Advanced choroplethr – Customized Bins
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Choroplethr is great to use straight out of the box, but sometimes you’ll want to fix up some of the details. In a previous post, I showed how to modify the color scheme. Now I’ll explain how to set up customized bins when using choroplethr. If you just run a choroplethr function, you will get the automatically generated bins:
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| county_choropleth_acs(tableId="B19301") |
You can pretty easily modify the number of bins (use 1 for continuous), but you will still just get automatically generated breakpoints for the number of bins you selected:
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| # Number of bins | |
| county_choropleth_acs(tableId="B19301") | |
| county_choropleth_acs(tableId="B19301",num_colors=4) | |
| county_choropleth_acs(tableId="B19301",num_colors=1) # continuous |
If you want to customize these breakpoints, then you will want to go into the data frame, create a new variable to store your value bins, and then overwrite the “value” variable. Essentially, you are changing the data in the data frame to suit your purposes. Here you can see that I’ve done that and applied breakpoints of $0-$10K, $10K-$20K, $20K-$30K, $30K-$40K, and $40K+:
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| ###### Set fixed break points | |
| df_2010$df$breaks<-"" | |
| for (i in 1:nrow(df_2010$df)) | |
| { | |
| if (is.na(df_2010$df[i,"value"])) | |
| { | |
| df_2010$df[i,"breaks"] <- "$20-$30K" | |
| } else if (df_2010$df[i,"value"] < 10000) { | |
| df_2010$df[i,"breaks"] <- "$0-$10K" | |
| } else if (df_2010$df[i,"value"]>=10000 & df_2010$df[i,"value"]<20000) { | |
| df_2010$df[i,"breaks"] <- "$10-$20K" | |
| } else if (df_2010$df[i,"value"]>=20000 & df_2010$df[i,"value"]<30000) { | |
| df_2010$df[i,"breaks"] <- "$20-$30K" | |
| } else if (df_2010$df[i,"value"]>=30000 & df_2010$df[i,"value"]<40000) { | |
| df_2010$df[i,"breaks"] <- "$30-$40K" | |
| } else if (df_2010$df[i,"value"]>=40000) { | |
| df_2010$df[i,"breaks"] <- "$40K+" | |
| } | |
| } | |
| df_2010$df$value<-df_2010$df$breaks | |
| county_choropleth(df_2010$df) |
Full R Code:
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| ###### Settings | |
| library(choroplethr) | |
| library(acs) | |
| library(ggplot2) | |
| library(RColorBrewer) | |
| setwd("C:/Github/DataBlog") | |
| col.pal<-brewer.pal(7,"Greens") | |
| ###### API key | |
| # Need to go to http://api.census.gov/data/key_signup.html to set API key | |
| api.key.install("95147043cfb3460867a1fc678808514e4a3d04fc") | |
| ###### Basic ACS Table IDs | |
| # B19301 = Per Capita Income | |
| # B01003 = Population | |
| ###### Plotting | |
| ## Basic by State | |
| state_choropleth_acs(tableId="B19301") | |
| state_choropleth_acs(tableId="B19301") + | |
| labs(title="US 2011 Per Capita Income by State") | |
| ## Per Capita Income by County | |
| county_choropleth_acs(tableId="B19301") | |
| county_choropleth_acs(tableId="B19301",state_zoom="california") | |
| ## Population by County by Ethnicity | |
| county_choropleth_acs(tableId="B01003")+labs(title="Total US Population by County (2011)") | |
| county_choropleth_acs(tableId="B02008")+labs(title="US Population by County (2011) - White") | |
| county_choropleth_acs(tableId="B02009")+labs(title="US Population by County (2011) - Black ") | |
| county_choropleth_acs(tableId="B02011")+labs(title="US Population by County (2011) - Asian") | |
| county_choropleth_acs(tableId="B03001")+labs(title="US Population by County (2011) - Hispanic") | |
| # Changing colors, easy but doesn't work for Alaska) | |
| county_choropleth_acs(tableId="B19301") + | |
| scale_fill_brewer(palette=2) | |
| df_2015<-get_acs_data(tableId="B19301",map="county",endyear=2015) | |
| df_2014<-get_acs_data(tableId="B19301",map="county",endyear=2014) | |
| df_2013<-get_acs_data(tableId="B19301",map="county",endyear=2013) | |
| df_2012<-get_acs_data(tableId="B19301",map="county",endyear=2012) | |
| df_2011<-get_acs_data(tableId="B19301",map="county",endyear=2011) | |
| df_2010<-get_acs_data(tableId="B19301",map="county",endyear=2010) | |
| # Changing colors, harder and works for all states | |
| choro1<-CountyChoropleth$new(df_2015$df) | |
| choro1$title = "2015 Per Capita Income" | |
| choro1$ggplot_scale <- scale_fill_manual(name="Per Capita Income",values=c("blue", "red","orange","green","black","purple","yellow"), drop=FALSE) | |
| choro1$render() | |
| choro1$ggplot_scale <- scale_fill_manual(name="Per Capita Income",values=col.pal, drop=FALSE) | |
| choro1$render() | |
| ###### Animated Choropleth | |
| choro_list<-list() | |
| choro_list[[1]]<-county_choropleth(df_2010$df) | |
| choro_list[[2]]<-county_choropleth(df_2011$df) | |
| choro_list[[3]]<-county_choropleth(df_2012$df) | |
| choro_list[[4]]<-county_choropleth(df_2013$df) | |
| choro_list[[5]]<-county_choropleth(df_2014$df) | |
| choro_list[[6]]<-county_choropleth(df_2015$df) | |
| choroplethr_animate(choro_list) | |
| # Number of bins | |
| county_choropleth_acs(tableId="B19301") | |
| county_choropleth_acs(tableId="B19301",num_colors=4) | |
| county_choropleth_acs(tableId="B19301",num_colors=1) # continuous | |
| ###### Set fixed break points | |
| df_2010$df$breaks<-"" | |
| for (i in 1:nrow(df_2010$df)) | |
| { | |
| if (is.na(df_2010$df[i,"value"])) | |
| { | |
| df_2010$df[i,"breaks"] <- "$20-$30K" | |
| } else if (df_2010$df[i,"value"] < 10000) { | |
| df_2010$df[i,"breaks"] <- "$0-$10K" | |
| } else if (df_2010$df[i,"value"]>=10000 & df_2010$df[i,"value"]<20000) { | |
| df_2010$df[i,"breaks"] <- "$10-$20K" | |
| } else if (df_2010$df[i,"value"]>=20000 & df_2010$df[i,"value"]<30000) { | |
| df_2010$df[i,"breaks"] <- "$20-$30K" | |
| } else if (df_2010$df[i,"value"]>=30000 & df_2010$df[i,"value"]<40000) { | |
| df_2010$df[i,"breaks"] <- "$30-$40K" | |
| } else if (df_2010$df[i,"value"]>=40000) { | |
| df_2010$df[i,"breaks"] <- "$40K+" | |
| } | |
| } | |
| df_2010$df$value<-df_2010$df$breaks | |
| county_choropleth(df_2010$df) |
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