NY")
head(qtile2)## 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 NYqtile2 <- qtile2[complete.cases(qtile2$Industry), ]
qtile2 <- qtile2[complete.cases(qtile2$Employees), ]
ny_median<-median(qtile2$Employees)
lower <- min(qtile2$Employees)
upper <- max(qtile2$Employees)
qtile2_test<-ggplot(qtile2, aes(reorder(Industry, Employees, FUN=median), Employees)) +
geom_boxplot(outlier.shape = NA, color = "black", fill = "red", alpha = 0.5) +
scale_y_continuous(trans = log2_trans(), limits = c(lower, upper)) +
geom_hline(yintercept = ny_median, color="blue") +
geom_text(aes(2.5,400,label = "NY State median of employees"), 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"))
qtile2_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.
# Answer Question 3 here
rev <- inc %>%
mutate(cases = complete.cases(inc)) %>%
filter(cases=="TRUE") %>%
mutate(rev_emp = Revenue/Employees) %>%
#looks to see if values are more than 2 standard deviations from the mean.
filter(!(abs(rev_emp - mean(rev_emp)) > 2*sd(rev_emp))) %>%
group_by(Industry)%>%
#Find the mean and standard error
summarise(Revenue_Employee = sum(Revenue)/sum(Employees),
n = length(Industry),
se = sd(Revenue/Employees)/sqrt(n))
ggplot(rev, aes(x = reorder(Industry, Revenue_Employee), y = Revenue_Employee)) +
geom_bar(fill = "lightblue", color="black", stat = "identity") +
geom_errorbar(aes(ymin=Revenue_Employee-se, ymax=Revenue_Employee+se), width=0.6) +
ggtitle("Average Revenue per Employee by Industry") +
labs(y= NULL, x = NULL) +
guides(fill=FALSE) +
scale_y_continuous(labels = scales::comma) +
coord_flip()