英文:
Create new column based on non-numerical variables from several columns in the same dataframe in R
问题
以下是您要翻译的代码部分：
A <- c("K", "K", "K", "S", "S", "S", "NA")B <- c("NA", "AA", "AC", "NA", "AA", "AB", "LD")C <- c("TT", "YY", "YY", "TT", "YY", "Y", "TT")df <- data.frame(A, B, C)
df <- df %>%mutate(K_status = case_when(all("K", "AA", "YY") %in% df) ~ "Mut",TRUE ~ "WT")) %>%mutate(S_status = case_when(all("S", "AB", "Y") %in% df) ~ "Mut",TRUE ~ "WT"))
这部分已经翻译完毕。
英文:
I have a large df which I have simplified where I would love to create two new column K_status and S_status with based on variables in another column and I am struggling on how best to code for this.
A <- c("K", "K", "K", "S", "S", "S", "NA")B <- c("NA", "AA", "AC", "NA", "AA", "AB", "LD")C <- c("TT", "YY", "YY", "TT", "YY", "Y", "TT")df <- data.frame(A, B, C)
To generate the K_status and S_status additional columns to df my current code is:
df <- df %>%mutate(K_status = case_when(all("K", "AA", "YY") %in% df) ~ "Mut",TRUE ~ "WT")) %>%mutate(S_status = case_when(all("S", "AB", "Y") %in% df) ~ "Mut",TRUE ~ "WT"))
This code is not working as my intended new df should look like this
A <- c("K", "K", "K", "S", "S", "S", "NA")B <- c("NA", "AA", "AC", "NA", "AA", "AB", "LD")C <- c("TT", "YY", "YY", "TT", "YY", "Y", "TT")K_status <- c("WT", "Mut", "WT", "WT", "WT", "WT", "WT")S_status <- c("WT", "WT", "WT", "WT", "WT", "Mut", "WT")df <- data.frame(A, B, C, K_status, S_status)
Any help in writing this code to generate K_status and S_status would be greatly appreciated. Thank you.
答案1
得分: 1
以下是翻译好的内容：
使用基本R:
我们可以使用base R，使用rowSums更高效，以创建一个逻辑向量，然后基于它进行赋值。
i1 <- rowSums(df == c("K", "AA", "YY")[col(df)]) == 3i2 <- rowSums(df == c("S", "AB", "Y")[col(df)]) == 3df$K_status <- "WT"df$K_status[i1] <- "Mut"df$S_status <- "WT"df$S_status[i2] <- "Mut"
-输出
> dfA B C K_status S_status1 K NA TT WT WT2 K AA YY Mut WT3 K AC YY WT WT4 S NA TT WT WT5 S AA YY WT WT6 S AB Y WT Mut7 NA LD TT WT WT
或者使用tidyverse:
以一种矢量化的方式进行高效的代码执行，只需创建一个键/值数据集或命名的list，然后在if_all中循环遍历列，在keydat数据集中提取相应的值，使用case_when创建新的列。
library(dplyr)keydat <- tibble(A = c("K", "S"), B = c("AA", "AB"), C = c("YY", "Y"))df %>%mutate(K_status = case_when(if_all(everything(),~ .x == keydat[[cur_column()]][1]) ~ "Mut", TRUE ~ "WT"),S_status = case_when(if_all(A:C, ~.x == keydat[[cur_column()]][2]) ~ "Mut", TRUE ~ "WT"))
-输出
A B C K_status S_status1 K NA TT WT WT2 K AA YY Mut WT3 K AC YY WT WT4 S NA TT WT WT5 S AA YY WT WT6 S AB Y WT Mut7 NA LD TT WT WT
英文:
We may use base R - would be more efficient with rowSums to create a logical vector and then do the assignment based on it
i1 <- rowSums(df == c("K", "AA", "YY")[col(df)]) == 3i2 <- rowSums(df == c("S", "AB", "Y")[col(df)]) == 3df$K_status <- "WT"df$K_status[i1] <- "Mut"df$S_status <- "WT"df$S_status[i2] <- "Mut"
-output
> dfA B C K_status S_status1 K NA TT WT WT2 K AA YY Mut WT3 K AC YY WT WT4 S NA TT WT WT5 S AA YY WT WT6 S AB Y WT Mut7 NA LD TT WT WT
Or with tidyverse in a vectorized way for efficient execution of code - just create a key/value dataset or a named list, then loop over the columns in if_all, extract the corresponding value from keydat dataset, compare and use case_when to create new columns
library(dplyr)keydat <- tibble(A = c("K", "S"), B = c("AA", "AB"), C = c("YY", "Y"))df %>%mutate(K_status = case_when(if_all(everything(),~ .x == keydat[[cur_column()]][1]) ~ "Mut", TRUE ~ "WT"),S_status = case_when(if_all(A:C, ~.x == keydat[[cur_column()]][2]) ~ "Mut", TRUE ~ "WT"))
-output
A B C K_status S_status1 K NA TT WT WT2 K AA YY Mut WT3 K AC YY WT WT4 S NA TT WT WT5 S AA YY WT WT6 S AB Y WT Mut7 NA LD TT WT WT
答案2
得分: 1
我们可以在纠正一些不一致性后使用类似的代码：
- 在比较
x %in% z时，包括rowwise，其中z是逐行使用的。 - 将
%in% df替换为对数据框的列 A:C 的正确引用，使用c_across。 - 使用
all(c(...) %in% x)代替all(...) %in% x
df %>%rowwise() %>%mutate(K_status = case_when(all(c("K", "AA", "YY") %in% c_across(A:C)) ~ "Mut",TRUE ~ "WT")) %>%mutate(S_status = case_when(all(c("S", "AB", "Y") %in% c_across(A:C)) ~ "Mut",TRUE ~ "WT")) %>%ungroup()# 一个 tibble: 7 × 5A B C K_status S_status<chr> <chr> <chr> <chr> <chr>1 K NA TT WT WT2 K AA YY Mut WT3 K AC YY WT WT4 S NA TT WT WT5 S AA YY WT WT6 S AB Y WT Mut7 NA LD TT WT WT
英文:
We can use similar code ofter correcting several inconsistencies:
-include rowwise as we are comparing x %in% z, in which z is used rowwise
-%in% df should be replaced with a proper reference to the columns A:C of the dataframe, with c_across.
-use all(c(...) %in% x) instead of all(...) %in% x
df %>%rowwise() %>%mutate(K_status = case_when(all(c("K", "AA", "YY") %in% c_across(A:C)) ~ "Mut",TRUE ~ "WT")) %>%mutate(S_status = case_when(all(c("S", "AB", "Y") %in% c_across(A:C)) ~ "Mut",TRUE ~ "WT")) %>%ungroup()# A tibble: 7 × 5A B C K_status S_status<chr> <chr> <chr> <chr> <chr>1 K NA TT WT WT2 K AA YY Mut WT3 K AC YY WT WT4 S NA TT WT WT5 S AA YY WT WT6 S AB Y WT Mut7 NA LD TT WT WT</details>
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