I have a question with regard to joining to datasets.

On the one hand I have information about the location of a conflict regarding its lat/long, radius, and area and how it changes over time. Here is a small snap for the conflict with the ID 91 in Chad.

enter image description here

On the other hand, I have georeferenced data about different aid projects in Chad over the time. Here as well a small snap of the dataset:

enter image description here

My goal is to aggregate the aid data to the conflict regions to finally analyze the relationship over time.

I have been reading many comments in the community but am still totally lost regarding how I can join the data using R. Would you recommend to first transform the conflict data set into polygons or is that not necessary? I would already be very very happy about a small hint.

1 Answer 1


Your primary challenge lies in the fact that the two datasets have no common key for you to use in joining them.

There are two ways of joining the two datasets - either based on country ('conflict territory' & 'loctext') or on the combination of lat-lon and radius from the first table.
However, it does appear that the geospatial information in the second table is not accurate at all, and that the lat-lon information available there is just for show (or crude plotting on maps), and not intended for georeferencing the information. I find it unlikely that all those projects took place on exactly 15-lat, 19-lon. As such, it would appear that the lat-lon information in the second table is not adequate for use, and you therefor only have the country-key as your remaining option. In order to use the country information, you'll have to make sure that the names match up, so that the join can work on correlating names - meaning, you have to make sure that all instances of 'Chad' are either called 'Chad' or 'Republic of Chad'.

Edit: just noticed the 'rname' element in the second table, which you can use for joining.

  • The second table shows just a snap and therefore contains also different lat/lon values for different aid projects in Chad. It was my mistake to make a snap only showing that part of the data. But it is defenitely important that I will mention this in my paper. Thank you for the input! I will therefore try to join the datasets on the combination of lat-lon and radius from the first table! May I ask, if you would recommend doing it in QGIS or in R?
    – ML8
    Apr 26, 2017 at 7:33
  • @ML8 - you have a lot more control in R - so I'd stick with it. Apr 30, 2017 at 20:58

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