How to merge a shapefile with a .csv and create a graph - r

Question

I am trying to merge a shapefile and .csv file to make a map of election results. I can make a graph when I load the shapefile, but as soon as I merge it with the .csv, it says "Error in plot.window(...) : need finite 'xlim' values".
I have been reading online, and I think maybe I have to merge the csv file to the shapefile (I have been merging the shapefile to the csv file). However, the csv file (which contains the election results) has more values than the shapefile (which gives the coordinates for the districts). How can I create more districts to match the election results? And will this even solve my problem, or is there something else that I am missing? Also, the data is in spanish, but the relevant values Distritos=districts, partidos=political party, cargo=type of election, votos=votes.
library(maptools)
library(rgdal)
library(rgeos)
library(sf)
municipios <-readOGR("/Users/Desktop/Limite_partidos/Shapefile/Partidos.shp")
elec <- read.csv("/Users/Desktop/elections excel.csv", header = TRUE, stringsAsFactors = FALSE)
library(dplyr)
#merge datasets together
elec <-merge(elec, municipios, by=c("nam"), na.rm=TRUE)
plot(elec)
Link to election results (elec)
Link for shapefile (municipios)

Answer 1

First some sample data. This should be similar to what you get when
you read in your shapefile. The R spatial object is called a
SpatialPolygonsDataFrame. It carries a data.frame with the covariate
information about your polygons.
library(sp)
Sr1 <- Polygon(cbind(c(2,4,4,1,2),c(2,3,5,4,2)))
Sr2 <- Polygon(cbind(c(5,4,2,5),c(2,3,2,2)))
Sr3 <- Polygon(cbind(c(4,4,5,10,4),c(5,3,2,5,5)))
Sr4 <- Polygon(cbind(c(5,6,6,5,5),c(4,4,3,3,4)), hole = TRUE)
Srs1 <- Polygons(list(Sr1), "s1")
Srs2 <- Polygons(list(Sr2), "s2")
Srs3 <- Polygons(list(Sr3, Sr4), "s3/4")
SpP <- SpatialPolygons(list(Srs1,Srs2,Srs3), 1:3)
Spdf <- SpatialPolygonsDataFrame(SpP, data.frame(name = c("a", "b", "c"), row.names = c("s1", "s2", "s3/4")))
Now you have a spatial object that you can plot:
plot(Spdf)
and have a look at the attached data.frame of your spatial object. Here you need to have some identifier that you will match to your election results:
Spdf#data
You also have another dataframe with your 'election results' (Also with this identifier)
election <- data.frame(name = c("a", "c", "b"), voted = c(0.1, 0.2, 0.3))
Now match in the election results to you spatial object so you can plot it:
Spdf#data$voted <- election$voted[match(Spdf$name, election$name)]
To plot the polygons with the voted result as the colour of the polygon you need a pallette:
Spdf#data$colour <- heat.colors(3)[as.numeric(cut(Spdf#data$voted, 3))]
Then just plot:
plot(Spdf, col = Spdf#data$colour)
You can image that you will want to have more than 3 breaks in you
scale and you'll have more polygons but this is just an example. Good luck!

Answer 2

You can either select a single party (and then have a single value per administrative region) or facet by the party (and have as many small multiples as there are parties).
This really depends on what is your aim with the visualization. Given that there seems to be 48 results in your file the "small" multiples would be rather large, and filtering makes more sense.
For joining the shapefile and data frame with election results I suggest using one of the *_join functions from tidyverse package.
Consider this approach, built on assumption of filtering:
library(tidyverse)
library(sf)
tf_elec <- tempfile(fileext = ".csv") # create a temporary csv file
download.file("https://catalogo.datos.gba.gob.ar/dataset/1ae289f8-532c-4f69-a3c8-0268fe0ee390/resource/f8168491-4c38-4b03-82f1-b05fe43f8349/download/generales-2017.csv", tf_elec, quiet = T)
elec <- read_csv2(tf_elec) # read the data
tf_zip <- tempfile(fileext = ".zip") # a temoprary zip file
download.file("https://catalogo.datos.gba.gob.ar/dataset/627f65de-2510-4bf4-976b-16035828b5ae/resource/de607a34-b782-420f-93ed-35073a016e01/download/limite_partidos.zip", tf_zip, quiet = T)
unzip(tf_zip, files = 'Limite_partidos/GeoJSON/Partidos.geojson', exdir = tempdir(), junkpaths = T)
municipios <- st_read(paste0(tempdir(), '/Partidos.geojson'), quiet = T) # read the metro stations
src <- municipios %>%
left_join(elec, by = c('nam' = 'distrito')) %>%
filter(partido == 'VOTOS NULOS') #or what not... :)
ggplot() +
geom_sf(data = src, aes(fill = votos))
The most difficult part is downloading the data. The left_join() is in the second last chunk, and the very last is visualization - for the sake of simplicty I took the easy ggplot2 route, but also consider the excellent tmap package if you want your maps to really shine.