We are attempting to create a US heatmap using only zipcode and corresponding values using R. While there are a number of examples that use lat and long data for creating such maps, is there one that will accept zipcodes and colour the corresponding regions on a map ? Preferably using some package that will produce presentation quality images (ggplot2, etc). Thanks in advance, – Raj.
Solved – US heatmap using zipcodes
data visualizationggplot2r
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Although there's room for improvement, here is a small attempt with simulated (heteroscedastic) data:
library(ggplot2)
set.seed(101)
x <- runif(100, min=1, max=10)
y <- rnorm(length(x), mean=5, sd=0.1*x)
df <- data.frame(x=x*70, y=y)
m <- lm(y ~ x, data=df)
fit95 <- predict(m, interval="conf", level=.95)
fit99 <- predict(m, interval="conf", level=.999)
df <- cbind.data.frame(df,
lwr95=fit95[,"lwr"], upr95=fit95[,"upr"],
lwr99=fit99[,"lwr"], upr99=fit99[,"upr"])
p <- ggplot(df, aes(x, y))
p + geom_point() +
geom_smooth(method="lm", colour="black", lwd=1.1, se=FALSE) +
geom_line(aes(y = upr95), color="black", linetype=2) +
geom_line(aes(y = lwr95), color="black", linetype=2) +
geom_line(aes(y = upr99), color="red", linetype=3) +
geom_line(aes(y = lwr99), color="red", linetype=3) +
annotate("text", 100, 6.5, label="95% limit", colour="black",
size=3, hjust=0) +
annotate("text", 100, 6.4, label="99.9% limit", colour="red",
size=3, hjust=0) +
labs(x="No. admissions...", y="Percentage of patients...") +
theme_bw()
Ok, so you can just look at the code by typing the name of the function at the R prompt, or use edit(pheatmap) to see it in your default editor.
Around line 14 and 23, you'll see that another function is called for computing the distance matrices (for rows and columns), given a distance function (R dist) and a method (compatible with hclust for hierarchical clustering in R). What does this function do? Use getAnywhere("cluster_mat") to print it on screen, and you soon notice that it does nothing more than returning an hclust object, that is your dendrogram computed from the specified distance and linkage options.
So, if you already have your distance matrix, change line 14 (rows) or 23 (columns) so that it reads, e.g.
tree_row = hclust(my.dist.mat, method="complete")
where my.dist.mat is your own distance function, and complete is one of the many methods available in hclust (see help(hclust)). Here, it is important to use fix(pheatmap) and not edit(pheatmap); otherwise, the edited function will not be callable in the correct environment/namespace.
This is a quick and dirty hack that I would not recommend with larger package. It seems to work for me at least, that is I can use a custom distance matrix with complete linkage for the rows.
In sum, assuming your distance matrix is stored in a variable named dd,
library(pheatmap)
fix(pheatmap)
# 1. change the function as you see fit
# 2. save and go back to R
# 3. if your custom distance matrix was simply read as a matrix, make sure
# it is read as a distance matrix
my.dist.map <- dd # or as.dist(dd)
Then, you can call pheatmap as you did but now it will use the results of hclust applied to my.dist.map with complete linkage. Please note that you just have to ensure that cluster_rows=TRUE (which is the default). Now, you may be able to change
- the linkage method
- choose between rows or columns
by editing the package function appropriately.
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Best Answer
Zip codes are not a good level because strictly speaking zip codes are assigned to streets and hence the map is actually a line file rather than polygon.
For mapping purpose, try zip code tabulation areas (ZCTA) by Census Bureau. You can find mapping resources in the link. For most zip codes, it's safe to attribute them the corresponding ZCTA, though lone zip codes may still exist (largely due to the volatility of the postal zip code system and special zip code like @PeterFlom mentioned in the comment). You may find some crosswalk files between zip code and ZCTA (like this) to better recode your data.
Also, see this thread for some technical hints on using R to plot zip code maps.