Tuesday, December 27, 2016

Analyzing the 2015 California Health Interview Survey in R

A few years ago, I wrote about how to analyze the 2012 California Health Interview Survey in R. In 2012, plans for Covered California (Obamacare in California) were just beginning to take shape. Today, Covered California is a relatively mature program and it is arguably the most successful implementation of the Affordable Care Act in the United States. This month, UCLA's Center for Health Policy released the 2015 California Health Interview Survey (CHIS for short). With this fantastic new data set, we can measure the impact of Covered California in its second year. In this brief post, I'll review the basics of the working with CHIS data in R by way of a simple example. My hope is to inspire other R users to dive into this unique data set.

The CHIS Quickstart guide for R

Though CHIS is a complex survey, it's simple to work with CHIS data in R. Here's how to get started:
• Head over to the CHIS site and create an account.
• CHIS data is divided into 3 groups, child, adolescent, and adult. We'll work with the adult data below.
• You'll also want to download the appropriate data dictionary for your data set. The dictionary provides excellent documentation about the hundreds of variables covered by CHIS. If it's your first time working with CHIS, I recommend a quick skim of the entire dictionary to get a sense of the kinds of things covered by the survey.
Once you've downloaded the data, to bring it into R you can use the foreign package:
# Read CHIS file
library(foreign)
CHIS   <- read.dta(file, convert.factors = TRUE)


The most important thing to understand about CHIS data is how to use the replicate weights RAKEDW0-RAKED80. I covered the use of replicate weights in detail in this post. The important points about replicate weights in CHIS are:
• Use RAKEDW0 for estimating means and counts in CHIS. RAKEDW0 is designed so that it's sum across all rows in the CHIS data is equal to the total non-institutionalized adult population of California.
• Use RAKEDW1-RAKED80 for estimating variances as described here.
As an example, let's start by getting counts of health insurance coverage by type. For this we have two insurance type variable INSTYPE and the new INS9TP which gives a more detailed breakdown of insurance types.
# tabulate the data
print(as.data.frame(xtabs(rakedw0~instype, CHIS, drop.unused.levels = TRUE)))
#              instype       Freq
# 1           UNINSURED  2910380.5
# 2 MEDICARE & MEDICAID  1561496.7
# 3   MEDICARE & OTHERS  1646743.6
# 4       MEDICARE ONLY  2129841.7
# 5            MEDICAID  6239539.9
# 6    EMPLOYMENT-BASED 12193686.7
# 7 PRIVATELY PURCHASED  1985807.9
# 8        OTHER PUBLIC   415154.8


One interesting health behavior that CHIS tracks is fast food consumption. To create the variable AC31, CHIS asked respondents about the number of times they ate fast food in the past week. This simple script explores how fast food consumption behavior interacts with health insurance coverage type:

Already with this superficial analysis we can see some interesting things. First we notice that the uninsured are eating fast food more often than the non-Medicaid insured. The uninsured's fast food behavior looks quite similar to the Medicaid population while the fast food behaviour of the employment-based insured resembles the behaviour of the private purchase group. And most importantly, everyone is eating too much fast food.

Conclusion

I hope this simple example inspires you to investigate CHIS data on your own. I think it would be especially interesting to see some further analysis of the nearly 3 million Californians who remain uninsured despite the relative success of Covered California. Some interesting background research on this topic can be found here and here. Feel free to get in touch if you are working with CHIS data to improve public health in California.

Sunday, January 3, 2016

Color Quantization in R

In this post, we'll look at a simple method to identify segments of an image based on RGB color values. The segmentation technique we'll consider is called color quantization. Not surprisingly, this topic lends itself naturally to visualization and R makes it easy to render some really cool graphics for the color quantization problem.

The code presented in detail below is packaged concisely in this github gist:

By sourcing this script in R, all the required images will be fetched and some demo visualizations will be rendered.

Color Quantization

Digital color images can be represented using the RGB color model. In a digital RGB image, each pixel is associated with a triple of 3 channel values red, green, and blue. For a given pixel in the image, each channel has an intensity value (e.g. an integer in the range from 0 to 255 for an 8-bit color representation or a floating point number in the range from 0 to 1). To render a pixel in a particular image, the intensity values of three RGB channels are combined to yield a specific color value. This RGB illumination image from Wikipedia give some idea of how the three RGB channels can combine to form new colors:

The goal of image segmentation, is to take a digital image and partition it into simpler regions. By breaking an image into simpler regions, it often becomes easier to identify interesting superstructure in an image such as edges of objects. For example, here's a possible segmentation of the Wikipedia RGB illumination image into 8 segments:

This segmentation picks out all of the solid color regions in the original image (excluding the white center) and discards much of the finer details of the image.

There are many approaches to segmenting an image but here we'll just consider a fairly simple one using K-means. The k-means algorithm attempts to partition a data set into k clusters. Our data set will be the RBG channel values for each pixel in a given image and we'll choose k to coincide with the number of partitions we'd like to extract from the region. By clustering over the RGB channel values, we'll tend to get clusters whose RGB channel values are relatively "close" in terms of Euclidean distance. If the choice of k is a good one, the color values of the pixels within a cluster will be very close to each other and the color values of pixels within two different clusters will be fairly distinct.

Implementing Color Segmentation in R

This beautiful image of a mandrill is famous in image processing (it's also in the public domain like all images in this post).

To load this PNG image into R, we'll use the PNG package:
library("png")
if(!file.exists("mandrill.png")){
destfile="mandrill.png")
}

# load the PNG into an RGB image object

# This mandrill is 512 x 512 x 3 array
dim(mandrill)
## [1] 512 512   3

In R, an RGB image is represented as an n by m by 3 array. The last dimension of this array is the channel (1 for red, 2 for green, 3 for blue). Here's what the three RGB channels of the image look like:

Here are some ways to view image data directly from within R:
library("grid")
library("gridExtra")

### EX 1: show the full RGB image
grid.raster(mandrill)

### EX 2: show the B channel in gray scale representing pixel intensity
grid.raster(mandrill[,,3])

### EX 3: show the 3 channels in separate images
# copy the image three times
mandrill.R = mandrill
mandrill.G = mandrill
mandrill.B = mandrill

# zero out the non-contributing channels for each image copy
mandrill.R[,,2:3] = 0
mandrill.G[,,1]=0
mandrill.G[,,3]=0
mandrill.B[,,1:2]=0

# build the image grid
img1 = rasterGrob(mandrill.R)
img2 = rasterGrob(mandrill.G)
img3 = rasterGrob(mandrill.B)
grid.arrange(img1, img2, img3, nrow=1)

Now let's segment this image. First, we need to reshape the array into a data frame with one row for each pixel and three columns for the RGB channels:
# reshape image into a data frame
df = data.frame(
red = matrix(mandrill[,,1], ncol=1),
green = matrix(mandrill[,,2], ncol=1),
blue = matrix(mandrill[,,3], ncol=1)
)

Now, we apply k-means to our data frame. We'll choose k=4 to break the image into 4 color regions.
### compute the k-means clustering
K = kmeans(df,4)
df$label = K$cluster

### Replace the color of each pixel in the image with the mean
### R,G, and B values of the cluster in which the pixel resides:

# get the coloring
colors = data.frame(
label = 1:nrow(K$centers), R = K$centers[,"red"],
G = K$centers[,"green"], B = K$centers[,"blue"]
)

# merge color codes on to df
# IMPORTANT: we must maintain the original order of the df after the merge!
df$order = 1:nrow(df) df = merge(df, colors) df = df[order(df$order),]
df$order = NULL  Finally, we have to reshape our data frame back into an image: # get mean color channel values for each row of the df. R = matrix(df$R, nrow=dim(mandrill)[1])
G = matrix(df$G, nrow=dim(mandrill)[1]) B = matrix(df$B, nrow=dim(mandrill)[1])

# reconstitute the segmented image in the same shape as the input image
mandrill.segmented = array(dim=dim(mandrill))
mandrill.segmented[,,1] = R
mandrill.segmented[,,2] = G
mandrill.segmented[,,3] = B

# View the result
grid.raster(mandrill.segmented)

Here is our segmented image:

Color Space Plots in Two and Three Dimensions

Color space is the three dimensional space formed by the three RGB channels. We can get a better understanding of color quantization by visualizing our images in color space. Here are animated 3d plots of the color space for the mandrill and the segmented mandrill:

These animations were generated with the help of the rgl package:
library("rgl")
# color space plot of mandrill
open3d()
plot3d(df$red, df$green, df$blue, col=rgb(df$red, df$green, df$blue),
xlab="R", ylab="G", zlab="B",
size=3, box=FALSE, axes=TRUE)
play3d( spin3d(axis=c(1,1,1), rpm=3), duration = 10 )

# color space plot of segmented mandrill
open3d()
plot3d(df$red, df$green, df$blue, col=rgb(df$R, df$G, df$B),
xlab="R", ylab="G", zlab="B",
size=3, box=FALSE)
play3d( spin3d(axis=c(1,1,1), rpm=3), duration = 10 )

# Use
# movie3d( spin3d(axis=c(1,1,1), rpm=3), duration = 10 )
# instead of play3d to generate GIFs (requires imagemagick).

To visualize color space in two dimensions, we can use principle components analysis. Principle components transforms the original RGB coordinate system into a new coordinate system UVW. In this system, the U coordinate captures as much of the variance in the original data as possible and the V coordinate captures as much of the variance as possible after factoring out U. So after performing PCA, most of the variation in the data should be visible by plotting in the UV plane. Here is the color space projection for the mandrill:

and for the segmented mandrill:

Here is the code to generate these projections:
require("ggplot2")

# perform PCA on the mandril data and add the uv coordinates to the dataframe
PCA = prcomp(df[,c("red","green","blue")], center=TRUE, scale=TRUE)
df$u = PCA$x[,1]
df$v = PCA$x[,2]

# Inspect the PCA
# most of the cumulative proportion of variance in PC2 should be close to 1.
summary(PCA)

#Importance of components:
#                          PC1    PC2     PC3
#Standard deviation     1.3903 0.9536 0.39695
#Proportion of Variance 0.6443 0.3031 0.05252
#Cumulative Proportion  0.6443 0.9475 1.00000

# mandrill
ggplot(df, aes(x=u, y=v, col=rgb(red,green,blue))) +
geom_point(size=2) + scale_color_identity()

# segmented mandrill
ggplot(df, aes(x=u, y=v, col=rgb(R,G,B))) +
geom_point(size=2) + scale_color_identity()