将数据框中的两个杂乱向量拆分为一个公共列
Splitting two messy vectors in a data frame into one common column
数据集样本：
library(dplyr)
sample <- structure(list(Rank = c(15, 17, 20, 2, 16, 8, 21, 5, 13, 31, 22, 18, 2, 19, 11, 11, 8, 7, 12, 9, 5, 23, 17, 16, 15, 14, 4, 20, 13, 2), Athlete = c("François Gourmet(BEL)", "Agustín Félix(ESP)", "Keisuke Ushiro", "Michael Schrader", "Pieter Braun", "Laurent Hernu(FRA)", "Dmitriy Karpov", "Laurent Hernu(FRA)", "Thomas van der Plaetsen", "Attila Szabó", "Nadir El Fassi", "Eduard Mikhan", "Leonel Suárez", "Janek Õiglane", "Hans van Alphen(BEL)", "Roman Šebrle", "André Niklaus(GER)", "Pascal Behrenbruch", "Pieter Braun", "Oleksandr Yurkov(UKR)", "Eelco Sintnicolaas", "Brent Newdick", "Kim Kun-woo", "Akihiko Nakamura", "Bastien Auzeil", "Frédéric Xhonneux", "Janek Õiglane", "Keisuke Ushiro", "Roman Šebrle", "Rico Freimuth"), Total = c(7974, 7749, 7498, 8670, 7890, 8280, 7550, 8218, 8069, 7610, 7922, 7968, 8640, 7581, 8034, 8266, 8020, 8211, 8114, 8264, 8298, 7915, 7860, 7745, 7922, 7616, 8371, 7532, 8069, 8564), `100m` = c(10.67, 11.17, 11.53, 10.73, 11.22, 10.97, 11.24, 11.2, 11.2, 11.15, 11.12, 10.97, 11.13, 11.51, 11.11, 11.16, 11.19, 11.08, 11.11, 10.93, 10.76, 11.11, 11.11, 10.86, 11.35, 11.28, 11.08, 11.51, 11.25, 10.53), LJ = c(7.15, 7.12, 6.64, 7.85, 7.17, 7.31, 6.86, 7.22, 7.79, 7.09, 7.26, 7.42, 7.24, 6.78, 7.35, 7.8, 7.21, 6.8, 7.29, 7.37, 7.29, 7.42, 7.24, 7.26, 6.87, 7.21, 7.33, 6.73, 7.3, 7.48), SP = c(13.74, 13.29, 13.43, 14.56, 14.48, 14.43, 15.69, 13.99, 12.76, 13.92, 13.62, 14.15, 15.2, 14.43, 14.67, 14.98, 13.87, 16.01, 13.9, 15.15, 14.13, 14.35, 12.96, 11.67, 15.23, 12.92, 15.13, 14.93, 15.2, 14.85), HJ = c(1.85, 2.03, 1.96, 1.99, 1.93, 2.03, 1.93, 2.03, 2.17, 1.84, 1.99, 1.96, 2.11, 1.92, 1.88, 2.11, 1.97, 1.93, 2.04, 1.97, 1.93, 1.99, 1.96, 1.95, 1.96, 2.03, 2.05, 1.89, 2.05, 1.99), `400m` = c(47.98, 52.08, 51.43, 47.66, 48.54, 49.31, 52.01, 48.95, 49.46, 49.79, 51.35, 48.8, 48, 50.95, 48.52, 50.42, 49.95, 49.9, 48.24, 49.45, 48.35, 50.1, 49.24, 47.81, 50.36, 49.04, 49.58, 50.85, 51.18, 48.41), `110mh` = c(15.02, 14.75, 15.35, 14.29, 14.67, 14.01, 14.64, 14.15, 14.79, 14.65, 14.9, 14.82, 14.45, 15.33, 14.77, 14.44, 14.5, 14.33, 14.37, 14.41, 14.42, 14.82, 14.95, 14.72, 14.59, 15.75, 14.56, 15.43, 14.75, 13.68), DT = c(39.87, 43.67, 47.64, 46.44, 42.59, 43.93, 47.1, 46.13, 37.2, 43.75, 42.25, 48, 44.71, 40.94, 44.3, 46.3, 42.68, 48.56, 42.09, 48.1, 42.23, 43.6, 39.53, 33.48, 46.86, 38.62, 42.11, 46.85, 46.93, 51.17), PV = c(5, 5, 4.6, 5, 4.7, 5.1, 4.8, 4.9, 5.1, 4.4, 4.8, 4.6, 5, 4.6, 4.3, 4.6, 5.1, 4.9, 4.9, 5, 5.2, 4.8, 4.9, 4.7, 4.8, 4.7, 5.1, 4.7, 4.8, 4.8), JT = c(57.73, 56.69, 63.28, 65.67, 59.26, 59.9, 46.91, 59.63, 58.91, 59.56, 57.65, 50.74, 75.19, 68.51, 65.71, 65.61, 57.55, 66.5, 56.95, 58.63, 61.07, 51.52, 53.33, 53.57, 60.8, 50.18, 71.73, 56.52, 67.28, 62.34), `1500m` = c(265.51, 288.27, 291.9, 265.38, 278.4, 277.41, 298.41, 268.4, 285.86, 285.64, 256.51, 273.71, 267.25, 283.06, 262.5, 290.33, 268.8, 276.64, 272.46, 278.43, 265.4, 270.57, 255.63, 256.36, 279.8, 262.71, 279.24, 283.51, 296.5, 281.57), Year = structure(c(4L, 4L, 9L, 7L, 9L, 1L, 6L, 2L, 6L, 5L, 5L, 7L, 5L, 8L, 4L, 5L, 2L, 6L, 8L, 1L, 6L, 5L, 6L, 8L, 9L, 3L, 9L, 8L, 6L, 9L), .Label = c("2001", "2003", "2005", "2007", "2009", "2011", "2013", "2015", "2017"), class = "factor"), Nationality = c(NA, NA, "Japan(JPN)", "Germany(GER)", "Netherlands(NED)", NA, "Kazakhstan(KAZ)", NA, "Belgium(BEL)", "Hungary", "France", "Belarus(BLR)", "Cuba", "Estonia(EST)", NA, "Czech Republic", NA, "Germany(GER)", "Netherlands(NED)", NA, "Netherlands(NED)", "New Zealand", "South Korea(KOR)", "Japan(JPN)", "France(FRA)", NA, "Estonia(EST)", "Japan(JPN)", "Czech Republic(CZE)", "Germany(GER)"), Notes = c(NA, NA, NA, "PB", NA, NA, NA, NA, NA, NA, "SB", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "PB", "NR", NA, "SB", NA, "PB", NA, NA, NA)), .Names = c("Rank", "Athlete", "Total", "100m", "LJ", "SP", "HJ", "400m", "110mh", "DT", "PV", "JT", "1500m", "Year", "Nationality", "Notes"), row.names = c(NA, -30L), class = c("tbl_df", "tbl", "data.frame"))
# A tibble: 30 x 16
Rank Athlete Total `100m` LJ SP HJ `400m` `110mh` DT PV JT `1500m` Year Nationality Notes
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fctr> <chr> <chr>
1 15 François Gourmet(BEL) 7974 10.67 7.15 13.74 1.85 47.98 15.02 39.87 5.0 57.73 265.51 2007 <NA> <NA>
2 17 Agustín Félix(ESP) 7749 11.17 7.12 13.29 2.03 52.08 14.75 43.67 5.0 56.69 288.27 2007 <NA> <NA>
3 20 Keisuke Ushiro 7498 11.53 6.64 13.43 1.96 51.43 15.35 47.64 4.6 63.28 291.90 2017 Japan(JPN) <NA>
4 2 Michael Schrader 8670 10.73 7.85 14.56 1.99 47.66 14.29 46.44 5.0 65.67 265.38 2013 Germany(GER) PB
5 16 Pieter Braun 7890 11.22 7.17 14.48 1.93 48.54 14.67 42.59 4.7 59.26 278.40 2017 Netherlands(NED) <NA>
6 8 Laurent Hernu(FRA) 8280 10.97 7.31 14.43 2.03 49.31 14.01 43.93 5.1 59.90 277.41 2001 <NA> <NA>
7 21 Dmitriy Karpov 7550 11.24 6.86 15.69 1.93 52.01 14.64 47.10 4.8 46.91 298.41 2011 Kazakhstan(KAZ) <NA>
8 5 Laurent Hernu(FRA) 8218 11.20 7.22 13.99 2.03 48.95 14.15 46.13 4.9 59.63 268.40 2003 <NA> <NA>
9 13 Thomas van der Plaetsen 8069 11.20 7.79 12.76 2.17 49.46 14.79 37.20 5.1 58.91 285.86 2011 Belgium(BEL) <NA>
10 31 Attila Szabó 7610 11.15 7.09 13.92 1.84 49.79 14.65 43.75 4.4 59.56 285.64 2009 Hungary <NA>
# ... with 20 more rows
我有两个字符向量，"Athlete and "国籍”，在我的数据集中，其中一些条目在末尾的括号中附有国家代码。我希望能够从这两个向量中仅拆分国家代码进入一个新变量，比如 "countrycode"，同时去掉括号。我不确定拆分的最佳方法或语法是什么 - dplyr::separate 可能吗？虽然我不确定如何在拆分期间将国家代码中的字符组合合并到括号内，以及某些条目不需要拆分的事实。
在从新变量中删除括号后，我会做类似的事情。
sample$countrycode<- gsub(pattern="\(",replacement="",x=sample$countrycode)
sample$countrycode<- gsub(pattern="\)",replacement="",x=sample$countrycode)
谢谢
一个丑陋的方法是使用 sub:
library(data.table)
DT = data.table(sample)
patt = "^.*\((.{3})\).*$"; rp = "\1"
DT[Athlete %like% patt, cc := sub(patt, rp, Athlete)]
DT[Nationality %like% patt, cc := sub(patt, rp, Nationality)]
如果您已经在使用 tidyverse 包，stringr 包中的 str_extract 之类的东西可能会更干净。此外，对于与上面代码类似的 dplyr，可以查看 case_when 函数。 （我对这些工具不够熟悉，不知道确切的语法。）
结果看起来像...
> DT[, .(Athlete, Nationality, cc)]
Athlete Nationality cc
1: François Gourmet(BEL) NA BEL
2: Agustín Félix(ESP) NA ESP
3: Keisuke Ushiro Japan(JPN) JPN
4: Michael Schrader Germany(GER) GER
5: Pieter Braun Netherlands(NED) NED
6: Laurent Hernu(FRA) NA FRA
7: Dmitriy Karpov Kazakhstan(KAZ) KAZ
8: Laurent Hernu(FRA) NA FRA
9: Thomas van der Plaetsen Belgium(BEL) BEL
10: Attila Szabó Hungary NA
11: Nadir El Fassi France NA
12: Eduard Mikhan Belarus(BLR) BLR
13: Leonel Suárez Cuba NA
14: Janek Õiglane Estonia(EST) EST
15: Hans van Alphen(BEL) NA BEL
16: Roman Šebrle Czech Republic NA
17: André Niklaus(GER) NA GER
18: Pascal Behrenbruch Germany(GER) GER
19: Pieter Braun Netherlands(NED) NED
20: Oleksandr Yurkov(UKR) NA UKR
21: Eelco Sintnicolaas Netherlands(NED) NED
22: Brent Newdick New Zealand NA
23: Kim Kun-woo South Korea(KOR) KOR
24: Akihiko Nakamura Japan(JPN) JPN
25: Bastien Auzeil France(FRA) FRA
26: Frédéric Xhonneux NA NA
27: Janek Õiglane Estonia(EST) EST
28: Keisuke Ushiro Japan(JPN) JPN
29: Roman Šebrle Czech Republic(CZE) CZE
30: Rico Freimuth Germany(GER) GER
Athlete Nationality cc
希望这对你有用：
library(dplyr)
res <- sample %>% mutate(
countrycode = case_when(
is.na(Nationality) & grepl('\(', Athlete) ~ gsub('.*?\((.*)\)', '\1', Athlete),
grepl('\(', Nationality) ~ gsub('.*?\((.*)\)', '\1', Nationality),
TRUE ~ Nationality
)
)
示例输出：
res %>% select(Athlete, Nationality, countrycode)
# # A tibble: 30 x 3
# Athlete Nationality countrycode
# <chr> <chr> <chr>
# 1 François Gourmet(BEL) NA BEL
# 2 Agustín Félix(ESP) NA ESP
# 3 Keisuke Ushiro Japan(JPN) JPN
# 4 Michael Schrader Germany(GER) GER
# 5 Pieter Braun Netherlands(NED) NED
# 6 Laurent Hernu(FRA) NA FRA
# 7 Dmitriy Karpov Kazakhstan(KAZ) KAZ
# 8 Laurent Hernu(FRA) NA FRA
# 9 Thomas van der Plaetsen Belgium(BEL) BEL
# 10 Attila Szabó Hungary Hungary
# # ... with 20 more rows
删除 TRUE ~ Nationality 以仅提取国家/地区代码，正如 Frank 评论的那样：
sample %>% mutate(
countrycode = case_when(
is.na(Nationality) & grepl('\(', Athlete) ~ gsub('.*?\((.*)\)', '\1', Athlete),
grepl('\(', Nationality) ~ gsub('.*?\((.*)\)', '\1', Nationality)
))
这个简单的解决方案也行得通。
library(stringr)
data1$country_code <- sapply(data1$Nationality, function(x) unlist(stri_extract_all(str = x, regex = '([A-Z]+)'))[2])
Nationality country_code
1: NA NA
2: NA NA
3: Japan(JPN) JPN
4: Germany(GER) GER
5: Netherlands(NED) NED
6: NA NA
数据集样本：
library(dplyr)
sample <- structure(list(Rank = c(15, 17, 20, 2, 16, 8, 21, 5, 13, 31, 22, 18, 2, 19, 11, 11, 8, 7, 12, 9, 5, 23, 17, 16, 15, 14, 4, 20, 13, 2), Athlete = c("François Gourmet(BEL)", "Agustín Félix(ESP)", "Keisuke Ushiro", "Michael Schrader", "Pieter Braun", "Laurent Hernu(FRA)", "Dmitriy Karpov", "Laurent Hernu(FRA)", "Thomas van der Plaetsen", "Attila Szabó", "Nadir El Fassi", "Eduard Mikhan", "Leonel Suárez", "Janek Õiglane", "Hans van Alphen(BEL)", "Roman Šebrle", "André Niklaus(GER)", "Pascal Behrenbruch", "Pieter Braun", "Oleksandr Yurkov(UKR)", "Eelco Sintnicolaas", "Brent Newdick", "Kim Kun-woo", "Akihiko Nakamura", "Bastien Auzeil", "Frédéric Xhonneux", "Janek Õiglane", "Keisuke Ushiro", "Roman Šebrle", "Rico Freimuth"), Total = c(7974, 7749, 7498, 8670, 7890, 8280, 7550, 8218, 8069, 7610, 7922, 7968, 8640, 7581, 8034, 8266, 8020, 8211, 8114, 8264, 8298, 7915, 7860, 7745, 7922, 7616, 8371, 7532, 8069, 8564), `100m` = c(10.67, 11.17, 11.53, 10.73, 11.22, 10.97, 11.24, 11.2, 11.2, 11.15, 11.12, 10.97, 11.13, 11.51, 11.11, 11.16, 11.19, 11.08, 11.11, 10.93, 10.76, 11.11, 11.11, 10.86, 11.35, 11.28, 11.08, 11.51, 11.25, 10.53), LJ = c(7.15, 7.12, 6.64, 7.85, 7.17, 7.31, 6.86, 7.22, 7.79, 7.09, 7.26, 7.42, 7.24, 6.78, 7.35, 7.8, 7.21, 6.8, 7.29, 7.37, 7.29, 7.42, 7.24, 7.26, 6.87, 7.21, 7.33, 6.73, 7.3, 7.48), SP = c(13.74, 13.29, 13.43, 14.56, 14.48, 14.43, 15.69, 13.99, 12.76, 13.92, 13.62, 14.15, 15.2, 14.43, 14.67, 14.98, 13.87, 16.01, 13.9, 15.15, 14.13, 14.35, 12.96, 11.67, 15.23, 12.92, 15.13, 14.93, 15.2, 14.85), HJ = c(1.85, 2.03, 1.96, 1.99, 1.93, 2.03, 1.93, 2.03, 2.17, 1.84, 1.99, 1.96, 2.11, 1.92, 1.88, 2.11, 1.97, 1.93, 2.04, 1.97, 1.93, 1.99, 1.96, 1.95, 1.96, 2.03, 2.05, 1.89, 2.05, 1.99), `400m` = c(47.98, 52.08, 51.43, 47.66, 48.54, 49.31, 52.01, 48.95, 49.46, 49.79, 51.35, 48.8, 48, 50.95, 48.52, 50.42, 49.95, 49.9, 48.24, 49.45, 48.35, 50.1, 49.24, 47.81, 50.36, 49.04, 49.58, 50.85, 51.18, 48.41), `110mh` = c(15.02, 14.75, 15.35, 14.29, 14.67, 14.01, 14.64, 14.15, 14.79, 14.65, 14.9, 14.82, 14.45, 15.33, 14.77, 14.44, 14.5, 14.33, 14.37, 14.41, 14.42, 14.82, 14.95, 14.72, 14.59, 15.75, 14.56, 15.43, 14.75, 13.68), DT = c(39.87, 43.67, 47.64, 46.44, 42.59, 43.93, 47.1, 46.13, 37.2, 43.75, 42.25, 48, 44.71, 40.94, 44.3, 46.3, 42.68, 48.56, 42.09, 48.1, 42.23, 43.6, 39.53, 33.48, 46.86, 38.62, 42.11, 46.85, 46.93, 51.17), PV = c(5, 5, 4.6, 5, 4.7, 5.1, 4.8, 4.9, 5.1, 4.4, 4.8, 4.6, 5, 4.6, 4.3, 4.6, 5.1, 4.9, 4.9, 5, 5.2, 4.8, 4.9, 4.7, 4.8, 4.7, 5.1, 4.7, 4.8, 4.8), JT = c(57.73, 56.69, 63.28, 65.67, 59.26, 59.9, 46.91, 59.63, 58.91, 59.56, 57.65, 50.74, 75.19, 68.51, 65.71, 65.61, 57.55, 66.5, 56.95, 58.63, 61.07, 51.52, 53.33, 53.57, 60.8, 50.18, 71.73, 56.52, 67.28, 62.34), `1500m` = c(265.51, 288.27, 291.9, 265.38, 278.4, 277.41, 298.41, 268.4, 285.86, 285.64, 256.51, 273.71, 267.25, 283.06, 262.5, 290.33, 268.8, 276.64, 272.46, 278.43, 265.4, 270.57, 255.63, 256.36, 279.8, 262.71, 279.24, 283.51, 296.5, 281.57), Year = structure(c(4L, 4L, 9L, 7L, 9L, 1L, 6L, 2L, 6L, 5L, 5L, 7L, 5L, 8L, 4L, 5L, 2L, 6L, 8L, 1L, 6L, 5L, 6L, 8L, 9L, 3L, 9L, 8L, 6L, 9L), .Label = c("2001", "2003", "2005", "2007", "2009", "2011", "2013", "2015", "2017"), class = "factor"), Nationality = c(NA, NA, "Japan(JPN)", "Germany(GER)", "Netherlands(NED)", NA, "Kazakhstan(KAZ)", NA, "Belgium(BEL)", "Hungary", "France", "Belarus(BLR)", "Cuba", "Estonia(EST)", NA, "Czech Republic", NA, "Germany(GER)", "Netherlands(NED)", NA, "Netherlands(NED)", "New Zealand", "South Korea(KOR)", "Japan(JPN)", "France(FRA)", NA, "Estonia(EST)", "Japan(JPN)", "Czech Republic(CZE)", "Germany(GER)"), Notes = c(NA, NA, NA, "PB", NA, NA, NA, NA, NA, NA, "SB", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "PB", "NR", NA, "SB", NA, "PB", NA, NA, NA)), .Names = c("Rank", "Athlete", "Total", "100m", "LJ", "SP", "HJ", "400m", "110mh", "DT", "PV", "JT", "1500m", "Year", "Nationality", "Notes"), row.names = c(NA, -30L), class = c("tbl_df", "tbl", "data.frame"))
# A tibble: 30 x 16
Rank Athlete Total `100m` LJ SP HJ `400m` `110mh` DT PV JT `1500m` Year Nationality Notes
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fctr> <chr> <chr>
1 15 François Gourmet(BEL) 7974 10.67 7.15 13.74 1.85 47.98 15.02 39.87 5.0 57.73 265.51 2007 <NA> <NA>
2 17 Agustín Félix(ESP) 7749 11.17 7.12 13.29 2.03 52.08 14.75 43.67 5.0 56.69 288.27 2007 <NA> <NA>
3 20 Keisuke Ushiro 7498 11.53 6.64 13.43 1.96 51.43 15.35 47.64 4.6 63.28 291.90 2017 Japan(JPN) <NA>
4 2 Michael Schrader 8670 10.73 7.85 14.56 1.99 47.66 14.29 46.44 5.0 65.67 265.38 2013 Germany(GER) PB
5 16 Pieter Braun 7890 11.22 7.17 14.48 1.93 48.54 14.67 42.59 4.7 59.26 278.40 2017 Netherlands(NED) <NA>
6 8 Laurent Hernu(FRA) 8280 10.97 7.31 14.43 2.03 49.31 14.01 43.93 5.1 59.90 277.41 2001 <NA> <NA>
7 21 Dmitriy Karpov 7550 11.24 6.86 15.69 1.93 52.01 14.64 47.10 4.8 46.91 298.41 2011 Kazakhstan(KAZ) <NA>
8 5 Laurent Hernu(FRA) 8218 11.20 7.22 13.99 2.03 48.95 14.15 46.13 4.9 59.63 268.40 2003 <NA> <NA>
9 13 Thomas van der Plaetsen 8069 11.20 7.79 12.76 2.17 49.46 14.79 37.20 5.1 58.91 285.86 2011 Belgium(BEL) <NA>
10 31 Attila Szabó 7610 11.15 7.09 13.92 1.84 49.79 14.65 43.75 4.4 59.56 285.64 2009 Hungary <NA>
# ... with 20 more rows
我有两个字符向量，"Athlete and "国籍”，在我的数据集中，其中一些条目在末尾的括号中附有国家代码。我希望能够从这两个向量中仅拆分国家代码进入一个新变量，比如 "countrycode"，同时去掉括号。我不确定拆分的最佳方法或语法是什么 - dplyr::separate 可能吗？虽然我不确定如何在拆分期间将国家代码中的字符组合合并到括号内，以及某些条目不需要拆分的事实。
在从新变量中删除括号后，我会做类似的事情。
sample$countrycode<- gsub(pattern="\(",replacement="",x=sample$countrycode)
sample$countrycode<- gsub(pattern="\)",replacement="",x=sample$countrycode)
谢谢
一个丑陋的方法是使用 sub:
library(data.table)
DT = data.table(sample)
patt = "^.*\((.{3})\).*$"; rp = "\1"
DT[Athlete %like% patt, cc := sub(patt, rp, Athlete)]
DT[Nationality %like% patt, cc := sub(patt, rp, Nationality)]
如果您已经在使用 tidyverse 包，stringr 包中的 str_extract 之类的东西可能会更干净。此外，对于与上面代码类似的 dplyr，可以查看 case_when 函数。 （我对这些工具不够熟悉，不知道确切的语法。）
结果看起来像...
> DT[, .(Athlete, Nationality, cc)]
Athlete Nationality cc
1: François Gourmet(BEL) NA BEL
2: Agustín Félix(ESP) NA ESP
3: Keisuke Ushiro Japan(JPN) JPN
4: Michael Schrader Germany(GER) GER
5: Pieter Braun Netherlands(NED) NED
6: Laurent Hernu(FRA) NA FRA
7: Dmitriy Karpov Kazakhstan(KAZ) KAZ
8: Laurent Hernu(FRA) NA FRA
9: Thomas van der Plaetsen Belgium(BEL) BEL
10: Attila Szabó Hungary NA
11: Nadir El Fassi France NA
12: Eduard Mikhan Belarus(BLR) BLR
13: Leonel Suárez Cuba NA
14: Janek Õiglane Estonia(EST) EST
15: Hans van Alphen(BEL) NA BEL
16: Roman Šebrle Czech Republic NA
17: André Niklaus(GER) NA GER
18: Pascal Behrenbruch Germany(GER) GER
19: Pieter Braun Netherlands(NED) NED
20: Oleksandr Yurkov(UKR) NA UKR
21: Eelco Sintnicolaas Netherlands(NED) NED
22: Brent Newdick New Zealand NA
23: Kim Kun-woo South Korea(KOR) KOR
24: Akihiko Nakamura Japan(JPN) JPN
25: Bastien Auzeil France(FRA) FRA
26: Frédéric Xhonneux NA NA
27: Janek Õiglane Estonia(EST) EST
28: Keisuke Ushiro Japan(JPN) JPN
29: Roman Šebrle Czech Republic(CZE) CZE
30: Rico Freimuth Germany(GER) GER
Athlete Nationality cc
希望这对你有用：
library(dplyr)
res <- sample %>% mutate(
countrycode = case_when(
is.na(Nationality) & grepl('\(', Athlete) ~ gsub('.*?\((.*)\)', '\1', Athlete),
grepl('\(', Nationality) ~ gsub('.*?\((.*)\)', '\1', Nationality),
TRUE ~ Nationality
)
)
示例输出：
res %>% select(Athlete, Nationality, countrycode)
# # A tibble: 30 x 3
# Athlete Nationality countrycode
# <chr> <chr> <chr>
# 1 François Gourmet(BEL) NA BEL
# 2 Agustín Félix(ESP) NA ESP
# 3 Keisuke Ushiro Japan(JPN) JPN
# 4 Michael Schrader Germany(GER) GER
# 5 Pieter Braun Netherlands(NED) NED
# 6 Laurent Hernu(FRA) NA FRA
# 7 Dmitriy Karpov Kazakhstan(KAZ) KAZ
# 8 Laurent Hernu(FRA) NA FRA
# 9 Thomas van der Plaetsen Belgium(BEL) BEL
# 10 Attila Szabó Hungary Hungary
# # ... with 20 more rows
删除 TRUE ~ Nationality 以仅提取国家/地区代码，正如 Frank 评论的那样：
sample %>% mutate(
countrycode = case_when(
is.na(Nationality) & grepl('\(', Athlete) ~ gsub('.*?\((.*)\)', '\1', Athlete),
grepl('\(', Nationality) ~ gsub('.*?\((.*)\)', '\1', Nationality)
))
这个简单的解决方案也行得通。
library(stringr)
data1$country_code <- sapply(data1$Nationality, function(x) unlist(stri_extract_all(str = x, regex = '([A-Z]+)'))[2])
Nationality country_code
1: NA NA
2: NA NA
3: Japan(JPN) JPN
4: Germany(GER) GER
5: Netherlands(NED) NED
6: NA NA