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)
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)
}
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"]]
)
I am currently trying to run some code (if you need to know the purpose to help me, ask me, but I'm trying to keep this question short). This is the code:
par<-c(a=.5,b=rep(1.3,4))
est<-rep(TRUE,length(par))
ncat<-5
Theta<-matrix(c(-6,-5.8,-5.6,-5.4,-5.2,-5,-4.8,-4.6,-4.4,-4.2,-4,-3.8,-3.6,-3.4,-3.2,-3,-2.8,-2.6,-2.4,-2.2,-2,-1.8,-1.6,-1.4,-1.2,-1,-0.8,-0.6,-0.4,-0.2,0,0.2,0.4,0.6,0.8,1,1.2,1.4,1.6,1.8,2,2.2,2.4,2.6,2.8,3,3.2,3.4,3.6,3.8,4,4.2,4.4,4.6,4.8,5,5.2,5.4,5.6,5.8,6))
p.grm<-function(par,Theta,ncat){
a<-par[1]
b<-par[2:length(par)]
z<-matrix(0,nrow(Theta),ncat)
y<-matrix(0,nrow(Theta),ncat)
y[,1]<-1
for(i in 1:ncat-1){
y[,i+1]<-(exp(a*(Theta-b[i])))/(1+exp(a*(Theta-b[i])))
}
for(i in 1:ncat-1){
z[,i]<-y[,i]-y[,i+1]
}
z[,ncat]<-y[,ncat]
z
}
However, when I try to run the code:
p.grm(par=par,Theta=Theta,ncat=ncat)
I get the following error:
Error: dims [product 61] do not match the length of object [0]
Traceback tells me that the error is occurring in the first for loop in the line:
y[,i+1]<-(exp(a*(Theta-b[i])))/(1+exp(a*(Theta-b[i])))
Could someone point me to what I'm doing wrong? When I try to run this code step by step outside of the custom p.grm function, everything seems to work fine.
It is a common mistake. When you write the for loop and you want it from 1 to ncat -1 remember to write it as for (i in 1:(ncat-1)) instead of for(i in 1:ncat-1) they are completly different.
You may also add to the function something to return return(z). Here it is the corrected code:
par<-c(a=.5,b=rep(1.3,4))
est<-rep(TRUE,length(par))
ncat<-5
Theta<-matrix(c(-6,-5.8,-5.6,-5.4,-5.2,-5,-4.8,-4.6,-4.4,-4.2,-4,-3.8,-3.6,-3.4,-3.2,-3,-2.8,-2.6,-2.4,-2.2,-2,-1.8,-1.6,-1.4,-1.2,-1,-0.8,-0.6,-0.4,-0.2,0,0.2,0.4,0.6,0.8,1,1.2,1.4,1.6,1.8,2,2.2,2.4,2.6,2.8,3,3.2,3.4,3.6,3.8,4,4.2,4.4,4.6,4.8,5,5.2,5.4,5.6,5.8,6))
p.grm<-function(par,Theta,ncat){
a<-par[1]
b<-par[2:length(par)]
z<-matrix(0,nrow(Theta),ncat)
y<-matrix(0,nrow(Theta),ncat)
y[,1]<-1
for(i in 1:(ncat-1)){
y[,i+1]<-(exp(a*(Theta-b[i])))/(1+exp(a*(Theta-b[i])))
}
for(i in 1:(ncat-1)){
z[,i]<-y[,i]-y[,i+1]
}
z[,ncat]<-y[,ncat]
return(z)
}
p.grm(par=par,Theta=Theta,ncat=ncat)
I am trying to delete all values in a list that have the tag ".dsw". My list is a list of files using the function list.files. This is my code:
for (file in GRef) {
if (strsplit(file, "[.]")[[1]][3] == "dsw") {
#GRef=GRef[-file]
for(n in 1:length(GRef)){
if (GRef[n] == file){
GRef=GRef[-n]
}
}
}
}
Where GRef is the list of file names. I get the error listed above, but I dont understand why. I have looked at this post: Error .. missing value where TRUE/FALSE needed, but I dont think it is the same thing.
You shouldn't attempt to to modify a vector while you are looping over it. The problem is your are removing items you are then trying to extract later which is causing the missing values. It's better to identify all the items you want remove first, then remove them. For example
GRef <- c("a.file.dsw", "b.file.txt", "c.file.gif", "d.file.dsw")
exts <- sapply(strsplit(GRef, "[.]"), `[`, 3)
GRef <- GRef[exts!="dsw"]
I am having a very hard time trying to subtract two vectors in my OpenBUGS model. The last line of the code below keeps giving the error "expected right parenthesis error":
model {
for ( i in 1:N) {
for(j in 1:q) {
vv[i,j] ~ dnorm(vz[i,j],tau.eta[j])
}
vz[i,1:q] ~ dmnorm(media.z[i,], K.delta[,])
for(j in 1:q) {
mean.z[i,j] <- inprod(K[i,] , vbeta[j,])
}
K[i,1] <- 1.0
for(j in 1:N) {
K[i,j+1] <- sum(ve[,i] - ve[,j])
}
}
If I change that line to K[i,j+1] <- sum(ve[,i]) - sum(ve[,j]), then the model works fine, but that is not what I want to do. I would like to subtract element-wise.
I searched SO for OpenBUGS, but there are only a few unrelated topics:
OpenBUGS - Variable is not defined
OpenBUGS: missing value in Bernoulli distribution
In Stats Stack Exchange there is this post which is close, but I still could not make how to implement this in my model:
https://stats.stackexchange.com/questions/20653/vector-multiplication-in-bugs-and-jags/20739#20739
I understand I have to write a for loop, but this thing is sure giving me a big headache. :)
I tried changing that line to:
for(k in 1:p) { temp [k] <- ve[k,i] - ve[k,j] }
K[i,j+1] <- sum(temp[])
where 'p' is the number of rows in each 've'. Now I keep getting the error "multiple definitions of node temp[1]".
I could definitely use some help. It will be much appreciated.
Best regards to all and thanks in advance!
PS: I wanted to add the tag "OpenBUGS" to this question but unfortunately I couldn't because it would be a new tag and I do not have enough reputation. I added "winbugs" instead.
The "multiple definitions" error is because temp[k] is redefined over and over again within a loop over i and another loop over j - you can only define it once. To get around that, use i and j subscripts like
for(k in 1:p) { temp[k,i,j] <- ve[k,i] - ve[k,j] }
K[i,j+1] <- sum(temp[,i,j])
Though if that compiles and runs, I'd check the results to make sure that's exactly what you want mathematically.