Adding a factor into the cut() function

I have a data frame Base such as

  ID Gender Strength
1  1      0      230
2  2      1       20
3  3      1       30
4  4      0       40
5  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$Strength
vec2 <- Base$Gender
Base$newvariable <- cut(vec1, breaks=c(0.00, 29.00, 60.00), labels=c("Stronger", "Weaker"))

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"))) 

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
))

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_group
1  1      0       30    Weaker
2  2      1       20    Weaker
3  3      1       30  Stronger
4  4      0       40  Stronger
5  5      0       40  Stronger

For gender == 0 strength of 30 indicates weakness, whereas gender == 1 strength value 30 is a strong person.

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")))