StackOverflow2013

Note that there are some explanatory texts on larger screens.

plurals

PO
primarykey
Id
6224761
data
AcceptedAnswerId
0
AnswerCount
0
ClosedDate
CommentCount
7
CommunityOwnedDate
CreationDate
2011-06-03T08:32:54.443
FavoriteCount
0
LastActivityDate
2014-04-25T20:40:22.650
LastEditDate
2014-04-25T20:40:22.650
LastEditorUserId
429846
OwnerUserId
429846
ParentId
6224341
PostTypeId
2
Score
8
ViewCount
0
LastEditorDisplayName
text
Body
Here is a simple base R solution (explanation below): <pre><code>spl <- with(df1, strsplit(as.charcter(B), split = "; ", fixed = TRUE)) lens <- sapply(spl, length) out <- with(df1, data.frame(A = rep(A, lens), B = unlist(spl))) </code></pre> Which gives us: <pre><code>R> out A B 1 aaa 111 2 aaa 222 3 bbb 333 4 ccc 444 5 ccc 555 6 ccc 666 7 ccc 777 8 ddd 888 9 ddd 999 </code></pre> What is the code doing? Line 1: <pre><code>spl <- with(df1, strsplit(as.character(B), split = "; ", fixed = TRUE)) </code></pre> breaks apart each of the strings in <code>B</code> using <code>"; "</code> as the characters to split on. We use <code>fixed = TRUE</code> (as suggested by @Marek in the comments) to speed up the matching and splitting as in this case we do not need to match using a regular expression, we simply want to match on the stated string. This gives us a list with the various elements split out: <pre><code>R> spl [[1]] [1] "111" "222" [[2]] [1] "333" [[3]] [1] "444" "555" "666" "777" [[4]] [1] "888" "999" </code></pre> The next line simply counts how many elements there are in each component of the list <code>spl</code> <pre><code>lens <- sapply(spl, length) </code></pre> which gives us a vectors of lengths: <pre><code>R> lens [1] 2 1 4 2 </code></pre> The final line of the solution plugs the outputs from the two previous steps into a new data frame. The trick is to repeat each element of <code>df1$A</code> <code>lens</code> number of times; for which we use the <code>rep()</code> function. We also need to unwrap the list <code>spl</code> into a vector which we do with <code>unlist()</code>: <pre><code>out <- with(df1, data.frame(A = rep(A, lens), B = unlist(spl))) </code></pre>
Tags
Title
singulars
PostAcceptedAnswerId
1. This table or related slice is empty.
PostParentId
1. POHow to create a new data frame with original data separated by ; and with different counts per category?
 singulars
 PostTypePostTypeId
 PTQuestion
PostTypePostTypeId
1. PTAnswer
UserLastEditorUserId
1. USGavin Simpson
UserOwnerUserId
1. USGavin Simpson
plurals
PostLinksPostIdRelatedPostId
1. This table or related slice is empty.
PostLinksRelatedPostIdPostId
1. This table or related slice is empty.
PostsAcceptedAnswerId
1. This table or related slice is empty.
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.