Unable to update data in dataframe - r

Question

i tried updating data in dataframe but its unable to get updating
//Initialize data and dataframe here
user_data=read.csv("train_5.csv")
baskets.df=data.frame(Sequence=character(),
Challenge=character(),
countno=integer(),
stringsAsFactors=FALSE)
/Updating data in dataframe here
for(i in 1:length((user_data)))
{
for(j in i:length(user_data))
{
if(user_data$challenge_sequence[i]==user_data$challenge_sequence[j]&&user_data$challenge[i]==user_data$challenge[j])
{
writedata(user_data$challenge_sequence[i],user_data$challenge[i])
}
}
}
writedata=function( seqnn,challng)
{
#print(seqnn)
#print(challng)
newRow <- data.frame(Sequence=seqnn,Challenge=challng,countno=1)
baskets.df=rbind(baskets.df,newRow)
}
//view data here
View(baskets.df)

Answer 1

I've modified your code to what I believe will work. You haven't provided sample data, so I can't verify that it works the way you want. I'm basing my attempt here on a couple of common novice mistakes that I'll do my best to explain.
Your writedata function was written to be a little loose with it's scope. When you create a new function, what happens in the function technically happens in its own environment. That is, it tries to look for things defined within the function, and then any new objects it creates are created only within that environment. R also has this neat (and sometimes tricky) feature where, if it can't find an object in an environment, it will try to look up to the parent environment.
The impact this has on your writedata function is that when R looks for baskets.df in the function and can't find it, R then turns to the Global Environment, finds baskets.df there, and then uses it in rbind. However, the result of rbind gets saved to a baskets.df in the function environment, and does not update the object of the same name in the global environment.
To address this, I added an argument to writedata that is simply named data. We can then use this argument to pass a data frame to the function's environment and do everything locally. By not making any assignment at the end, we implicitly tell the function to return it's result.
Then, in your loop, instead of simply calling writedata, we assign it's result back to baskets.df to replace the previous result.
for(i in 1:length((user_data)))
{
for(j in i:length(user_data))
{
if(user_data$challenge_sequence[i] == user_data$challenge_sequence[j] &&
user_data$challenge[i] == user_data$challenge[j])
{
baskets.df <- writedata(baskets.df,
user_data$challenge_sequence[i],
user_data$challenge[i])
}
}
}
writedata=function(data, seqnn,challng)
{
#print(seqnn)
#print(challng)
newRow <- data.frame(Sequence = seqnn,
Challenge = challng,
countno = 1)
rbind(data, newRow)
}

Answer 2

I'm not sure what you're programming background is, but your loops will be very slow in R because it's an interpreted language. To get around this, many functions are vectorized (which simply means that you give them more than one data point, and they do the looping inside compiled code where the loops are fast).
With that in mind, here's what I believe will be a much faster implementation of your code
user_data=read.csv("train_5.csv")
# challenge_indices will be a matrix with TRUE at every place "challenge" and "challenge_sequence" is the same
challenge_indices <- outer(user_data$challenge_sequence, user_data$challenge_sequence, "==") &
outer(user_data$challenge, user_data$challenge, "==")
# since you don't want duplicates, get rid of them
challenge_indices[upper.tri(challenge_indices, diag = TRUE)] <- FALSE
# now let's get the indices of interest
index_list <- which(challenge_indices,arr.ind = TRUE)
# now we make the resulting data set all at once
# this is much faster, because it does not require copying the data frame many times - which would be required if you created a new row every time.
baskets.df <- with(user_data, data.frame(
Sequence = challenge_sequence[index_list[,"row"]],
challenge = challenge[index_list[,"row"]]
)