Question

我有许多具有相同列的数据框。我想要的是将环境中的所有数据帧的分位数（15％和80％）功能应用于第三列（“ cpm”），并将结果作为新列添加到每个数据帧环境中的所有数据帧都是相同的，这是它们的示例：

BD.ios = structure(list(geo = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = "BD", class = "factor"), os = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "ios", class = "factor"), 
    cpm = c(0.00026978417266187, 0.000276497695852535, 0.00442228161827238, 
    0.00396317260301814, 0.0191772698764066, 0.700811773637797, 
    0.00482934642627173, 0.00201429499675114, 0.00021494623655914, 
    0.0000520855057351408)), row.names = c(12925L, 13011L, 15189L, 
18469L, 19494L, 22385L, 22594L, 29467L, 31907L, 38037L), class = "data.frame")

AE.mac = structure(list(geo = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L), .Label = "AE", class = "factor"), os = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "mac", class = "factor"), 
    cpm = c(0.000353264424964019, 0.00390138781055901, 0.000893105609526794, 
    0.0099634872417983, 0.00119375573921028, 0.00535134321942833, 
    0.00318471337579618, 0.000983284169124877, 0.116180371352785
    )), row.names = c(2622L, 6483L, 6898L, 9383L, 25280L, 25923L, 
29649L, 37977L, 40411L), class = "data.frame")

AF.android = structure(list(geo = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L), .Label = "AF", class = "factor"), os = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "android", class = "factor"), 
    cpm = c(0.193592767295597, 0.153727276424417, 0.30376596601237, 
    0.43615845874945, 0.552450120363948, 0.214786723495654, 0.206123674204523, 
    0.0250727462779332, 0.157723828668625)), row.names = c(955L, 
7975L, 8899L, 9297L, 11223L, 14963L, 17452L, 19883L, 20555L), class = "data.frame")

我相信，该解决方案很简单，需要使用eapply函数，但我只是想不通

env = .GlobalEnv
eapply(env, quantile, probs = c(.15,.8))

此命令导致错误：

    Error in `[.data.frame`(x, order(x, na.last = na.last, decreasing = decreasing)) :
   undefined columns selected

编辑为了明确起见，这是我的工作以及结果的需要：

我有这样的数据

data = structure(list(geo = structure(c(15L, 1L, 3L, 16L, 1L, 9L, 17L, 
23L, 29L, 52L, 26L, 55L, 34L, 46L, 25L, 52L, 17L, 15L, 27L, 35L, 
45L, 8L, 21L, 24L, 6L, 16L, 52L, 31L, 14L, 38L, 21L, 5L, 41L, 
16L, 34L, 52L, 27L, 16L, 7L, 13L, 10L, 35L, 52L, 44L, 27L, 19L, 
35L, 6L, 42L, 25L, 40L, 31L, 43L, 33L, 13L, 2L, 4L, 12L, 30L, 
44L, 51L, 38L, 35L, 28L, 52L, 32L, 20L, 19L, 34L, 56L, 51L, 53L, 
54L, 22L, 49L, 18L, 4L, 36L, 34L, 4L, 47L, 11L, 25L, 9L, 6L, 
46L, 39L, 25L, 12L, 50L, 27L, 39L, 48L, 27L, 23L, 9L, 19L, 9L, 
44L, 37L), .Label = c("AE", "AR", "AT", "AU", "AZ", "BD", "BG", 
"BO", "CA", "CD", "CH", "CO", "DK", "DZ", "EC", "EG", "ES", "FI", 
"FR", "GA", "GB", "GE", "HK", "HU", "ID", "IE", "IN", "IR", "IT", 
"KE", "KR", "LB", "LY", "MX", "MY", "NL", "PE", "PH", "PK", "PL", 
"PT", "QA", "RO", "RU", "RW", "SE", "SG", "SK", "SY", "TH", "TR", 
"US", "UY", "VN", "YE", "ZA"), class = "factor"), os = structure(c(3L, 
2L, 1L, 1L, 1L, 6L, 4L, 1L, 1L, 4L, 6L, 1L, 1L, 1L, 6L, 7L, 1L, 
4L, 1L, 3L, 1L, 6L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 1L, 1L, 1L, 
6L, 1L, 1L, 1L, 1L, 4L, 6L, 1L, 1L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 
1L, 6L, 1L, 1L, 1L, 4L, 4L, 1L, 3L, 1L, 5L, 1L, 6L, 6L, 1L, 3L, 
1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 6L, 4L, 2L, 
6L, 1L, 1L, 1L, 1L, 6L, 1L, 1L, 6L, 3L, 3L, 1L, 1L, 1L, 1L, 6L, 
4L, 3L, 1L), .Label = c("android", "blackberry", "ios", "mac", 
"other", "windows", "windows_phone"), class = "factor"), cpm = c(0.259529602595296, 
0.008325, 0.664507018855387, 0.000646161798914448, 0.117647058823529, 
0.630132741077424, 0.00398838150289017, 0.0986788005043583, 0.483832900637243, 
0.631904877252478, 0.00499783423573511, 0.408063887806778, 0.0916731378464372, 
1.3325069724202, 0.0112485708069297, 0.00171537666632221, 0.0129665435458787, 
0.00296443300606869, 0.22941417451864, 0.000426580184572523, 
0.206888580674988, 0.000622490272373541, 0.016084968041569, 0.119169168392267, 
0.0216352172946694, 0.0552526416330796, 0.0150883006745904, 0.324403186817902, 
0.188053932659688, 0.00389006342494715, 0.0625410833224263, 0.00111134385665529, 
0.000198831231813773, 0.00551511140525039, 1.02902374670185, 
0.574300071787509, 0.371022474579782, 0.111970606352996, 0.0000313953488372093, 
0.380035469977198, 0.0159468438538206, 0.0274524158125915, 0.237448482577744, 
0.083452302337827, 0.371352785145889, 0.129754756459319, 0.0261164794985636, 
0.602409638554217, 0.0157611216101295, 0.347620654741816, 0.130193264668441, 
0.34434946165254, 0.0693131695022054, 0.673575129533679, 0.0272002127093858, 
0.0295980803571429, 0.482425913163336, 0.00235336471280429, 0.00508469886782341, 
0.0000840689365279529, 0.236539258503618, 0.0799443865137296, 
0.296296296296296, 0.0236127508854782, 0.0152198636822762, 0.00339285714285714, 
0.150753768844221, 0.0859481582537517, 0.000587920688617856, 
0.00127715231788079, 0.150836862270619, 0.0849810111668886, 0.279757646414598, 
0.00113308871141809, 0.996427153632394, 0.00269808881394042, 
0.374087591240876, 0.228267072474796, 0.0516169572925784, 0.00902986826347305, 
0.000207365145228216, 0.244244977712646, 0.169128424850603, 0.573023255813954, 
0.0152944175375988, 1.11731843575419, 0.426646706586826, 0.0544090571844687, 
0.271433919880195, 0.0271570068233128, 0.00445611403693561, 0.00160892057026477, 
0.671800318640467, 0.0216794334441393, 0.00285318261516391, 0.295866741619575, 
0.0843108504398827, 1.60302577359969, 0.0132230143658259, 0.00246752277351996
)), row.names = c(6L, 22L, 25L, 28L, 31L, 41L, 43L, 45L, 47L, 
59L, 68L, 70L, 71L, 72L, 73L, 80L, 94L, 95L, 96L, 101L, 115L, 
117L, 121L, 123L, 125L, 140L, 144L, 149L, 151L, 165L, 169L, 170L, 
179L, 182L, 186L, 189L, 190L, 206L, 207L, 208L, 221L, 238L, 239L, 
259L, 271L, 275L, 276L, 280L, 281L, 294L, 303L, 308L, 311L, 315L, 
318L, 345L, 354L, 355L, 362L, 374L, 377L, 383L, 384L, 385L, 386L, 
394L, 405L, 407L, 408L, 419L, 422L, 424L, 425L, 427L, 442L, 445L, 
454L, 455L, 465L, 466L, 482L, 484L, 485L, 487L, 496L, 506L, 510L, 
513L, 517L, 518L, 523L, 528L, 544L, 548L, 552L, 557L, 570L, 579L, 
586L, 596L), class = "data.frame")

使用split函数来获取数据帧列表，这些数据帧将geo + os组合彼此分离并记录在数据帧列表中：

X <- split(data, list(data$geo,data$os))

比起将数据帧从该列表中拉出到环境中并删除零行的数据帧

list2env(X, envir = .GlobalEnv)
## create a function that returns a logical value
isEmpty <- function(x) {
  is.data.frame(x) && nrow(x) == 0L
}
## apply it over the environment
empty <- unlist(eapply(.GlobalEnv, isEmpty))
## remove the empties
rm(list = names(empty)[empty])

所需结果是一个数据框，该框有4列：

geo, os, quantile_15,quantile_80

geo + os是唯一的并且具有一定的quantile_15，quantile_80

Answer 1

我强烈建议putting your data frames in a list而不是仅仅将它们放在全球环境中。我链接到的答案应该可以帮助您了解列表为什么更好，并且还可以显示如何从一开始就创建列表，而不是这种“查找所有数据帧并将它们放入列表”的方法。

eapply很困难，因为没有内置函数可以让您仅将其应用于数据帧。并且eapply以list的形式返回结果，因此在现有数据帧中添加列没有多大意义。

df_names = ls()[sapply(mget(ls()), is.data.frame)]
df_list = mget(df_names)
result_list = lapply(df_list, function(d) d$new_col = <code for new column>)

我不确定您想要什么，因为您没有发布所需的输出。 quantile(x, c(.15, .8))返回2个值，并且您的数据框有多于2行，因此我不确定要添加的内容-2个新列？ 1个新的回收柱？还有其他东西吗？

或者，也许您只希望每个数据帧有2个数字的摘要？在这种情况下，sapply可以简化并保留名称：

sapply(df_list, function(d) quantile(d$cpm, c(0.15, 0.8)))
#           AE.mac AF.android       BD.ios
# 15% 0.0009111413  0.1545266 0.0002341395
# 80% 0.0071962008  0.3567230 0.0076989311

根据您的编辑

编辑，让我们直接使用data。我们不需要split，我们肯定地在list2env之后不需要split。使用dplyr或data.table可以轻松，高效地按组添加列。例如：

library(dplyr)
data %>%
  group_by(geo, os) %>%
  summarize(quantile_15 = quantile(cpm, .15),
            quantile_80 = quantile(cpm, 0.8))
# # A tibble: 81 x 4
# # Groups:   geo [?]
#    geo   os         quantile_15 quantile_80
#    <fct> <fct>            <dbl>       <dbl>
#  1 AE    android      0.118       0.118    
#  2 AE    blackberry   0.00833     0.00833  
#  3 AR    mac          0.0296      0.0296   
#  4 AT    android      0.665       0.665    
#  5 AU    android      0.482       0.482    
#  6 AU    ios          0.374       0.374    
#  7 AU    mac          0.00903     0.00903  
# ...

或使用data.table：

library(data.table)
setDT(data)
data[, as.list(quantile(cpm, c(0.15, 0.8))), by = .(geo, os)]
 #    geo            os          15%          80%
 # 1:  EC           ios 2.595296e-01 2.595296e-01
 # 2:  AE    blackberry 8.325000e-03 8.325000e-03
 # 3:  AT       android 6.645070e-01 6.645070e-01
 # 4:  EG       android 1.702811e-02 8.928342e-02
 # 5:  AE       android 1.176471e-01 1.176471e-01
 # 6:  CA       windows 6.301327e-01 6.301327e-01

如何在R中环境中的所有数据帧的某个列上应用函数

1 个答案: