How to apply a long list of functions automatically in 10 imputed datasets in R - r

Question

I have 10 datasets that are the result of multiple imputation, which i named: data1, data2, ..., data10. For each of them, I want to do:
Create a logistic regression model
Do multiple steps which include creating a LASSO model, resampling 200 times from my imputed dataset, recreate LASSO model in each resampling, evaluate measures of performance.
I'm able to do it separately for each dataset but I was wondering if there was a way to automatically do all of the steps for each imputed dataset. Below, I included an example of all the steps I do to get results separately for each imputation.
To do it automatically, i first thought about using lapply to create regressions for every imputation:
log01.1 <- lapply(paste0("data",1:10), function(x){lrm(y ~ x1 + x2 + x3, data=eval(parse(text = x)), x=T, y=T)})
Then I wanted to use lapply again on the whole block of code below with something like :
lapply(log01.1,fun(x){*All the steps following the regression*}
But I realized it doesn't work since lapply can only be applied to one function at a time as I understand it + at model.L1 <- glmnet(x=log01.1$x, y=log01.1$y, alpha=1, lambda=cv.glmmod$lambda.1se, family="binomial")
it wouldn't work since my lambda would come from a list. And I can't use lapply both on log01.1 and on cv.glmmod at the same time. Add to that the resampling with the 200 repetitons and I'm sure I would run into other problems I can't even think of right now.
And that's about the extent of my knowledge on lapply and other functions that could do similar things. Is there a way to take the chunk of code I wrote below and tell R to repeat it for every one of my 10 imputations and then store into separate lists the objects that would have been created? Or maybe not in lists but I would get for example App1, App2, App3, etc.?
Or am I better off just repeating it 10 times and storing the results?
log01.1 <- lrm(y ~ x1 + x2 + x3 , data=data1, x=T, y=T)})
reps <- 200;App=numeric(reps);Test=numeric(reps)
for(i in 1:reps){
#1.Construct LASSO model in sample i
cv.glmmod <- cv.glmnet(x=log01.1$x, y=log01.1$y, alpha=1, family="binomial")
model.L1 <- glmnet(x=log01.1$x, y=log01.1$y, alpha=1,
lambda=cv.glmmod$lambda.1se, family="binomial") #use optimum penalty
lp1 <- log01.1$x %*% model.L1$beta #for apparent performance
#2. Draw bootstrap sample with replacement from sample i
j <- sample(nrow(data1), replace=T) #for sample Bi
#3. Construct a model in sample Bi replaying every step that was done in the imputed sample
#I, especially model specification steps such as selection of predictors.
#Determine the bootstrap performance as the apparent performance in sample Bi.
#3 Construct LASSO model in sample i replaying every step done in imputed sample i
cv.j <- cv.glmnet (x=log01.1$x[j,], y=log01.1$y[j,], alpha = 1, family="binomial")
model.L1j <- glmnet (x=log01.1$x[j,], y=log01.1$y[j,], alpha=1,
lambda=cv.j$lambda.1se, family="binomial") #use optimum penalty for Bi
lp1j <- log01.1$x[j,] %*% model.L1j$beta #apparent performance in Bi
App[i] <- lrm.fit(y=log01.1$y[j,], x=lp1j)$stats[6] #apparent c for Bi
#4. Apply model from Bi to the original sample i without any modification to determine the test performance
lp1 <- log01.1$x %*% model.L1j$beta #Validated performance in I
Test[i] <- lrm.fit(y=log01.1$y, x=lp1)$stats[6]} #Test c in I
That is the code I would like to repeat automatically for every imputed set.