transform a dataframe of frequencies to a wider format - r

Question

I have a dataframe that looks like this.
input dataframe
position,mean_freq,reference,alternative,sample_id
1,0.002,A,C,name1
2,0.04,G,T,name1
3,0.03,A,C,name2
These data are nucleotide differences at a given position in a hypothetical genome, mean_freq is relative to the reference, so the first row means the proportion of C's are 0.002 implying the A are at 0.998.
I want to transform this to a different structure by creating new columns such that,
desired_output
position,G,C,T,A,sampleid
1,0,0.002,0,0.998,name1
2, 0.96,0,0.04,0,name
3,0,0.93,0,0.07,name2
I have attempted this approach
per_position_full_nt_freq <- function(x){
df <- data.frame(A=0, C=0, G=0, T=0)
idx <- names(df) %in% x$alternative
df[,idx] <- x$mean_freq
idx2 <- names(df) %in% x$reference
df[,idx2] <- 1 - x$mean_freq
df$position <- x$position
df$sampleName <- x$sampleName
return(df)
}
desired_output_dataframe <- per_position_full_nt_freq(input_dataframe)
I ran into an error
In matrix(value, n, p) :
data length [8905] is not a sub-multiple or multiple of the number of columns
additionally, I feel there has to be a more intuitive solution and presumably using tidyr or dplyr.
How do I conveniently transform the input dataframe to the desired output dataframe format?
Thank you.

Answer 1

One option would be to create a matrix of 0's with the 'G', 'C', 'T', 'A' column names, match with the column names of the original dataset, use the row/column index to assign the values and then cbind with the original dataset's 'position' and 'sample_id', columns
m1 <- matrix(0, ncol=4, nrow=nrow(df1), dimnames = list(NULL, c("G", "C", "T", "A")))
m1[cbind(seq_len(nrow(df1)), match(df1$alternative, colnames(m1)))] <- df1$mean_freq
m1[cbind(seq_len(nrow(df1)), match(df1$reference, colnames(m1)))] <- 0.1 - df1$mean_freq
cbind(df1['position'], m1, df1['sample_id'])
# position G C T A sample_id
#1 1 0.00 0.002 0.00 0.098 name1
#2 2 0.06 0.000 0.04 0.000 name1
#3 3 0.00 0.030 0.00 0.070 name2

Answer 2

The following should do the trick:
library(readr)
library(dplyr)
library(tidyr)
input_df <- read_csv(
'position,mean_freq,reference,alternative,sample_id
1,0.002,A,C,name1
2,0.04,G,T,name1
3,0.03,A,C,name2'
)
input_df %>%
mutate( ref_val = 0.1 -mean_freq) %>%
spread(alternative, mean_freq, fill=0) %>%
spread(reference, ref_val, fill=0) %>%
select( position, G, C, T, A, sample_id )
One assumption you have here is that the alternative and reference are distinct, otherwise you will get two columns with the same name, but different values. You need to handle for that with a couple of command at the beginning of your code if need be.