Data608 - Homework 1
Alec
9/11/2022
library(tidyverse)## Warning: package 'tidyr' was built under R version 4.1.2## Warning: package 'readr' was built under R version 4.1.2## Warning: package 'dplyr' was built under R version 4.1.2library(ggplot2)
library(psych)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 :12Think 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:
# glimpse is helpful if there are lots of features. However, in this case it does not provide much utility
glimpse(inc)## Rows: 5,001
## Columns: 8
## $ Rank <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…
## $ Name <chr> "Fuhu", "FederalConference.com", "The HCI Group", "Bridger…
## $ Growth_Rate <dbl> 421.48, 248.31, 245.45, 233.08, 213.37, 179.38, 174.04, 17…
## $ Revenue <dbl> 1.179e+08, 4.960e+07, 2.550e+07, 1.900e+09, 8.700e+07, 4.5…
## $ Industry <chr> "Consumer Products & Services", "Government Services", "He…
## $ Employees <int> 104, 51, 132, 50, 220, 63, 27, 75, 97, 15, 149, 165, 250, …
## $ City <chr> "El Segundo", "Dumfries", "Jacksonville", "Addison", "Bost…
## $ State <chr> "CA", "VA", "FL", "TX", "MA", "TX", "TN", "CA", "UT", "RI"…# describe() is a powerful summary tool, giving the user everything in summary() and more
describe(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 se
## Rank 1853.25 1.0e+00 5.0000e+03 4.9990e+03 0.00 -1.20 20.41
## Name* 1853.25 1.0e+00 5.0010e+03 5.0000e+03 0.00 -1.20 20.42
## Growth_Rate 1.22 3.4e-01 4.2148e+02 4.2114e+02 12.55 242.34 0.20
## Revenue 10674720.00 2.0e+06 1.0100e+10 1.0098e+10 22.17 722.66 3401441.44
## Industry* 8.90 1.0e+00 2.5000e+01 2.4000e+01 -0.10 -1.18 0.10
## Employees 53.37 1.0e+00 6.6803e+04 6.6802e+04 29.81 1268.67 19.16
## City* 604.90 1.0e+00 1.5190e+03 1.5180e+03 -0.04 -1.26 6.24
## State* 19.27 1.0e+00 5.2000e+01 5.1000e+01 0.12 -1.46 0.22# the last thing I typically check is for null values
colSums(is.na(inc))## Rank Name Growth_Rate Revenue Industry Employees
## 0 0 0 0 0 12
## City State
## 0 0# Lets check out the rows with missing entries
inc %>%
filter(is.na(Employees))## Rank Name Growth_Rate Revenue
## 1 183 First Flight Solutions 22.32 2700000
## 2 1064 Popchips 3.98 93300000
## 3 1124 Vocalocity 3.72 42900000
## 4 1653 Higher Logic 2.36 6000000
## 5 1686 Global Communications Group 2.30 3600000
## 6 2197 JeffreyM Consulting 1.68 12100000
## 7 2743 Excalibur Exhibits 1.27 9900000
## 8 3001 Heartland Business Systems 1.12 156300000
## 9 3978 SSEC 0.68 80400000
## 10 4112 Carolinas Home Medical Equipment 0.64 3300000
## 11 4566 Oakbrook 0.48 8900000
## 12 4968 Popcorn Palace 0.35 5500000
## Industry Employees City State
## 1 Logistics & Transportation NA Emerald Isle NC
## 2 Food & Beverage NA San Francisco CA
## 3 Telecommunications NA Atlanta GA
## 4 Software NA Washington DC
## 5 Telecommunications NA Englewood CO
## 6 Business Products & Services NA Bellevue WA
## 7 Business Products & Services NA houston TX
## 8 IT Services NA Little Chute WI
## 9 Manufacturing NA Horsham PA
## 10 Health NA Matthews NC
## 11 Real Estate NA Madison WI
## 12 Food & Beverage NA Schiller Park ILOne can approach the case of missing values in a few ways. Imputation is one option, and removing the rows is another. In this case, because the revenues of the companies in question vary widely (they are not all small companies) it could be appropriate to impute with the mean.
inc$Employees[is.na(inc$Employees)] <- mean(inc$Employees,na.rm = TRUE)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.
aggregated <- inc %>%
group_by(State) %>%
count(State) %>%
arrange(desc(n))
aggregated %>%
ggplot(aes(y=reorder(State, n), x=n)) +
geom_bar(stat="identity") +
xlab("Count") +
ylab("State")
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.
# New York is the third largest by # companies
ny_state <- inc %>% filter(State=="NY")
summary(ny_state)## Rank Name Growth_Rate Revenue
## Min. : 26 Length:311 Min. : 0.350 Min. :2.000e+06
## 1st Qu.:1186 Class :character 1st Qu.: 0.670 1st Qu.:4.300e+06
## Median :2702 Mode :character Median : 1.310 Median :8.800e+06
## Mean :2612 Mean : 4.371 Mean :5.872e+07
## 3rd Qu.:4005 3rd Qu.: 3.580 3rd Qu.:2.570e+07
## Max. :4981 Max. :84.430 Max. :4.600e+09
## Industry Employees City State
## Length:311 Min. : 1.0 Length:311 Length:311
## Class :character 1st Qu.: 21.0 Class :character Class :character
## Mode :character Median : 45.0 Mode :character Mode :character
## Mean : 271.3
## 3rd Qu.: 105.5
## Max. :32000.0# plotting NY state
ny_state %>%
filter(complete.cases(.)) %>% # complete cases only
group_by(Industry) %>%
select(Industry, Employees) %>%
ggplot(mapping=aes(x=Industry, y=Employees)) +
geom_boxplot()
This will not do. There are many high outliers which are skewing the image. Additionally, the text at the bottom is illegible.
# plotting NY state
ny_state %>%
filter(complete.cases(.)) %>% # complete cases only
group_by(Industry) %>%
select(Industry, Employees) %>%
ggplot(mapping=aes(x=Industry, y=Employees)) +
geom_boxplot(outlier.shape=NA) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
coord_cartesian(ylim = c(0, 1500))
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.
# first create the new variable
inc$rev_per_employee = inc$Revenue / inc$Employeeshead(inc, 20)## 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
## 7 7 Value Payment Systems 174.04 2.550e+07
## 8 8 Emerge Digital Group 170.64 2.390e+07
## 9 9 Goal Zero 169.81 3.310e+07
## 10 10 Yagoozon 166.89 1.860e+07
## 11 11 OBXtek 164.33 2.960e+07
## 12 12 AdRoll 150.65 3.410e+07
## 13 13 uBreakiFix 141.02 1.700e+07
## 14 14 Sparc 128.63 2.110e+07
## 15 15 LivingSocial 123.33 5.360e+08
## 16 16 Amped Wireless 110.68 1.430e+07
## 17 17 Intelligent Audit 105.73 1.450e+08
## 18 18 Integrity Funding 104.62 1.110e+07
## 19 19 Vertex Body Sciences 100.10 1.180e+07
## 20 20 BlueKai 92.45 2.680e+07
## Industry Employees City State rev_per_employee
## 1 Consumer Products & Services 104 El Segundo CA 1133653.8
## 2 Government Services 51 Dumfries VA 972549.0
## 3 Health 132 Jacksonville FL 193181.8
## 4 Energy 50 Addison TX 38000000.0
## 5 Advertising & Marketing 220 Boston MA 395454.5
## 6 Real Estate 63 Austin TX 725396.8
## 7 Financial Services 27 Nashville TN 944444.4
## 8 Advertising & Marketing 75 San Francisco CA 318666.7
## 9 Consumer Products & Services 97 Bluffdale UT 341237.1
## 10 Retail 15 Warwick RI 1240000.0
## 11 Government Services 149 Tysons Corner VA 198657.7
## 12 Advertising & Marketing 165 San Francisco CA 206666.7
## 13 Retail 250 Orlando FL 68000.0
## 14 Software 160 Charleston SC 131875.0
## 15 Consumer Products & Services 4100 Washington DC 130731.7
## 16 Computer Hardware 26 Chino CA 550000.0
## 17 Logistics & Transportation 15 Rochelle Park NJ 9666666.7
## 18 Financial Services 11 Sarasota FL 1009090.9
## 19 Food & Beverage 51 columbus OH 231372.5
## 20 Advertising & Marketing 107 Cupertino CA 250467.3# plot with facet wrap
ggplot(inc, aes(rev_per_employee)) +
geom_density() +
facet_wrap(~Industry)
ny_state %>%
group_by(Industry) %>%
summarize(total_rev = sum(Revenue),
total_emp = sum(Employees),
rev_per_emp = total_rev/total_emp) %>%
arrange(desc(rev_per_emp)) %>%
na.omit() %>%
ggplot(aes(x=reorder(Industry, rev_per_emp), y=rev_per_emp)) +
geom_bar(stat="identity") +
coord_flip()