如何根据 R 中的多个条件将变量解析为多个列？
How can I parse a variable into multiple columns according to multiple conditions in R?
我是 R 的新手，所以请多多包涵。我正在查看监禁数据，并且有一个变量 conviction，这是一个看起来像这样的混乱字符串：
[1] "Ct. 1: Conspiracy to distribute"
[2] "Aggravated Assault"
[3] "Ct. 1: Possession of prohibited object; Ct. 2: criminal forfeiture"
[4] "Ct. 1-6: Human Trafficking; Cts. 7, 8 Unlawful contact; Ct. 11: Involuntary Servitude; Ct. 36: Smuggling"
理想情况下，我想做两件事。首先，我想将 Ct. 解析为多列。对于前三行，数据将如下所示：
convictions conviction_1 conviction_2
[1,] "Ct. 1: Conspiracy to distribute" "Conspiracy to distribute" NA
[2,] "Aggravated Assault" "Aggravated Assault" NA
[3,] "Ct. 1: Possession of prohibited object" "Possession of prohibited object" "criminal forfeiture"
但是当我到达第三行时事情变得很麻烦，因为我想将字符串的第一部分 (Ct. 1-6: Human Trafficking) 解析为 6 列，然后 Ct. 7,8: Unlawful contact 解析为另外 2 列列。
第二部分是我想生成一个变量 convictions_total，它会在 Ct: 之后的 conviction 字符串中找到最大的数字。对于我在此处包含的三个示例条目，convictions_total 看起来像：
[1] 1 2 36
这是我用来解析更直接的字符串变量的代码，但我不确定如何针对这个变量调整它：
cols <- data.frame(str_split_fixed(data$convictions`,",",Inf))
colnames(cols) <- paste0("conviction_",rep(1:length(cols)))
data <- cbind(data,cols)
提前致谢！
以下适用于您的示例，无需使用太多正则表达式，主要是数字提取或其他字符串检测：
library(stringr)
library(magrittr)
library(purrr)
library(plyr)
convictions_total <- sapply(stringr::str_extract_all(convictions, "\d+"),
function(x) max(as.numeric(x), 1))
convictions_split <- strsplit(convictions, ";")
reps <- lapply(convictions_split, FUN = function(x) {
sapply(x, FUN = function(i) {
num <- paste(stringr::str_extract_all(i, "[\d+\-,]")[[1]], collapse = "")
# "-" indicates a range: take largest value
if (stringr::str_detect(num, "-")){
stringr::str_extract_all(num, "\d+") %>%
unlist() %>%
as.numeric() %>%
max() %>%
return()
# "," indicates a sequence: get length of sequence
} else if(stringr::str_detect(num, ",")){
stringr::str_count(num, ",") + 1 %>%
as.numeric() %>%
return()
# otherwise return 1
} else {
return(1)
}
})
})
convictions_str <- lapply(convictions_split,
function(x) gsub(".*\d:?\s(.*)$", "\1", x))
df <- purrr::map2(convictions_str, reps, rep) %>%
plyr::ldply(rbind) %>%
cbind(convictions_total, .) %>%
data.frame() %>%
dplyr::rename_with(~ gsub("X", "conviction_", .x), starts_with("X"))
输出
convictions_total conviction_1 conviction_2 conviction_3
1 1 Conspiracy to distribute <NA> <NA>
2 1 Aggravated Assault <NA> <NA>
3 2 Possession of prohibited object criminal forfeiture <NA>
4 36 Human Trafficking Human Trafficking Human Trafficking
conviction_4 conviction_5 conviction_6 conviction_7 conviction_8
1 <NA> <NA> <NA> <NA> <NA>
2 <NA> <NA> <NA> <NA> <NA>
3 <NA> <NA> <NA> <NA> <NA>
4 Human Trafficking Human Trafficking Human Trafficking Unlawful contact Unlawful contact
conviction_9 conviction_10
1 <NA> <NA>
2 <NA> <NA>
3 <NA> <NA>
4 Involuntary Servitude Smuggling
数据
convictions <- c("Ct. 1: Conspiracy to distribute",
"Aggravated Assault",
"Ct. 1: Possession of prohibited object; Ct.: 2 criminal forfeiture",
"Ct. 1-6: Human Trafficking; Cts. 7, 8 Unlawful contact; Ct. 11: Involuntary Servitude; Ct. 36: Smuggling")
工作原理
convictions_total 很容易通过使用 stringr::str_extract_all 来提取 convictions 中每一行的所有数字。 return 是一个向量列表。 sapply 然后从列表中的每个向量中取最大值，return 是一个向量。reps 是一个列表，其中的元素对应于 convictions 的元素，它存储一个数值向量，表示每个定罪计数要重复多少次。
该代码首先将 convictions 拆分为一个向量列表，其中向量包含以下提取的信息：数字 (\d+)、破折号 (\-) 和逗号(,)。该逻辑通过搜索这些字符串提取来工作：
- 首先，如果它在定罪计数中找到
"-"，则表示一个范围，它再次取最大值。例如 "Ct. 1-6: Human Trafficking" 将 return 6.
- 接下来，如果它没有找到
"-"，而是表示计数分隔符的 ","。所以它计算逗号分隔符的数量并加一。例如 "Cts. 7, 8 Unlawful contact" 将 return 2
- 假定其他所有内容仅重复一次，因为它不是顺序列表或范围。
reps
[[1]]
Ct. 1: Conspiracy to distribute
1
[[2]]
Aggravated Assault
1
[[3]]
Ct. 1: Possession of prohibited object Ct.: 2 criminal forfeiture
1 1
[[4]]
Ct. 1-6: Human Trafficking Cts. 7, 8 Unlawful contact Ct. 11: Involuntary Servitude
6 2 1
Ct. 36: Smuggling
1
convictions_str 只是提取实际的定罪信息。例如，代码将从 "Ct. 1: Conspiracy to distribute" 中提取所有定罪的 "Conspiracy to distribute" 等等。
[[1]]
[1] "Conspiracy to distribute"
[[2]]
[1] "Aggravated Assault"
[[3]]
[1] "Possession of prohibited object" "criminal forfeiture"
[[4]]
[1] "Human Trafficking" "Unlawful contact" "Involuntary Servitude"
[4] "Smuggling"
此时reps和convictions_str有一个相关的结构：
convictions_str[[1]][1] 应重复 reps[[1]][1] 次convictions_str[[1]][2] 应重复 reps[[1]][2] 次
purrr::map2 利用此结构，使用 rep 函数通过存储在 reps 中的值重复 convictions_str 中的元素并输出一个列表。 plyr::ldply 行绑定此列表填充 NA 因为不是每个人都有相同数量的定罪。 cbind 添加列 convictions_total，dplyr::rename_with 更改列名称。
经过两天的探索之后，我想出了一个简洁的@LMc 代码版本，它最终运行得更好，因为调用 plyr 弄乱了我写的其他代码：
test_data <-
tibble(id = 1:5,
convictions = c("Ct. 1: Conspiracy to distribute" ,
"Aggravated Assault" ,
"Ct. 1: Possession of prohibited object; Ct. 2: criminal forfeiture" ,
"Ct. 1-6: Human Trafficking; Cts. 7, 8 Unlawful contact; Ct. 11: Involuntary Servitude; Ct. 36: Smuggling 50 grams",
"Ct. 1: Conspiracy; Cts. 2-7: Wire Fraud; Cts. 8-28: Money Laundering"))
test_data <- test_data %>%
mutate(c2 = convictions) #this just duplicates the original variable convictions because I want to preserve it
test_data <- test_data %>%
separate_rows(c2, sep = ";") %>%
mutate(c2 = str_remove(c2, "Ct(s)?(\. )(\d|-|:|,|\s)+")) %>%
group_by(id) %>%
mutate(conviction_number = paste0("c_", row_number())) %>%
pivot_wider(values_from = c2, names_from = conviction_number)
test_data <- test_data %>%
mutate(c2 = convictions) #again, just preserving the original variable
test_data <- test_data %>%
separate_rows(c2, sep = ";") %>%
mutate(total_counts = as.numeric(ifelse(is.na(str_extract(c2, "((?<=\-)\d+)")), str_extract(c2, "\d+"), str_extract(c2, "((?<=\-)\d+)")))) %>%
mutate(total_counts = ifelse(is.na(total_counts), 1, total_counts)) %>%
group_by(id) %>%
slice_max(total_counts)
产生以下数据帧：
id convictions c_1 c_2 c_3 c_4 c2 total_counts
<int> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
1 1 Ct. 1: Conspiracy to distribute Conspiracy to dis~ NA NA NA "Ct. 1: Conspirac~ 1
2 2 Aggravated Assault Aggravated Assault NA NA NA "Aggravated Assau~ 1
3 3 Ct. 1: Possession of prohibited object; Ct. 2: criminal for~ Possession of pro~ " criminal f~ NA NA " Ct. 2: criminal~ 2
4 4 Ct. 1-6: Human Trafficking; Cts. 7, 8 Unlawful contact; Ct.~ Human Trafficking " Unlawful c~ " Involuntary~ " Smuggling~ " Ct. 36: Smuggli~ 36
5 5 Ct. 1: Conspiracy; Cts. 2-7: Wire Fraud; Cts. 8-28: Money ~ Conspiracy " Wire Fraud" " Money Laund~ NA " Cts. 8-28: Mon~ 28
第一段代码将计数解析为单独的行，然后转向回 c_ 列。第二个代码块执行相同的解析，但随后查看每个条目以解析出数字，而不是单词。
//d+ 寻找任何数字，但事实证明我有看起来像 Cts. 2-7 的数据，我想要值 7，而不是 2。
((?<=\-)\d+)")) 查找连字符，然后解析其后的数字。如果没有连字符，则默认返回 \d+.
最后，slice_max 根据 total_counts 的最高值将数据折叠为每个 ID 1 个条目。
我是 R 的新手，所以请多多包涵。我正在查看监禁数据，并且有一个变量 conviction，这是一个看起来像这样的混乱字符串：
[1] "Ct. 1: Conspiracy to distribute"
[2] "Aggravated Assault"
[3] "Ct. 1: Possession of prohibited object; Ct. 2: criminal forfeiture"
[4] "Ct. 1-6: Human Trafficking; Cts. 7, 8 Unlawful contact; Ct. 11: Involuntary Servitude; Ct. 36: Smuggling"
理想情况下，我想做两件事。首先，我想将 Ct. 解析为多列。对于前三行，数据将如下所示：
convictions conviction_1 conviction_2
[1,] "Ct. 1: Conspiracy to distribute" "Conspiracy to distribute" NA
[2,] "Aggravated Assault" "Aggravated Assault" NA
[3,] "Ct. 1: Possession of prohibited object" "Possession of prohibited object" "criminal forfeiture"
但是当我到达第三行时事情变得很麻烦，因为我想将字符串的第一部分 (Ct. 1-6: Human Trafficking) 解析为 6 列，然后 Ct. 7,8: Unlawful contact 解析为另外 2 列列。
第二部分是我想生成一个变量 convictions_total，它会在 Ct: 之后的 conviction 字符串中找到最大的数字。对于我在此处包含的三个示例条目，convictions_total 看起来像：
[1] 1 2 36
这是我用来解析更直接的字符串变量的代码，但我不确定如何针对这个变量调整它：
cols <- data.frame(str_split_fixed(data$convictions`,",",Inf))
colnames(cols) <- paste0("conviction_",rep(1:length(cols)))
data <- cbind(data,cols)
提前致谢！
以下适用于您的示例，无需使用太多正则表达式，主要是数字提取或其他字符串检测：
library(stringr)
library(magrittr)
library(purrr)
library(plyr)
convictions_total <- sapply(stringr::str_extract_all(convictions, "\d+"),
function(x) max(as.numeric(x), 1))
convictions_split <- strsplit(convictions, ";")
reps <- lapply(convictions_split, FUN = function(x) {
sapply(x, FUN = function(i) {
num <- paste(stringr::str_extract_all(i, "[\d+\-,]")[[1]], collapse = "")
# "-" indicates a range: take largest value
if (stringr::str_detect(num, "-")){
stringr::str_extract_all(num, "\d+") %>%
unlist() %>%
as.numeric() %>%
max() %>%
return()
# "," indicates a sequence: get length of sequence
} else if(stringr::str_detect(num, ",")){
stringr::str_count(num, ",") + 1 %>%
as.numeric() %>%
return()
# otherwise return 1
} else {
return(1)
}
})
})
convictions_str <- lapply(convictions_split,
function(x) gsub(".*\d:?\s(.*)$", "\1", x))
df <- purrr::map2(convictions_str, reps, rep) %>%
plyr::ldply(rbind) %>%
cbind(convictions_total, .) %>%
data.frame() %>%
dplyr::rename_with(~ gsub("X", "conviction_", .x), starts_with("X"))
输出
convictions_total conviction_1 conviction_2 conviction_3
1 1 Conspiracy to distribute <NA> <NA>
2 1 Aggravated Assault <NA> <NA>
3 2 Possession of prohibited object criminal forfeiture <NA>
4 36 Human Trafficking Human Trafficking Human Trafficking
conviction_4 conviction_5 conviction_6 conviction_7 conviction_8
1 <NA> <NA> <NA> <NA> <NA>
2 <NA> <NA> <NA> <NA> <NA>
3 <NA> <NA> <NA> <NA> <NA>
4 Human Trafficking Human Trafficking Human Trafficking Unlawful contact Unlawful contact
conviction_9 conviction_10
1 <NA> <NA>
2 <NA> <NA>
3 <NA> <NA>
4 Involuntary Servitude Smuggling
数据
convictions <- c("Ct. 1: Conspiracy to distribute",
"Aggravated Assault",
"Ct. 1: Possession of prohibited object; Ct.: 2 criminal forfeiture",
"Ct. 1-6: Human Trafficking; Cts. 7, 8 Unlawful contact; Ct. 11: Involuntary Servitude; Ct. 36: Smuggling")
工作原理
convictions_total很容易通过使用stringr::str_extract_all来提取convictions中每一行的所有数字。 return 是一个向量列表。sapply然后从列表中的每个向量中取最大值，return 是一个向量。reps是一个列表，其中的元素对应于convictions的元素，它存储一个数值向量，表示每个定罪计数要重复多少次。
该代码首先将 convictions 拆分为一个向量列表，其中向量包含以下提取的信息：数字 (\d+)、破折号 (\-) 和逗号(,)。该逻辑通过搜索这些字符串提取来工作：
- 首先，如果它在定罪计数中找到
"-"，则表示一个范围，它再次取最大值。例如"Ct. 1-6: Human Trafficking"将 return6. - 接下来，如果它没有找到
"-"，而是表示计数分隔符的","。所以它计算逗号分隔符的数量并加一。例如"Cts. 7, 8 Unlawful contact"将 return2 - 假定其他所有内容仅重复一次，因为它不是顺序列表或范围。
reps
[[1]]
Ct. 1: Conspiracy to distribute
1
[[2]]
Aggravated Assault
1
[[3]]
Ct. 1: Possession of prohibited object Ct.: 2 criminal forfeiture
1 1
[[4]]
Ct. 1-6: Human Trafficking Cts. 7, 8 Unlawful contact Ct. 11: Involuntary Servitude
6 2 1
Ct. 36: Smuggling
1
convictions_str只是提取实际的定罪信息。例如，代码将从"Ct. 1: Conspiracy to distribute"中提取所有定罪的"Conspiracy to distribute"等等。
[[1]]
[1] "Conspiracy to distribute"
[[2]]
[1] "Aggravated Assault"
[[3]]
[1] "Possession of prohibited object" "criminal forfeiture"
[[4]]
[1] "Human Trafficking" "Unlawful contact" "Involuntary Servitude"
[4] "Smuggling"
此时reps和convictions_str有一个相关的结构：
convictions_str[[1]][1]应重复reps[[1]][1]次convictions_str[[1]][2]应重复reps[[1]][2]次
purrr::map2利用此结构，使用rep函数通过存储在reps中的值重复convictions_str中的元素并输出一个列表。plyr::ldply行绑定此列表填充NA因为不是每个人都有相同数量的定罪。cbind添加列convictions_total，dplyr::rename_with更改列名称。
经过两天的探索之后，我想出了一个简洁的@LMc 代码版本，它最终运行得更好，因为调用 plyr 弄乱了我写的其他代码：
test_data <-
tibble(id = 1:5,
convictions = c("Ct. 1: Conspiracy to distribute" ,
"Aggravated Assault" ,
"Ct. 1: Possession of prohibited object; Ct. 2: criminal forfeiture" ,
"Ct. 1-6: Human Trafficking; Cts. 7, 8 Unlawful contact; Ct. 11: Involuntary Servitude; Ct. 36: Smuggling 50 grams",
"Ct. 1: Conspiracy; Cts. 2-7: Wire Fraud; Cts. 8-28: Money Laundering"))
test_data <- test_data %>%
mutate(c2 = convictions) #this just duplicates the original variable convictions because I want to preserve it
test_data <- test_data %>%
separate_rows(c2, sep = ";") %>%
mutate(c2 = str_remove(c2, "Ct(s)?(\. )(\d|-|:|,|\s)+")) %>%
group_by(id) %>%
mutate(conviction_number = paste0("c_", row_number())) %>%
pivot_wider(values_from = c2, names_from = conviction_number)
test_data <- test_data %>%
mutate(c2 = convictions) #again, just preserving the original variable
test_data <- test_data %>%
separate_rows(c2, sep = ";") %>%
mutate(total_counts = as.numeric(ifelse(is.na(str_extract(c2, "((?<=\-)\d+)")), str_extract(c2, "\d+"), str_extract(c2, "((?<=\-)\d+)")))) %>%
mutate(total_counts = ifelse(is.na(total_counts), 1, total_counts)) %>%
group_by(id) %>%
slice_max(total_counts)
产生以下数据帧：
id convictions c_1 c_2 c_3 c_4 c2 total_counts
<int> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
1 1 Ct. 1: Conspiracy to distribute Conspiracy to dis~ NA NA NA "Ct. 1: Conspirac~ 1
2 2 Aggravated Assault Aggravated Assault NA NA NA "Aggravated Assau~ 1
3 3 Ct. 1: Possession of prohibited object; Ct. 2: criminal for~ Possession of pro~ " criminal f~ NA NA " Ct. 2: criminal~ 2
4 4 Ct. 1-6: Human Trafficking; Cts. 7, 8 Unlawful contact; Ct.~ Human Trafficking " Unlawful c~ " Involuntary~ " Smuggling~ " Ct. 36: Smuggli~ 36
5 5 Ct. 1: Conspiracy; Cts. 2-7: Wire Fraud; Cts. 8-28: Money ~ Conspiracy " Wire Fraud" " Money Laund~ NA " Cts. 8-28: Mon~ 28
第一段代码将计数解析为单独的行，然后转向回 c_ 列。第二个代码块执行相同的解析，但随后查看每个条目以解析出数字，而不是单词。
//d+ 寻找任何数字，但事实证明我有看起来像 Cts. 2-7 的数据，我想要值 7，而不是 2。
((?<=\-)\d+)")) 查找连字符，然后解析其后的数字。如果没有连字符，则默认返回 \d+.
最后，slice_max 根据 total_counts 的最高值将数据折叠为每个 ID 1 个条目。