Exploratory Data Analysis
Eric Mazzola
7/4/2017
- Exploratory Data Analysis
- Chapter 1: Exploring Categorical Data
- Chapter 2: Exploring Numerical Data
- Chapter 3: Numerical Summaries
- Chapter 4: Case Study
- Quiz 4
- How can we revitalize a region’s economy?
- Part 1: Use the code below to create a new variable, rural takes the value of rural if it has less than 1,000 people per square mile and the value of urban otherwise.
- Part 2: Examine the living standard of the US counties, using
per_capita_incomeas a measure, and how it differs by whether the county is rural or urban. To that end, do the following and interpret the results: - Part 3:Use the box plot you created above and identify an outlier, a rural county that has a per_capita_income higher than $60,000 a year when a typical rural county’s per capita income is less than $23,000 a year. What makes this rural county successful? Compare it with a typical rural county in the country (compare the county’s data with means of that of the rest of the rural counties)
Disclaimer: The content of this RMarkdown note came from a course called Exploratory Data Analysis in datacamp.
Exploratory Data Analysis
Chapter 1: Exploring Categorical Data
1.1 Contingency table review
comics <- read.csv("/resources/rstudio/comics.csv")
# Check levels of align
levels(comics$align)
## [1] "Bad" "Good" "Neutral"
## [4] "Reformed Criminals"
# Check the levels of gender
levels(comics$gender)
## [1] "Female" "Male" "Other"
# Create a 2-way contingency table
table(comics$align, comics$gender)
##
## Female Male Other
## Bad 1573 7561 32
## Good 2490 4809 17
## Neutral 836 1799 17
## Reformed Criminals 1 2 01.2 Dropping levels
# Load dplyr
library(dplyr)
tab <- table(comics$align, comics$gender)
# Print tab
tab
##
## Female Male Other
## Bad 1573 7561 32
## Good 2490 4809 17
## Neutral 836 1799 17
## Reformed Criminals 1 2 0
# Remove align level
comics <- comics %>%
filter(align != "Reformed Criminals") %>%
droplevels()1.3 Side-by-side barcharts
# Load ggplot2
library(ggplot2)
# Create side-by-side barchart of gender by alignment
ggplot(comics, aes(x = align, fill = gender)) +
geom_bar(position = "dodge")
# Create side-by-side barchart of alignment by gender
ggplot(comics, aes(x = gender, fill = align)) +
geom_bar(position = "dodge") +
theme(axis.text.x = element_text(angle = 90))
1.4 Counts vs. proportions (2)
# Plot of gender by align
ggplot(comics, aes(x = align, fill = gender)) +
geom_bar()
# Plot proportion of gender, conditional on align
ggplot(comics, aes(x = align, fill = gender)) +
geom_bar(position = "fill")
1.5 Marginal barchart
comics$align <- factor(comics$align,
levels = c("Bad", "Neutral", "Good"))
# Create plot of align
ggplot(comics, aes(x = align)) +
geom_bar()
1.6 Conditional barchart
# Plot of alignment broken down by gender
ggplot(comics, aes(x = align)) +
geom_bar() +
facet_wrap(~ gender)
1.7 Improve piechart
pies <- data.frame(read.csv("/resources/rstudio/pie.csv"))
# Put levels of flavor in decending order
lev <- c("apple", "key lime", "boston creme", "blueberry", "cherry", "pumpkin", "strawberry")
pies$flavor <- factor(pies$flavor, levels = lev)
# Create barchart of flavor
ggplot(pies, aes(x = flavor)) +
geom_bar(fill = "chartreuse") +
theme(axis.text.x = element_text(angle = 90))
Chapter 2: Exploring Numerical Data
cars <- read.csv("/resources/rstudio/cars.csv")2.1 Faceted histogram
# Load package
library(ggplot2)
# Learn data structure
str(cars)
## 'data.frame': 428 obs. of 19 variables:
## $ name : Factor w/ 425 levels "Acura 3.5 RL 4dr",..: 66 67 68 69 70 114 115 133 129 130 ...
## $ sports_car : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ suv : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ wagon : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ minivan : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ pickup : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ all_wheel : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ rear_wheel : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ msrp : int 11690 12585 14610 14810 16385 13670 15040 13270 13730 15460 ...
## $ dealer_cost: int 10965 11802 13697 13884 15357 12849 14086 12482 12906 14496 ...
## $ eng_size : num 1.6 1.6 2.2 2.2 2.2 2 2 2 2 2 ...
## $ ncyl : int 4 4 4 4 4 4 4 4 4 4 ...
## $ horsepwr : int 103 103 140 140 140 132 132 130 110 130 ...
## $ city_mpg : int 28 28 26 26 26 29 29 26 27 26 ...
## $ hwy_mpg : int 34 34 37 37 37 36 36 33 36 33 ...
## $ weight : int 2370 2348 2617 2676 2617 2581 2626 2612 2606 2606 ...
## $ wheel_base : int 98 98 104 104 104 105 105 103 103 103 ...
## $ length : int 167 153 183 183 183 174 174 168 168 168 ...
## $ width : int 66 66 69 68 69 67 67 67 67 67 ...
# Create faceted histogram
ggplot(cars, aes(x = city_mpg)) +
geom_histogram() +
facet_grid(~ suv)
2.2 Boxplots and density plots
# Filter cars with 4, 6, 8 cylinders
common_cyl <- filter(cars, ncyl %in% c(4, 6, 8))
# Create box plots of city mpg by ncyl
ggplot(common_cyl, aes(x = as.factor(ncyl), y = city_mpg)) +
geom_boxplot()
# Create overlaid density plots for same data
ggplot(common_cyl, aes(x = city_mpg, fill = as.factor(ncyl))) +
geom_density(alpha = .3)
2.3 Marginal and conditional histograms
# Create hist of horsepwr
cars %>%
ggplot(aes(horsepwr)) +
geom_histogram() +
ggtitle("Distribution of horsepower")
# Create hist of horsepwr for affordable cars
cars %>%
filter(msrp < 25000) %>%
ggplot(aes(horsepwr)) +
geom_histogram() +
xlim(c(90, 550)) +
ggtitle("Distribution of horsepower for cars less than $25,000")
2.4 Three binwidths
# Create hist of horsepwr with binwidth of 3
cars %>%
ggplot(aes(horsepwr)) +
geom_histogram(binwidth = 3) +
ggtitle("The distribution of horsepower with a binwidth of 3")
# Create hist of horsepwr with binwidth of 30
cars %>%
ggplot(aes(horsepwr)) +
geom_histogram(binwidth = 30) +
ggtitle("The distribution of horsepower with a binwidth of 30")
# Create hist of horsepwr with binwidth of 60
cars %>%
ggplot(aes(horsepwr)) +
geom_histogram(binwidth = 60) +
ggtitle("The distribution of horsepower with a binwidth of 60")
2.5 Box plots for outliers
# Construct box plot of msrp
cars %>%
ggplot(aes(x = 1, y = msrp)) +
geom_boxplot()
# Exclude outliers from data
cars_no_out <- cars %>%
filter(msrp < 100000)
# Construct box plot of msrp using the reduced dataset
cars_no_out %>%
ggplot(aes(x = 1, y = msrp)) +
geom_boxplot()
2.6 Plot selection
# Create plot of city_mpg
cars %>%
ggplot(aes(x = 1, y = city_mpg)) +
geom_boxplot()
# Create plot of width
cars %>%
ggplot(aes(x = width)) +
geom_density()
2.7 3 variable plot
# Facet hists using hwy mileage and ncyl
common_cyl %>%
ggplot(aes(x = hwy_mpg)) +
geom_histogram() +
facet_grid(ncyl ~ suv, labeller = label_both) +
ggtitle("Distribution of Highway MPG by ncyl and SUV")
Chapter 3: Numerical Summaries
3.1 Calculate center measures
library(gapminder)
# Create dataset of 2007 data
gap2007 <- filter(gapminder, year ==2007)
# Compute groupwise mean and median lifeExp
gap2007 %>%
group_by(continent) %>%
summarize(mean(lifeExp),
median(lifeExp))
## # A tibble: 5 x 3
## continent `mean(lifeExp)` `median(lifeExp)`
## <fctr> <dbl> <dbl>
## 1 Africa 54.80604 52.9265
## 2 Americas 73.60812 72.8990
## 3 Asia 70.72848 72.3960
## 4 Europe 77.64860 78.6085
## 5 Oceania 80.71950 80.7195
# Generate box plots of lifeExp for each continent
gap2007 %>%
ggplot(aes(x = continent, y = lifeExp)) +
geom_boxplot()
3.2 Calculate spread measures
# Compute groupwise measures of spread
gap2007 %>%
group_by(continent) %>%
summarize(sd(lifeExp),
IQR(lifeExp),
n())
## # A tibble: 5 x 4
## continent `sd(lifeExp)` `IQR(lifeExp)` `n()`
## <fctr> <dbl> <dbl> <int>
## 1 Africa 9.6307807 11.61025 52
## 2 Americas 4.4409476 4.63200 25
## 3 Asia 7.9637245 10.15200 33
## 4 Europe 2.9798127 4.78250 30
## 5 Oceania 0.7290271 0.51550 2
# Generate overlaid density plots
gap2007 %>%
ggplot(aes(x = lifeExp, fill = continent)) +
geom_density(alpha = 0.3)
3.3 Choose measures for center and spread
# Compute stats for lifeExp in Americas
gap2007 %>%
filter(continent == "Americas") %>%
summarize(mean(lifeExp),
sd(lifeExp))
## # A tibble: 1 x 2
## `mean(lifeExp)` `sd(lifeExp)`
## <dbl> <dbl>
## 1 73.60812 4.440948
# Compute stats for population
gap2007 %>%
summarize(median(pop),
IQR(pop))
## # A tibble: 1 x 2
## `median(pop)` `IQR(pop)`
## <dbl> <dbl>
## 1 10517531 267020083.4 Transformations
# Create density plot of old variable
gap2007 %>%
ggplot(aes(x = pop)) +
geom_density()
# Transform the skewed pop variable
gap2007 <- gap2007 %>%
mutate(log_pop = log(pop))
# Create density plot of new variable
gap2007 %>%
ggplot(aes(x = log_pop)) +
geom_density()
3.5 Identify outliers
# Filter for Asia, add column indicating outliers
gap_asia <- gap2007 %>%
filter(continent == "Asia") %>%
mutate(is_outlier = lifeExp < 50)
# Remove outliers, create box plot of lifeExp
gap_asia %>%
filter(!is_outlier) %>%
ggplot(aes(x = 1, y = lifeExp)) +
geom_boxplot()
Chapter 4: Case Study
4.1 Spam and num_char
# Load packages
library(openintro)
library(ggplot2)
library(dplyr)
# Compute summary statistics
email %>%
group_by(spam) %>%
summarize(median(num_char),
IQR(num_char))
## # A tibble: 2 x 3
## spam `median(num_char)` `IQR(num_char)`
## <dbl> <dbl> <dbl>
## 1 0 6.831 13.58225
## 2 1 1.046 2.81800
# Create plot
email %>%
mutate(log_num_char = log(num_char)) %>%
ggplot(aes(x = spam, y = log_num_char)) +
geom_boxplot()
4.2 Spam and !!!
# Compute center and spread for exclaim_mess by spam
email %>%
group_by(spam) %>%
summarize(median(exclaim_mess),
IQR(exclaim_mess))
## # A tibble: 2 x 3
## spam `median(exclaim_mess)` `IQR(exclaim_mess)`
## <dbl> <dbl> <dbl>
## 1 0 1 5
## 2 1 0 1
# Create plot for spam and exclaim_mess
email %>%
mutate(log_exclaim_mess = log(exclaim_mess + .01)) %>%
ggplot(aes(x = log_exclaim_mess)) +
geom_histogram() +
facet_wrap(~ spam)
4.3 Collapsing levels
# Create plot of proportion of spam by image
email %>%
mutate(has_image = image > 0) %>%
ggplot(aes(x = has_image, fill = spam)) +
geom_bar(position = "fill")
4.4 Data Integrity
# Test if images count as attachments
email$image < 0
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## [3862] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3873] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3884] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3895] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3906] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3917] FALSE FALSE FALSE FALSE FALSE
sum(email$image < 0)
## [1] 0
sum(email$attach < 0)
## [1] 04.5 Answering questions with chains
# Question 1
email %>%
filter(dollar > 0) %>%
group_by(spam) %>%
summarize(median(dollar))
## # A tibble: 2 x 2
## spam `median(dollar)`
## <dbl> <dbl>
## 1 0 4
## 2 1 2
# Question 2
email %>%
filter(dollar > 10) %>%
ggplot(aes(x = spam)) +
geom_bar()
4.6 What’s in a number?
# Reorder levels
email$number <- factor(email$number, levels = c("none", "small", "big"))
# Construct plot of number
ggplot(email, aes(x = number)) +
geom_bar() +
facet_wrap(~ spam)
Quiz 4
How can we revitalize a region’s economy?
Assume that you live in a rural community and are interested in helping your community develop its economy. So you want to identify a rural community in the U.S. that enjoys a high living standards and investigate what makes them different from the rest of the rural America. For this brief analysis, use the countyComplete data set in the openintro R package. Click the link to open the manual and find the definitions of the variables in the data set.
Part 1: Use the code below to create a new variable, rural takes the value of rural if it has less than 1,000 people per square mile and the value of urban otherwise.
# Load data
data(countyComplete) # It comes from the openintro package
head(countyComplete, rows = 5)
## state name FIPS pop2010 pop2000 age_under_5 age_under_18
## 1 Alabama Autauga County 1001 54571 43671 6.6 26.8
## 2 Alabama Baldwin County 1003 182265 140415 6.1 23.0
## 3 Alabama Barbour County 1005 27457 29038 6.2 21.9
## 4 Alabama Bibb County 1007 22915 20826 6.0 22.7
## 5 Alabama Blount County 1009 57322 51024 6.3 24.6
## 6 Alabama Bullock County 1011 10914 11714 6.8 22.3
## age_over_65 female white black native asian pac_isl two_plus_races
## 1 12.0 51.3 78.5 17.7 0.4 0.9 NA 1.6
## 2 16.8 51.1 85.7 9.4 0.7 0.7 NA 1.5
## 3 14.2 46.9 48.0 46.9 0.4 0.4 NA 0.9
## 4 12.7 46.3 75.8 22.0 0.3 0.1 NA 0.9
## 5 14.7 50.5 92.6 1.3 0.5 0.2 NA 1.2
## 6 13.5 45.8 23.0 70.2 0.2 0.2 NA 0.8
## hispanic white_not_hispanic no_move_in_one_plus_year foreign_born
## 1 2.4 77.2 86.3 2.0
## 2 4.4 83.5 83.0 3.6
## 3 5.1 46.8 83.0 2.8
## 4 1.8 75.0 90.5 0.7
## 5 8.1 88.9 87.2 4.7
## 6 7.1 21.9 88.5 1.1
## foreign_spoken_at_home hs_grad bachelors veterans mean_work_travel
## 1 3.7 85.3 21.7 5817 25.1
## 2 5.5 87.6 26.8 20396 25.8
## 3 4.7 71.9 13.5 2327 23.8
## 4 1.5 74.5 10.0 1883 28.3
## 5 7.2 74.7 12.5 4072 33.2
## 6 3.8 74.7 12.0 943 28.1
## housing_units home_ownership housing_multi_unit
## 1 22135 77.5 7.2
## 2 104061 76.7 22.6
## 3 11829 68.0 11.1
## 4 8981 82.9 6.6
## 5 23887 82.0 3.7
## 6 4493 76.9 9.9
## median_val_owner_occupied households persons_per_household
## 1 133900 19718 2.70
## 2 177200 69476 2.50
## 3 88200 9795 2.52
## 4 81200 7441 3.02
## 5 113700 20605 2.73
## 6 66300 3732 2.85
## per_capita_income median_household_income poverty
## 1 24568 53255 10.6
## 2 26469 50147 12.2
## 3 15875 33219 25.0
## 4 19918 41770 12.6
## 5 21070 45549 13.4
## 6 20289 31602 25.3
## private_nonfarm_establishments private_nonfarm_employment
## 1 877 10628
## 2 4812 52233
## 3 522 7990
## 4 318 2927
## 5 749 6968
## 6 120 1919
## percent_change_private_nonfarm_employment nonemployment_establishments
## 1 16.6 2971
## 2 17.4 14175
## 3 -27.0 1527
## 4 -14.0 1192
## 5 -11.4 3501
## 6 -18.5 390
## firms black_owned_firms native_owned_firms asian_owned_firms
## 1 4067 15.2 NA 1.3
## 2 19035 2.7 0.4 1.0
## 3 1667 NA NA NA
## 4 1385 14.9 NA NA
## 5 4458 NA NA NA
## 6 417 NA NA NA
## pac_isl_owned_firms hispanic_owned_firms women_owned_firms
## 1 NA 0.7 31.7
## 2 NA 1.3 27.3
## 3 NA NA 27.0
## 4 NA NA NA
## 5 NA NA 23.2
## 6 NA NA 38.8
## manufacturer_shipments_2007 mercent_whole_sales_2007 sales
## 1 NA NA 598175
## 2 1410273 NA 2966489
## 3 NA NA 188337
## 4 0 NA 124707
## 5 341544 NA 319700
## 6 NA NA 43810
## sales_per_capita accommodation_food_service building_permits
## 1 12003 88157 191
## 2 17166 436955 696
## 3 6334 NA 10
## 4 5804 10757 8
## 5 5622 20941 18
## 6 3995 3670 1
## fed_spending area density
## 1 331142 594.44 91.8
## 2 1119082 1589.78 114.6
## 3 240308 884.88 31.0
## 4 163201 622.58 36.8
## 5 294114 644.78 88.9
## 6 108846 622.81 17.5
# Create a new variable, rural
countyComplete$rural <- ifelse(countyComplete$density < 1000, "rural", "urban")
countyComplete$rural <- factor(countyComplete$rural)Part 2: Examine the living standard of the US counties, using per_capita_income as a measure, and how it differs by whether the county is rural or urban. To that end, do the following and interpret the results:
Compute groupwise mean and median per_capita_income by rural
countyComplete %>%
group_by(rural) %>%
summarize(mean(per_capita_income),
median(per_capita_income))
## # A tibble: 2 x 3
## rural `mean(per_capita_income)` `median(per_capita_income)`
## <fctr> <dbl> <dbl>
## 1 rural 22117.13 21598.0
## 2 urban 30576.30 28521.5Compute groupwise measures of spread for per_capita_income by rural
countyComplete %>%
group_by(rural) %>%
summarize(sd(per_capita_income),
IQR(per_capita_income))
## # A tibble: 2 x 3
## rural `sd(per_capita_income)` `IQR(per_capita_income)`
## <fctr> <dbl> <dbl>
## 1 rural 4883.993 5498.00
## 2 urban 8597.632 9593.75Create faceted histogram for per_capita_income by rural
countyComplete %>%
ggplot(aes(x = per_capita_income)) +
geom_histogram() +
facet_wrap(~rural)
Create overlaid density plots for per_capita_income by rural
countyComplete %>%
ggplot(aes(x = per_capita_income, fill = rural)) +
geom_density(alpha = 0.3)
Create box plots of per_capita_income by rural
countyComplete %>%
ggplot(aes(x = 1, y = per_capita_income)) +
geom_boxplot()+ facet_wrap(~rural)
Part 3:Use the box plot you created above and identify an outlier, a rural county that has a per_capita_income higher than $60,000 a year when a typical rural county’s per capita income is less than $23,000 a year. What makes this rural county successful? Compare it with a typical rural county in the country (compare the county’s data with means of that of the rest of the rural counties)
countyComplete %>%
filter(per_capita_income > 60000) %>%
group_by(rural) %>%
summarize(mean(white), mean(black), mean(hs_grad), mean(bachelors))
## # A tibble: 1 x 5
## rural `mean(white)` `mean(black)` `mean(hs_grad)` `mean(bachelors)`
## <fctr> <dbl> <dbl> <dbl> <dbl>
## 1 rural 93.5 0.5 96.1 59.7
countyComplete %>%
filter(per_capita_income < 23000) %>%
group_by(rural) %>%
summarize(mean(white), mean(black), mean(hs_grad), mean(bachelors))
## # A tibble: 2 x 5
## rural `mean(white)` `mean(black)` `mean(hs_grad)` `mean(bachelors)`
## <fctr> <dbl> <dbl> <dbl> <dbl>
## 1 rural 81.4911 NA 79.98210 14.83098
## 2 urban 56.7000 33.96667 79.81429 23.00000