Question

我正在尝试使用data.frame排列dplyr降序的所有变量。我尝试过这个post和other，但没有运气。

我可以使用sapply(my_data, function(x) sort(x, decreasing = T))来完成它，但我想知道是否有dplyr方式。

谢谢。

预期输出：

## A tibble: 14 x 8
#      mean_EDR  total_EDR  mean_ED total_ED meanEDGE totalEDGE       PD    SR
#         <dbl>      <dbl>    <dbl>    <dbl>    <dbl>     <dbl>    <dbl> <dbl>
# 1 0.006364422 0.25457688 87.45347 3591.964 4.402291  190.8988 5012.058    45
# 2 0.004558913 0.18287734 85.17115 3585.592 4.334637  182.0548 5006.400    43
# 3 0.004354222 0.18235650 82.18129 3577.188 4.296272  180.4434 4847.001    42
# 4 0.003916397 0.17623788 81.62851 3428.398 4.257691  177.3847 4826.244    42
# 5 0.003371391 0.13485566 81.39085 3287.251 4.242196  176.0916 4761.613    42
# 6 0.002764087 0.11609165 80.42706 3255.634 4.236135  173.6815 4553.213    41
# 7 0.002600013 0.10400050 79.82142 3217.082 4.207982  173.5540 4479.349    41
# 8 0.002490112 0.09960447 76.88072 3152.110 4.186332  171.0301 4376.620    40
# 9 0.002375897 0.09604333 76.62444 3112.277 4.171466  170.3076 4329.670    40
#10 0.002233566 0.09503590 74.10183 3066.979 4.132238  168.3193 4326.234    40
#11 0.002177910 0.09147222 73.85801 3064.977 4.125226  167.4533 4239.042    40
#12 0.002125356 0.08713958 71.32510 2954.320 4.113421  164.5368 4195.718    40
#13 0.002111953 0.08447812 71.26738 2850.695 4.108972  164.3589 4178.706    40
#14 0.001883719 0.07723249 69.17816 2767.127 4.101265  164.0506 3951.562    40

我的数据：

mydata <- structure(list(mean_EDR = c(0.00217790991358218, 0.0022335657178876,
    0.00636442199939338, 0.00237589747478451, 0.00391639742140039,
    0.00212535568313589, 0.00337139144056166, 0.00455891259894763,
    0.00211195297458007, 0.00435422243053321, 0.00249011168862914,
    0.00276408679487771, 0.00260001258489477, 0.00188371927668553
    ), total_EDR = c(0.0914722163704516, 0.0960433258691667, 0.254576879975735,
    0.0950358989913805, 0.176237883963018, 0.0871395830085717, 0.134855657622467,
    0.182356503957905, 0.0844781189832028, 0.182877342082395, 0.0996044675451654,
    0.116091645384864, 0.104000503395791, 0.0772324903441067), mean_ED = c(74.1018288513791,
    71.3251043864098, 71.2673767111652, 69.1781633678164, 79.8214230265286,
    87.4534730091657, 80.4270622827382, 82.1812860167843, 73.8580061679473,
    85.1711489295962, 76.6244373025454, 81.6285146116456, 81.3908547968942,
    76.880719592627), total_ED = c(3112.27681175792, 3066.97948861562,
    2850.69506844661, 2767.12653471266, 3591.96403619379, 3585.59239337579,
    3217.08249130953, 3287.25144067137, 2954.32024671789, 3577.18825504304,
    3064.97749210182, 3428.39761368911, 3255.63419187577, 3152.10950329771
    ), meanEDGE = c(4.13223792760153, 4.12522568181855, 4.10126475315219,
    4.10897215839184, 4.24219626910329, 4.23613487246523, 4.18633177964857,
    4.40229086298827, 4.11342097420357, 4.33463744539261, 4.20798247355965,
    4.29627178239136, 4.25769090087689, 4.17146614021448), totalEDGE = c(173.553992959264,
    177.384704318198, 164.050590126088, 164.358886335674, 190.898832109648,
    173.681529771074, 167.453271185943, 176.091634519531, 164.536838968143,
    182.05477270649, 168.319298942386, 180.443414860437, 170.307636035076,
    171.030111748794), PD = c(4376.619712149, 4326.234320518, 4178.705568494,
    3951.562387357, 4847.001017106, 5006.400370579, 4553.213048526,
    4479.348501062, 4329.670425728, 5012.05828655, 4239.042466738,
    4826.243742531, 4761.612966919, 4195.718301322), SR = c(42, 43,
    40, 40, 45, 41, 40, 40, 40, 42, 40, 42, 40, 41)), class = c("tbl_df",
    "tbl", "data.frame"), row.names = c(NA, -14L), .Names = c("mean_EDR",
    "total_EDR", "mean_ED", "total_ED", "meanEDGE", "totalEDGE",
    "PD", "SR"))

Answer 1

我们sort列独立的类似选项会循环map和sort

中的列

library(dplyr)
library(purrr)
mydata %>%
    map_df(sort, decreasing = TRUE)
# A tibble: 14 x 8
#   mean_EDR total_EDR mean_ED total_ED meanEDGE totalEDGE    PD    SR
#      <dbl>     <dbl>   <dbl>    <dbl>    <dbl>     <dbl> <dbl> <dbl>
# 1  0.00636    0.255     87.5     3592     4.40       191  5012  45.0
# 2  0.00456    0.183     85.2     3586     4.33       182  5006  43.0
# 3  0.00435    0.182     82.2     3577     4.30       180  4847  42.0
# 4  0.00392    0.176     81.6     3428     4.26       177  4826  42.0
# 5  0.00337    0.135     81.4     3287     4.24       176  4762  42.0
# 6  0.00276    0.116     80.4     3256     4.24       174  4553  41.0
# 7  0.00260    0.104     79.8     3217     4.21       174  4479  41.0
# 8  0.00249    0.0996    76.9     3152     4.19       171  4377  40.0
# 9  0.00238    0.0960    76.6     3112     4.17       170  4330  40.0
#10  0.00223    0.0950    74.1     3067     4.13       168  4326  40.0
#11  0.00218    0.0915    73.9     3065     4.13       167  4239  40.0
#12  0.00213    0.0871    71.3     2954     4.11       165  4196  40.0
#13  0.00211    0.0845    71.3     2851     4.11       164  4179  40.0
#14  0.00188    0.0772    69.2     2767     4.10       164  3952  40.0

或另一种选择是在arrange

中使用map

mydata %>%
     map(~ .x %>% 
            as_tibble %>%
            arrange(desc(value))) %>%
     bind_cols %>%
     set_names(names(mydata))

dplyr arrange_所有列

1 个答案: