Processing the list of data.frames with “apply” family of functions
I have a data frame which I then split into three (or any number) of dataframes.
What I’m trying to do is to automatically process each column in each dataframe and add
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Here is one option:
## reuse @Andrie's clag() function as lag() is silly clag <- function(x, n = 1) c(rep(NA, n), head(x, -n)) ## wrapper function to do the addition of lag variables for single DF foo <- function(df, n = 1) { nums <- sapply(df, is.numeric) ## which are numeric vars nams <- paste(names(df)[nums], "lag", sep = ".") ## generate new names foo.lag df[, nams] <- lapply(which(nums), function(id, df, n) clag(df[[id]], n = n), df = df, n = n) ## apply clag to each numeric variable df ## return } lapply(dds, foo)Which gives:
> lapply(dds, foo) $A v1 v2 v3 dim1 dim2 v1.lag v2.lag v3.lag 1 -1.15107343 1.47671548 -0.146501739 A J NA NA NA 2 -1.61068272 -0.85397093 -1.240187604 A J -1.15107343 1.47671548 -0.146501739 3 -1.23470282 -0.26194027 1.938344030 A J -1.61068272 -0.85397093 -1.240187604 4 -0.57874043 -0.44600138 0.326069423 A J -1.23470282 -0.26194027 1.938344030 5 0.16139066 -1.95804742 -0.744678169 A J -0.57874043 -0.44600138 0.326069423 6 -1.01497027 0.36850034 1.532640065 A J 0.16139066 -1.95804742 -0.744678169 7 0.72288058 -0.40115543 -0.686450596 A J -1.01497027 0.36850034 1.532640065 8 -0.51300447 0.19686310 0.441649595 A J 0.72288058 -0.40115543 -0.686450596 9 0.95439966 -2.03513002 -0.897784897 A J -0.51300447 0.19686310 0.441649595 10 -1.36736081 -0.41040962 -0.459403176 A J 0.95439966 -2.03513002 -0.897784897 11 0.59503846 0.28925760 -0.003095389 A J -1.36736081 -0.41040962 -0.459403176 12 -0.37951869 0.49551357 0.269412108 A J 0.59503846 0.28925760 -0.003095389 13 -0.52953401 -0.28433351 1.125505917 A J -0.37951869 0.49551357 0.269412108 14 -1.73466020 0.25442637 -1.094139749 A J -0.52953401 -0.28433351 1.125505917 15 0.08479137 -0.11688894 -1.034378216 A J -1.73466020 0.25442637 -1.094139749 16 -2.45854464 0.15806266 -2.275995527 A J 0.08479137 -0.11688894 -1.034378216 17 1.10663502 1.28587230 0.070334868 A J -2.45854464 0.15806266 -2.275995527 18 -0.01945585 1.63659116 -0.137040232 A J 1.10663502 1.28587230 0.070334868 19 0.59026606 -1.95724134 -0.480014930 A J -0.01945585 1.63659116 -0.137040232 20 -0.32245933 1.35372005 1.348717525 A J 0.59026606 -1.95724134 -0.480014930 21 -0.42560327 -1.30145328 2.020609480 A J -0.32245933 1.35372005 1.348717525 22 1.19550777 0.18417336 0.099232994 A J -0.42560327 -1.30145328 2.020609480 23 1.20198621 0.05926023 -0.171505810 A J 1.19550777 0.18417336 0.099232994 24 -1.00667141 1.32441782 0.056696824 A J 1.20198621 0.05926023 -0.171505810 $B v1 v2 v3 dim1 dim2 v1.lag v2.lag v3.lag 25 0.7878614 0.10354576 -0.69308980 B J NA NA NA 26 0.5824551 0.42319616 0.42734938 B J 0.7878614 0.10354576 -0.69308980 27 -0.2769730 1.51559382 -0.64106570 B J 0.5824551 0.42319616 0.42734938 28 -0.5736416 -1.58745816 -1.13274631 B J -0.2769730 1.51559382 -0.64106570 29 -1.9082145 -0.26148604 -0.04699411 B J -0.5736416 -1.58745816 -1.13274631 30 -1.6254549 0.39390814 -1.79993619 B J -1.9082145 -0.26148604 -0.04699411 31 0.3963274 1.79667985 0.92873142 B J -1.6254549 0.39390814 -1.79993619 32 -0.5889415 -0.04690351 1.43394978 B J 0.3963274 1.79667985 0.92873142 33 0.4683819 -1.34023029 0.18749782 B J -0.5889415 -0.04690351 1.43394978 34 0.7373052 -0.93470320 -1.14528378 B J 0.4683819 -1.34023029 0.18749782 35 -0.7751348 -1.26533917 0.11246728 B J 0.7373052 -0.93470320 -1.14528378 36 1.7786627 -0.19757164 0.14150980 B J -0.7751348 -1.26533917 0.11246728 37 1.8570412 -2.15174901 1.07751105 B K 1.7786627 -0.19757164 0.14150980 38 0.5128697 0.40112948 -0.94826274 B K 1.8570412 -2.15174901 1.07751105 39 0.8710264 -0.59978467 0.54462858 B K 0.5128697 0.40112948 -0.94826274 40 -0.3711512 -0.15632337 0.15832543 B K 0.8710264 -0.59978467 0.54462858 41 1.4505624 0.20915835 2.59369653 B K -0.3711512 -0.15632337 0.15832543 42 0.0871329 0.25440471 0.30096063 B K 1.4505624 0.20915835 2.59369653 43 -0.7398342 -1.72678544 0.45534941 B K 0.0871329 0.25440471 0.30096063 44 0.1953264 -0.60560630 -0.36884626 B K -0.7398342 -1.72678544 0.45534941 45 -0.2702493 0.50747209 -0.50699830 B K 0.1953264 -0.60560630 -0.36884626 46 0.2987449 0.46347722 1.20725190 B K -0.2702493 0.50747209 -0.50699830 47 -0.5682779 -0.71470625 -0.07865078 B K 0.2987449 0.46347722 1.20725190 48 -1.5291983 1.80092050 -1.73317395 B K -0.5682779 -0.71470625 -0.07865078 $C v1 v2 v3 dim1 dim2 v1.lag v2.lag v3.lag 49 0.06095825 -0.518263220 0.510999371 C K NA NA NA 50 0.40077713 0.477989115 0.855752036 C K 0.06095825 -0.518263220 0.510999371 51 0.06763037 0.802110426 -0.102536186 C K 0.40077713 0.477989115 0.855752036 52 -0.90530986 -0.005452101 -0.089703589 C K 0.06763037 0.802110426 -0.102536186 53 -0.79360209 0.299844218 -0.765164525 C K -0.90530986 -0.005452101 -0.089703589 54 1.34050298 -1.093705314 -0.955952912 C K -0.79360209 0.299844218 -0.765164525 55 0.45377712 0.054978470 0.382874895 C K 1.34050298 -1.093705314 -0.955952912 56 0.95283101 -0.564193352 1.458002944 C K 0.45377712 0.054978470 0.382874895 57 1.09157807 -1.351894599 -1.366084414 C K 0.95283101 -0.564193352 1.458002944 58 2.71993062 -1.126272793 1.374046159 C K 1.09157807 -1.351894599 -1.366084414 59 -0.04685281 0.423085481 -0.455903151 C K 2.71993062 -1.126272793 1.374046159 60 -0.31055449 0.818291875 0.400386018 C K -0.04685281 0.423085481 -0.455903151 61 -0.54904545 1.542272313 0.648135340 C K -0.31055449 0.818291875 0.400386018 62 -0.72914142 1.495482707 -0.212135011 C K -0.54904545 1.542272313 0.648135340 63 -0.27374611 -1.309254707 -0.005125047 C K -0.72914142 1.495482707 -0.212135011 64 0.87439910 -2.666588138 1.043778597 C K -0.27374611 -1.309254707 -0.005125047 65 1.07142042 0.446233778 -0.286784683 C K 0.87439910 -2.666588138 1.043778597 66 -0.10431808 0.510820156 0.405309569 C K 1.07142042 0.446233778 -0.286784683 67 -1.04006019 -0.041327622 1.202855549 C K -0.10431808 0.510820156 0.405309569 68 0.41084794 -0.376796559 -1.147032471 C K -1.04006019 -0.041327622 1.202855549 69 0.88329788 -0.344611311 1.862998306 C K 0.41084794 -0.376796559 -1.147032471 70 -0.67916248 1.396061431 0.697517685 C K 0.88329788 -0.344611311 1.862998306 71 3.55359528 -0.207825480 -0.949834845 C K -0.67916248 1.396061431 0.697517685 72 0.11329113 0.294747300 -0.955891419 C K 3.55359528 -0.207825480 -0.949834845For the last bit, the combine step, save the above:
dds <- lapply(dds, foo)then use
do.call()torbind()the individual data frames together, as in:df2 <- do.call(rbind, dds)which gives:
> head(df2) v1 v2 v3 dim1 dim2 v1.lag v2.lag v3.lag A.1 -1.1510734 1.4767155 -0.1465017 A J NA NA NA A.2 -1.6106827 -0.8539709 -1.2401876 A J -1.1510734 1.4767155 -0.1465017 A.3 -1.2347028 -0.2619403 1.9383440 A J -1.6106827 -0.8539709 -1.2401876 A.4 -0.5787404 -0.4460014 0.3260694 A J -1.2347028 -0.2619403 1.9383440 A.5 0.1613907 -1.9580474 -0.7446782 A J -0.5787404 -0.4460014 0.3260694 A.6 -1.0149703 0.3685003 1.5326401 A J 0.1613907 -1.9580474 -0.7446782讨论(0) -
Use the
plyrpackage to do all of this one step:library(plyr) clag <- function(x, n=1)c(rep(NA, n), head(x, -n)) x <- ddply(dd, .(dim1), transform, v1.lag=clag(v1), v2.lag=clag(v2), v3.lag=clag(v3)) head(x) v1 v2 v3 dim1 dim2 v1.lag v2.lag v3.lag 1 0.4465910 -0.2564334 -0.9122640 A J NA NA NA 2 -0.3748563 -0.9461061 0.1641274 A J 0.4465910 -0.2564334 -0.9122640 3 -0.5010834 -0.4413026 -0.7509968 A J -0.3748563 -0.9461061 0.1641274 4 -0.5278584 -0.6377017 0.5528831 A J -0.5010834 -0.4413026 -0.7509968 5 -0.4290586 0.4687849 0.6885102 A J -0.5278584 -0.6377017 0.5528831 6 0.1179935 -0.2742456 -0.1945482 A J -0.4290586 0.4687849 0.6885102讨论(0)
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