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<code>merge()</code> is a suitable tool for this job. Here is an example: <pre><code>set.seed(1) d1 <- data.frame(lat = 1:10, lon = 1:10, V2 = runif(10), V4 = rnorm(10)) d2 <- data.frame(lat = 1:10, lon = 1:10, V1 = runif(10), V3 = rnorm(10)) ## merge the data using `lat` and `lon` res <- merge(d1, d2, sort = FALSE) ## `sort = FALSE` stops R reordering rows ## get columns in right order res <- res[, c(1:2, order(colnames(res[, -(1:2)])) + 2)] </code></pre> Which gives: <pre><code>> res lat lon V1 V2 V3 V4 1 1 1 0.4820801 0.26550866 0.91897737 -0.8204684 2 2 2 0.5995658 0.37212390 0.78213630 0.4874291 3 3 3 0.4935413 0.57285336 0.07456498 0.7383247 4 4 4 0.1862176 0.90820779 -1.98935170 0.5757814 5 5 5 0.8273733 0.20168193 0.61982575 -0.3053884 6 6 6 0.6684667 0.89838968 -0.05612874 1.5117812 7 7 7 0.7942399 0.94467527 -0.15579551 0.3898432 8 8 8 0.1079436 0.66079779 -1.47075238 -0.6212406 9 9 9 0.7237109 0.62911404 -0.47815006 -2.2146999 10 10 10 0.4112744 0.06178627 0.41794156 1.1249309 </code></pre> Update based on revised Q: <pre><code>## dummy data set.seed(1) df3 <- data.frame(matrix(runif(60), ncol = 6)) names(df3) <- paste("df3Var", 1:6, sep = "") df3 <- cbind.data.frame(lat = 1:10, lon = 1:10, df3) df4 <- data.frame(matrix(runif(30), ncol = 3)) names(df4) <- paste("df4Var", 1:3, sep = "") df4 <- cbind.data.frame(lat = 1:10, lon = 1:10, df4) ## merge res2 <- merge(df3, df4, sort = FALSE) </code></pre> This gives: <pre><code>> head(res2) lat lon df3Var1 df3Var2 df3Var3 df3Var4 df3Var5 df3Var6 1 1 1 0.2655087 0.2059746 0.9347052 0.4820801 0.8209463 0.47761962 2 2 2 0.3721239 0.1765568 0.2121425 0.5995658 0.6470602 0.86120948 3 3 3 0.5728534 0.6870228 0.6516738 0.4935413 0.7829328 0.43809711 4 4 4 0.9082078 0.3841037 0.1255551 0.1862176 0.5530363 0.24479728 5 5 5 0.2016819 0.7698414 0.2672207 0.8273733 0.5297196 0.07067905 6 6 6 0.8983897 0.4976992 0.3861141 0.6684667 0.7893562 0.09946616 df4Var1 df4Var2 df4Var3 1 0.9128759 0.3390729 0.4346595 2 0.2936034 0.8394404 0.7125147 3 0.4590657 0.3466835 0.3999944 4 0.3323947 0.3337749 0.3253522 5 0.6508705 0.4763512 0.7570871 6 0.2580168 0.8921983 0.2026923 > names(res2) [1] "lat" "lon" "df3Var1" "df3Var2" "df3Var3" "df3Var4" "df3Var5" [8] "df3Var6" "df4Var1" "df4Var2" "df4Var3" </code></pre> OK, so now note the ordering. Assume we want to take variables in groups of 2 from <code>df3</code> with 1 variable from <code>df4</code> and within each of <code>df3</code> and <code>df4</code> the variables are in the correct order within themselves. For this we need to create an index vector <code>ord</code> that is: <pre><code>> ord [1] 1 2 7 3 4 8 5 6 9 </code></pre> which we then add <code>2</code> too (to cover the <code>lat</code> and <code>lon</code> columns in the merged data frame) <pre><code>> ord + 2 [1] 3 4 9 5 6 10 7 8 11 </code></pre> Once you have the sequence, we just need a way to use R's vectorised tools and a tiny bit of math to produce the sequence we want. I build the index up in two stages; i) first I work out where the columns <code>(1:6) + 2</code> of the merged data frame should be in <code>ord</code>, and then ii) I fill in the remaining spaces with the indexes in the merged data frame of the columns from the second data frame. <pre><code>ord <- numeric(length = sum(ncol(df3), ncol(df4)) - 4) ngrps <- 3 ningrps <- 2 ## i) want <- rep(seq_len(ningrps), ngrps) + rep(seq(from = 0, by = 3, length = prod(ngrps, ningrps) / 2), each = ningrps) ord[want] <- seq_len(prod(ngrps, ningrps)) ## ii) want <- ngrps * seq_len(ngrps) ord[want] <- seq(to = sum(ncol(df3), ncol(df4)) - 4, by = 1, length = ngrps) res3 <- res2[, c(1:2, ord+2)] </code></pre> That gives: <pre><code>> head(res3) lat lon df3Var1 df3Var2 df4Var1 df3Var3 df3Var4 df4Var2 df3Var5 1 1 1 0.2655087 0.2059746 0.9128759 0.9347052 0.4820801 0.3390729 0.8209463 2 2 2 0.3721239 0.1765568 0.2936034 0.2121425 0.5995658 0.8394404 0.6470602 3 3 3 0.5728534 0.6870228 0.4590657 0.6516738 0.4935413 0.3466835 0.7829328 4 4 4 0.9082078 0.3841037 0.3323947 0.1255551 0.1862176 0.3337749 0.5530363 5 5 5 0.2016819 0.7698414 0.6508705 0.2672207 0.8273733 0.4763512 0.5297196 6 6 6 0.8983897 0.4976992 0.2580168 0.3861141 0.6684667 0.8921983 0.7893562 df3Var6 df4Var3 1 0.47761962 0.4346595 2 0.86120948 0.7125147 3 0.43809711 0.3999944 4 0.24479728 0.3253522 5 0.07067905 0.7570871 6 0.09946616 0.2026923 </code></pre> which is the ordering you wanted. Now we can cook that into a little function: <pre><code>myMerge <- function(x, y, ngrps, ningrps, ...) { out <- merge(x, y, ...) ncols <- ncol(out) - 2 ord <- numeric(length = ncols) want <- rep(seq_len(ningrps), ngrps) + rep(seq(from = 0, by = ngrps, length = prod(ngrps, ningrps) / 2), each = ningrps) ord[want] <- seq_len(prod(ngrps, ningrps)) want <- ngrps * seq_len(ngrps) ord[want] <- seq(to = ncols, by = 1, length = ngrps) out <- out[, c(1:2, ord+2)] out } </code></pre> Which when used on <code>df3</code> and <code>df4</code> above gives: <pre><code>> myMerge(df3, df4, ngrps = 3, ningrps = 2, sort = FALSE) lat lon df3Var1 df3Var2 df4Var1 df3Var3 df3Var4 df4Var2 1 1 1 0.26550866 0.2059746 0.91287592 0.93470523 0.4820801 0.3390729 2 2 2 0.37212390 0.1765568 0.29360337 0.21214252 0.5995658 0.8394404 3 3 3 0.57285336 0.6870228 0.45906573 0.65167377 0.4935413 0.3466835 4 4 4 0.90820779 0.3841037 0.33239467 0.12555510 0.1862176 0.3337749 5 5 5 0.20168193 0.7698414 0.65087047 0.26722067 0.8273733 0.4763512 6 6 6 0.89838968 0.4976992 0.25801678 0.38611409 0.6684667 0.8921983 7 7 7 0.94467527 0.7176185 0.47854525 0.01339033 0.7942399 0.8643395 8 8 8 0.66079779 0.9919061 0.76631067 0.38238796 0.1079436 0.3899895 9 9 9 0.62911404 0.3800352 0.08424691 0.86969085 0.7237109 0.7773207 10 10 10 0.06178627 0.7774452 0.87532133 0.34034900 0.4112744 0.9606180 df3Var5 df3Var6 df4Var3 1 0.8209463 0.47761962 0.4346595 2 0.6470602 0.86120948 0.7125147 3 0.7829328 0.43809711 0.3999944 4 0.5530363 0.24479728 0.3253522 5 0.5297196 0.07067905 0.7570871 6 0.7893562 0.09946616 0.2026923 7 0.0233312 0.31627171 0.7111212 8 0.4772301 0.51863426 0.1216919 9 0.7323137 0.66200508 0.2454885 10 0.6927316 0.40683019 0.1433044 </code></pre> Which is again what you wanted. You could fiddle with the function definition so you don't need to specify both <code>ngrps</code> and <code>ningrps</code> as you can work one out from the other plus the number of columns in <code>df3</code> - 2. But I'll leave that as an exercise for the reader.
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