Two ways to programmatically select variable(s): with = FALSE: DT = data.table(col1 = 1:3) colname = “col1” DT[, colname, with = FALSE] # col1 # 1: 1 # 2: 2 # 3: 3 ‘dot dot’ (..) prefix: DT[, ..colname] # col1 # 1: 1 # 2: 2 # 3: 3 For further description of the … Read more
These solutions (1) maintain the pipeline, (2) do not overwrite the input and (3) only require that the condition be specified once: 1a) mutate_cond Create a simple function for data frames or data tables that can be incorporated into pipelines. This function is like mutate but only acts on the rows satisfying the condition: mutate_cond … Read more
The match approach works when there is a unique key in the second data frame for each key value in the first. If there are duplicates in the second data frame then the match and merge approaches are not the same. Match is, of course, faster since it is not doing as much. In particular … Read more
Slightly more elegant: library(data.table) DT[ , .SD[which.min(Employees)], by = State] State Company Employees 1: AK D 24 2: RI E 19 Slighly less elegant than using .SD, but a bit faster (for data with many groups): DT[DT[ , .I[which.min(Employees)], by = State]$V1] Also, just replace the expression which.min(Employees) with Employees == min(Employees), if your data … Read more
From dplyr 1.0.0, “slice_min() and slice_max() select the rows with the minimum or maximum values of a variable, taking over from the confusing top_n().“ d %>% group_by(grp) %>% slice_max(order_by = x, n = 5) # # A tibble: 15 x 2 # # Groups: grp [3] # x grp # <dbl> <fct> # 1 0.994 … Read more
Use the quote() and eval() functions to pass a variable to j. You don’t need double-quotes on the column names when you do it this way, because the quote()-ed string will be evaluated inside the DT[] temp <- quote(x) DT[ , eval(temp)] # [1] “b” “b” “b” “a” “a” With a single column name, the … Read more
In addition to this answer, please refer to the vignettes Secondary indices and auto indexing and Keys and fast binary search based subset as well. This issue highlights the other vignettes that we plan to. I’ve updated this answer again (Feb 2016) in light of the new on= feature that allows ad-hoc joins as well. … Read more
Option 1 Use the fact that a data.frame is a list of columns, then use do.call to recreate a data.frame. do.call(data.frame,lapply(DT, function(x) replace(x, is.infinite(x),NA))) Option 2 — data.table You could use data.table and set. This avoids some internal copying. DT <- data.table(dat) invisible(lapply(names(DT),function(.name) set(DT, which(is.infinite(DT[[.name]])), j = .name,value =NA))) Or using column numbers (possibly faster … Read more
For a single column: dtnew <- dt[, Quarter:=as.character(Quarter)] str(dtnew) Classes ‘data.table’ and ‘data.frame’: 10 obs. of 3 variables: $ ID : Factor w/ 2 levels “A”,”B”: 1 1 1 1 1 2 2 2 2 2 $ Quarter: chr “1” “2” “3” “4” … $ value : num -0.838 0.146 -1.059 -1.197 0.282 … Using … Read more
To quote from the data.table FAQ 1.11 What is the difference between X[Y] and merge(X, Y)? X[Y] is a join, looking up X’s rows using Y (or Y’s key if it has one) as an index. Y[X] is a join, looking up Y’s rows using X (or X’s key if it has one) merge(X,Y) does … Read more