R Notebook
Leticia Salazar
09/05/2022
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Data 608 - Module 1: Principles of Data Visualization and Introduction to ggplot2
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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 :12\(~\)
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:
# Loading libraries
library(tidyverse)
library(ggplot2)
library(psych)# Insert your code here, create more chunks as necessary
# Offers an overview of what the data looks like, has 5,001 rows with 8 columns, along with the column names
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"…# looking deeper into the data set with the describe function
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\(~\)
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.
The bar chart below shows California is the top state with the largest amount of companies while Puerto Rico has the least.
# Answer Question 1 here
# sort by statem in descending order
ques_1 <- inc %>%
group_by(State) %>%
count(State) %>%
arrange(desc(n)) %>%
as_tibble(ques_1)
# plot bar chart
ggplot(ques_1, aes(x = reorder(State, n), y = n)) +
geom_bar(stat = "identity") +
theme_classic() +
coord_flip() +
xlab("State") +
ylab("Number of Companies") +
ggtitle("Number of Companies by State") +
geom_text(aes(label = n), vjust = 0.6, hjust = 1.2, size = 2, color="white")
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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.
Here’s a comparison for NY state and the country’s data set using boxplots. Notice the increase in outliers for the country’s plot than in NY and the change in mean and median for all the industries.
# Answer Question 2 here
# Based on question 1 we know NY is the third state with most companies so we filter it out
ny_state <- filter(inc, 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# using the whole data set to compare NY with
summary(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 :12# plotting NY state
ques_2a <- ny_state %>%
filter(complete.cases(.)) %>% # complete cases only
group_by(Industry) %>%
select(Industry, Employees)
# boxplot showing NY by industry
ggplot(ques_2a, mapping = aes(x = Industry, y = Employees)) +
geom_boxplot() +
theme_classic() +
labs(title = 'Distribution of Employment by Industry in NY', x = 'Industry', y = 'Number of Employees') +
coord_cartesian(ylim = c(0, 1500)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# comparison plot of the country
ques_2b <- inc %>%
filter(complete.cases(.)) %>% # complete cases only
group_by(Industry) %>%
select(Industry, Employees)
ggplot(ques_2b, mapping = aes(x = Industry, y = Employees)) +
geom_boxplot() +
theme_classic() +
labs(title = 'Distribution of Employment by Industry in the Country', x = 'Industry', y = 'Number of Employees') +
coord_cartesian(ylim = c(0, 1500)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
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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.
The charts created below assumes we are still looking into NY state but I also created a chart for the country’s data. In terms of which industries generate the most revenue, for NY you have Energy, Logistics & Transportation and IT services as the top 3. While for the country we have Computer Hardware, Energy and Construction.
# turning off scientific notation
options(scipen = 999)
# showing NY state only
ques_3a <- 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(ques_3a, aes(x = reorder(Industry, rev_per_emp), y = rev_per_emp)) +
geom_bar(stat = "identity") +
labs(title = "Revenue per Employee by Industry in NY", x = "Industry", y = "Revenue per Employee") +
theme_classic() +
coord_flip()
# Answer Question 3 here
# showing the country as a whole
ques_3b <- inc %>%
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(ques_3b, aes(x = reorder(Industry, rev_per_emp), y = rev_per_emp)) +
geom_bar(stat = "identity") +
labs(title = "Revenue per Employee by Industry in the Country", x = "Industry", y = "Revenue per Employee") +
theme_classic() +
coord_flip()