WebNov 5, 2016 · I have a dataframe with one observation per row and two observations per subject. I'd like to filter out just the rows with duplicate 'day' numbers. ex <- data.frame('id'= rep(1:5,2), 'day'= c(1:5, 1:3,5:6)) The following code filters out just the second duplicated row, but not the first. Again, I'd like to filter out both of the duplicated rows. WebThis can be achieved using dplyr package, which is available in CRAN. The simple way to achieve this: Install dplyr package. Run the below code library (dplyr) df<- select (filter (dat,name=='tom' name=='Lynn'), c ('days','name)) Explanation:
r - Filter certain values and multiple previous rows with another ...
WebJun 14, 2024 · How to Filter Rows In R, it’s common to want to subset a data frame based on particular conditions. Fortunately, using the filter() function from the dplyr package … WebMar 4, 2015 · Another option could be using complete.cases in your filter to for example remove the NA in the column A. Here is some reproducible code: library (dplyr) df %>% filter (complete.cases (a)) #> # A tibble: 2 × 3 #> a b c #> #> 1 1 2 3 #> 2 1 NA 3 Created on 2024-03-26 with reprex v2.0.2 Share Improve this answer Follow howard lake herald journal
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WebAug 3, 2012 · There are two columns. I want to remove any rows that are duplicated in both columns. Previously for a data.frame I would have done this: df -> unique (df [,c ('V1', 'V2')]) but this doesn't work with data.table. I have tried unique (df [,c (V1,V2), with=FALSE]) but it seems to still only operate on the key of the data.table and not the whole row. WebMay 23, 2024 · The filter () function is used to produce a subset of the data frame, retaining all rows that satisfy the specified conditions. The filter () method in R can be applied to both grouped and ungrouped data. The expressions include comparison operators (==, >, >= ) , logical operators (&, , !, xor ()) , range operators (between (), near ()) as ... Web2 days ago · Filter columns by group and condition. I have a kind of easy task but still can't figure it out. I have a csv binary matrix, with genes as rows and samples as columns, like this: Gene sampleA sampleB sampleC sampleD sampleE sampleF sampleG gene1 1 0 0 1 0 0 0 gene2 0 0 0 0 1 1 0 gene3 0 0 0 0 0 0 1 gene4 0 1 0 0 0 0 0 gene5 1 1 1 1 0 0 0 … how many joints to get high