Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with r
Search options answers only
not deleted
user 18514
R is a free, open source programming language and software environment for statistical computing and graphics.
4
votes
Accepted
Find the minimum value that data could have had before it was rounded
You can firstly change by difference != 0 and then use na.locf to replace NAs by last available value recursively.
minimum_new <- function(data) {
answer <- rep(NA, length(data))
difference <- c( …
- 366
5
votes
Accepted
Row-wise mean imputation in R
You can use rowMeans with indexing.
k <- which(is.na(cancer1), arr.ind=TRUE)
cancer1[k] <- rowMeans(cancer1, na.rm=TRUE)[k[,1]]
Where k is an indices of the rows with NA values.
- 366
3
votes
Accepted
Predict new ratings for each user based on their pearson correlation with other users
You have incorrect indexing in the loop. Better version with mapply and correct indexing:
df <- mapply(function(i, j) {
sim_user = which(cor_mat[i,] >= lower & cor_mat[i,] < upper)
final_x[j, i] …
- 366