Description Usage Arguments Details Value Input Output References See Also Examples
Call a multi-argument function with values taken from columns of an data frame or array, and combine results into a data frame
1 2 |
.data |
matrix or data frame to use as source of arguments |
.fun |
function to apply to each piece |
... |
other arguments passed on to |
.expand |
should output be 1d (expand = FALSE), with an element for each row; or nd (expand = TRUE), with a dimension for each variable. |
.progress |
name of the progress bar to use, see
|
.inform |
produce informative error messages? This is turned off by default because it substantially slows processing speed, but is very useful for debugging |
.parallel |
if |
.paropts |
a list of additional options passed into
the |
The m*ply
functions are the plyr
version of mapply
,
specialised according to the type of output they produce. These functions
are just a convenient wrapper around a*ply
with margins = 1
and .fun
wrapped in splat
.
A data frame, as described in the output section.
Call a multi-argument function with values taken from columns of an data frame or array
The most unambiguous behaviour is achieved when .fun
returns a
data frame - in that case pieces will be combined with
rbind.fill
. If .fun
returns an atomic vector of
fixed length, it will be rbind
ed together and converted to a data
frame. Any other values will result in an error.
If there are no results, then this function will return a data
frame with zero rows and columns (data.frame()
).
Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29. http://www.jstatsoft.org/v40/i01/.
Other data frame output: adply
,
ddply
, ldply
Other multiple arguments input: m_ply
,
maply
, mlply
1 2 3 4 |
mean sd V1 V2
1 1 1 0.0281318 0.9603721
2 2 2 3.6647118 5.7467023
3 3 3 6.4070505 -2.7127433
4 4 4 1.2098160 9.5733648
5 5 5 -5.9447450 -1.0400300
mean sd V1 V2
1 1 1 1.2506739 0.7594330
2 2 1 3.3733821 2.0537217
3 3 1 5.4556120 2.3165622
4 4 1 3.5802295 5.3253231
5 5 1 5.4384257 5.7115446
6 1 2 1.2419571 -2.0223169
7 2 2 -1.6976060 2.3125690
8 3 2 2.1137534 2.2454386
9 4 2 4.2050767 4.2625454
10 5 2 3.2627306 6.3577257
11 1 3 -3.6870592 7.8840932
12 2 3 5.6079898 1.5860156
13 3 3 -4.0223132 -0.1752903
14 4 3 -2.6084191 2.0463315
15 5 3 1.5435458 3.5188400
16 1 4 -2.4372223 0.5647384
17 2 4 4.8898557 2.1102315
18 3 4 -0.3103671 3.6353763
19 4 4 1.8073910 3.0327481
20 5 4 1.6112743 3.4861216
21 1 5 2.3765304 -5.1859103
22 2 5 9.6538316 -0.5331513
23 3 5 -4.2507880 -3.7359422
24 4 5 8.2971204 0.9058580
25 5 5 7.5059693 11.8869157
mean sd V1 V2 V3 V4 V5
1 1 1 2.3735868 0.458740 0.433311 0.4914755 0.1916439
2 2 2 0.5876147 6.308201 4.792018 0.9086048 2.3942631
3 3 3 0.8936273 9.725387 3.006424 3.3535177 8.3397970
4 4 4 2.7855436 3.689438 3.858531 6.2899890 3.6810345
5 5 5 4.4560882 1.327426 7.759834 8.1707367 5.6068403
mean sd value
1 1 1 2.5241037
2 1 1 1.1133229
3 1 1 1.2999567
4 1 1 1.6144720
5 1 1 0.1030883
6 2 2 1.3409487
7 2 2 1.6047732
8 2 2 1.9683570
9 2 2 5.2817933
10 2 2 2.4033436
11 3 3 9.1567933
12 3 3 1.9877205
13 3 3 7.3993067
14 3 3 3.1268524
15 3 3 5.2726129
16 4 4 5.7998091
17 4 4 0.1764000
18 4 4 2.0118731
19 4 4 4.7002937
20 4 4 5.6200545
21 5 5 -4.0449753
22 5 5 0.5471754
23 5 5 2.0633721
24 5 5 4.6482450
25 5 5 7.4577557
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