data.table

问题 I'm trying to prepare some demographic data retrieved from Eurostat for further processing, amongst others replacing any missing data with corresponding approximated ones. First I was using data.frames only, but then I got convinced that data.tables might offer some advantages over regular data.frames, so I migrated to data.tables. One thing I've observed while doing so was getting different results when using "na.spline" in combination with "apply" versus "na.spline" as part of the data

问题 I have a dataframe df.sample like this id <- c("A","A","A","A","A","A","A","A","A","A","A") date <- c("2018-11-12","2018-11-12","2018-11-12","2018-11-12","2018-11-12", "2018-11-12","2018-11-12","2018-11-14","2018-11-14","2018-11-14", "2018-11-12") hour <- c(8,8,9,9,13,13,16,6,7,19,7) min <- c(47,59,6,18,22,36,12,32,12,21,47) value <- c(70,70,86,86,86,74,81,77,79,83,91) df.sample <- data.frame(id,date,hour,min,value,stringsAsFactors = F) df.sample$date <- as.Date(df.sample$date,format="%Y-%m-

问题 I have a dataframe df.sample like this id <- c("A","A","A","A","A","A","A","A","A","A","A") date <- c("2018-11-12","2018-11-12","2018-11-12","2018-11-12","2018-11-12", "2018-11-12","2018-11-12","2018-11-14","2018-11-14","2018-11-14", "2018-11-12") hour <- c(8,8,9,9,13,13,16,6,7,19,7) min <- c(47,59,6,18,22,36,12,32,12,21,47) value <- c(70,70,86,86,86,74,81,77,79,83,91) df.sample <- data.frame(id,date,hour,min,value,stringsAsFactors = F) df.sample$date <- as.Date(df.sample$date,format="%Y-%m-

问题 I have a dataframe df.sample like this id <- c("A","A","A","A","A","A","A","A","A","A","A") date <- c("2018-11-12","2018-11-12","2018-11-12","2018-11-12","2018-11-12", "2018-11-12","2018-11-12","2018-11-14","2018-11-14","2018-11-14", "2018-11-12") hour <- c(8,8,9,9,13,13,16,6,7,19,7) min <- c(47,59,6,18,22,36,12,32,12,21,47) value <- c(70,70,86,86,86,74,81,77,79,83,91) df.sample <- data.frame(id,date,hour,min,value,stringsAsFactors = F) df.sample$date <- as.Date(df.sample$date,format="%Y-%m-

问题 I have a dataframe df.sample like this id <- c("A","A","A","A","A","A","A","A","A","A","A") date <- c("2018-11-12","2018-11-12","2018-11-12","2018-11-12","2018-11-12", "2018-11-12","2018-11-12","2018-11-14","2018-11-14","2018-11-14", "2018-11-12") hour <- c(8,8,9,9,13,13,16,6,7,19,7) min <- c(47,59,6,18,22,36,12,32,12,21,47) value <- c(70,70,86,86,86,74,81,77,79,83,91) df.sample <- data.frame(id,date,hour,min,value,stringsAsFactors = F) df.sample$date <- as.Date(df.sample$date,format="%Y-%m-

问题 Using my example below, how can I rank multiple columns using different orders, so for example rank y as descending and z as ascending? require(data.table) dt <- data.table(x = c(rep("a", 5), rep("b", 5)), y = abs(rnorm(10)) * 10, z = abs(rnorm(10)) * 10) cols <- c("y", "z") dt[, paste0("rank_", cols) := lapply(.SD, function(x) frankv(x, ties.method = "min")), .SDcols = cols, by = .(x)] 回答1: data.table 's frank() function has some useful features which aren't available in base R's rank()

问题 I am looking to add a field that counts the number of consecutive days within each group (captured by id field). I start with this: dt <- structure(list(id = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), date = c("1/01/2000", "2/01/2000", "2/01/2000", "5/01/2000", "6/01/2000", "7/01/2000", "8/01/2000", "13/01/2000", "14/01/2000", "18/01/2000", "19/01/2000", "21/01/2000", "25/01/2000", "26/01/2000", "30/01/2000", "31/01/2000")), .Names = c("id", "date"), row.names = c(NA,

问题 I have data as follows: EDIT: Sample of Original Data DT <- structure(list(Abbreviation = "AK", date = "1/31/2011", month = "01", year = "2011", c1 = "P", male = 12288, female = 6107, c4 = 2, upto22 = 870, from22to24 = 1441, from25to34 = 5320, from35to44 = 3568, from45to54 = 4322, from55to59 = 1539, from60to64 = 886, over65 = 451, c20 = 0, hispanic = 771, non_hispanic = 17458, c42 = 168, native = 4856, asian = 791, black = 611, hawaii = 289, white = 11209, c48 = 641), row.names = c(NA, -1L),

问题 I have this badly organized data table given to me, in which there are hundreds of columns (subset is given below) Names of columns are dot delimited where the first field holds information about a type of object (e.g. Item123, object_AB etc.) without any naming convention. There is no specific order for these columns as well. Other columns share the type of object field and also have the name of some property for that object (e.g. color, manufacturer etc.). Item123.type.value Item123.mass

问题 Here is the output that I want from data.table . library(data.table) dt_mtcars <- as.data.table(mtcars) ## desired output ---- dt_mtcars[mpg >20 , .(mean_mpg = mean(mpg) ,median_mpg = median(mpg)) , .(cyl, gear)] cyl gear mean_mpg median_mpg 1: 6 4 21.000 21.00 2: 4 4 26.925 25.85 3: 6 3 21.400 21.40 4: 4 3 21.500 21.50 5: 4 5 28.200 28.20 I want to get the output by passing arguments to a function. processFUN <- function(dt, where, select, group){ out <- dt[i=eval(parse(text = where)) ,j