根据列中的公共值将大型数据框拆分为一个数据框列表

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最佳答案

You can just as easily access each element in the list using e.g. path[[1]]. You can't put a set of matrices into an atomic vector and access each element. A matrix is an atomic vector with dimension attributes. I would use the list structure returned by split, it's what it was designed for. Each list element can hold data of different types and sizes so it's very versatile and you can use *apply functions to further operate on each element in the list. Example below.

#  For reproducibile data
set.seed(1)


#  Make some data
userid <- rep(1:2,times=4)
data1 <- replicate(8 , paste( sample(letters , 3 ) , collapse = "" ) )
data2 <- sample(10,8)
df <- data.frame( userid , data1 , data2 )


#  Split on userid
out <- split( df , f = df$userid )
#$`1`
#  userid data1 data2
#1      1   gjn     3
#3      1   yqp     1
#5      1   rjs     6
#7      1   jtw     5


#$`2`
#  userid data1 data2
#2      2   xfv     4
#4      2   bfe    10
#6      2   mrx     2
#8      2   fqd     9

Access each element using the [[ operator like this:

out[[1]]
#  userid data1 data2
#1      1   gjn     3
#3      1   yqp     1
#5      1   rjs     6
#7      1   jtw     5

Or use an *apply function to do further operations on each list element. For instance, to take the mean of the data2 column you could use sapply like this:

sapply( out , function(x) mean( x$data2 ) )
#   1    2
#3.75 6.25

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Stumbled across this answer and I actually wanted BOTH groups (data containing that one user and data containing everything but that one user). Not necessary for the specifics of this post, but I thought I would add in case someone was googling the same issue as me.

df <- data.frame(
ran_data1=rnorm(125),
ran_data2=rnorm(125),
g=rep(factor(LETTERS[1:5]), 25)
)


test_x = split(df,df$g)[['A']]
test_y = split(df,df$g!='A')[['TRUE']]

Here's what it looks like:

head(test_x)
x          y g
1   1.1362198  1.2969541 A
6   0.5510307 -0.2512449 A
11  0.0321679  0.2358821 A
16  0.4734277 -1.2889081 A
21 -1.2686151  0.2524744 A


> head(test_y)
x          y g
2 -2.23477293  1.1514810 B
3 -0.46958938 -1.7434205 C
4  0.07365603  0.1111419 D
5 -1.08758355  0.4727281 E
7  0.28448637 -1.5124336 B
8  1.24117504  0.4928257 C

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From version 0.8.0, dplyr offers a handy function called group_split():

# On sample data from @Aus_10


df %>%
group_split(g)


[[1]]
# A tibble: 25 x 3
ran_data1 ran_data2 g
<dbl>     <dbl> <fct>
1     2.04      0.627 A
2     0.530    -0.703 A
3    -0.475     0.541 A
4     1.20     -0.565 A
5    -0.380    -0.126 A
6     1.25     -1.69  A
7    -0.153    -1.02  A
8     1.52     -0.520 A
9     0.905    -0.976 A
10     0.517    -0.535 A
# … with 15 more rows


[[2]]
# A tibble: 25 x 3
ran_data1 ran_data2 g
<dbl>     <dbl> <fct>
1     1.61      0.858 B
2     1.05     -1.25  B
3    -0.440    -0.506 B
4    -1.17      1.81  B
5     1.47     -1.60  B
6    -0.682    -0.726 B
7    -2.21      0.282 B
8    -0.499     0.591 B
9     0.711    -1.21  B
10     0.705     0.960 B
# … with 15 more rows

To not include the grouping column:

df %>%
group_split(g, keep = FALSE)