In R I am trying to aggregate points data to a grid. The data for the grid (G-Econ) comes as a csv-file. In this file each row represent a grid-cell at 1x1 degree resolution and the given coordinates, in longitude and latitude, label the south-west corner of the grid-cell.

In trying to convert these points to a grid I run into the issue that the number of grid-cells in the output (so the eventual grid-raster) does not correspond with the number of cells in the input.

Basically the short version of my question is: How do I solve this?

The simplified G-Econ csv-file can be found here and below I describe step-by-step what I've been doing.

First I load the csv-file, limit the number of observations to only include cells in African countries, and create a spatial object out of the data frame.

# Load data
gecon$Cell<-1:nrow(gecon) # Create a grid cell identifier 

# Limit data to African countries

# Create spatial object
coordinates(g.africa)<-~LONGITUDE+LAT # Creates spatial object
proj4string(g.africa)=CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")


From this we see that Africa is covered by 3550 grid cells in the dataset
Next step it to transform the points into a grid:

# Transform points to grid
extent # Determine number of cols. and rows for grid
length(-26:63) # Number of cols: 90
length(-38:37) # Number of rows: 76
         crs="+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
grid<-rasterize(g.africa,rast,g.africa$Cell,fun="last") # Create grid

During this step the number of cells has increased -to 6840- this is due to the inclusion of cells that cover the oceans. I included a cell identifier so I can check how many of the new cells do not belong to a country.


There are 4053 NAs. Subtract these from the total number of grid-cells and the result is 2787 grid-cells, which is way less than the original number of cells for Africa. I transform the grid into polygons and this number is confirmed.

africa<-rasterToPolygons(grid) # Create spatial polygons
africa # See nfeatures 

So I guess something goes wrong when I rasterize the data, that somehow the interpolation of points to grid-cells results in this reduction. Due to my inexperience with GIS in R -and in general- I have been unable to solve this issue.

Does anyone have any suggestions on how I could solve this? Or maybe recommendations for a better method?

1 Answer 1


Your input file has multiple records at many coordinates: only 2787 unique coordinate pairs appear in it.

> g.coords <- g.africa@coords
> y <- g.coords %*% c(1,1000) # Turns coordinates into unique integral codes
> length(unique(y))

[1] 2787

Since 2787 + 4053 = 6840 = 90 * 76, everything works out correctly. Nothing is wrong with what R is doing.

  • Thanks. I hadn't spotted that yet although I could've known it. Do you have a suggestion with regard to what the best method would be if I want to keep those multiple records for cell that traverse country boundaries? Commented Jan 17, 2014 at 17:34
  • It depends on what those records mean and what kind of analysis you wish to perform. You might want to post a new question about that. Depending on its nature, it would be appropriate here (if you know what you want to do and need help performing the GIS processing) or on Cross Validated (if you need statistical advice on how to analyze your data).
    – whuber
    Commented Jan 17, 2014 at 17:39
  • Yeah I will probably post a new question. Basically what I am trying to do is merge event data (points) with grid cells. But for each grid cell I have to account for country characteristics (e.g. regime type). So when a grid-cell covers two separate countries the cell has to be in the data frame for each country. Like in the G-Econ dataset. Commented Jan 17, 2014 at 17:49

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