DATA 608 Assignment 1
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 TXdescribe(inc)## vars n mean sd median trimmed
## Rank 1 5001 2501.64 1443.51 2.502e+03 2501.73
## Name* 2 5001 2501.00 1443.81 2.501e+03 2501.00
## Growth_Rate 3 5001 4.61 14.12 1.420e+00 2.14
## Revenue 4 5001 48222535.49 240542281.14 1.090e+07 17334966.26
## Industry* 5 5001 12.10 7.33 1.300e+01 12.05
## Employees 6 4989 232.72 1353.13 5.300e+01 81.78
## City* 7 5001 732.00 441.12 7.610e+02 731.74
## State* 8 5001 24.80 15.64 2.300e+01 24.44
## mad min max range skew kurtosis
## Rank 1853.25 1.0e+00 5.0000e+03 4.9990e+03 0.00 -1.20
## Name* 1853.25 1.0e+00 5.0010e+03 5.0000e+03 0.00 -1.20
## Growth_Rate 1.22 3.4e-01 4.2148e+02 4.2114e+02 12.55 242.34
## Revenue 10674720.00 2.0e+06 1.0100e+10 1.0098e+10 22.17 722.66
## Industry* 8.90 1.0e+00 2.5000e+01 2.4000e+01 -0.10 -1.18
## Employees 53.37 1.0e+00 6.6803e+04 6.6802e+04 29.81 1268.67
## City* 604.90 1.0e+00 1.5190e+03 1.5180e+03 -0.04 -1.26
## State* 19.27 1.0e+00 5.2000e+01 5.1000e+01 0.12 -1.46
## se
## Rank 20.41
## Name* 20.42
## Growth_Rate 0.20
## Revenue 3401441.44
## Industry* 0.10
## Employees 19.16
## City* 6.24
## State* 0.22Think 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:
Thoughts
The median growth rate is around 1.5, with a SD of 14 and mean of 4.61…I’m sensing some serious skew towards lower growth rates, which is corroborated in the psych package’s ‘describe’ method.
City frequency medians/means match up, should probably do a visual exploratory analysis on that.
We see the employee range is 66802, with a minimum of 1…This indicates these businesses are not sampled from their respectively tiered size. (We should see quite a bit of variation between businesses)
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.
inc = transform(inc,freq =ave(seq(nrow(inc)),State,FUN=length))
#inc = inc[order(inc$freq),]
inc1 = subset(inc, !duplicated(State))
ggplot(inc1,aes(x=reorder(State,freq, height=1),y=freq)) + geom_bar(stat='identity') +coord_flip()+labs(title='Frequency of Companies by State') +xlab('State')+ylab('Frequency')
Question 2
Lets dig in on the state with the 3rd most companies in the data set. Imagine you work for the state and are interested in how many people are employed by companies in different industries. Create a plot that shows the average and/or median employment by industry for companies in this state (only use cases with full data, use R’s complete.cases() function.) In addition to this, your graph should show how variable the ranges are, and you should deal with outliers.
inc2 = inc[complete.cases(inc),]
inc2 = inc2[inc2$State=='NY',]
inc2 = group_by(inc2,Industry)
inc2 = summarize(inc2, Mean_=mean(Employees),Median_=median(Employees))
inc2 = gather(inc2,'Metric','Value',2:3)
ggplot(inc2,aes(x=Industry,y=Value)) +geom_bar(stat='identity',aes(fill=Metric),position='dodge')+coord_flip()+labs(title='Total employed by Industry in NY')+ylab('Employees')
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.
Thoughts
My initial thought was to scale the average revenue against one another. While this helped delineate which businesses were above/below the average threshhold..I had to remove two bad leverage points, which existed in very key points on the graph, and this was just not pleasant to the eyes.
inc3 = inc[complete.cases(inc),]
#inc3 = inc3[inc3$State=='NY',]
inc3 = group_by(inc3,Industry)
inc3 = summarize(inc3, total_employees=sum(Employees),total_revenue=sum(Revenue),growth_rate = mean(Growth_Rate))
inc3$avgRev = inc3$total_revenue/inc3$total_employees
compDF = inc3[!inc3$Industry=='Computer Hardware' & !inc3$Industry=='Human Resources',]
compDF$scaledAR = scale(compDF$avgRev)ggplot(compDF,aes(x=reorder(Industry,scaledAR),y=scaledAR,fill=growth_rate)) + geom_bar(stat="identity") + coord_flip()
Simpler, better graph
ggplot(inc3,aes(x=reorder(Industry,avgRev),y=avgRev,fill=growth_rate)) + geom_bar(stat='identity') +coord_flip()