First painful attempt to do a Spatial map - r

Question

I am struggling to get my first map to work. I have read every document I could find but I am not able to pull it all together to view my data on a map.
This is what I have done so far.
1. I created a very basic data table with 3 observations and 5 variables as a very simple starting point.
str(Datawithlatlongnotvector)
'data.frame': 3 obs. of 5 variables:
$ Client: Factor w/ 3 levels "Jan","Piet","Susan": 2 1 3
$ Sales : int 100 1000 15000
$ Lat : num 26.2 33.9 23.9
$ Lon : num 28 18.4 29.4
$ Area : Factor w/ 3 levels "Gauteng","Limpopo",..: 1 3 2
(the Area is the provinces of South Africa and also is as per the SHP file that I downloaded, see below)
I downloaded a map of South Africa and placed all 3 files (.dbf, shp and shx) files in the same directory - previous error but I found the answer from another user's question. http://www.mapmakerdata.co.uk.s3-website-eu-west-1.amazonaws.com/library/stacks/Africa/South%20Africa/index.htm and selected Simple base map.
I created a map as follows :
SAMap <- readOGR(dsn = ".", layer = "SOU-level_1")
and I can plot the map of the country showing the provinces with plot(SAMap)
I can also plot the data
plot(datawithlatlong)
I saw the instructions how to make a SpatialPointsData frame and I did that :
coordinates(Datawithlatlong) = ~Lat + Lon
I do not know how to pull it all together and do the following :
Show the data (100,1000 and 15000) on the map with different colours i.e. between 1 and 500 is one colour, between 501 and 10 000 is one colour and above 10 000 is one colour.

Answer 1

Maybe trying ggplot2 with some function like:
map = ggplot(df, aes(long, lat, fill = Sales_cat)) + scale_fill_brewer(type = "seq", palette = "Oranges", name = "Sales") + geom_polygon()
With scale_fill_brewer you can represent scales in terms of colours on the map. You should create a factor variable that represents categories according to the range of sales ("Sales_cat"). In any case, the shape file must be transformed into a data.frame.

Answer 2

Try this for 'SAMap' as the country shapefile and 'datawithlatlong' as your data convereted to SpatialPointDataFrame:
library(maptools)
library(classInt)
library(RColorBrewer)
# Prepare colour pallete
plotclr <- brewer.pal(3,"PuRd")
class<-classIntervals(datawithlatlong#data$sales, n=3, style="fixed", fixedBreaks=c(0, 500,1000,10000)) # you can adjust the intervals here
colcode <- findColours(class, plotclr)
# Plot country map
plot(SAMap,xlim=c(16, 38.0), ylim=c(-46,-23))# plot your polygon shapefile with appropriate xlim and ylim (extent)
# Plot dataframe convereted to SPDF (in your step 5)
plot(datawithlatlong, col=colcode, add=T,pch=19)
# Creating the legend
legend(16.2, -42, legend=names(attr(colcode, "table")), fill=attr(colcode, "palette"), cex=0.6, bty="n") # adjust the x and y for fixing appropriate location for the legend

Answer 3

I generated a bigger dataset because I think with only 3 points it hard to see how things are working.
library(rgdal)
library(tmap)
library(ggmap)
library(randomNames)
#I downloaded the shapefile with the administrative area polygons
map <- readOGR(dsn = ".", layer = "SOU")
#the coordinate system is not part of the loaded object hence I added this information
proj4string(map) <- CRS("+init=epsg:4326")
# Some sample data with random client names and random region
ADM2 <- sample(map#data$ADM2, replace = TRUE, 50)
name <- randomNames(50)
sales <- sample(0:5000, 50)
clientData <- data.frame(id = 1:50, name, region = as.character(ADM2), sales,
stringsAsFactors = FALSE)
#In order to add the geoinformation for each client I used the awesome
#function `ggmap::geocode` which takes a character string as input an
#provides the lon and lat for the region, city ...
geoinfo <- geocode(clientData$region, messaging = FALSE)
# Use this information to build a Point layer
clientData_point <- SpatialPointsDataFrame(geoinfo, data = clientData)
proj4string(clientData_point) <- CRS("+init=epsg:4326")
Now the part I hope that answers the question:
# Adding all sales which occured in one region
# If there are 3 clients in one region, the sales of the three are
# summed up and returned in a new layer
sales_map <- aggregate(x = clientData_point[ ,4], by = map, FUN = sum)
# Building a map using the `tmap` package`
tm_shape(sales_map) + tm_polygons(col = "sales")
Edit:
Here is a ggplot2 solution because it seems you want to stick with it.
First, for ggplot you have to transform your SpatialPolygonDataFrame to an ordinary data.frame. Fortunately, broom::tidy() will do the job automatically.
Second, your Lat values are missing a -. I added it.
Third, I renamed your objects for less typing.
point_layer<- structure(list(Client = structure(c(2L, 1L, 3L),
.Label = c("Jan", "Piet", "Susan"),
class = "factor"),
Sales = c(100, 1000, 15000 ),
Lat = c(-26.2041, -33.9249, -23.8962),
Lon = c(28.0473, 18.4241, 29.4486),
Area = structure(c(1L, 3L, 2L),
.Label = c("Gauteng", "Limpopo", "Western Cape"),
class = "factor"),
Sale_range = structure(c(1L, 2L, 4L),
.Label = c("(1,500]", "(500,2e+03]", "(2e+03,5e+03]", "(5e+03,5e+04]"),
class = "factor")),
.Names = c("Client", "Sales", "Lat", "Lon", "Area", "Sale_range"),
row.names = c(NA, -3L), class = "data.frame")
point_layer$Sale_range <- cut(point_layer$Sales, c(1,500.0,2000.0,5000.0,50000.0 ))
library(broom)
library(ggplot2)
ggplot_map <- tidy(map)
ggplot() + geom_polygon(ggplot_map, mapping = aes(x = long, y = lat, group = group),
fill = "grey65", color = "black") +
geom_point(point_layer, mapping = aes(x = Lon, y = Lat, col = Sale_range)) +
scale_colour_brewer(type = "seq", palette = "Oranges", direction = 1)