r creating an adjacency matrix from columns in a dataframe - r

Question

I am interested in testing some network visualization techniques but before trying those functions I want to build an adjacency matrix (from, to) using the dataframe which is as follows.
Id Gender Col_Cold_1 Col_Cold_2 Col_Cold_3 Col_Hot_1 Col_Hot_2 Col_Hot_3
10 F pain sleep NA infection medication walking
14 F Bump NA muscle NA twitching flutter
17 M pain hemoloma Callus infection
18 F muscle pain twitching medication
My goal is to create an adjacency matrix as follows
1) All values in columns with keyword Cold will contribute to the rows
2) All values in columns with keyword Hot will contribute to the columns
For example, pain, sleep, Bump, muscle, hemaloma are cell values under the columns with keyword Cold and they will form the rows and cell values such as infection, medication, Callus, walking, twitching, flutter are under columns with keywords Hot and this will form the columns of the association matrix.
The final desired output should appear like this:
infection medication walking twitching flutter Callus
pain 2 2 1 1 1
sleep 1 1 1
Bump 1 1
muscle 1 1
hemaloma 1 1
[pain, infection] = 2 because the association between pain and infection occurs twice in the original dataframe: once in row 1 and again in row 3.
[pain, medication]=2 because association between pain and medication occurs twice once in row 1 and again in row 4.
Any suggestions or advice on producing such an association matrix is much appreciated thanks.
Reproducible Dataset
df = structure(list(id = c(10, 14, 17, 18), Gender = structure(c(1L, 1L, 2L, 1L), .Label = c("F", "M"), class = "factor"), Col_Cold_1 = structure(c(4L, 2L, 1L, 3L), .Label = c("", "Bump", "muscle", "pain"), class = "factor"), Col_Cold_2 = structure(c(4L, 2L, 3L, 1L), .Label = c("", "NA", "pain", "sleep"), class = "factor"), Col_Cold_3 = structure(c(1L, 3L, 2L, 4L), .Label = c("NA", "hemaloma", "muscle", "pain" ), class = "factor"), Col_Hot_1 = structure(c(4L, 3L, 2L, 1L), .Label = c("", "Callus", "NA", "infection"), class = "factor"), Col_Hot_2 = structure(c(2L, 3L, 1L, 3L), .Label = c("infection", "medication", "twitching"), class = "factor"), Col_Hot_3 = structure(c(4L, 2L, 1L, 3L), .Label = c("", "flutter", "medication", "walking" ), class = "factor")), .Names = c("id", "Gender", "Col_Cold_1", "Col_Cold_2", "Col_Cold_3", "Col_Hot_1", "Col_Hot_2", "Col_Hot_3" ), row.names = c(NA, -4L), class = "data.frame")

Answer 1

One way is to make the dataset into a "tidy" form, then use xtabs. First, some cleaning up:
df[] <- lapply(df, as.character) # Convert factors to characters
df[df == "NA" | df == "" | is.na(df)] <- NA # Make all blanks NAs
Now, tidy the dataset:
library(tidyr)
library(dplyr)
out <- do.call(rbind, sapply(grep("^Col_Cold", names(df), value = T), function(x){
vars <- c(x, grep("^Col_Hot", names(df), value = T))
setNames(gather_(select(df, one_of(vars)),
key_col = x,
value_col = "value",
gather_cols = vars[-1])[, c(1, 3)], c("cold", "hot"))
}, simplify = FALSE))
The idea is to "pair" each of the "cold" columns with each of the "hot" columns to make a long dataset. out looks like this:
out
# cold hot
# 1 pain infection
# 2 Bump <NA>
# 3 <NA> Callus
# 4 muscle <NA>
# 5 pain medication
# ...
Finally, use xtabs to make the desired output:
xtabs(~ cold + hot, na.omit(out))
# hot
# cold Callus flutter infection medication twitching walking
# Bump 0 1 0 0 1 0
# hemaloma 1 0 1 0 0 0
# muscle 0 1 0 1 2 0
# pain 1 0 2 2 1 1
# sleep 0 0 1 1 0 1