data.table

问题 Inspired by the experimental fuzzy_join function from the statar package I wrote a function myself which combines exact and fuzzy (by string distances) matching. The merging job I have to do is quite big (resulting into multiple string distance matrices with a little bit less than one billion cells) and I had the impression that the fuzzy_join function is not written very efficiently (with regard to memory usage) and the parallelization is implemented in a weird manner (the computation of the

问题 I have a data.table that I am wanting to aggregate library(data.table) dt1 <- data.table(year=c("2001","2001","2001","2002","2002","2002","2002"), group=c("a","a","b","a","a","b","b"), amt=c(20,40,20,35,30,28,19)) I am wanting to sum the amt by year and group and then filter where the summed amt for any given group is greater than 100. I've got the data.table sum nailed. dt1[, sum(amt),by=list(year,group)] year group V1 1: 2001 a 60 2: 2001 b 20 3: 2002 a 65 4: 2002 b 47 I am having trouble

问题 I have a dataset as follows: library(data.table) DT <- structure(list(State_Ab = c("MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD"), County = c("Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore"), year = c(1994, 1994, 1998, 1998, 2000, 2000, 2004, 2004, 2006, 2006, 2010, 2010, 2016, 2016), Population = c(140942, 219673, 235413,

问题 I have a dataset as follows: library(data.table) DT <- structure(list(State_Ab = c("MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD"), County = c("Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore"), year = c(1994, 1994, 1998, 1998, 2000, 2000, 2004, 2004, 2006, 2006, 2010, 2010, 2016, 2016), Population = c(140942, 219673, 235413,

问题 I have a dataset as follows: library(data.table) DT <- structure(list(State_Ab = c("MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD"), County = c("Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore", "Baltimore"), year = c(1994, 1994, 1998, 1998, 2000, 2000, 2004, 2004, 2006, 2006, 2010, 2010, 2016, 2016), Population = c(140942, 219673, 235413,

问题 I have data as follows: library(data.table) DT <- structure(list(State_Ab = c("VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA"), year = c(1995, 1995, 1995, 1995, 1999, 1999, 1999, 1999, 2001, 2001, 2001, 2001, 2005, 2005, 2005, 2005, 2007, 2007, 2007, 2007, 2011, 2011,

问题 I am trying to join data.table x to z using a non-equi join. Table x contains two columns X1 and X2 that are used as the range to use for joining with column Z1 in z. The current code successfully does the non-equi join however certain columns are removed or renamed. I would like to return the 'ideal' data.table supplied, instead of the one I currently have which I would have to rename columns or join data further to get the 'ideal' data supplied. > library(data.table) > > x <- data.table(Id

问题 I would like to match multiple conditions from independent data tables onto my main data table. How can I do this using the data.table package? What would be the most efficient/fastest way? I have a mock example, with some mock conditions here to illustrate my question: main_data <- data.frame( pnum = c(1,2,3,4,5,6,7,8,9,10), age = c(24,35,43,34,55,24,36,43,34,54), gender = c("f","m","f","f","m","f","m","f","f","m")) data_1 <- data.frame( pnum = c(1,4,5,8,9), value_data_1 = c(1, 2, 1, 1, 1),

问题 I would like to match multiple conditions from independent data tables onto my main data table. How can I do this using the data.table package? What would be the most efficient/fastest way? I have a mock example, with some mock conditions here to illustrate my question: main_data <- data.frame( pnum = c(1,2,3,4,5,6,7,8,9,10), age = c(24,35,43,34,55,24,36,43,34,54), gender = c("f","m","f","f","m","f","m","f","f","m")) data_1 <- data.frame( pnum = c(1,4,5,8,9), value_data_1 = c(1, 2, 1, 1, 1),

问题 I have a data.table with ID, dates and values like the following one: DT <- setDT(data.frame(ContractID= c(1,1,1,2,2), Date = c("2018-02-01", "2018-02-20", "2018-03-12", "2018-02-01", "2018-02-12"), Value = c(10,20,30,10,20))) ContractID Date Value 1: 1 2018-02-01 10 2: 1 2018-02-20 20 3: 1 2018-03-12 30 4: 2 2018-02-01 10 5: 2 2018-02-12 20 I'd like to get a new column with the total cumulative sum per ID from a month ago until the current day for each row, like in the table below. NB: the