merge

问题 I have two dataframes. df1contains number and timestamps. It is a very large set. df1.head() Out[292]: 2016-08-31 08:09:00 1.0 2016-08-31 08:11:00 7.0 2016-08-31 08:14:00 90.0 df2 contains intervals d2.head() Out[292]: start stop C 2016-08-31 08:09:00 2016-08-31 08:12:00 'a' 2016-08-31 08:13:00 2016-08-31 08:20:00 'b' 2016-08-31 08:20:00 2016-08-31 08:45:00 'c' I would like to add a new column C to df1 such that the value of C is corresponding to the value in df2 for the interval which

问题 I have a spreadsheet that have >100 columns, and many columns have the same names. I would like to merge those columns with the same names and row-sum the values in those columns. I think conditional execution, if(), should do it, but I'm stuck at writing the condition for identical column names? And what will be the function to merge and sum the columns? merge()? or rowsum()? aa <- read.table() if ( colnames(aa) == ) merge/rowsum() Thanks. This is a sample of what it looks like now: B C U B

问题 I have a spreadsheet that have >100 columns, and many columns have the same names. I would like to merge those columns with the same names and row-sum the values in those columns. I think conditional execution, if(), should do it, but I'm stuck at writing the condition for identical column names? And what will be the function to merge and sum the columns? merge()? or rowsum()? aa <- read.table() if ( colnames(aa) == ) merge/rowsum() Thanks. This is a sample of what it looks like now: B C U B

问题 These are the key/values in JSON [ { "country":"First", "coupon":["1"] }, { "country":"First", "coupon":["10"] }, { "country":"First", "coupon":["12"] }, { "country":"Second", "coupon":"2" }, { "country":"third", "coupon":"3" }, { "country":"fourth", "coupon":"4" }, { "country":"fifth", "coupon":"5" } ] I sorted out the duplicates in JSON and displayed on dropdown var sortedCountries = []; if (sortedCountries.indexOf(value.country) == -1) { $('#sel').append('<option value="' + value.coupon +

问题 This question already has answers here : join datasets using a quosure as the by argument (2 answers) Closed 2 years ago . I am trying to create a merge function as follows: Defined function merge_tables <- function(inputdata1, inputdata2, byvar1, byvar2) { byvar1 <- enquo(byvar1) byvar2 <- enquo(byvar2) outputdata <- inputdata1 %>% full_join(inputdata2, by = c(rlang::quo_text(byvar1) = rlang::quo_text(byvar2))) return(outputdata) } I am getting an error when I run using the following data

问题 our company try to fork a github project to our own git server,then we can add our own features on it. We just want to checkout a specific branch, and keep all branches and tags up to this branch, then copy(mirror ?) to our git server. 回答1: Create the repo on your server. Elsewhere (not in the server repo), clone just the branch with git clone --single-branch --branch branch_name github_repo_url Tell git where your repo is: git remote add mine your_repo_url Then, push the branch to your repo

问题 our company try to fork a github project to our own git server,then we can add our own features on it. We just want to checkout a specific branch, and keep all branches and tags up to this branch, then copy(mirror ?) to our git server. 回答1: Create the repo on your server. Elsewhere (not in the server repo), clone just the branch with git clone --single-branch --branch branch_name github_repo_url Tell git where your repo is: git remote add mine your_repo_url Then, push the branch to your repo

问题 I want to merge some rows if they are nearly similar. Similarity can be checked by using spaCy. df: string yellow color yellow color looks like yellow color bright red color okay red color blood output: string yellow color looks like bright red color okay blood solution: brute force approach is - for every item in string check similarity with other n-1 item if greater than some threshold value then merge. Is there any other approach ? As i am not in contact with much people idk how they do it

问题 I want to merge some rows if they are nearly similar. Similarity can be checked by using spaCy. df: string yellow color yellow color looks like yellow color bright red color okay red color blood output: string yellow color looks like bright red color okay blood solution: brute force approach is - for every item in string check similarity with other n-1 item if greater than some threshold value then merge. Is there any other approach ? As i am not in contact with much people idk how they do it

问题 Both Uwe's and GKi's answer are correct. Gki received the bounty because Uwe was late for that, but Uwe's solution runs about 15x as fast I have two datasets that contain scores for different patients on multiple measuring moments like so: df1 <- data.frame("ID" = c("patient1","patient1","patient1","patient1","patient2","patient3"), "Days" = c(0,25,235,353,100,538), "Score" = c(NA,2,3,4,5,6), stringsAsFactors = FALSE) df2 <- data.frame("ID" = c("patient1","patient1","patient1","patient1",