Transforming a dataframe in r to apply pivot table - r

Question

I have a data frame like below:
Red Green Black
John A B C
Sean A D C
Tim B C C
How can I transform it to below form to apply a pivot table (or if it can be done directly in r without transforming data):
Names Code Type
John Red A
John Green B
John Black C
Sean Red A
Sean Green D
Sean Black C
Tim Red B
Tim Green C
Tim Black C
So then my ultimate goal is to count the types as below by a pivot table on the transformed dataframe:
Count of Code for each type:
Row Labels A B C D Grand Total
John 1 1 1 3
Sean 1 1 1 3
Tim 1 2 3
Grand Total 2 2 4 1 9
```
reading similar topics did not help that much.
Thanks in advance!
Regards

Answer 1

Using a literal dump from your first matrix-like frame above:
dat <- structure(list(Red = c("A", "A", "B"), Green = c("B", "D", "C"
), Black = c("C", "C", "C")), class = "data.frame", row.names = c("John",
"Sean", "Tim"))
I can do this:
library(dplyr)
library(tidyr)
tibble::rownames_to_column(dat, var = "Names") %>%
gather(Code, Type, -Names)
# Names Code Type
# 1 John Red A
# 2 Sean Red A
# 3 Tim Red B
# 4 John Green B
# 5 Sean Green D
# 6 Tim Green C
# 7 John Black C
# 8 Sean Black C
# 9 Tim Black C
We can extend that to get your next goal:
tibble::rownames_to_column(dat, var = "Names") %>%
gather(Code, Type, -Names) %>%
xtabs(~ Names + Type, data = .)
# Type
# Names A B C D
# John 1 1 1 0
# Sean 1 0 1 1
# Tim 0 1 2 0
which then just needs marginals:
tibble::rownames_to_column(dat, var = "Names") %>%
gather(Code, Type, -Names) %>%
xtabs(~ Names + Type, data = .) %>%
addmargins()
# Type
# Names A B C D Sum
# John 1 1 1 0 3
# Sean 1 0 1 1 3
# Tim 0 1 2 0 3
# Sum 2 2 4 1 9

Answer 2

You can use reshape(). I'm not sure about your data structure, if there is a column with names or if they are row names. I've added both versions.
reshape(dat1, idvar="Names",
varying=2:4,
v.names="Type", direction="long",
timevar="Code", times=c("red", "green", "black"),
new.row.names=1:9)
reshape(transform(dat2, Names=rownames(dat2)), idvar="Names",
varying=1:3,
v.names="Type", direction="long",
timevar="Code", times=c("red", "green", "black"),
new.row.names=1:9)
# V1 Code Type
# 1 John red A
# 2 Sean red A
# 3 Tim red B
# 4 John black B
# 5 Sean black D
# 6 Tim black C
# 7 John green C
# 8 Sean green C
# 9 Tim green C
To get kind of a raw version you could do:
res <- reshape(transform(dat2, Names=rownames(dat2)), idvar="Names",
varying=1:3,
v.names="Type", direction="long",
timevar="Code")
res
# Names Code Type
# John.1 John 1 A
# Sean.1 Sean 1 A
# Tim.1 Tim 1 B
# John.2 John 2 B
# Sean.2 Sean 2 D
# Tim.2 Tim 2 C
# John.3 John 3 C
# Sean.3 Sean 3 C
# Tim.3 Tim 3 C
After that you may assign labels at will to "Code" column by transforming to factor like so:
res$Code <- factor(res$Code, labels=c("red", "green", "black"))
Data
dat1 <- structure(list(Names = c("John", "Sean", "Tim"), Red = c("A",
"A", "B"), Green = c("B", "D", "C"), Black = c("C", "C", "C")), row.names = c(NA,
-3L), class = "data.frame")
dat2 <- structure(list(Red = c("A", "A", "B"), Green = c("B", "D", "C"
), Black = c("C", "C", "C")), row.names = c("John", "Sean", "Tim"
), class = "data.frame")

Answer 3

What you aim to do is (1) creating a contingency table and then (2) compute the sum of table entries for both rows and columns.
Step1: Create a contingency table
I first pivoted the data using pivot_longer() rather than gather() because it's more intuitive. Then, apply table() to the two variables of your interest.
# Toy example
df <- structure(list(Red = c("A", "A", "B"), Green = c("B", "D", "C"
), Black = c("C", "C", "C")), class = "data.frame", row.names = c("John",
"Sean", "Tim"))
# Pivot the data
long_df <- tibble::rownames_to_column(df, var = "Names") %>%
tidyverse::pivot_longer(cols = c(-Names),
names_to = "Type",
values_to = "Code")
# Create a contingency table
df_table <- table(long_df$Names, long_df$Code)
Step 2: Compute the sum of entries for both rows and columns.
Again, I only used a base R function margin.table(). Using this approach also allows you to save the sum of the row and column entries for further analysis.
# Grand total (margin = 1 indicates rows)
df_table %>%
margin.table(margin = 1)
# Grand total (margin = 2 indicates columns)
df_table %>%
margin.table(margin = 2)