StackOverflow2013

Note that there are some explanatory texts on larger screens.

plurals

PO
primarykey
Id
14157292
data
AcceptedAnswerId
0
AnswerCount
0
ClosedDate
CommentCount
6
CommunityOwnedDate
CreationDate
2013-01-04T12:32:59.473
FavoriteCount
0
LastActivityDate
2013-01-04T16:28:41.657
LastEditDate
2013-01-04T16:28:41.657
LastEditorUserId
1320535
OwnerUserId
1320535
ParentId
14154952
PostTypeId
2
Score
4
ViewCount
0
LastEditorDisplayName
text
Body
Here is a generalized function: <pre><code>PatternMatcher <- function(data, pattern, idx = NULL) { p <- unlist(pattern[1]) if(is.null(idx)){ p <- unlist(pattern[length(pattern)]) PatternMatcher(data, rev(pattern)[-1], idx = Filter(function(n) all(p %in% intersect(data[n, ], p)), 1:nrow(data))) } else if(length(pattern) > 1) { PatternMatcher(data, pattern[-1], idx = Filter(function(n) all(p %in% intersect(data[n, ], p)), idx - 1)) } else Filter(function(n) all(p %in% intersect(data[n, ], p)), idx - 1) } </code></pre> This is a recursive function which is reducing <code>pattern</code> in every iteration and checks only rows that go right after ones identified in the previous iteration. List structure allows passing the pattern in a convenient way: <pre><code>PatternMatcher(m, list(37, list(10, 29), 42)) # [1] 57 PatternMatcher(m, list(list(45, 24, 1), 7, list(45, 31), 4)) # [1] 2 PatternMatcher(m, list(1,3)) # [1] 47 48 93 </code></pre> Edit: The idea of the function above seems fine: check all rows for the vector <code>pattern[[1]]</code> and get indices <code>r1</code>, then check rows <code>r1+1</code> for <code>pattern[[2]]</code> and get <code>r2</code>, etc. But it takes really much time at the first step when going through all rows. Of course, every step would take much time with e.g. <code>m <- matrix(sample(1:10, 800, replace=T), ncol=8)</code>, i.e. when there is not much of a change in indices <code>r1</code>, <code>r2</code>, ... So here is another approach, here <code>PatternMatcher</code> looks very similar, but there is another function <code>matchRow</code> for finding rows that have all elements of <code>vector</code>. <pre><code>matchRow <- function(data, vector, idx = NULL){ if(is.null(idx)){ matchRow(data, vector[-1], as.numeric(unique(rownames(which(data == vector[1], arr.ind = TRUE))))) } else if(length(vector) > 0) { matchRow(data, vector[-1], as.numeric(unique(rownames(which(data[idx, , drop = FALSE] == vector[1], arr.ind = TRUE))))) } else idx } PatternMatcher <- function(data, pattern, idx = NULL) { p <- pattern[[1]] if(is.null(idx)){ rownames(data) <- 1:nrow(data) p <- pattern[[length(pattern)]] PatternMatcher(data, rev(pattern)[-1], idx = matchRow(data, p)) } else if(length(pattern) > 1) { PatternMatcher(data, pattern[-1], idx = matchRow(data, p, idx - 1)) } else matchRow(data, p, idx - 1) } </code></pre> Comparison with the previous function: <pre><code>library(rbenchmark) bigM <- matrix(sample(1:50, 800000, replace=T), ncol=8) benchmark(PatternMatcher(bigM, list(37, c(10, 29), 42)), PatternMatcher(bigM, list(1, 3)), OldPatternMatcher(bigM, list(37, list(10, 29), 42)), OldPatternMatcher(bigM, list(1, 3)), replications = 10, columns = c("test", "elapsed")) # test elapsed # 4 OldPatternMatcher(bigM, list(1, 3)) 61.14 # 3 OldPatternMatcher(bigM, list(37, list(10, 29), 42)) 63.28 # 2 PatternMatcher(bigM, list(1, 3)) 1.58 # 1 PatternMatcher(bigM, list(37, c(10, 29), 42)) 2.02 verybigM1 <- matrix(sample(1:40, 8000000, replace=T), ncol=20) verybigM2 <- matrix(sample(1:140, 8000000, replace=T), ncol=20) benchmark(PatternMatcher(verybigM1, list(37, c(10, 29), 42)), PatternMatcher(verybigM2, list(37, c(10, 29), 42)), find.combo(verybigM1, convert.gui.input("37;10,29;42")), find.combo(verybigM2, convert.gui.input("37;10,29;42")), replications = 20, columns = c("test", "elapsed")) # test elapsed # 3 find.combo(verybigM1, convert.gui.input("37;10,29;42")) 17.55 # 4 find.combo(verybigM2, convert.gui.input("37;10,29;42")) 18.72 # 1 PatternMatcher(verybigM1, list(37, c(10, 29), 42)) 15.84 # 2 PatternMatcher(verybigM2, list(37, c(10, 29), 42)) 19.62 </code></pre> Also now the <code>pattern</code> argument should be like <code>list(37, c(10, 29), 42)</code> instead of <code>list(37, list(10, 29), 42)</code>. And finally: <pre><code>fastPattern <- function(data, pattern) PatternMatcher(data, lapply(strsplit(pattern, ";")[[1]], function(i) as.numeric(unlist(strsplit(i, split = ","))))) fastPattern(m, "37;10,29;42") # [1] 57 fastPattern(m, "37;;42") # [1] 57 4 fastPattern(m, "37;;;42") # [1] 33 56 77 </code></pre>
Tags
Title
singulars
PostAcceptedAnswerId
1. This table or related slice is empty.
PostParentId
1. POFinding pattern in a matrix in R
 singulars
 PostTypePostTypeId
 PTQuestion
PostTypePostTypeId
1. PTAnswer
UserLastEditorUserId
1. USJulius Vainora
UserOwnerUserId
1. USJulius Vainora
plurals
PostLinksPostIdRelatedPostId
1. This table or related slice is empty.
PostLinksRelatedPostIdPostId
1. This table or related slice is empty.
PostsAcceptedAnswerId
1. POFinding pattern in a matrix in R
 singulars
 PostTypePostTypeId
 PTQuestion
PostsParentIdCreationDate
1. This table or related slice is empty.
VotesPostIdCreationDate
1. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTUpMod
2. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTUpMod
3. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTUpMod
CommentsPostId

Querying!

Guidance

A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.