Homework 1
Homework One
Joshua Hummell
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 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 :12colnames(inc)## [1] "Rank" "Name" "Growth_Rate" "Revenue" "Industry"
## [6] "Employees" "City" "State"Think 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:
Let’s get the datatype for each column
# Insert your code here, create more chunks as necessary
sapply(inc, class)## Rank Name Growth_Rate Revenue Industry Employees
## "integer" "character" "numeric" "numeric" "character" "integer"
## City State
## "character" "character"Let’s get the count of how many items there are (1-5000, there should be 5001)
nrow(inc)## [1] 5001unique(inc$Industry)## [1] "Consumer Products & Services" "Government Services"
## [3] "Health" "Energy"
## [5] "Advertising & Marketing" "Real Estate"
## [7] "Financial Services" "Retail"
## [9] "Software" "Computer Hardware"
## [11] "Logistics & Transportation" "Food & Beverage"
## [13] "IT Services" "Business Products & Services"
## [15] "Education" "Construction"
## [17] "Manufacturing" "Telecommunications"
## [19] "Security" "Human Resources"
## [21] "Travel & Hospitality" "Media"
## [23] "Environmental Services" "Engineering"
## [25] "Insurance"Question 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.
# Answer Question 1 here
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(ggplot2)
inc_company <- inc %>% select(State) %>% group_by(State) %>% summarise(n = n()) %>% arrange(desc(n))
p <- ggplot(inc_company,aes(x = reorder(State, -n), y = n))+geom_bar(stat="identity")+coord_flip()
p
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.
First, let’s find out which state is third
# Answer Question 2 here
inc_company[3,]## # A tibble: 1 x 2
## State n
## <chr> <int>
## 1 NY 311Now let’s take a closure look at it
NY <- inc %>% select_all() %>% filter(State == "NY")hist(NY$Employees,breaks = 100)
It looks like there are a few that really stand out, let’s see what they are
NY %>% select_all() %>% filter(Employees > 5000)## Rank Name Growth_Rate Revenue
## 1 4577 Sutherland Global Services 0.48 5.976e+08
## 2 4936 Coty 0.36 4.600e+09
## Industry Employees City State
## 1 Business Products & Services 32000 Pittsford NY
## 2 Consumer Products & Services 10000 New York NYIt looks like they may have typed in an extra 0, but since I don’t know them well enough, I’ll just remove them for now
NY <- NY %>% select_all() %>% filter(Employees < 5000)hist(NY$Employees,breaks = 100)
STill there are a few outliers but overall more believable
Let’s get a look at the industries these employees fall in
ny_plot <- NY %>% select(Industry, Employees, Name) %>% group_by(Industry, Name) %>% summarise(n = sum(Employees)) %>% arrange(desc(n))## `summarise()` has grouped output by 'Industry'. You can override using the `.groups` argument.ny_plot <- ny_plot %>% filter(complete.cases(Industry,n))
ny_plot <- ny_plot%>%
group_by(Industry) %>%
summarise_at(vars(n),funs(mean, sd, n()))## Warning: `funs()` was deprecated in dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.p <- ggplot(ny_plot,aes(x = reorder(Industry, -mean), y = mean)) +
geom_point()+
geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,
position=position_dodge(0.05)) +
coord_flip()
p
Question 3
Now imagine you work for an investor and want to see which industries generate the most revenue per employee. Create a chart that makes this information clear. Once again, the distribution per industry should be shown.
library("scales")## Warning: package 'scales' was built under R version 4.1.2inc_plot <- inc %>% filter(complete.cases(.))
inc_plot$Revenue <- format(inc_plot$Revenue, scientific = FALSE)
inc_plot <- NY %>% select(Industry, Revenue, Name) %>% group_by(Industry, Name) %>% arrange(desc(Revenue))
inc_dis <- inc_plot%>%
group_by(Industry) %>%
summarise_at(vars(Revenue),funs(mean, sd, n()))
p <- ggplot(inc_dis,aes(x = reorder(Industry, -mean), y = mean)) +
geom_point()+
geom_errorbar(aes(ymin=0, ymax=mean+sd), width=.2,
position=position_dodge(0.05)) +
coord_flip()
p
Looks like IT services has the best bet at making the most money, but the large Standard Deviation makes it look more risky, a more secure bet would be Travel and hospitality, which has a lower mean revenue but also less of a standard deviation.