#r map graphics

[sasrandme]

Obesity maps 2011 - BRFSS - the data 2011 are online

Data are available here

I added the maps for overweight and obesity, download

script available here

rebuild cdc obesity maps with ggplot

[sasrandme]

R ggplot - rebuild cdc obesity maps - 1984-2011

redoing the cdc obesity maps with ggplot2

rebuild cdc obesity maps with ggplot

Slideshow

0.1 in the legend stands for 10%

first obese rates

second overweight rates

and if the slide show does not work - here is the link to the pictures

Table of Contents

1 get the data

2 compute rates and plot graphs

1 get the data

I downloaded the data from http://www.cdc.gov/brfss/technical_infodata/surveydata.htm

for the years 1984 - 1997 I use read.xport() (foreign package) on the sas xpt files

then the data sets became to large, so I used the ascii files read.fortran() and choose just a few columns

here is a resulting example data set (2006 - I computed the bmi2 column for checking)

2012-09: I added the maps for 2011 since the new data were out

head(x2006)

State month day year age weight height sex htm wkg bmi bmigr bmirisk 1 1 5 2 2006 66 263 503 2 160 11955 4669 3 2 2 1 9 19 2006 56 290 603 1 191 13182 3632 3 2 3 1 12 12 2006 40 230 511 1 180 10455 3215 3 2 4 1 4 29 2006 38 320 603 1 191 14545 4008 3 2 5 1 4 29 2006 52 120 504 2 163 5455 2064 1 1 6 1 8 2 2006 32 165 510 2 178 7500 2372 1 1 heightcm weightkg bmi2 1 160.02 119.29417 46.58764 2 190.50 131.54110 36.24695 3 180.34 104.32570 32.07799 4 190.50 145.14880 39.99664 5 162.56 54.43080 20.59763 6 177.80 74.84235 23.67467

2 compute rates and plot the graphs

library(ggplot2) library(scales) library(plyr) library(maps) ## map of the states (part of the map package) states_map <- map_data("state") states_map$region <- factor(states_map$region) ## got fips form here and saved it as txt; http://www.epa.gov/enviro/html/codes/state.html fips <- read.table("states.txt",sep="\t",header=T) fips$State.Name <- tolower(as.character(fips$State.Name)) ## build the graphs filenames <- paste("bmi",1984:2010,".rdata",sep="") for(file in filenames){ load(file) year <- substr(file,4,7) x <- get(paste("x",year,sep="")) ## for adding the year to the plot testdf <- data.frame(x2=-70,y2=49,year=year) ## for the first 4 years was no bmi in the data set ## I named my computed one "bmi" so I need another "bmi2" for the loop, not very sophisticated, ## but it works if(!("bmi2" %in% names(x))){ print(file) x$bmi2 <- x$bmi } ## bmi groups x$bmi2gr <- cut(x$bmi2,breaks=c(0,25,30,300),include.lowest=T,labels=c("1","2","3")) ## count x <- ddply(x,.(State),transform,perstate=sum(!is.na(bmi2))) x <- ddply(x,.(State,bmi2gr),transform,perstate.gr=sum(!is.na(bmi2))) dats <- unique(x[,c("State","bmi2gr","perstate","perstate.gr")]) dats <- dats[!is.na(dats$bmi2gr),] ## percents dats$perc <- dats$perstate.gr/dats$perstate dats$ow <- as.numeric(dats$bmi2gr) > 1 ## I just want the obese and overweight ## >= 25 dats2 <- dats[dats$ow==T,] dats2 <- ddply(dats2,.(State),summarize,perc=sum(perc)) dats2$gr <- "ow" ## >= 30 dats3 <- dats[dats$bmi2gr=="3",c("State","perc")] dats3$gr <- "obese" dats <- rbind(dats2,dats3) ## identify the states in the data set using the region names in the map (fips coded) dats <- merge(dats,fips[,2:3],by.x="State",by.y="FIPS.Code",all=T) dats$gr[is.na(dats$gr)] <- "obese" dats$State.Name <- factor(dats$State.Name) ## graph ggplot(dats[dats$gr=="obese",],aes(map_id = State.Name)) + geom_map(aes(fill=perc),colour="black",map = states_map) + expand_limits(x = states_map$long, y = states_map$lat) + scale_fill_gradientn(limits=c(0.1,0.7),colours=cols,guide = guide_colorbar(),na.value="grey50") + geom_text(data=testdf,aes(x=x2,y=y2,label=year),inherit.aes=F) ## save image ggsave(file=paste("obese",substr(file,4,7),".png",sep="")) }

output

[1] "bmi1984.rdata" Saving 12.7 x 7.01 in image [1] "bmi1985.rdata" Saving 12.7 x 7.01 in image [1] "bmi1986.rdata" Saving 12.7 x 7.01 in image [1] "bmi1987.rdata" Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image Saving 12.7 x 7.01 in image

Date: 2012-08-04 21:24:07 CEST

Author: mandy

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