Intro
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:Code And lets preview this data:
library(magrittr)## Warning: package 'magrittr' was built under R version 3.5.2inc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE)
inc_df<-data.frame(inc, stringsAsFactors = TRUE)head(inc_df)## 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_df)## 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: Looking at the summary one can see that some of the data is highly skewed, i.e. the number of Emplyoees as well as the Growth Rate
The summary of this data shows that a lot of these fast growing companies are in California and are in the IR services industry.
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.
library(ggplot2)
library(ggthemes)## Warning: package 'ggthemes' was built under R version 3.5.2library(dplyr)## Warning: package 'dplyr' was built under R version 3.5.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniontheme_set(theme_tufte()) # from ggthemes
data <- inc_df %>%
group_by(State) %>%
count()
# plot
g <- ggplot(data, aes(x=reorder(State, n), y=n))
g + geom_tufteboxplot() +
theme(axis.text.x = element_text(angle=65, vjust=0.6)) + coord_flip() +
labs(title="Fastest Growing Companies by State",
subtitle="",
caption="Source: mpg",
x="State",
y="Growth Rate")
The graphic indicates that CA is the state with the fastest growing rate
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 complete.cases() function.) In addition to this, your graph should show how variable the ranges are, and you should deal with outliers.
filter(data, n == sort(data$n, T)[3])## # A tibble: 1 x 2
## # Groups: State [1]
## State n
## <fct> <int>
## 1 NY 311The State with the most 3rd companies is NY with 311
library(ggplot2)
theme_set(theme_bw())
#Create subset for NY
ny <- subset(inc_df, State=="NY")
ny <- ny[complete.cases(ny$Industry), ]
ny <- ny[complete.cases(ny$Employees), ] # plot
# find mean employees by industry
means <- aggregate(Employees ~ Industry, ny, mean)
# find maximum average employee no.
means_max <- max(means$Employees)
# prepare plot data: box plots (with outliers removed) to show variation; dots for mean EEs
g <- ggplot(ny, aes(x = reorder(Industry,Employees,mean), y = Employees))
g <- g + geom_boxplot(outlier.shape = NA, show.legend=F) + coord_flip()
g <- g + labs(x = "Industry", y = "Employees", title="Mean Employee Size by Industry in NY")
g <- g + labs(subtitle = "")
g <- g + geom_point(data = means, aes(x=Industry, y=Employees), color='red', size = 2)
g <- g + theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))
g <- g + scale_y_continuous(limits = c(0,means_max), breaks = seq(0, means_max, 200))
g## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
The plot above indicates that the mean data are highly skewed thus logarithmic scale mights give a better representation of the Employee count by Industry
g <- g + scale_y_log10(limits = c(1, means_max))## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.g <- g + labs(caption = "(grid line spacing on log scale)")
g <- g + theme(plot.caption = element_text(size = 8))
g## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
#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.
r <- inc_df[complete.cases(inc_df$Revenue), ]
r<- r[complete.cases(r$Employees), ]
r <- r %>%
group_by(Industry) %>%
summarise(RevenuePer = sum(Revenue)/sum(Employees)/1000000)
g<- ggplot(r, aes(x=reorder(Industry, RevenuePer), y=RevenuePer)) +
geom_bar(stat="identity", width=.5, fill="blue")+
labs(title="Revenue Per Employee by Industry",
y="Revenue per Employee",
x="Industry") +
theme_light(12) +
coord_flip()
g
The above plot indicates that Computer Hardware genrates the most Revenue