Creating a dataset (data frame,df) in R consisting of all possible combinations of 10 different variables - dataframe

Question

Thanks in advance for any advice. As part of a study, I need to:
Part 1:
I need to create a .csv dataset (or r data frame?) that produces all possible combinations of 10 different variables. Each of the 10 variables has either 2 (i.e., binary 0,1) or 4 levels. I think this should be possible easily in both excel and r, but r would be preferable. They are provided in the table below:
For example, the first set of combinations would keep "druga_LIFE" at 0.5 and then would cycle through all combinations of the other variables, then it would follow by fixing "druga_LIFE" at 1 and cycling through all other combinations of variables. Eventually, it would move on to fix "druga.NEED" at 0 changing other variables, then at 1 and so on.
The dataset should be a full set of combinations with no repeat combinations.
I understand there is a large number of possible combinations - this is as expected, but I don't think this should be too difficult to compute.
Part 2:
I then need to go through this dataset, selecting only the possible combinations where:
"druga.LIFE" is the same as "drugb.LIFE"
AND
2)"druga.NEED" is the same as "drugb.NEED"
I think this should be simple with the dplyr package in R.
I have created the df in r, but do not know how to begin with cycling through to produce all possible combinations.
#DATASET OF ALL POSSIBLE CHOICE SETS#
#Creating the Vectors of choices
DrugA_LIFE <- c(0.5, 1, 2,5)
DrugA_NEED <- c(0,1)
DrugA_CERT <- c(0, 0.2, 0.4, 0.6)
DrugA_RISK <- c(0.1, 0.2, 0.4, 0.6)
DrugA_WAIT <- c(0, 0.5, 1, 2)
DrugB_LIFE <- c(0.5, 1, 2,5)
DrugB_NEED <- c(0,1)
DrugB_CERT <- c(0, 0.2, 0.4, 0.6)
DrugB_RISK <- c(0.1, 0.2, 0.4, 0.6)
DrugB_WAIT <- c(0, 0.5, 1, 2)
#Create data frame
df <- data.frame(DrugA_LIFE, DrugA_NEED, DrugA_CERT, DrugA_RISK, DrugA_WAIT, DrugB_LIFE, DrugB_NEED, DrugB_CERT, DrugB_RISK, DrugB_WAIT)

Answer 1

All possible combinations? expand.grid or tidyr::expand_big. We can apply either function to an already-made frame using do.call.
Unique? Use R's unique or dplyr::distinct.
Filtering? Use ... dplyr::filter (or base R subset).
library(dplyr)
# library(tidyr) # expand_grid
do.call(tidyr::expand_grid, df) %>%
distinct() %>%
filter(DrugA_LIFE == DrugB_LIFE, DrugA_NEED == DrugB_NEED)
# # A tibble: 32,768 × 10
# DrugA_LIFE DrugA_NEED DrugA_CERT DrugA_RISK DrugA_WAIT DrugB_LIFE DrugB_NEED DrugB_CERT DrugB_RISK DrugB_WAIT
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 0.5 0 0 0.1 0 0.5 0 0 0.1 0
# 2 0.5 0 0 0.1 0 0.5 0 0 0.1 0.5
# 3 0.5 0 0 0.1 0 0.5 0 0 0.1 1
# 4 0.5 0 0 0.1 0 0.5 0 0 0.1 2
# 5 0.5 0 0 0.1 0 0.5 0 0 0.2 0
# 6 0.5 0 0 0.1 0 0.5 0 0 0.2 0.5
# 7 0.5 0 0 0.1 0 0.5 0 0 0.2 1
# 8 0.5 0 0 0.1 0 0.5 0 0 0.2 2
# 9 0.5 0 0 0.1 0 0.5 0 0 0.4 0
# 10 0.5 0 0 0.1 0 0.5 0 0 0.4 0.5
# # … with 32,758 more rows
# # ℹ Use `print(n = ...)` to see more rows
Data:
df <- structure(list(DrugA_LIFE = c(0.5, 1, 2, 5), DrugA_NEED = c(0, 1, 0, 1), DrugA_CERT = c(0, 0.2, 0.4, 0.6), DrugA_RISK = c(0.1, 0.2, 0.4, 0.6), DrugA_WAIT = c(0, 0.5, 1, 2), DrugB_LIFE = c(0.5, 1, 2, 5), DrugB_NEED = c(0, 1, 0, 1), DrugB_CERT = c(0, 0.2, 0.4, 0.6), DrugB_RISK = c(0.1, 0.2, 0.4, 0.6), DrugB_WAIT = c(0, 0.5, 1, 2)), class = "data.frame", row.names = c(NA, -4L))