 # Adding "NA" factors to the "levels" function

I am working with the R programming language. In this example, I have the following data:

``````library("dplyr")

df <- data.frame(b = rnorm(100,5,5), d = rnorm(100,2,2),
c = rnorm(100,10,10))

a <- c("a", "b", "c", "d", "e")
a <- sample(a, 100, replace=TRUE, prob=c(0.3, 0.2, 0.3, 0.1, 0.1))

a<- as.factor(a)
df\$a = a

f <- c("a", "b", "c", "d", "e")
f <- sample(f, 100, replace=TRUE, prob=c(0.3, 0.2, 0.3, 0.1, 0.1))

f<- as.factor(f)
df\$f = f

b        d         c a f
1  6.896434 2.037835  2.867707 e a
2 -3.314758 2.681726 20.038918 d d
3  2.018130 2.229342 -8.341578 c a
4  9.738082 1.127069 18.337212 c c
5  2.442182 3.475735 27.875924 c c
6  5.061937 1.098709  6.166077 a e
``````

I then have the following function ("my_subset_mean") that evaluates the "mean" value of df\$c for different subsets of "a,b,d,f ":

``````my_subset_mean <- function(r1, r2, r3, r4){
subset <- df %>% filter(a %in% r1, f %in% r4, b > r2, d < r3 )
return(mean(subset\$c))
}
``````

Here is a loop that evaluates the function "my_subset_mean" at random subsets of "a,b,d,f " :

``````create_output <- function() {
uv <- levels(df\$a)
r1 <- sample(uv, sample(length(uv)))
uv1 <- levels(df\$f)
r4 <- sample(uv1, sample(length(uv1)))
rgb <- range(df\$b)
rgd <- range(df\$d)
r2 <- runif(1, rgb, rgb)
r3 <- runif(1, rgd, rgd)
my_subset_mean <- my_subset_mean(r1, r2, r3, r4)
data.frame(r1 = toString(r1), r4 = toString(r4), r2, r3, my_subset_mean)
}

out <- do.call(rbind, replicate(100, create_output(), simplify = FALSE))

r1         r4        r2         r3 my_subset_mean
1 a, c, b, e, d          d 14.560821  3.4251138            NaN
2          d, e e, d, b, c  9.027482 -1.7108754            NaN
3             d e, b, a, d  1.447395  0.4279652      18.019990
4 a, e, b, c, d          e -6.807861  2.6301878       7.424415
5          a, d          d  8.307980 -1.8923647            NaN
6             a    b, c, a  7.180056 -0.4022791            NaN
``````

Question: Is it possible to write this loop ("create_output") so that sometimes, values of "r1, r2, r3, r4" are not considered? E.g.

``````             r1         r4        r2         r3     my_subset_mean
1            NA          d     14.56    3.4251138            5
2          d, e, d, b,   NA    NA        -1.7108754         3.1
3             e, b,  d         1.447         NA           18.019990
``````

I was thinking that maybe this can be specified within the "levels" statement:

``````uv <- levels(df\$a)
r1 <- sample(uv, sample(length(uv)))
``````

Here, we can see the values of "uv":

``````uv
 "a" "b" "c" "d" "e"
``````

Can something be done so that sometimes, the function "my_subset_mean" sometimes ignores the some of the subset conditions for "a, b, d,f"? E.g. the "mean" is only calculated using subset conditions on "a,d"?

Thanks

sometimes generate character(0) by

``````r1 <- sample(uv, sample(length(uv)+1)-1)
``````

if you want conditional behaviour like to skip or pass a filter you can try

``````my_subset_mean <- function(r1, r2, r3, r4){
if(identical(r1,character(0)){
subset <- df %>% filter(f %in% r4, b > r2, d < r3 )
} else{
subset <- df %>% filter(a %in% r1, f %in% r4, b > r2, d < r3 )}
return(mean(subset\$c))
}``````
1 Like

Is this how the final code should look?

``````### generate data ####

library("dplyr")

df <- data.frame(b = rnorm(100,5,5), d = rnorm(100,2,2),
c = rnorm(100,10,10))

a <- c("a", "b", "c", "d", "e")
a <- sample(a, 100, replace=TRUE, prob=c(0.3, 0.2, 0.3, 0.1, 0.1))

a<- as.factor(a)
df\$a = a

f <- c("a", "b", "c", "d", "e")
f <- sample(f, 100, replace=TRUE, prob=c(0.3, 0.2, 0.3, 0.1, 0.1))

f<- as.factor(f)
df\$f = f

#### define function ####

my_subset_mean <- function(r1, r2, r3, r4){
if(identical(r1,character(0)){
subset <- df %>% filter(f %in% r4, a %in% r1, b > r2, d < r3 )
} else{
subset <- df %>% filter(a %in% r1, f %in% r4, b > r2, d < r3 )}
return(mean(subset\$c))
}

### run loop ####

create_output <- function() {
uv <- levels(df\$a)
r1 <- sample(uv, sample(length(uv)))
uv1 <- levels(df\$f)
r4 <- sample(uv1, sample(length(uv1)))
rgb <- range(df\$b)
rgd <- range(df\$d)
r2 <- runif(1, rgb, rgb)
r3 <- runif(1, rgd, rgd)
my_subset_mean <- my_subset_mean(r1, r2, r3, r4)
data.frame(r1 = toString(r1), r4 = toString(r4), r2, r3, my_subset_mean)
}

out <- do.call(rbind, replicate(100, create_output(), simplify = FALSE))

``````

Thank you!

You didn't change r1 to not always produce an output

1 Like

Thank you for your reply! I am still a bit confused - I tried to edit parts of the code but I still didn't quite understand. If you have time, can you please show me what you meant?

Thank you so much!

``````library("dplyr")

df <- data.frame(b = rnorm(100,5,5), d = rnorm(100,2,2),
c = rnorm(100,10,10))

a <- c("a", "b", "c", "d", "e")
a <- sample(a, 100, replace=TRUE, prob=c(0.3, 0.2, 0.3, 0.1, 0.1))

a<- as.factor(a)
df\$a = a

f <- c("a", "b", "c", "d", "e")
f <- sample(f, 100, replace=TRUE, prob=c(0.3, 0.2, 0.3, 0.1, 0.1))

f<- as.factor(f)
df\$f = f

my_subset_mean <- function(r1, r2, r3, r4){
if(identical(r1,character(0))){
subset <- df %>% filter(f %in% r4, b > r2, d < r3 )
} else{
subset <- df %>% filter(a %in% r1, f %in% r4, b > r2, d < r3 )}
return(mean(subset\$c))
}

create_output <- function() {
uv <- levels(df\$a)
r1 <- sample(uv, sample(length(uv)+1)-1)
uv1 <- levels(df\$f)
r4 <- sample(uv1, sample(length(uv1)))
rgb <- range(df\$b)
rgd <- range(df\$d)
r2 <- runif(1, rgb, rgb)
r3 <- runif(1, rgd, rgd)
my_subset_mean <- my_subset_mean(r1, r2, r3, r4)
data.frame(r1 = toString(r1), r4 = toString(r4), r2, r3, my_subset_mean)
}

out <- do.call(rbind, replicate(100, create_output(), simplify = FALSE))``````
1 Like

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