data.table

问题 I'm having difficulty counting overlaps of intervals as I would expect. Here is an R data.table with intervals defined by start to end: > library(data.table) > dt1 = data.table(start=c(1, 5, 3), end=c(10, 15, 8)) > print(dt1) start end 1: 1 10 2: 5 15 3: 3 8 Here is how I would consider overlaps for these intervals, from 0 to 20: [0, 1]: 0 (there are no intervals here) [1, 3]: 1 (there is only one interval here, from [1, 10]) [3, 5]: 2 (two intervals here, both [1, 10] and [3, 8]) [5, 8]: 3

问题 I'm having difficulty counting overlaps of intervals as I would expect. Here is an R data.table with intervals defined by start to end: > library(data.table) > dt1 = data.table(start=c(1, 5, 3), end=c(10, 15, 8)) > print(dt1) start end 1: 1 10 2: 5 15 3: 3 8 Here is how I would consider overlaps for these intervals, from 0 to 20: [0, 1]: 0 (there are no intervals here) [1, 3]: 1 (there is only one interval here, from [1, 10]) [3, 5]: 2 (two intervals here, both [1, 10] and [3, 8]) [5, 8]: 3

问题 I have a dataframe structured like this (but it actually has ~400k rows): library(data.table) df <- fread(" id start end 174095 2018-12-19 2018-12-31 227156 2018-12-19 2018-12-31 210610 2018-04-13 2018-09-27 27677 2018-04-12 2018-04-26 370474 2017-07-13 2017-08-19 303693 2017-02-20 2017-04-09 74744 2016-10-03 2016-11-05 174095 2018-12-01 2018-12-20 27677 2018-03-01 2018-05-29 111111 2018-01-01 2018-01-31 111111 2018-11-11 2018-12-31") (edited, thanks to Uwe) For each row, I want to count how

问题 MAJOR EDIT to clarify as answers are wrong I have a data.table with group columns (split_by), key columns (key_by) and trait ids columns (intersect_by) I want in each group of split_by, keep only the rows where the trait ids are shared by all the present keys in the group. For example: dt <- data.table(id = 1:6, key1 = 1, key2 = c(1:2, 2), group_id1= 1, group_id2= c(1:2, 2:1, 1:2), trait_id1 = 1, trait_id2 = 2:1) setkey(dt, group_id1, group_id2, trait_id1, trait_id2) dt id key1 key2 group_id1

问题 I need to to read a .txt file from an URL, but would like to skip the rows until a row with a certain value. The URL is https://fred.stlouisfed.org/data/HNOMFAQ027S.txt and the data takes the following form: " ... (number of rows) ... (number of rows) ... (number of rows) DATE VALUE 1945-01-01 144855 1946-01-01 138515 1947-01-01 136405 1948-01-01 135486 1949-01-01 142455 " I would like to skip all rows until the row with "DATE // VALUE" and start importing the data from this line onwards

问题 This question already has answers here : Quickly reading very large tables as dataframes (11 answers) Closed 4 years ago . I have a large dataset (about 13GB uncompressed) and I need to load it repeatedly. The first load (and save to a different format) can be very slow but every load after this should be as fast as possible. What is the fastest way and fastest format from which to load a data set? My suspicion is that the optimal choice is something like saveRDS(obj, file = 'bigdata.Rda',

问题 Note: this question is a copy of this one but with different wording, and a suggestion for data.table instead of dplyr I have two datasets that contain scores for different patients on multiple measuring moments like so: dt1 <- data.frame("ID" = c("patient1","patient1","patient1","patient1","patient2","patient3"), "Days" = c(0,10,25,340,100,538), "Score" = c(NA,2,3,99,5,6), stringsAsFactors = FALSE) dt2 <- data.frame("ID" = c("patient1","patient1","patient1","patient1","patient2","patient2",

问题 Note: this question is a copy of this one but with different wording, and a suggestion for data.table instead of dplyr I have two datasets that contain scores for different patients on multiple measuring moments like so: dt1 <- data.frame("ID" = c("patient1","patient1","patient1","patient1","patient2","patient3"), "Days" = c(0,10,25,340,100,538), "Score" = c(NA,2,3,99,5,6), stringsAsFactors = FALSE) dt2 <- data.frame("ID" = c("patient1","patient1","patient1","patient1","patient2","patient2",

问题 Here is a data.table: library(data.table) DT <- data.table(airquality) This example produces the output I want: DT[, `:=`(New_Ozone= log(Ozone), New_Wind=log(Wind))] How can I write a function log_those_columns such that the following code snippet outputs the same result? old_names <- c("Ozone", "Wind") new_names <- c("New_Ozone", "New_Wind") log_those_columns(DT, old_names, new_names) Note that I need old_names and new_names to be flexible enough to contain any number of columns. (I see from

问题 I have a data.table of allele identities (rows are individuals, columns are loci), grouped by a separate column. I want to calculate allele frequencies (proportions) for each locus efficiently, by group. An example data table: DT = data.table(Loc1=rep(c("G","T"),each=5), Loc2=c("C","A"), Loc3=c("C","G","G","G", "C","G","G","G","G","G"), Group=c(rep("G1",3),rep("G2",4),rep("G3",3))) for(i in 1:3) set(DT, sample(10,2), i, NA) > DT Loc1 Loc2 Loc3 Group 1: G NA C G1 2: G A G G1 3: G C G G1 4: NA