mapstats: An R Package for Geographic Visualization of Survey Data
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1 mapstats: An R Package for Geographic Visualization of Survey Data Samuel Ackerman Department of Statistics, Temple University ackerman@temple.edu June 14, 2015 amuel Ackerman (Temple University Department mapstats: of Statistics) An R Package for Geographic Visualization of Survey June Data 14, / 26
2 Outline 1 Motivation 2 Demonstration Samuel Ackerman (Temple University Department mapstats: of Statistics) An R Package for Geographic Visualization of Survey June Data 14, / 26
3 Choropleth maps Often in datasets (especially surveys), the distribution of a variable depends on a geographic variable. For instance, in a survey, mean income of respondents may vary by state, and visualizing this distribution is an important part of data exploration. In the general case of choropleth maps, each geographic unit (e.g. state) is associated with a number. In our case, these numbers are the values of the statistic calculated for each geographic unit. If you take all of the numbers across the units you have a distribution which is partitioned into several ranges, each of which is associated with a color. The distribution of the statistic can be visualized by coloring each geographic unit with the color of the range the statistic s value falls in. Samuel Ackerman (Temple University Department mapstats: of Statistics) An R Package for Geographic Visualization of Survey June Data 14, / 26
4 Motivation Since such geographic analysis is an essential visualization task for exploratory analysis, it should be as easy and flexible to implement as possible. Manually creating such maps in R involves several steps and packages: (1) calculation of variable statistics (survey package), (2) manipulation of data matrices, (3) calculation of the data intervals (classint package), (4) merging of the statistics to a map shapefile (maptools package), and (5) plotting (usually sp package). Each step requires some manual code, errors can occur at each step, and a user needs to be familiar with all tools to generate a quality map. Any flexibility would require significant modifications in code. Samuel Ackerman (Temple University Department mapstats: of Statistics) An R Package for Geographic Visualization of Survey June Data 14, / 26
5 Other tools in R Many shapefiles can be easily downloaded online and used in R using tools in the maptools package. Choroplethr package offers quick choropleth maps from common data sources (e.g. ACS, Census) and common shapefiles (e.g., US States, world map), but little flexibility for arbitrary shapefiles and visual options. Shiny package, which generates interactive graphics, has tools for choropleth maps from common shapefiles; however, there is some learning curve to using shiny applications. rmaps also has interactive choropleth maps but I am not familiar with it. In the future I would like to have an option to make maps created in my package interactive. Suggestions? Samuel Ackerman (Temple University Department mapstats: of Statistics) An R Package for Geographic Visualization of Survey June Data 14, / 26
6 Benefits of mapstats package Generation of choropleth maps for multiple analysis variables, statistics, and class variables in a single command. It is thus easy for casual users to use without needing to be familiar with R plotting and other utilities. Can be used with arbitrary shapefile maps and datasets where the geographic units match. Easy and flexible control over plot titles, layout, colors and interval breaks, and other visual options through R lattice package parameters. Variable statistics are returned by the function. Many options are demonstrated in the package s included demo file when downloading from CRAN. Samuel Ackerman (Temple University Department mapstats: of Statistics) An R Package for Geographic Visualization of Survey June Data 14, / 26
7 Demonstration on EPA data U.S. Environmental Protection Agency (EPA) daily-observed data on carbon monoxide (CO) and ozone measurements (parts per million) observed at outdoor monitor stations; data is analyzed for measurements from June and August of Use only the continental 48 states for analysis. Of these, Vermont has no station, nineteen states have only one station, thirteen have two, and Pennsylvania, Texas, and California have 13, 18, and 37, respectively. Samuel Ackerman (Temple University Department mapstats: of Statistics) An R Package for Geographic Visualization of Survey June Data 14, / 26
8 Initial visualization mapstats(d=chem_data, var=c("co","ozone"), stat="mean", d.geo.var="abbreviation", map.geo.var="stusps", map.file=usmap, num.row=2, cex.title=1.5) d is the dataset, var and stat are vectors of the analysis variables and statistics to be calculated. d.geo.var, map.geo.var are the names of the geographic variables in the dataset and the shapefile used (parameter map.file). By defualt, all plots are on their own page. Here, use num.row (num.col can also be used) to print two plots on a single page. cex.title controls the title font size. Samuel Ackerman (Temple University Department mapstats: of Statistics) An R Package for Geographic Visualization of Survey June Data 14, / 26
9 Initial visualization (continued) Mean of CO by STUSPS (unwtd.) WA OR ID NV CA UT AZ MT WY CO NM ND SD NE KS TX OK MN ME WI MI VTNH NY IA CT MA RI OH PA IL IN NJ DCDE MD MO WV KY VA AR TN NC SC MS AL GA LA FL Mean of ozone by STUSPS (unwtd.) WA OR ID NV CA UT AZ MT WY CO NM ND SD NE KS TX OK MN ME WI MI VTNH NY IA CT MA RI OH PA IL IN NJ DCDE MD MO WV KY VA AR TN NC SC MS AL GA LA FL Samuel Ackerman (Temple University Department mapstats: of Statistics) An R Package for Geographic Visualization of Survey June Data 14, / 26
10 Additional visual parameters mapstats(d=chem_data, var=c("co","ozone"), stat=c("mean", "quantile"), quantiles=.75, cex.title=1.2, d.geo.var="abbreviation", map.geo.var="stusps", map.file=usmap, num.row=2, num.col=2, separate=1, wt.label=false, map.label=false, geo.pretty="state", var.pretty=paste(c("co","ozone"),"measurements (ppm)")) quantiles lists the quantiles to calculate. separate is how to calculate the color breaks for the statistic values (either separately for each plot [3], all together [0], or separately by statistic [1] or variable [2]). Here they are by statistic. wt.label removes the weighting indicator from the titles, and map.label removes the state names to save space. geo.pretty and var.pretty rename the geographic and analysis variables in the plot titles. 14, / 26
11 Additional visual parameters (continued) Mean of CO measurements (ppm) by state Mean of ozone measurements (ppm) by state Q75 of CO measurements (ppm) by state Q75 of ozone measurements (ppm) by state Samuel Ackerman (Temple University Department mapstats: of Statistics) An R Package for Geographic Visualization of Survey JuneData 14, / 26
12 Groups and color palettes mapstats(d=chem_data, var=c("co","ozone"), stat=c("mean", "quantile"), quantiles=.75, cex.title=1.2, d.geo.var="abbreviation", map.geo.var="stusps", map.file=usmap, num.row=2, num.col=2, separate=2, wt.label=false, map.label=false, geo.pretty="state", var.pretty=paste(c("co","ozone"), "measurements (ppm)"), palettename=c("reds","greens"), ngroups=c(4,5), plotbyvar=false) separate=2, meaning breaks are by variable; plotbyvar means plots are ordered by statistic, not variable. ngroups and palettename indicate the number of groups (default is 4) to split into and the colors to use. These parameters work so that since breaks are by variable, and CO is first, the first settings of ngroups and palettename apply to CO. 14, / 26
13 Groups and color palettes (continued) Mean of CO measurements (ppm) by state Mean of ozone measurements (ppm) by state Q75 of CO measurements (ppm) by state Q75 of ozone measurements (ppm) by state Samuel Ackerman (Temple University Department mapstats: of Statistics) An R Package for Geographic Visualization of Survey JuneData 14, / 26
14 Groups and palettes (continued) If have multiple plots on the same page with class divisions where the same colors mean different intervals in different plots, it is better to use different palettes. The function also supports the use of user-defined sequential HCL color sequences (from R package colorspace sequential_hcl function), rather than the palettes from package RColorBrewer, through parameter colorvec rather than palettename. Can also define geographic divisions consisting of multiple units (for instance Census regions made up of multiple states). From now on just concentrate on ozone variable and analyze the statistics by month of observation. 14, / 26
15 Using class variables mapstats(d=chem_data, var="ozone", by.var="month", stat=c("mean", "quantile"), quantiles=.75, cex.title=1.5, d.geo.var="abbreviation", map.geo.var="stusps", map.file=usmap, num.row=2, separate=1, wt.label=false, map.label=false, geo.pretty="state", var.pretty="ozone measurements (ppm)", palettename=c("reds","greens"), layout=c(2,1)) by.var is a vector of class variables to calculate statistics by. layout (a lattice plotting parameter) controls the arrangement of the sub-panels for class variables within the statistics plots. Use a separate palette for statistic to directly compare the class variable panels to each other. 14, / 26
16 Using class variables (continued) Mean of ozone measurements (ppm) by state month=june month=august Q75 of ozone measurements (ppm) by state month=june month=august Samuel Ackerman (Temple University Department mapstats: of Statistics) An R Package for Geographic Visualization of Survey JuneData 14, / 26
17 Excluding small cells combinations (censoring) mapstats(d=chem_data, var="ozone", by.var="month", stat="mean", cex.title=1.5, d.geo.var="abbreviation", map.geo.var="stusps", map.file=usmap, wt.label=false, map.label=false, geo.pretty="state", var.pretty="ozone measurements (ppm)", layout=c(1,2), cell.min=32) Return to simple example of just mean of ozone by month. We want to exclude states with only one monitoring station from calculating the breaks for mean measurement by state. Setting parameter cell.min=32 (more than the number of days in a month) means all state-month combinations with fewer than 32 observations are excluded from calculating color breaks. These states will be left in white since they are not assigned a color. 14, / 26
18 Excluding small cells (continued) Mean of ozone measurements (ppm) by state month=june month=august , / 26
19 Overriding default titles chem_data$ozone_high <- 100*(chem_data$ozone > 0.025) mapstats(d=chem_data, var="ozone_high", by.var="month", stat="mean", cex.title=1.5, d.geo.var="abbreviation", map.geo.var="stusps", map.file=usmap, wt.label=false, map.label=false, layout=c(1,2), titles="percentage of station ozone observations by state exceeding ppm") Create indicator variable (ozone_high) for whether an ozone measurement exceeds ppm. Calculate its mean to show the percentage of measurements by state and month where ozone is high. Sometimes the default titles ("[Statistic] of [variable] by [geography]") don t make sense in a situation. Here we use the parameter titles to supply our own. 14, / 26
20 Overriding default titles (continued) Percentage of station ozone observations by state exceeding ppm month=june month=august , / 26
21 Overlaying maps with shapefiles site.locs <- unique(chem_data[, c("abbreviation","site.id", "Latitude","Longitude")]) coordinates(site.locs)= ~Longitude + Latitude sites.shp <- list("sp.points", site.locs, pch=1, cex=.6) mapstats(d=chem_data, var="ozone", stat="mean", d.geo.var="abbreviation", map.geo.var="stusps", map.file=usmap, var.pretty="ozone measurements (ppm)", geo.pretty="state", wt.label=false, map.label=false, cex.title=1.5, sp_layout.pars=list(sites.shp)) Create object (sites.shp) of station monitor locations. Overlay all plots with these points with parameter sp_layout.pars. Can similarly define shapefile objects of shapes (sp.polygons) and text labels (panel.text), for instance, to overlay on maps. 14, / 26
22 Overlaying maps with shapefiles (continued) Mean of CO measurements (ppm) by state , / 26
23 Overlaying maps with shapefiles (continued) ozone.shp <- readshapespatial("ozone_8hr_2008std_naa.shp") ozone.shp <- list("sp.polygons", ozone.shp, col="green", fill="green") mapstats(d=chem_data, var="ozone", stat="mean", d.geo.var="abbreviation", map.geo.var="stusps", map.file=usmap, var.pretty="ozone measurements (ppm)", geo.pretty="state", wt.label=false, map.label=false, cex.title=1.5, sp_layout.pars=list(ozone.shp)) Use external shapefile (ozone.shp) of nonattainment areas (areas where emissions consistently exceed national standards). The projections of the maps need to match. As before, use parameter sp_layout.pars to overlay the maps with shapefiles. 14, / 26
24 Overlaying maps with shapefiles (continued) Mean of ozone measurements (ppm) by state , / 26
25 References Ackerman, Samuel. "mapstats: Geographic Display of Survey Data Statistics" (2014). Available from CRAN for R versions and later. http: //cran.r-project.org/web/packages/mapstats/index.html U.S. Environmental Protection Agency (EPA). "8-Hour Ozone Area Information (2008 Standard)." U.S. Environmental Protection Agency (EPA). "Tables of Daily and Daily Summary Data" (2014). aqstmp/airdata/download_files.html 14, / 26
26 The End 14, / 26
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