In R, there is often the need to merge two data.frame objects (say one with individual samples and the other with population coordinates. The merge() function is a pretty awesome though it may take a little getting used to.
Here are some things to remember:
- You need to have two data.frame objects to merge
- The first one in the function call will be the one merged _on-to _the second one is added to the first.
- Each will need a column to use as an index—it is a column that will be used to match rows of data. If they are the same column names then the function will do it automagically, if no common names are found in the names() of either data.frame objects, you can specify the columns using the optional by.x= and by.y= function arguments.
Here is an example. I’m going to load in some data from the
popgraph library. First, I’ll load up the library and hten grab the population meta data for the lophocereus data set we analyzed in Dyer & Nason (2004).
require(popgraph)
data(baja)
summary(baja)
Region Population Latitude Longitude
Baja :16 BaC : 1 Min. :22.93 Min. :-114.7
Sonora:13 Cabo : 1 1st Qu.:24.45 1st Qu.:-112.6
CP : 1 Median :27.95 Median :-111.8
Ctv : 1 Mean :27.33 Mean :-111.8
ELR : 1 3rd Qu.:29.59 3rd Qu.:-110.7
IC : 1 Max. :31.95 Max. :-109.5
(Other):23
The graph itself has nodes indicated as populations and perhaps we are interested in plotting node size as a function of spatial location. We can grab the names and sizes from the popgraph object (it is a kind of igraph ) by:
data(lopho) df.nodes <- data.frame( Population=V(lopho)$name, Size=V(lopho)$size ) summary(df.nodes) Population Size BaC : 1 Min. : 2.500 CP : 1 1st Qu.: 5.291 Ctv : 1 Median : 6.860 LaV : 1 Mean : 7.770 LF : 1 3rd Qu.:10.925 Lig : 1 Max. :16.001 (Other):15
Now we have baja and df.nodes as two data.frames and can merge them by their common column `Population`. If we merge df.nodesonto baja then we get the new data.frame:
merge( baja, df.nodes ) Population Region Latitude Longitude Size 1 BaC Baja 26.59 -111.79 12.158810 2 CP Sonora 27.95 -110.66 7.870725 3 Ctv Baja 29.73 -114.72 3.880886 4 LaV Baja 24.04 -109.99 3.533757 5 LF Sonora 30.68 -112.27 8.472215 6 Lig Baja 25.73 -111.27 4.731355 7 PL Sonora 30.39 -112.58 6.692795 8 PtC Baja 24.19 -111.15 4.684652 9 PtP Baja 29.03 -113.90 10.925375 10 SenBas Sonora 31.95 -112.87 9.116705 11 Seri Sonora 28.88 -111.96 2.500000 12 SG Sonora 29.40 -112.05 11.027530 13 SI Sonora 29.75 -112.50 11.521450 14 SLG Baja 29.59 -114.40 5.955645 15 SN Sonora 28.82 -111.80 8.325785 16 SnE Baja 24.45 -110.70 11.828220 17 SnF Baja 30.76 -114.73 6.325655 18 SnI Baja 27.29 -113.02 5.466695 19 StR Baja 24.91 -111.62 6.859545 20 TS Sonora 28.41 -111.37 16.001165 21 TsS Baja 23.58 -110.34 5.290570
but if we do it the other way, we get:
merge(df.nodes,baja) Population Size Region Latitude Longitude 1 BaC 12.158810 Baja 26.59 -111.79 2 CP 7.870725 Sonora 27.95 -110.66 3 Ctv 3.880886 Baja 29.73 -114.72 4 LaV 3.533757 Baja 24.04 -109.99 5 LF 8.472215 Sonora 30.68 -112.27 6 Lig 4.731355 Baja 25.73 -111.27 7 PL 6.692795 Sonora 30.39 -112.58 8 PtC 4.684652 Baja 24.19 -111.15 9 PtP 10.925375 Baja 29.03 -113.90 10 SenBas 9.116705 Sonora 31.95 -112.87 11 Seri 2.500000 Sonora 28.88 -111.96 12 SG 11.027530 Sonora 29.40 -112.05 13 SI 11.521450 Sonora 29.75 -112.50 14 SLG 5.955645 Baja 29.59 -114.40 15 SN 8.325785 Sonora 28.82 -111.80 16 SnE 11.828220 Baja 24.45 -110.70 17 SnF 6.325655 Baja 30.76 -114.73 18 SnI 5.466695 Baja 27.29 -113.02 19 StR 6.859545 Baja 24.91 -111.62 20 TS 16.001165 Sonora 28.41 -111.37 21 TsS 5.290570 Baja 23.58 -110.34
Hope this helps.