如何使用任何 tidyverse 函数整理这些数据，以使市场、部门、子部门及其相应数据(即单元格内每个 = 符号的 RHS，例如 0.2934)更有用？有没有一种形式和方法可以将这些信息放在单独的行或列中？
这是我的玩具数据:
df <- tibble::tribble(
~var1, ~year, ~Markets, ~Sectors,
"AA", 2015, "A=0.2934;B=0.1483;C=0.5583", "Technology=0.0566;Health Care=0.1396;Financial=0.0925;Consumer Staples=0.0642;C=0.4252;Basic Materials=0.0358",
"BB", 2015, "D=0.8548;E=0.0869;A=0.0529", "Technology=0.1924;Financial=0.3262;Communications=0.0844;Consumer Discretionary=0.1181;Utilities=0.0484",
"CC", 2015, "A=0.4159;C=0.3615;B=0.1522;D=0.0665;F=0.0018;E=0.0022", "Technology=0.0733;Consumer Discretionary=0.0788;Financial=0.1401;Industrials=0.0691;Energy=0.0377;C=0.3598",
"BB", 2019, "C=22.2;G=16.4;H=9.9;I=9.3;J=6.6", "C=23.3;Financials=21.8;Consumer Staples=11.3;Industrials=10.8;Consumer Discretionary=10.1;Information Technology=8.6",
"CC", 2019, "C=23.9;K=12.7;L=12.2;M=11.2;N=9.6;O=7.8", "C=33.4;Financials=25.6;Consumer Discretionary=6.8;Information Technology=6.7;Energy=5.8;Consumer Staples=5.6",
"DD", 2019, "N=82.4;C=13.9;P=1.1;Q=1.0;R=0.5;S=0.3;T=0.3;U=0.1", "Information Technology=19.9;Financials=14.8;C=13.7;Health Care=11.8;Consumer Discretionary=11.7;Industrials=9.1")
我的真实数据有更多这样的变量，每个变量在每个单元格中包含更多的值。
最佳答案
我们可以使用 separate_rows 通过 ; 为 Markets 和 Sectors 列扩展行。之后，我们可以使用 separate 将列拆分为 = 。结果是以下长格式数据帧。
library(tidyverse)
df2 <- df %>%
separate_rows(Markets, sep = ";") %>%
separate_rows(Sectors, sep = ";") %>%
separate(Markets, into = c("Markets", "Markets_Number"), sep = "=", convert = TRUE) %>%
separate(Sectors, into = c("Sectors", "Sectors_Number"), sep = "=", convert = TRUE)
df2
# # A tibble: 183 x 6
# var1 year Markets Markets_Number Sectors Sectors_Number
# <chr> <dbl> <chr> <dbl> <chr> <dbl>
# 1 AA 2015 A 0.293 Technology 0.0566
# 2 AA 2015 A 0.293 Health Care 0.140
# 3 AA 2015 A 0.293 Financial 0.0925
# 4 AA 2015 A 0.293 Consumer Staples 0.0642
# 5 AA 2015 A 0.293 C 0.425
# 6 AA 2015 A 0.293 Basic Materials 0.0358
# 7 AA 2015 B 0.148 Technology 0.0566
# 8 AA 2015 B 0.148 Health Care 0.140
# 9 AA 2015 B 0.148 Financial 0.0925
# 10 AA 2015 B 0.148 Consumer Staples 0.0642
# # ... with 173 more rows
关于r - 具有许多组/值对的变量的整洁数据，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/54840165/