Data-608 Homework-1
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Student Name : Sachid Deshmukh
Date : 09/28/2018
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**Refernce required libraries
library(ggplot2)
library(dplyr)##
## 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, unionPrinciples 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, stringsAsFactors = FALSE)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
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## Industry Employees City
## Length:5001 Min. : 1.0 Length:5001
## Class :character 1st Qu.: 25.0 Class :character
## Mode :character Median : 53.0 Mode :character
## Mean : 232.7
## 3rd Qu.: 132.0
## Max. :66803.0
## NA's :12
## State
## Length:5001
## Class :character
## Mode :character
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##
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## 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:
The summary above provides quick qualitative statistics of the data. We can check important qualitative stats like min, max, median and mean values. We can use this stats to describe the data to stakeholders. These stats also helps us to figure out the outliers if any and provides a guide for data cleanup and data imputation
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.
theme_set(theme_classic())
require(forcats)## Loading required package: forcatstop.State = data.frame(inc%>%dplyr::group_by(State)%>%dplyr::mutate(TotCnt = n())%>%dplyr::group_by(State, Industry, TotCnt)%>%dplyr::summarise(IndCnt = n()))
top.10 = sort(unique(top.State$TotCnt), decreasing=TRUE)[1:10]
top.State = top.State%>%dplyr::filter(TotCnt %in% top.10)%>%dplyr::arrange(desc(TotCnt),desc(IndCnt))
ggplot(top.State, aes(x=reorder(State, TotCnt), y=IndCnt, fill=Industry)) +
geom_bar(stat='identity') +
labs(title="Bar Chart",
subtitle="Top 10 States by Industry Count",
caption="Source: INC company dataset", x='States', y='Industry Counts') +
geom_text(aes(y=TotCnt-50, label=TotCnt), color='white', size=3) +
coord_flip()
Quesiton 2
Lets dig in on the state with the 3rd most companies in the data set. Imagine you work for the state and are interested in how many people are employed by companies in different industries. Create a plot that shows the average and/or median employment by industry for companies in this state (only use cases with full data, use R’s complete.cases() function.) In addition to this, your graph should show how variable the ranges are, and you should deal with outliers.
third.comp = inc%>%dplyr::filter(State=='NY')
third.comp = third.comp[complete.cases(third.comp), ]
third.comp = third.comp%>%dplyr::group_by(Industry)%>%dplyr::summarise(meanemp = mean(Employees, na.rm=TRUE))
ggplot(third.comp, aes(x=reorder(Industry, meanemp), y=meanemp)) +
geom_bar(stat='identity', fill='Orange') +
labs(title="Bar Chart",
subtitle="NY State Emp Count per Industry",
caption="For third largest state as per Industry Count", x='Industry', y='Emp Counts') +
geom_text(aes(y=meanemp-40, label=round(meanemp,0)), color='white', size=3) +
coord_flip()
Question 3
Now imagine you work for an investor and want to see which industries generate the most revenue per employee. Create a chart that makes this information clear. Once again, the distribution per industry should be shown.
rev.emp = inc[complete.cases(inc),]
rev.emp = inc%>%dplyr::group_by(Industry)%>%dplyr::summarise(TotEmp = sum(Employees, na.rm = TRUE), TotRev = sum(Revenue, na.rm = TRUE))
rev.emp$revperemp = rev.emp$TotRev/rev.emp$TotEmp
ggplot(rev.emp, aes(x=reorder(Industry, revperemp), y=revperemp)) +
geom_bar(stat='identity', fill='Orange') +
labs(title="Bar Chart",
subtitle="Industry wise Revenue per Employee",
caption="Source: INC company dataset", x='Industry', y='Revenue per Employee') +
geom_text(aes(y=revperemp-50000, label=round(revperemp,0)), color='white', size=3) +
coord_flip()