Dplyr ile verilerin rasgele nasıl alt kümesine ayrılır?

Question

Ben dplyrsample_n fonksiyonu ile bir sorun var. Ben data.frame alt kümeleri rasgele ayıklamak için çalışıyorum ama başarısız oldu. Çünkü sample_n sadece rastgele satırları ayıklıyor. Her bir alt-rasgele satır elde etmek için nasıl kullanıldığını gösteren bazı örnekler ara Burada

.

sample-rows-of-subgroups-from-dataframe-with-dplyr

selecting-n-random-rows-across-all-levels-of-a-factor-within-a-dataframe

Bu ne istiyorum değil

. Bir veri çerçevesi olup, her alt-rasgele satır rastgele grupları çıkarmak istiyoruz. Örneğin

,

xx <- rep(rep(seq(0,800,200),each=10),times=2) 
     yy<-c(replicate(2,sort(10^runif(10,-1,0),decreasing=TRUE)),replicate(2,sort(10^runif(10,-1,0),decreasing=TRUE)), replicate(2,sort(10^runif(10,-2,0),decreasing=TRUE)),replicate(2,sort(10^runif(10,-3,0),decreasing=TRUE)), replicate(2,sort(10^runif(10,-4,0), decreasing=TRUE)))  
     V <- rep(seq(100,2500,length.out=10),times=2) 
     No <- rep(1:10,each=10) 
     df <- data.frame(V,xx,yy,No) 
library(dplyr) 
    random <- df %>% 
      group_by(No)%>% 
     sample_n(5,replace=T) ## This part is the problem. 

Örneğin kadar rastgele kendi sıraları muhafaza tüm 3 alt kümelerini çıkarmak?

   V xx   yy No 
    1 100.0000 0 0.9877468589 1 
    2 366.6667 0 0.6658268649 1 
    3 633.3333 0 0.4408336374 1 
    4 900.0000 0 0.4136939054 1 
    5 1166.6667 0 0.4104986026 1 
    6 1433.3333 0 0.3899468530 1 
    7 1700.0000 0 0.3042157845 1 
    8 1966.6667 0 0.1585948347 1 
    9 2233.3333 0 0.1307305044 1 
    10 2500.0000 0 0.1079459480 1 
    11 100.0000 200 0.7437972385 2 
    12 366.6667 200 0.7130753133 2 
    13 633.3333 200 0.6000577122 2 
    14 900.0000 200 0.5038569759 2 
    15 1166.6667 200 0.3740146819 2 
    16 1433.3333 200 0.3605675251 2 
    17 1700.0000 200 0.1821736571 2 
    18 1966.6667 200 0.1542015388 2 
    19 2233.3333 200 0.1453810015 2 
    20 2500.0000 200 0.1142553452 2 
    21 100.0000 400 0.9712414163 3 
    22 366.6667 400 0.5420861908 3 
    23 633.3333 400 0.4622129942 3 
    24 900.0000 400 0.3634606046 3 
    25 1166.6667 400 0.3541710297 3 
    26 1433.3333 400 0.3451167353 3 
    27 1700.0000 400 0.2413016960 3 
    28 1966.6667 400 0.2356020402 3 
    29 2233.3333 400 0.2054358298 3 
    30 2500.0000 400 0.1132074106 3 
    31 100.0000 600 0.9220690387 4 
    32 366.6667 600 0.8772938566 4 
    33 633.3333 600 0.7560569362 4 
    34 900.0000 600 0.5395093190 4 
    35 1166.6667 600 0.3696490756 4 
    36 1433.3333 600 0.1585255169 4 
    37 1700.0000 600 0.1425756544 4 
    38 1966.6667 600 0.1135199782 4 
    39 2233.3333 600 0.1061660399 4 
    40 2500.0000 600 0.1052644706 4 
    41 100.0000 800 0.6175240054 5 
    42 366.6667 800 0.5527556076 5 
    43 633.3333 800 0.4339775258 5 
    44 900.0000 800 0.2462104866 5 
    45 1166.6667 800 0.1955550477 5 
    46 1433.3333 800 0.1701907232 5 
    47 1700.0000 800 0.0824833313 5 
    48 1966.6667 800 0.0483463760 5 
    49 2233.3333 800 0.0246629341 5 
    50 2500.0000 800 0.0186177562 5 
    51 100.0000 0 0.8977179587 6 
    52 366.6667 0 0.8087930175 6 
    53 633.3333 0 0.5547978713 6 
    54 900.0000 0 0.4395436341 6 
    55 1166.6667 0 0.2972449261 6 
    56 1433.3333 0 0.0925262903 6 
    57 1700.0000 0 0.0665688788 6 
    58 1966.6667 0 0.0309263319 6 
    59 2233.3333 0 0.0238500731 6 
    60 2500.0000 0 0.0213679919 6 
    61 100.0000 200 0.7777420232 7 
    62 366.6667 200 0.2299083233 7 
    63 633.3333 200 0.0611370244 7 
    64 900.0000 200 0.0228982941 7 
    65 1166.6667 200 0.0150085546 7 
    66 1433.3333 200 0.0076922035 7 
    67 1700.0000 200 0.0066120335 7 
    68 1966.6667 200 0.0062052827 7 
    69 2233.3333 200 0.0037895910 7 
    70 2500.0000 200 0.0011051211 7 
    71 100.0000 400 0.3829786486 8 
    72 366.6667 400 0.1901274442 8 
    73 633.3333 400 0.1775864007 8 
    74 900.0000 400 0.0567928196 8 
    75 1166.6667 400 0.0414294193 8 
    76 1433.3333 400 0.0127875497 8 
    77 1700.0000 400 0.0105576089 8 
    78 1966.6667 400 0.0051503839 8 
    79 2233.3333 400 0.0035216836 8 
    80 2500.0000 400 0.0
    81 100.0000 600 0.0370072219 9 
    82 366.6667 600 0.0297765049 9 
    83 633.3333 600 0.0219866835 9 
    84 900.0000 600 0.0140510807 9 
    85 1166.6667 600 0.0021593963 9 
    86 1433.3333 600 0.0018936887 9 
    87 1700.0000 600 0.0017860546 9 
    88 1966.6667 600 0.0001551491 9 
    89 2233.3333 600 0.0001345905 9 
    90 2500.0000 600 0.0001048041 9 
    91 100.0000 800 0.7343220323 10 
    92 366.6667 800 0.1653557177 10 
    93 633.3333 800 0.1006331452 10 
    94 900.0000 800 0.0083407709 10 
    95 1166.6667 800 0.0043037301 10 
    96 1433.3333 800 0.0032461136 10 
    97 1700.0000 800 0.0015843809 10 
    98 1966.6667 800 0.0004819055 10 
    99 2233.3333 800 0.0002991639 10 
    100 2500.0000 800 0.0001447263 10 

Answer 1

Belki de bu sonra ne:

# sample from distinct values of No 
my_groups <- 
    df %>% 
    select(No) %>% 
    distinct %>% 
    sample_n(5) 

# merge the two datasets 
my_df <- 
    left_join(my_groups, df)