Currently I have a variable "Sex" that contains 1's and 2's for respectively men and women. I want to add random noise to this variable. Therefore I generated random numbers using a normal distribution. The next step is to determine if some of the values have to change to the other sex. I use a z-value of 2 and -2 as boundaries. So if a man (1) is assigned to a value >2, it has to change to a woman. It works also the other way around, so when a woman (2) is assigned to a random z-value of <-2, the sex variable has to change to man (1). In all the other options, the value has to remain the same value.
I thought a ifelse statement would do the trick. Unfortunately it did not work. My statement looks like:
with(Dataset18$New_sex,
ifelse(Sex== 1 & Norm_dist_random > 2, 2 , ifelse(Sex== 1 & Norm_dist_random <= 2, 1,
ifelse(Sex== 2 & Norm_dist_random < -2, 1, ifelse(Sex== 2 & Norm_dist_random >= -2, 2))))
)
My data looks like:
Sex Norm_dist_random
1 0.622221897
1 2.573726407
1 -0.298095612
1 0.717745305
2 -2.597695772
2 2.534427904
2 0.089732903
2 -0.329274570
2 -1.173434147
In the end my data has to look like
Sex Norm_dist_random Sex_new
1 0.622221897 1
1 2.573726407 2
1 -0.298095612 1
1 0.717745305 1
2 -2.597695772 1
2 2.534427904 2
2 0.089732903 2
2 -0.329274570 2
2 -1.173434147 2
One approach is with case_when which allows an arbitrary set of logical condition value pairs. Each argument is a left hand side that evaluates to TRUE or FALSE and a right hand side that defines the value. The two sides are separated by ~.
Conditions are tried in order until one is TRUE and that value is assigned. I added TRUE ~ NA_real_ to catch the rows that don't fulfill any conditions.
library(dplyr)
Dataset18 %>%
mutate(Sex_new = case_when(Sex == 1 & Norm_dist_random <= 2 ~ 1,
Sex == 1 & Norm_dist_random > 2 ~ 2,
Sex == 2 & Norm_dist_random < -2 ~ 1,
Sex == 2 & Norm_dist_random >= -2 ~ 2,
TRUE ~ NA_real_))
# Sex Norm_dist_random Sex_new
#1 1 0.6222219 1
#2 1 2.5737264 2
#3 1 -0.2980956 1
#4 1 0.7177453 1
#5 2 -2.5976958 1
#6 2 2.5344279 2
#7 2 0.0897329 2
#8 2 -0.3292746 2
#9 2 -1.1734341 2