R Notebook
Ajay Arora
Principles of Data Visualization and Introduction to ggplot2
#References:
##https://www.rdocumentation.org/packages/psych/versions/1.9.12.31/topics/describe
##https://github.com/AjayArora35/Data-607-Final-Project
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.5.3library(dplyr)## Warning: package 'dplyr' was built under R version 3.5.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(kableExtra)## Warning: package 'kableExtra' was built under R version 3.5.3##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
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## group_rowslibrary(Hmisc)## Warning: package 'Hmisc' was built under R version 3.5.3## Loading required package: lattice## Loading required package: survival## 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, unitsI 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
## 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:
inc <- na.omit(inc)
# Insert your code here, create more chunks as necessary
# n, nmiss, unique, mean, 5,10,25,50,75,90,95th percentiles
# 5 lowest and 5 highest scores
# Lastly, in addition to the percentiles, the following command provides lower and upper boundaries of data.
describe(inc) ## inc
##
## 8 Variables 4989 Observations
## ---------------------------------------------------------------------------
## Rank
## n missing distinct Info Mean Gmd .05 .10
## 4989 0 4987 1 2501 1667 252.4 501.8
## .25 .50 .75 .90 .95
## 1252.0 2502.0 3750.0 4500.2 4750.6
##
## lowest : 1 2 3 4 5, highest: 4996 4997 4998 4999 5000
## ---------------------------------------------------------------------------
## Name
## n missing distinct
## 4989 0 4989
##
## lowest : (Add)ventures @Properties 1-Stop Translation USA 110 Consulting 11thStreetCoffee.com
## highest: Zoup! ZT Wealth and Altus Group of Companies Zumasys Zurple ZweigWhite
## ---------------------------------------------------------------------------
## Growth_Rate
## n missing distinct Info Mean Gmd .05 .10
## 4989 0 1145 1 4.615 6.498 0.430 0.500
## .25 .50 .75 .90 .95
## 0.770 1.420 3.290 9.132 17.104
##
## lowest : 0.34 0.35 0.36 0.37 0.38, highest: 213.37 233.08 245.45 248.31 421.48
## ---------------------------------------------------------------------------
## Revenue
## n missing distinct Info Mean Gmd .05
## 4989 0 1066 1 48253357 75177089 2400000
## .10 .25 .50 .75 .90 .95
## 3000000 5100000 10900000 28600000 76800000 155200000
##
## lowest : 2.00e+06 2.10e+06 2.20e+06 2.30e+06 2.40e+06
## highest: 3.80e+09 4.50e+09 4.60e+09 4.70e+09 1.01e+10
## ---------------------------------------------------------------------------
## Industry
## n missing distinct
## 4989 0 25
##
## lowest : Advertising & Marketing Business Products & Services Computer Hardware Construction Consumer Products & Services
## highest: Retail Security Software Telecommunications Travel & Hospitality
## ---------------------------------------------------------------------------
## Employees
## n missing distinct Info Mean Gmd .05 .10
## 4989 0 691 1 232.7 365.6 10.0 14.0
## .25 .50 .75 .90 .95
## 25.0 53.0 132.0 351.2 688.0
##
## lowest : 1 2 3 4 5, highest: 17057 18887 20000 32000 66803
## ---------------------------------------------------------------------------
## City
## n missing distinct
## 4989 0 1517
##
## lowest : Acton Addison Adrian Agoura Hills Aiea
## highest: Worthington Wyomissing Yonkers Youngsville Zumbrota
## ---------------------------------------------------------------------------
## State
## n missing distinct
## 4989 0 52
##
## lowest : AK AL AR AZ CA, highest: VT WA WI WV WY
## ---------------------------------------------------------------------------#What is the growth rate by Industry?
(inc %>% dplyr::filter(Growth_Rate >= 100) %>%
select(Rank, Name,Growth_Rate,Industry ,Employees ,City, State) %>%
group_by (Industry) %>%
mutate(mean_growth_rate = mean(Growth_Rate)) %>%
mutate(min_growth_rate = min(Growth_Rate)) %>%
mutate(max_growth_rate = max(Growth_Rate)))## # A tibble: 19 x 10
## # Groups: Industry [12]
## Rank Name Growth_Rate Industry Employees City State mean_growth_rate
## <int> <fct> <dbl> <fct> <int> <fct> <fct> <dbl>
## 1 1 Fuhu 421. Consume~ 104 El S~ CA 238.
## 2 2 Fede~ 248. Governm~ 51 Dumf~ VA 206.
## 3 3 The ~ 245. Health 132 Jack~ FL 245.
## 4 4 Brid~ 233. Energy 50 Addi~ TX 233.
## 5 5 Data~ 213. Adverti~ 220 Bost~ MA 178.
## 6 6 Mile~ 179. Real Es~ 63 Aust~ TX 179.
## 7 7 Valu~ 174. Financi~ 27 Nash~ TN 139.
## 8 8 Emer~ 171. Adverti~ 75 San ~ CA 178.
## 9 9 Goal~ 170. Consume~ 97 Bluf~ UT 238.
## 10 10 Yago~ 167. Retail 15 Warw~ RI 154.
## 11 11 OBXt~ 164. Governm~ 149 Tyso~ VA 206.
## 12 12 AdRo~ 151. Adverti~ 165 San ~ CA 178.
## 13 13 uBre~ 141. Retail 250 Orla~ FL 154.
## 14 14 Sparc 129. Software 160 Char~ SC 129.
## 15 15 Livi~ 123. Consume~ 4100 Wash~ DC 238.
## 16 16 Ampe~ 111. Compute~ 26 Chino CA 111.
## 17 17 Inte~ 106. Logisti~ 15 Roch~ NJ 106.
## 18 18 Inte~ 105. Financi~ 11 Sara~ FL 139.
## 19 19 Vert~ 100. Food & ~ 51 colu~ OH 100.
## # ... with 2 more variables: min_growth_rate <dbl>, max_growth_rate <dbl>#Standard Deviation of Growth_Rate
sd(inc$Growth_Rate)## [1] 14.13767#What companies exceed the Growth Rate of 100?
(inc %>% dplyr::filter(Growth_Rate >= 100) %>%
select(Rank, Name,Growth_Rate,Revenue,Industry ,Employees ,City, State))## 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
## 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 TX
## 7 Financial Services 27 Nashville TN
## 8 Advertising & Marketing 75 San Francisco CA
## 9 Consumer Products & Services 97 Bluffdale UT
## 10 Retail 15 Warwick RI
## 11 Government Services 149 Tysons Corner VA
## 12 Advertising & Marketing 165 San Francisco CA
## 13 Retail 250 Orlando FL
## 14 Software 160 Charleston SC
## 15 Consumer Products & Services 4100 Washington DC
## 16 Computer Hardware 26 Chino CA
## 17 Logistics & Transportation 15 Rochelle Park NJ
## 18 Financial Services 11 Sarasota FL
## 19 Food & Beverage 51 columbus OH#What is the Revenue by Industry?
(inc %>%
select(Rank, Name,Industry ,Revenue, Employees ,City, State) %>%
group_by (Industry) %>%
mutate(mean_rev = mean(Revenue)) %>%
mutate(median_rev = median(Revenue)) %>%
mutate(min_rev = min(Revenue)) %>%
mutate(max_rev = max(Revenue)))## # A tibble: 4,989 x 11
## # Groups: Industry [25]
## Rank Name Industry Revenue Employees City State mean_rev median_rev
## <int> <fct> <fct> <dbl> <int> <fct> <fct> <dbl> <dbl>
## 1 1 Fuhu Consume~ 1.18e8 104 El S~ CA 7.37e7 9400000
## 2 2 Fede~ Governm~ 4.96e7 51 Dumf~ VA 2.97e7 11450000
## 3 3 The ~ Health 2.55e7 132 Jack~ FL 5.05e7 11400000
## 4 4 Brid~ Energy 1.90e9 50 Addi~ TX 1.26e8 29400000
## 5 5 Data~ Adverti~ 8.70e7 220 Bost~ MA 1.65e7 7900000
## 6 6 Mile~ Real Es~ 4.57e7 63 Aust~ TX 3.11e7 13300000
## 7 7 Valu~ Financi~ 2.55e7 27 Nash~ TN 5.06e7 15550000
## 8 8 Emer~ Adverti~ 2.39e7 75 San ~ CA 1.65e7 7900000
## 9 9 Goal~ Consume~ 3.31e7 97 Bluf~ UT 7.37e7 9400000
## 10 10 Yago~ Retail 1.86e7 15 Warw~ RI 5.05e7 8200000
## # ... with 4,979 more rows, and 2 more variables: min_rev <dbl>,
## # max_rev <dbl>#Standard Deviation of Revenue
sd(inc$Revenue)## [1] 240819469#What is the count of distinct cities?
result2 <- inc %>%
group_by(City) %>%
summarise(n())
nrow(result2)## [1] 1517#What are the distinct industries in the data?
result3 <- inc %>%
dplyr::group_by(Industry) %>%
dplyr::summarise(n())
result3## # A tibble: 25 x 2
## Industry `n()`
## <fct> <int>
## 1 Advertising & Marketing 471
## 2 Business Products & Services 480
## 3 Computer Hardware 44
## 4 Construction 187
## 5 Consumer Products & Services 203
## 6 Education 83
## 7 Energy 109
## 8 Engineering 74
## 9 Environmental Services 51
## 10 Financial Services 260
## # ... with 15 more rows#What are median, mean, etc. for employees?
(inc %>%
select(Rank, Name,Industry ,Revenue, Employees ,City, State) %>%
group_by (Industry) %>%
mutate(mean_employee = mean(Employees)) %>%
mutate(min_employee = min(Employees)) %>%
mutate(max_employee = max(Employees)) %>%
mutate(sum_employee = sum(Employees)))## # A tibble: 4,989 x 11
## # Groups: Industry [25]
## Rank Name Industry Revenue Employees City State mean_employee
## <int> <fct> <fct> <dbl> <int> <fct> <fct> <dbl>
## 1 1 Fuhu Consume~ 1.18e8 104 El S~ CA 224.
## 2 2 Fede~ Governm~ 4.96e7 51 Dumf~ VA 130.
## 3 3 The ~ Health 2.55e7 132 Jack~ FL 233.
## 4 4 Brid~ Energy 1.90e9 50 Addi~ TX 243.
## 5 5 Data~ Adverti~ 8.70e7 220 Bost~ MA 84.4
## 6 6 Mile~ Real Es~ 4.57e7 63 Aust~ TX 199.
## 7 7 Valu~ Financi~ 2.55e7 27 Nash~ TN 183.
## 8 8 Emer~ Adverti~ 2.39e7 75 San ~ CA 84.4
## 9 9 Goal~ Consume~ 3.31e7 97 Bluf~ UT 224.
## 10 10 Yago~ Retail 1.86e7 15 Warw~ RI 183.
## # ... with 4,979 more rows, and 3 more variables: min_employee <int>,
## # max_employee <int>, sum_employee <int>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
result4 = inc %>%
group_by(State) %>%
count(State)%>%
arrange(desc(n))
graph1 <- ggplot(data = result4,aes(x=reorder(State, n), y=n, fill = "lightblue", )) +
theme(legend.position = "none", axis.text.y = element_text(size=8), axis.text.x = element_text(size=8), panel.background = element_blank()) +
geom_bar(stat = "identity") +
#geom_label(aes(label=(result4$n)), position = position_dodge(width = 0.5), size = 3.0, label.padding = unit(0.08, "lines"), label.size = 0.15, inherit.aes = TRUE)+
labs(title = "Distribution By States", x = "States", y = "Number of Companies")+
coord_flip()
graph1
Quesiton 2
Lets dig in on the state with the 3rd most companies in the data set. Imagine you work for the state and are interested in how many people are employed by companies in different industries. Create a plot that shows the average and/or median employment by industry for companies in this state (only use cases with full data, use R’s complete.cases() function.) In addition to this, your graph should show how variable the ranges are, and you should deal with outliers.
# Answer Question 2 here
#Retrieve the top 3 states
result5 <- inc %>%
group_by(State) %>%
summarise(n=n()) %>%
arrange(desc(n)) %>%
top_n(3)## Selecting by nresult5## # A tibble: 3 x 2
## State n
## <fct> <int>
## 1 CA 700
## 2 TX 386
## 3 NY 311#Isolate 3rd state.
result6 <- inc[complete.cases(inc),] %>%
filter(State=='NY') %>%
group_by(Industry) %>%
summarise(median=median(Employees)) %>%
arrange(desc(Industry))
result6## # A tibble: 25 x 2
## Industry median
## <fct> <dbl>
## 1 Travel & Hospitality 61
## 2 Telecommunications 31
## 3 Software 80
## 4 Security 32.5
## 5 Retail 13.5
## 6 Real Estate 18
## 7 Media 45
## 8 Manufacturing 30
## 9 Logistics & Transportation 23.5
## 10 IT Services 54
## # ... with 15 more rowsgraph2 <- ggplot(result6, aes(x=reorder(result6$Industry, result6$median), y=result6$median, fill = "lightblue", )) +
theme(legend.position = "none", axis.text.y = element_text(size=8), axis.text.x = element_text(size=8), panel.background = element_blank()) +
geom_bar(stat = "identity") +
#geom_label(aes(label=(result6$median)), position = position_dodge(width = 0.5), size = 3.0, label.padding = unit(0.08, "lines"), label.size = 0.15, inherit.aes = TRUE)+
labs(title = "Distribution By Industries for NY", x = "Industries", y = "Average number of employees for NY")+
coord_flip()
graph2
Question 3
Now imagine you work for an investor and want to see which industries generate the most revenue per employee. Create a chart that makes this information clear. Once again, the distribution per industry should be shown.
# Answer Question 3 here
result7 = inc[complete.cases(inc),] %>%
group_by(Industry) %>%
summarise(Revenue=sum(Revenue), Employees=sum(Employees)) %>%
mutate(Revenue_per_Employee = Revenue/Employees)
result7## # A tibble: 25 x 4
## Industry Revenue Employees Revenue_per_Employee
## <fct> <dbl> <int> <dbl>
## 1 Advertising & Marketing 7785000000 39731 195943.
## 2 Business Products & Services 26345900000 117357 224494.
## 3 Computer Hardware 11885700000 9714 1223564.
## 4 Construction 13174300000 29099 452741.
## 5 Consumer Products & Services 14956400000 45464 328972.
## 6 Education 1139300000 7685 148250.
## 7 Energy 13771600000 26437 520921.
## 8 Engineering 2532500000 20435 123930.
## 9 Environmental Services 2638800000 10155 259852.
## 10 Financial Services 13150900000 47693 275741.
## # ... with 15 more rowsgraph3 <- ggplot(result7, aes(x=reorder(result7$Industry, result7$Revenue_per_Employee), y=result7$Revenue_per_Employee, fill = "lightblue", )) +
theme(legend.position = "none", axis.text.y = element_text(size=8), axis.text.x = element_text(size=8), panel.background = element_blank()) +
geom_bar(stat = "identity") +
#geom_label(aes(label=paste((result7$Employees), " Total EEs", sep = "")), position = position_dodge(width = 0.5), size = 3.0, label.padding = unit(0.08, "lines"), label.size = 0.15, inherit.aes = TRUE)+
labs(title = "Distribution By Industries -- Revenue Per Employee", x = "Industries", y = "Revenue Per Employee")+
coord_flip()
graph3