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    <p>Here is a solution which gives you six dataframes, in the following order:</p> <pre><code>diff_eve &lt;1 diff_mor &lt;1 diff_tot &lt;1 diff_eve &gt;1 diff_mor &gt;1 diff_tot &gt;1 </code></pre> <p>Code:</p> <pre><code> cols &lt;- c("diff_eve", "diff_mor", "diff_tot") c(lapply(cols, function(x)subset(tbl_comp, eval(parse(text=x))&lt;1)), lapply(cols, function(x)subset(tbl_comp, eval(parse(text=x))&gt;1))) </code></pre> <p>gives you </p> <pre><code>[[1]] Meve_mean Mmor_mean Mtot_mean tot_meanMe tot_meanMm tot_sdMe tot_sdMm Wteve_mean Wtmor_mean Wttot_mean tot_meanwte tot_meanwtm tot_sdwte tot_sdwtm diff_eve diff_mor diff_tot 1 7774.442 39113.53 23443.99 258492587 246665241 35569170 30522100 10752.44 29254.64 18681.95 278901500 235415567 16743477 3922418 0.7230399 1.3370026 1.2549002 2 43374.117 119476.16 81425.14 NA NA NA NA 53658.84 98804.80 73007.11 NA NA NA NA 0.8083312 1.2092141 1.1153042 3 585562.724 1296530.34 941046.53 NA NA NA NA 715547.92 1001344.20 838032.04 NA NA NA NA 0.8183417 1.2947899 1.1229243 5 320338.198 209092.54 264715.37 NA NA NA NA 335384.20 217110.41 284695.43 NA NA NA NA 0.9551380 0.9630701 0.9298195 6 918368.020 577355.58 747861.80 NA NA NA NA 1013708.19 571826.16 824330.18 NA NA NA NA 0.9059491 1.0096698 0.9072357 [[2]] Meve_mean Mmor_mean Mtot_mean tot_meanMe tot_meanMm tot_sdMe tot_sdMm Wteve_mean Wtmor_mean Wttot_mean tot_meanwte tot_meanwtm tot_sdwte tot_sdwtm diff_eve diff_mor diff_tot 5 320338.2 209092.5 264715.4 NA NA NA NA 335384.2 217110.4 284695.4 NA NA NA NA 0.955138 0.9630701 0.9298195 [[3]] Meve_mean Mmor_mean Mtot_mean tot_meanMe tot_meanMm tot_sdMe tot_sdMm Wteve_mean Wtmor_mean Wttot_mean tot_meanwte tot_meanwtm tot_sdwte tot_sdwtm diff_eve diff_mor diff_tot 5 320338.2 209092.5 264715.4 NA NA NA NA 335384.2 217110.4 284695.4 NA NA NA NA 0.9551380 0.9630701 0.9298195 6 918368.0 577355.6 747861.8 NA NA NA NA 1013708.2 571826.2 824330.2 NA NA NA NA 0.9059491 1.0096698 0.9072357 [[4]] Meve_mean Mmor_mean Mtot_mean tot_meanMe tot_meanMm tot_sdMe tot_sdMm Wteve_mean Wtmor_mean Wttot_mean tot_meanwte tot_meanwtm tot_sdwte tot_sdwtm diff_eve diff_mor diff_tot 4 3866.547 23511.3 13688.92 NA NA NA NA 3422.172 11541.89 6902.05 NA NA NA NA 1.129852 2.037041 1.983313 [[5]] Meve_mean Mmor_mean Mtot_mean tot_meanMe tot_meanMm tot_sdMe tot_sdMm Wteve_mean Wtmor_mean Wttot_mean tot_meanwte tot_meanwtm tot_sdwte tot_sdwtm diff_eve diff_mor diff_tot 1 7774.442 39113.53 23443.99 258492587 246665241 35569170 30522100 10752.438 29254.64 18681.95 278901500 235415567 16743477 3922418 0.7230399 1.337003 1.2549002 2 43374.117 119476.16 81425.14 NA NA NA NA 53658.844 98804.80 73007.11 NA NA NA NA 0.8083312 1.209214 1.1153042 3 585562.724 1296530.34 941046.53 NA NA NA NA 715547.922 1001344.20 838032.04 NA NA NA NA 0.8183417 1.294790 1.1229243 4 3866.547 23511.30 13688.92 NA NA NA NA 3422.172 11541.89 6902.05 NA NA NA NA 1.1298517 2.037041 1.9833127 6 918368.020 577355.58 747861.80 NA NA NA NA 1013708.188 571826.16 824330.18 NA NA NA NA 0.9059491 1.009670 0.9072357 [[6]] Meve_mean Mmor_mean Mtot_mean tot_meanMe tot_meanMm tot_sdMe tot_sdMm Wteve_mean Wtmor_mean Wttot_mean tot_meanwte tot_meanwtm tot_sdwte tot_sdwtm diff_eve diff_mor diff_tot 1 7774.442 39113.53 23443.99 258492587 246665241 35569170 30522100 10752.438 29254.64 18681.95 278901500 235415567 16743477 3922418 0.7230399 1.337003 1.254900 2 43374.117 119476.16 81425.14 NA NA NA NA 53658.844 98804.80 73007.11 NA NA NA NA 0.8083312 1.209214 1.115304 3 585562.724 1296530.34 941046.53 NA NA NA NA 715547.922 1001344.20 838032.04 NA NA NA NA 0.8183417 1.294790 1.122924 4 3866.547 23511.30 13688.92 NA NA NA NA 3422.172 11541.89 6902.05 NA NA NA NA 1.1298517 2.037041 1.983313 </code></pre> <p>If you want the columns for the selection criterion in front of your dataframes, as in your example, you can use:</p> <pre><code>c(lapply(cols, function(x)subset(tbl_comp[,c(cols, setdiff(colnames(tbl_comp), cols))], eval(parse(text=x))&lt;1)), lapply(cols, function(x)subset(tbl_comp[,c(cols, setdiff(colnames(tbl_comp), cols))], eval(parse(text=x))&gt;1))) </code></pre>
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    1. COCan I somehow seperate them into different data frames ?
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    2. CO`lapply` gives you a list. You can store the output of the command above in a variable, e.g. `allframes` and then access the single alements with `allframes[[1]]` and so on.
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    3. COIf you want a single dataframe, you can do it similar to DJack's solution: `subset(tbl_comp, diff_eve<1)` for the first dataframe etc...
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