如何从具有因子的变量中创建子集或分类变量

Question

我正在处理大型数据集。我在数据框中有变量例如称为.

Part<-c(1,2,3,4,5,6,7)
Disease_codes>- c(A100,A145,B165,B187,B102,C132,D156)
df<-data.frame(Part,Disease_codes)

其实我想把"A"开始的疾病代码都归为"Blood cancer"。从字母 A 开始的疾病代码（例如 A100、A145）是血癌。因为我需要从我的研究中排除患有血癌的参与者。当然，我不能手动执行此操作，因为我有大量参与者。那么我如何制作一个疾病代码以 A 开头的人的子集，然后将他们从我的数据框中排除。例如，我想要以下类型的输出。

Blood_Cancer_Part<-c(1,2)
Part_without_Blood_cancer<-c(3,4,5,6,7)

Answer 1

这是一种方法，您可以使用 stringr 包来检查给定文本中的第一个字母，并相应地从已经存在的 Part 列中创建一个列。

library(stringr)
library(dplyr)

# Creating the dataframe
Part <- c(1,2,3,4,5,6,7)
Disease_codes <- c("A100","A145","B165","B187","B102","C132","D156")
df <- data.frame(Part, Disease_codes)

df <-
  df %>%
  # If first letter of Disease_codes contains A then create column from value of Part
  mutate(Blood_Cancer_Part = ifelse(str_sub(Disease_codes, 1, 1) == "A", Part, NA_character_),
         # If first letter of Disease_codes does not contains A then 
         # create column from value of Part
         Part_without_Blood_cancer = ifelse(str_sub(Disease_codes, 1, 1) != "A", Part, 
                                            NA_character_))

# To view as vectors
df$Blood_Cancer_Part[!is.na(df$Blood_Cancer_Part)]
# [1] "1" "2"

df$Part_without_Blood_cancer[!is.na(df$Part_without_Blood_cancer)]
# [1] "3" "4" "5" "6" "7"

Answer 2

在基础 R 中，我们可以使用 subset :

BloodCancer <- subset(df, grepl('^A', Disease_codes), select = Part)
#OR
#BloodCancer <- subset(df, startsWith(Disease_codes, "A"))
BloodCancer

#  Part
#1    1
#2    2


Part_without_Blood_cancer <- subset(df, !grepl('^A', Disease_codes))
#OR
#Part_without_Blood_cancer <- subset(df, !startsWith(Disease_codes, "A"))
Part_without_Blood_cancer

#  Part
#3    3
#4    4
#5    5
#6    6
#7    7

数据

Part<-c(1,2,3,4,5,6,7)
Disease_codes <- c("A100","A145","B165","B187","B102","C132","D156")
df<-data.frame(Part,Disease_codes, stringsAsFactors = FALSE)

如何从具有因子的变量中创建子集或分类变量

How to make a subset or categorical variable from a Variable having factors

r

bioinformatics