Using dplyr to create a ICCs table - r

Question

I am trying to create a table with ICCs for multiple raters and multiple variables, I am trying to use a function and dplyr, but it is not working as I expected.
This is the structure of the data frame and the expected ICCs table:
# Create data frame
ID <- c("r1", "r1", "r1", "r1", "r1", "r2", "r2", "r2", "r2", "r2", "r3", "r3", "r3", "r3", "r3")
V1.1 <- c(3, 3, 3, 3, 3, 3, 2, 3, 3, 1, 2, 2, 1, 1, 2)
V2.1 <- c(1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 3)
V3.1 <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
V4.1 <- c(2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 2)
V1.2 <- c(3, 3, 3, 3, 3, 3, 2, 3, 2, 2, 3, 2, 1, 2, 1)
V2.2 <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2)
V3.2 <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
V4.2 <- c(2, 4, 2, 1, 3, 2, 1, 3, 2, 2, 3, 2, 1, 2, 1)
df <- data.frame(ID, V1.1, V2.1, V3.1, V4.1, V1.2, V2.2, V3.2, V4.2)
# Empty data frame for ICCs
ids <- c("r1", "r2", "r3")
vars <- c("V1", "V2", "V3", "V4")
icc_table <- data.frame(ID = ids)
icc_table <- cbind(icc_table, matrix(NA, nrow = length(ids), ncol = length(vars)))
names(icc_table)[2:ncol(icc_table)] <- vars
Here is the attempt to create the ICCs table with a function and dplyr:
# ICC function
icc.fun <- function(data, x1, x2){
result <- irr::icc(subset(data, select = c(x1, x2)),
model = "twoway",
type = "agreement",
unit = "single")
result$value
}
# Table attempt
icc_table <- df %>%
pivot_longer(cols = -ID, names_to = c("criteria", ".value"), names_pattern = "(V\\d)\\.(\\d)") %>%
group_by(ID, criteria) %>%
rename("val1" = `1`, "val2" = `2`) %>%
summarise(icc = icc.fun(df, val1, val2), .groups = "drop") %>%
pivot_wider(id_cols = ID, names_from = criteria, values_from = icc)
However, it is not working and it returns a table with a lot of NAs. When I tried the function it seems to be working fine, so I guess it is a problem with the dplyr code. If you have any other solution apart from dplyr it is also welcomed!
Thanks!

Answer 1

I think the issue is between the subset() in your icc.fun and summarise(), try:
# ICC function
icc.fun <- function(x1, x2){
result <- irr::icc(data.frame(x1, x2)),
model = "twoway",
type = "agreement",
unit = "single")
result$value
}
# Table attempt
icc_table <- df %>%
pivot_longer(cols = -ID, names_to = c("criteria", ".value"), names_pattern = "(V\\d)\\.(\\d)") %>%
group_by(ID, criteria) %>%
rename("val1" = `1`, "val2" = `2`) %>%
summarise(icc = icc.fun(val1, val2), .groups = "drop") %>%
pivot_wider(id_cols = ID, names_from = criteria, values_from = icc)

Answer 2

In case it is useful for someone, here is the solution that I found:
I simplified the function by subsetting the data using R base
# ICC function
icc.fun <- function(data, x1, x2){
result <- icc(data[ ,c(x1, x2)],
model = "twoway",
type = "agreement",
unit = "single")
result$value
}
I used the group_modify() instead of summarise(), plus enframe()
# Create ICC table
icc_table <- df %>%
pivot_longer(cols = -ID, names_to = c("criteria", ".value"), names_pattern = "(V\\d)\\.(\\d)") %>%
group_by(ID, criteria) %>%
rename("val1" = `1`, "val2" = `2`) %>%
group_modify(~ {
icc.fun(.x, "val1", "val2") %>%
tibble::enframe(name = "variable", value = "icc")
}) %>%
pivot_wider(id_cols = ID, names_from = criteria, values_from = icc)