Résumer les données dans table par groupe pour chaque variable dans r

Question

J'ai quelques données que je voudrais formater correctement avec quelques valeurs récapitulatives dans R. J'ai joué avec aggregate et d'autres choses comme summaryBy, mais aucun n'a produit quoi Je voulais.

est ici les données

data <- data.frame(id = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48), 
        x1 = c(0.2846,0.3741,0.4208,0.3756,0.3476,0.3664,0.2852,0.3537,0.3116,0.3124,0.364,0.3934,0.3456,0.3034,0.3139,0.2766,0.3034,0.3159,0.3648,0.4046,0.3961,0.3451,0.2059,0.3184,0.2481,0.3503,0.331,0.3166,0.3203,0.1868,0.245,0.1625,0.2227,0.196,0.1697,0.2064,0.1369,0.1938,0.1498,0.1315,0.1523,0.2151,0.168,0.1427,0.3083,0.301,0.2328,0.2747), 
        x2 = c(-0.4364,-0.5262,-0.5338,-0.5037,-0.4758,-0.5003,-0.4359,-0.5002,-0.4027,-0.424,-0.4811,-0.5492,-0.3846,-0.3899,-0.4473,-0.3688,-0.3946,-0.4112,-0.4833,-0.4909,-0.4865,-0.368,0.295,-0.3221,-0.2482,-0.5424,-0.5021,-0.4453,-0.3952,0.3915,0.4472,0.364,0.436,0.3877,0.4077,0.2737,0.3104,0.3514,0.3256,0.287,0.3126,0.3648,-0.2596,-0.1913,-0.3656,-0.4598,-0.3198,-0.3685), 
        x3 = c(0.6043,0.5141,0.4638,0.486,0.3691,0.4104,0.426,0.3846,0.3191,0.4347,0.5842,0.4638,0.4418,0.523,0.5009,0.4568,0.5105,0.5421,0.4857,0.4063,0.391,0.4114,0.5189,0.5248,0.4942,0.2855,0.6107,0.4712,0.2009,0.4632,0.4457,0.3914,0.4547,0.4801,0.4873,0.5501,0.4442,0.4458,0.4651,0.5748,0.5231,0.4869,0.1769,0.099,0.5013,0.4543,0.4601,0.4396), 
        x4 = c(0.4895,0.6991,0.6566,0.6106,0.6976,0.6883,0.6533,0.6951,0.6852,0.5062,0.5682,0.6172,0.5073,0.6514,0.577,0.5228,0.6571,0.6132,0.4893,0.7904,0.6519,0.6582,0.6919,0.6011,0.6145,0.5943,0.4608,0.5997,0.4431,0.4082,0.5641,0.4535,0.5448,0.4632,0.4237,0.6187,0.4115,0.4995,0.4504,0.4103,0.4511,0.527,0.3654,0.2537,0.6317,0.478,0.5915,0.5283), 
        trt = c("A","A","A","A","A","A","A","A","A","A","A","A","A","A","B","B","B","B","B","B","B","B","B","B","B","B","B","B","B","C","C","C","C","C","C","C","C","C","C","C","C","C","D","D","D","D","D","D") 
        ) 

et je voudrais les données de résumer de la façon suivante.

  A    |   B   |   C   |   D 
-------------------+------------+----------+-----------+-----------+------------+-----------+------------- 
|  Mean  | Std.Dev | Mean  | Std.Dev | Mean  | Std.Dev | Mean  | Std.Dev | 
-----+-------------+------------+----------+-----------+-----------+------------+-----------+------------- 
| X1 | 0.3456  | 0.04104 |0.3207333 | 0.0514311 | 0.1821923 | 0.0350107 | 0.2379167 | 0.06966645 | 
-----+-------------+------------+----------+-----------+-----------+------------+-----------+------------- 
| X2 | -0.4674143 | 0.05489628 |-0.37406 | 0.2003379 | 0.3584308 | 0.05489583 | -0.3274333| 0.0936547 | 
-----+-------------+------------+----------+-----------+-----------+------------+-----------+------------- 
| X3 | 0.4589214 | 0.07952784 |0.45406 | 0.1036369 | 0.4778769 | 0.04866813 | 0.3552 | 0.1713025 | 
-----+-------------+------------+----------+-----------+-----------+------------+-----------+------------- 
| X4 | 0.6232571 | 0.0762495 |0.5976867 | 0.0914621 | 0.4789231 | 0.06686731 | 0.4747667 | 0.1428023 | 
-------------------+------------+----------+-----------+-----------+------------+-----------+------------- 

Une des façons que j'essayé de faire à l'aide globale est la suivante:

library(dplyr) 
t(data[,2:5] %>% group_by(data$trt) %>% summarise_each(funs(mean, sd))) 

mais il produit dans ce format:

  [,1]   [,2]   [,3]   [,4]   
data$trt "A"   "B"   "C"   "D"   
x1_mean "0.3456000" "0.3207333" "0.1821923" "0.2379167" 
x2_mean "-0.4674143" "-0.3740600" " 0.3584308" "-0.3274333" 
x3_mean "0.4589214" "0.4540600" "0.4778769" "0.3552000" 
x4_mean "0.6232571" "0.5976867" "0.4789231" "0.4747667" 
x1_sd "0.04104517" "0.05143110" "0.03501070" "0.06966645" 
x2_sd "0.05489628" "0.20033792" "0.05489583" "0.09365470" 
x3_sd "0.07952784" "0.10363689" "0.04866813" "0.17130249" 
x4_sd "0.07624950" "0.09146218" "0.06686731" "0.14280235" 

Est-il possible de faire ce que je veux dans R?

Answer 1

est ici une façon de le faire:

data %>% 
    select(-id) %>% 
    gather(row, val, -trt) %>% 
    group_by(trt, row) %>% 
    summarise_all(funs(Mean=mean, `Std.Dev`=sd)) %>% 
    gather(col, val, Mean, `Std.Dev`) %>% 
    unite("col", trt, col) %>% 
    spread(col, val) 
# # A tibble: 4 x 9 
# row A_Mean A_Std.Dev B_Mean B_Std.Dev C_Mean C_Std.Dev D_Mean D_Std.Dev 
# * <chr> <dbl>  <dbl> <dbl>  <dbl> <dbl>  <dbl> <dbl>  <dbl> 
# 1 x1  0.346 0.0410 0.321 0.0514 0.182 0.0350 0.238 0.0697 
# 2 x2 -0.467 0.0549 -0.374 0.200 0.358 0.0549 -0.327 0.0937 
# 3 x3  0.459 0.0795 0.454 0.104 0.478 0.0487 0.355 0.171 
# 4 x4  0.623 0.0762 0.598 0.0915 0.479 0.0669 0.475 0.143 

Vous pourriez ajouter %>% tibble::column_to_rownames("row") pour transformer la première colonne en noms de ligne, cependant, il est dépréciée.

Answer 2

Voici une façon de le faire en utilisant la base R et aggregate

apply(data[,2:5], 2, function(x) aggregate(x, by=list(data$trt), FUN=summary)) 
$x1 
    Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max. 
1  A 0.2846 0.3118 0.3506 0.3456 0.3722 0.4208 
2  B 0.2059 0.3086 0.3184 0.3207 0.3477 0.4046 
3  C 0.1315 0.1523 0.1868 0.1822 0.2064 0.2450 
4  D 0.1427 0.1842 0.2538 0.2379 0.2944 0.3083 

$x2 
    Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max. 
1  A -0.5492 -0.5028 -0.4784 -0.4674 -0.4270 -0.3846 
2  B -0.5424 -0.4849 -0.4112 -0.3741 -0.3684 0.2950 
3  C 0.2737 0.3126 0.3640 0.3584 0.3915 0.4472 
4  D -0.4598 -0.3678 -0.3427 -0.3274 -0.2746 -0.1913 

$x3 
    Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max. 
1  A 0.3191 0.4143 0.4528 0.4589 0.5071 0.6043 
2  B 0.2009 0.4088 0.4857 0.4541 0.5147 0.6107 
3  C 0.3914 0.4458 0.4651 0.4779 0.4873 0.5748 
4  D 0.0990 0.2426 0.4470 0.3552 0.4586 0.5013 

$x4 
    Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max. 
1  A 0.4895 0.5788 0.6524 0.6233 0.6875 0.6991 
2  B 0.4431 0.5499 0.6011 0.5977 0.6545 0.7904 
3  C 0.4082 0.4237 0.4535 0.4789 0.5270 0.6187 
4  D 0.2537 0.3936 0.5032 0.4748 0.5757 0.6317