"Object '...' not found when referenced object is a nested function in R - r

Question

Trying to nest functions with in a function to return a list in R after taking in a data frame. But running into a problem right away getting the error:
Error in ------frqTbl <- function(df) { : object 'frqTbl' not found
Is there some way to define a variable that's a function before the function definition? Or is the nesting incorrect?
Tested with:
data(diamonds, package = "ggplot2")
test <- diamonds[1:100,]
mstrFnct(test)
mstrFnct <- function(df){
output <- list()
frqTbl <- function(df){
fctvr <- df[sapply(df,is.factor)]
logicvr <- df[sapply(df,is.logical)]
nwDf <- data.frame(fctvr,logicvr)
if(ncol(nwDf)>0){
freq <-list()
for (i in 1:ncol(nwDf)){
freq[[i]] <- as.data.frame(table((nwDf)[,i]))
names(freq[[i]])[1]=colnames(nwDf[i])
}
return(freq)
}
else{
print("There are no categorical or logical variables in the data
frame.")
}
}
output[[length(output)+1]] <- frqTbl(df)
rSqd <- function(df){
y <- df[sapply(df,is.numeric)]
if(ncol(y)>=2){
c <- combn(colnames(y), 2)
vrPrs <- paste(c[1,], c[2,], sep = "-")
m <- cor(y, method = "pearson")
r <- m[which(lower.tri(m))]
vlus <- r^2
df2 <- data.frame(vrPrs, values)
names(df2) <- sub("^VrPrs$", "Variable Pairs",
names(df2))
names(df2) <- sub("^vlus$", "R-Square", names(df2))
format.data.frame(df2)
return(df2)
}
else{
print(paste("This Data Frame does not have two or more numerical
columns to compute the Pearson correlation coefficient(s)."))
}
}
output[[length(output)+1]] <- rSqd(df)
}

Answer 1

Is there some way to define a variable that's a function before the
function definition?
No. (see first code chunk)
Or is the nesting incorrect?
Actually not. You just messed up the variable names. (see second code chunk)
I suggest the following code to cover your example:
frqTbl <- function(df){
fctvr <- df[sapply(df,is.factor)]
logicvr <- df[sapply(df,is.logical)]
nwDf <- data.frame(fctvr,logicvr)
if(ncol(nwDf)>0){
freq <-list()
for (i in 1:ncol(nwDf)){
freq[[i]] <- as.data.frame(table((nwDf)[,i]))
names(freq[[i]])[1]=colnames(nwDf[i])
}
return(freq)
}
else{
print("There are no categorical or logical variables in the data
frame.")
}
}
rSqd <- function(df){
y <- df[sapply(df,is.numeric)]
if(ncol(y)>=2){
c <- combn(colnames(y), 2)
vrPrs <- paste(c[1,], c[2,], sep = "-")
m <- cor(y, method = "pearson")
r <- m[which(lower.tri(m))]
vlus <- r^2
df2 <- data.frame(vrPrs, vlus)
names(df2) <- sub("^vrPrs$", "Variable Pairs",
names(df2))
names(df2) <- sub("^vlus$", "R-Square", names(df2))
format.data.frame(df2)
return(df2)
}
else{
print(paste("This Data Frame does not have two or more numerical
columns to compute the Pearson correlation coefficient(s)."))
}
}
mstrFnct <- function(df){
output <- list()
output[[length(output)+1]] <- frqTbl(df)
output[[length(output)+1]] <- rSqd(df)
return(output)
}
data(diamonds, package = "ggplot2")
test <- diamonds[1:100,]
mstrFnct(test)
But you could also pack the function definitions into the master function. Like this:
mstrFnct <- function(df){
# create output list
output <- list()
# define function frqTbl()
frqTbl <- function(df){
fctvr <- df[sapply(df,is.factor)]
logicvr <- df[sapply(df,is.logical)]
nwDf <- data.frame(fctvr,logicvr)
if(ncol(nwDf)>0){
freq <-list()
for (i in 1:ncol(nwDf)){
freq[[i]] <- as.data.frame(table((nwDf)[,i]))
names(freq[[i]])[1]=colnames(nwDf[i])
}
return(freq)
}
else{
print("There are no categorical or logical variables in the data
frame.")
}
}
# call function frqTbl() and store result in list
output[[length(output)+1]] <- frqTbl(df)
# define function rSqd()
rSqd <- function(df){
y <- df[sapply(df,is.numeric)]
if(ncol(y)>=2){
c <- combn(colnames(y), 2)
vrPrs <- paste(c[1,], c[2,], sep = "-")
m <- cor(y, method = "pearson")
r <- m[which(lower.tri(m))]
vlus <- r^2
df2 <- data.frame(vrPrs, vlus)
names(df2) <- sub("^vrPrs$", "Variable Pairs",
names(df2))
names(df2) <- sub("^vlus$", "R-Square", names(df2))
format.data.frame(df2)
return(df2)
}
else{
print(paste("This Data Frame does not have two or more numerical
columns to compute the Pearson correlation coefficient(s)."))
}
}
# call function rSqd() and store result in list
output[[length(output)+1]] <- rSqd(df)
return(output)
}
data(diamonds, package = "ggplot2")
test <- diamonds[1:100,]
mstrFnct(test)