I am constantly getting warning message like :
as.is should be specified by the caller using true
Code is like :
difficulty_data <- data_original[,c(-1)] %>% colMeans() %>% t() %>% as.data.frame() %>% t()
difficulty_data <- reshape::melt(difficulty_data, id.vars=c("id")) %>% dplyr::select(-X2)
Description :
I wanted to calculate the colmeans of the dataframe and separate them in two distinct column. I deleted "X2" because in the end I wanted to plot them with ggplot. (Result was combined with the value and the id of the value)
CodePudding user response:
The issue is in the code line in reshape::melt
dn[char] <- lapply(dn[char], type.convert)
which should be type.convert
with as.is = TRUE
as in the next step it shows the warning
indices <- do.call(expand.grid, dn)
Warning message: In type.convert.default(X[[i]], ...) : 'as.is' should be specified by the caller; using TRUE
i.e. if we look at the source code
getAnywhere("melt.matrix")[1]
function (data, varnames = names(dimnames(data)), ...)
{
values <- as.vector(data)
dn <- dimnames(data)
if (is.null(dn))
dn <- vector("list", length(dim(data)))
dn_missing <- sapply(dn, is.null)
dn[dn_missing] <- lapply(dim(data), function(x) 1:x)[dn_missing]
char <- sapply(dn, is.character)
dn[char] <- lapply(dn[char], type.convert)
indices <- do.call(expand.grid, dn)
names(indices) <- varnames
data.frame(indices, value = values)
}
and try to reproduce the issue with mtcars
data(mtcars)
difficulty_data <- mtcars %>%
colMeans() %>%
t() %>%
as.data.frame %>%
t()
The data is a matrix with one column and rownames
attribute
> reshape::melt(difficulty_data)
X1 X2 value
1 mpg 1 20.090625
2 cyl 1 6.187500
3 disp 1 230.721875
4 hp 1 146.687500
5 drat 1 3.596563
6 wt 1 3.217250
7 qsec 1 17.848750
8 vs 1 0.437500
9 am 1 0.406250
10 gear 1 3.687500
11 carb 1 2.812500
Warning message:
In type.convert.default(X[[i]], ...) :
'as.is' should be specified by the caller; using TRUE
As mentioned above, the fix is in adding the type.convert
as.is = TRUE
data <- difficulty_data
varnames <- names(dimnames(data))
values <- as.vector(data)
dn <- dimnames(data)
if (is.null(dn))
dn <- vector("list", length(dim(data)))
dn_missing <- sapply(dn, is.null)
dn[dn_missing] <- lapply(dim(data), function(x) 1:x)[dn_missing]
char <- sapply(dn, is.character)
dn[char] <- lapply(dn[char], type.convert, as.is = TRUE) #change here
indices <- do.call(expand.grid, dn)
names(indices) <- varnames
data.frame(indices, value = values)
Var1 Var2 value
1 mpg 1 20.090625
2 cyl 1 6.187500
3 disp 1 230.721875
4 hp 1 146.687500
5 drat 1 3.596563
6 wt 1 3.217250
7 qsec 1 17.848750
8 vs 1 0.437500
9 am 1 0.406250
10 gear 1 3.687500
11 carb 1 2.812500
No warnings with the fix
So, we may create a duplicate of the the function and change the specific line
meltnew <- reshape::melt.matrix
body(meltnew)[8][[1]] <- dn[char] <- lapply(dn[char], type.convert, as.is = TRUE)
Now, test it
> meltnew(difficulty_data)
X1 X2 value
1 mpg 1 20.090625
2 cyl 1 6.187500
3 disp 1 230.721875
4 hp 1 146.687500
5 drat 1 3.596563
6 wt 1 3.217250
7 qsec 1 17.848750
8 vs 1 0.437500
9 am 1 0.406250
10 gear 1 3.687500
11 carb 1 2.812500
No warnings