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### Outline lab 7 - three examples of Principle Component Analysis:

1. 10th grade student demographics & school safety (ELS, 2002 public-use data)

Next two PCA examples included are adapted from Allison Horst’s ESM-206 course at UCSB (Horst, 2020):

1. California pollution burden example (from: OEHHA)
2. Combined pollution data & California county demographic data (from: CA census 2010)

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DATA SOURCE: This lab exercise utilizes the NCES public-use dataset: Education Longitudinal Study of 2002 (Lauff & Ingels, 2014) $$\color{blue}{\text{See website: nces.ed.gov}}$$

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library(FactoMineR)
library(factoextra)
library(skimr)
library(naniar)
library(ggfortify)
library(janitor)
library(tidyverse)
library(here)

### read in ELS-2002 lab data:

lab_data <- read_csv(here("data", "els_sub4.csv"))

### make all column names “lower_snake_case” style

lab_tidy <- lab_data %>%
clean_names()

### Prepare data for PCA

# remove variables that don't make sense in a PCA
lab_sub1 <- lab_tidy %>%
select(-stu_id,    # these are random numbers
-sch_id,
-byrace,    # nominal (non-ordered variable)
-byparace,  # nominal (non-ordered variable)
-byparlng,  # nominal (non-ordered variable)
-byfcomp,  # nominal (non-ordered variable)
-bypared, -bymothed, -byfathed,
-bysctrl, -byurban, -byregion)

# select columns and rename variables to have descriptive names
lab_sub2 <- lab_sub1 %>%
select(1:9,
bys20a, bys20h, bys20j, bys20k, bys20m, bys20n,
bys21b, bys21d, bys22a, bys22b, bys22c, bys22d,
bys22e, bys22g, bys22h, bys24a, bys24b) %>%
rename("stu_exp" = "bystexp",
"par_asp" = "byparasp",
"mth_test" = "bytxmstd",
"rd_test" = "bytxrstd",
"freelnch" = "by10flp",
"stu_tch" = "bys20a",
"putdownt" = "bys20h",
"safe" = "bys20j",
"disrupt" = "bys20k",
"gangs" = "bys20m",
"rac_fght" = "bys20n",
"fair" = "bys21b",
"strict" = "bys21d",
"stolen" = "bys22a",
"drugs" = "bys22b",
"t_hurt" = "bys22c",
"p_fight" = "bys22d",
"hit" = "bys22e",
"damaged" = "bys22g",
"bullied" = "bys22h",
"late" = "bys24a",
"skipped" = "bys24b")

# write a CSV datafile of the new subset with renamed columns (will use for lab 8)
write_csv(lab_sub2, here("data", "lab7-8_els2002_data_subset.csv"))

### Investigate missingness {naniar} & make data summary with {skimr}

# Plot number of missings by variable
gg_miss_var(lab_sub2)

# Look at summary of data using skimr::skim()
skim(lab_sub2)

pca1 <- lab_sub2 %>%
drop_na()

### run PCA with prcomp() (function does not permit NA values)

pca_out1 <- prcomp(pca1, scale = TRUE)

plot(pca_out1)

#summary(pca_out1)

### plot PCA biplot

jpeg(here("figures", "biplot_pca1.jpg"), res = 100) # to save the biplot

my_biplot <- autoplot(pca_out1,
colour = NA,
theme_minimal()

my_biplot

dev.off()
my_biplot

### alternative funtion to run & plot PCA biplot

PCA(pca1, scale.unit = TRUE, ncp = 20, graph = TRUE)

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### A. Get the data

Data:

ca_pb <- read_csv(here("data", "ca_pollution_burden.csv"))
ca_dem <- read_csv(here("data", "ca_census_demographics_2010.csv"))

### B. Do some cleaning

1. For the pollution burden data:
• Clean up the column headers
• Exclude any column that is a calculated percentile (contains ‘percentile’, ‘perc’, or ‘pctl’)
ca_pb_nopct <- ca_pb %>%
clean_names() %>%
select(-contains("pctl")) %>%
select(-contains("perc")) %>%
select(-latitude, - longitude)
1. For the demographic data:
• Clean up column names
ca_dem_clean <- ca_dem %>%
clean_names()

### C. PCA for pollution burden indicator variables

First, starting with ca_pb_nopct:

Note: The pollution burden and population characteristic variables are aggregates (averages) of existing variables in the data frame, so we won’t include those. That means we’ll include columns:

• From ozone:solid_waste, and
• From asthma:housing_burden

First, just selecting those:

ca_pb_subset <- ca_pb_nopct %>%
select(ozone:solid_waste, asthma:housing_burden)

To run pca we will use the prcomp function:

pb_pca <- prcomp(ca_pb_subset, scale = TRUE) # hmmm an error 

#### Look at the NA situation:

A little aside: the naniar package for exploring missingness! See: https://naniar.njtierney.com/

Use naniar::gg_miss_var() to plot the number of missings by variable:

# Plot number of missings by variable
gg_miss_var(ca_pb_subset)

Let’s say our conclusion is that there are missings, but not many (compared to the actual scope of the data). We’ll only keep our complete cases (census tracts without any missings).

Use tidyr::drop_na() with no variables specified to keep ONLY complete cases across all variables:

ca_pb_nona <- ca_pb_subset %>%
drop_na()

# Now check for NAs:
summary(ca_pb_nona)

# Or use skimr::skim()!
skim(ca_pb_nona)

Cool. No NAs, NOW let’s try PCA again:

my_ca_pca <- prcomp(ca_pb_nona, scale = TRUE)

plot(my_ca_pca)
jpeg(here("figures", "biplot_pca2.jpg"), res = 100) # to save the biplot

# Hmmm let's try something else (this requires ggfortify):
my_biplot <- autoplot(my_ca_pca,
colour = NA,
theme_minimal()

my_biplot

dev.off()
1. Join data by census tract (inner join)
ca_df <- ca_dem_clean %>%
inner_join(ca_pb_nopct, by = c("census_tract_number" = "census_tract"))

Look at the dataframe first & then use drop_na() to get complete cases only:

ca_df_nona <- ca_df %>%
drop_na()
1. Make a new subset for PCA, that includes % white and elderly, and some interesting pollution burden & health indicators:

Like (you can choose a different set):

• white_percent
• elderly_65_percent
• pm2_5
• pesticides
• traffic
• asthma
• cardiovascular_disease
• poverty

Make our subset:

my_sub <- ca_df_nona %>%
select(white_percent,
elderly_65_percent,
pm2_5,
pesticides,
traffic,
asthma,
cardiovascular_disease,
poverty)

Then run PCA:

my_dem_pca <- prcomp(my_sub, scale = TRUE)

Check it out a bit:

# Proportion of variance (& cumulative variance) explained by each PC
summary(my_dem_pca)

# Rotations (linear combinations for each PC):
my_dem_pca

Make a sweet biplot:

jpeg(here("figures", "biplot_pca3.jpg"), res = 100) # to save the biplot

my_dem_biplot <- autoplot(my_dem_pca,
colour = NA,
theme_minimal() +
scale_y_continuous(limits = c(-0.05, 0.05))

my_dem_biplot

dev.off()
• What are a few main things we can take out of this?
• What are the main correlations you notice?
• Are they in line with what you would expect, or is anything surprising?