如何解决如何将列值的下半部分移动到新创建的列中？
我有一列包含前 50% 行中三个不同测量值的平均值，以及后 50% 行中的相关标准误差。上一列是用于每个名称的名称（meanNativeSR、meanExoticSR、meanTotalSR、seN、seE、seT）。我想创建 2 个新列，在第一列中包含 se_ 名称，在第二列中包含它们的值，然后去掉底部 50% 的行。这是我的数据集：
df <- structure(list(Invasion = structure(c(1L,1L,2L,3L,3L
),.Label = c("Low","Medium","High"),class = "factor"),Growth = structure(c(1L,3L),.Label = c("cover","herb","woody"),mean_se = c("meanNativeSR","meanNativeSR","meanExoticSR","meanTotalSR","seN","seE","seT","seT"
),value = c(0.769230769230769,0.230769230769231,0.923076923076923,2.46153846153846,6.84615384615385,0.538461538461538,1.69230769230769,1.76923076923077,1.15384615384615,0.384615384615385,1.38461538461538,2.23076923076923,2.07692307692308,0.769230769230769,2.53846153846154,4.23076923076923,3.23076923076923,3.76923076923077,2.76923076923077,3.84615384615385,0.280883362823162,0.12162606385263,0.329364937914491,0.312463015562922,0.705710715103738,0.24325212770526,0.36487819155789,0.191021338791684,0.140441681411581,0.180400606147055,0.201081886427668,0.273771237231572,0.394738572265145,0.440772139427464,0.532938710021193,0.257050482766198,0.336767321450351)),row.names = c(NA,-54L),class = c("tbl_df","tbl","data.frame"))
我能够弄清楚我想用下面的代码做什么，但肯定有一种更优雅的方式，因为这种方式需要我创建不必要的中间体。
#create an intermediate data.frame that contains just the means and their values from the first half of original df
df.mean <- head(df,-27)
#rename columns 3 and 4
colnames(df.mean)[3] <- "mean"
colnames(df.mean)[4] <- "mean_value"
#create another intermediate data.frame with standard error values from the bottom half of original df
df.se <- df[28:54,]
#rename columns 3 and 4
colnames(df.se)[3] <- "se"
colnames(df.se)[4] <- "se_value"
#cbind those together to get desired result
df.final <- cbind(df.mean,df.se[,3:4])
#remove intermediates
rm(df.mean); rm(df.se)
是否有更简单的方法来实现这一点，也许使用管道或 tidyverse 中的某些函数，甚至使用基础 R？
解决方法
这是一种使用 pivot_wider 和 unnest 的方法：
library(tidyverse)
df %>%
mutate(class = str_extract(mean_se,"(N|E|T)"),fun = str_extract(mean_se,"(mean|se)")) %>%
pivot_wider(id_cols = c("Invasion","Growth"),names_from = "fun",values_from = c("mean_se","value")) %>%
unnest()
# A tibble: 27 x 6
Invasion Growth mean_se_mean mean_se_se value_mean value_se
<fct> <fct> <chr> <chr> <dbl> <dbl>
1 Low cover meanNativeSR seN 0.769 0.281
2 Low cover meanExoticSR seE 0.385 0.140
3 Low cover meanTotalSR seT 1.15 0.274
4 Low herb meanNativeSR seN 0.231 0.122
5 Low herb meanExoticSR seE 0 0
6 Low herb meanTotalSR seT 0.231 0.122
7 Low woody meanNativeSR seN 0.923 0.329
8 Low woody meanExoticSR seE 1.38 0.180
9 Low woody meanTotalSR seT 2.54 0.243
10 Medium cover meanNativeSR seN 2.46 0.312
# … with 17 more rows
您会收到一些警告，但它应该可以正常工作。
,使用 tidyverse，我们可以执行 group_split，更改列名称，然后执行 inner_join
library(dplyr)
library(purrr)
df %>%
group_split(grp = row_number() > 27,.keep = FALSE) %>%
map2(list(c('mean','mean_value'),c('se','se_value')),~ {nm1 <- .y
.x %>%
rename_at(3:4,~ nm1) %>%
mutate(rn = row_number())} ) %>%
reduce(inner_join) %>%
select(-rn)
-输出
# A tibble: 27 x 6
# Invasion Growth mean mean_value se se_value
# <fct> <fct> <chr> <dbl> <chr> <dbl>
# 1 Low cover meanNativeSR 0.769 seN 0.281
# 2 Low herb meanNativeSR 0.231 seN 0.122
# 3 Low woody meanNativeSR 0.923 seN 0.329
# 4 Medium cover meanNativeSR 2.46 seN 0.312
# 5 Medium herb meanNativeSR 6.85 seN 0.706
# 6 Medium woody meanNativeSR 0.538 seN 0.243
# 7 High cover meanNativeSR 1.69 seN 0.365
# 8 High herb meanNativeSR 1.77 seN 0.281
# 9 High woody meanNativeSR 1.15 seN 0.191
#10 Low cover meanExoticSR 0.385 seE 0.140
# … with 17 more rows
我认为，除了将事情整合在一起之外，没有什么更短、更简单的方法可以实现您的目标。代码中最长的部分是分配新的列名，它不能真正缩短。其余的可以放在一行中。但实际上，您必须始终在简洁性和可读性之间取得平衡。
上面显示的 dplyr 方法非常简洁，但我相信它们旨在处理比您更复杂/更一般的情况。
df_final_2 <- cbind(head(df,-27),df[28:54,3:4])
colnames(df_final_2)[3:6] <- c("mean","mean_value","se","se_value")
版权声明：本文内容由互联网用户自发贡献，该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 dio@foxmail.com 举报，一经查实，本站将立刻删除。
