英文:
Adding a factor into the cut() function
问题
我有一个名为`Base`的数据框，如下所示：ID Gender Strength1 1 0 2302 2 1 203 3 1 304 4 0 405 5 0 40我想使用`cut`函数创建一个新变量，将人们按照力量的多少进行分类，但是根据不同性别使用不同的分割点。在性别为1时，力量大于28被视为更强；在性别为0时，力量大于10被视为更强。我可以创建一个新变量，但我不知道在哪里放入另一个向量，以便根据这两个变量创建新变量。我正在使用以下代码行，但不知道如何继续：vec1 <- Base$Strengthvec2 <- Base$GenderBase$newvariable <- cut(vec1, breaks=c(0.00, 29.00, 60.00), labels=c("Stronger", "Weaker"))
英文:
I have a data frame Base such as
ID Gender Strength1 1 0 2302 2 1 203 3 1 304 4 0 405 5 0 40
I want to create a new variable with the cut function to categorise people with more strength vs lower but divided by gender by different cut-off points. Cut-off point for more strength in 1 is 28 and for 0 is 10.
I can create a new variable but I don´t know where I can put the other vec for creating the variable according the two variables. I´m using this line of code but I don´t know how to go forward:
vec1 <- Base$Strengthvec2 <- Base$GenderBase$newvariable <- cut(vec1, breaks=c(0.00, 29.00, 60.00), labels=c("Stronger", "Weaker"))
答案1
得分: 2
你可以通过cur_group()进行分组，然后使用组值。
df %>%group_by(Gender) %>%mutate(newVariable = factor(Strength > (if (cur_group() == 1) 29 else 10), labels = c("Weaker", "Stronger")))
英文:
You can group by and the use the group value via cur_group()
df %>%group_by(Gender) %>%mutate(newVariable = factor(Strength>(if(cur_group()==1) 29 else 10),labels = c("Weaker", "Stronger")))
答案2
得分: 0
不确定的是60，但是你需要添加max或Inf。
transform(Base, str_cat=cut(Strength, c(0, 29, max(Strength)), labels=c('weaker', 'strong')))# ID Gender Strength str_cat# 1 1 0 230 strong# 2 2 1 20 weaker# 3 3 1 30 strong# 4 4 0 40 strong# 5 5 0 40 strong
如果60表示你想要三个分界点，那么执行以下操作：
transform(Base, str_cat=cut(Strength, c(0, 29, 60, Inf), labels=c('weaker', 'normal', 'strong')))# ID Gender Strength str_cat# 1 1 0 230 strong# 2 2 1 20 weaker# 3 3 1 30 normal# 4 4 0 40 normal# 5 5 0 40 normal
数据：
Base <- structure(list(ID = 1:5, Gender = c(0, 1, 1, 0, 0), Strength = c(230,20, 30, 40, 40)), class = "data.frame", row.names = c(NA, -5L))
英文:
Not sure with the 60, but you need to add the max or Inf.
transform(Base, str_cat=cut(Strength, c(0, 29, max(Strength)), labels=c('weaker', 'strong')))# ID Gender Strength str_cat# 1 1 0 230 strong# 2 2 1 20 weaker# 3 3 1 30 strong# 4 4 0 40 strong# 5 5 0 40 strong
If the 60 meant you want three cuttofs, do
transform(Base, str_cat=cut(Strength, c(0, 29, 60, Inf), labels=c('weaker', 'normal', 'strong')))# ID Gender Strength str_cat# 1 1 0 230 strong# 2 2 1 20 weaker# 3 3 1 30 normal# 4 4 0 40 normal# 5 5 0 40 normal
Data:
Base <- structure(list(ID = 1:5, Gender = c(0, 1, 1, 0, 0), Strength = c(230,20, 30, 40, 40)), class = "data.frame", row.names = c(NA, -5L))
答案3
得分: 0
以下是翻译好的部分：
这里为每个gender组准备了不同的cut()。请注意，factor是R中用于表示分类变量的特定术语，而你的gender虚拟变量只是0和1。因此，我们可以为每个值筛选并分配特定的切割点：
df <- data.frame(id = c(1,2,3,4,5),gender = c(0,1,1,0,0),strength = c(30,20,30,40,40))library(tidyverse)df %>%mutate(cut_group =ifelse(gender == 1,cut(strength, breaks=c(0.00, 20.00, 60.00), labels = c("较弱", "较强")) %>% as.character,cut(strength, breaks=c(0.00, 39.00, 60.00), labels = c("较弱", "较强")) %>% as.character))
输出结果如下：
id gender strength cut_group1 1 0 30 较弱2 2 1 20 较弱3 3 1 30 较强4 4 0 40 较强5 5 0 40 较强
对于gender == 0，强度为30表示弱点，而对于gender == 1，强度值30表示一个强壮的人。
英文:
Here is a different cut() for each gender group. Note that factor is a specific R term for a categorical variable, whereas your gender dummy is simply 0 and 1. So we can filter for each value and assign a specific cut break:
df <- data.frame(id = c(1,2,3,4,5),gender = c(0,1,1,0,0),strength = c(30,20,30,40,40))library(tidyverse)df %>%mutate(cut_group =ifelse(gender == 1,cut(strength, breaks=c(0.00, 20.00, 60.00), labels = c("Weaker", "Stronger")) %>% as.character,cut(strength, breaks=c(0.00, 39.00, 60.00), labels = c("Weaker", "Stronger")) %>% as.character))id gender strength cut_group1 1 0 30 Weaker2 2 1 20 Weaker3 3 1 30 Stronger4 4 0 40 Stronger5 5 0 40 Stronger
For gender == 0 strength of 30 indicates weakness, whereas gender == 1 strength value 30 is a strong person.
答案4
得分: 0
I usually prefer cut but since you simply have two factor levels and combinations you can consider this as well. Your conditions either return TRUE or FALSE, then convert that to a factor with the labels you want.
df %>%mutate(grp = factor((gender == 0 & strength > 10) | (gender == 1 & strength > 28), levels = c(T, F), labels = c("Strong", "Weak")))
英文:
I usually prefer cut but since you simply have two factor levels and combinations you can consider this as well. Your conditions either return TRUE or FALSE, then convert that to a factor with the labels you want.
df %>%mutate(grp = factor((gender == 0 & strength > 10) | (gender == 1 & strength > 28), levels = c(T, F), labels = c("Strong", "Weak")))
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