subset

I have two data sets that are supposed to be the same size but aren't. I need to trim the values from A that are not in B and vice versa in order to eliminate noise from a graph that's going into a report. (Don't worry, this data isn't being permanently deleted!) I have read the following: Selecting columns in R data frame based on those *not* in a vector http://www.ats.ucla.edu/stat/r/faq/subset_R.htm How to combine multiple conditions to subset a data-frame using "OR"? But I'm still not able to get this to work right. Here's my code: bg2011missingFromBeg <- setdiff(x=eg2011$ID, y=bg2011$ID)

I have a data frame 'math.numeric' with 32 variables. Each row represents a student and each variable is an attribute. The students have been put into 5 groups based on their final grade. The data looks as follows: head(math.numeric) school sex age address famsize Pstatus Medu Fedu Mjob Fjob reason ... group 1 1 18 2 1 1 4 4 1 5 1 2 1 1 17 2 1 2 1 1 1 3 1 2 1 1 15 2 2 2 1 1 1 3 3 3 1 1 15 2 1 2 4 2 2 4 2 4 1 1 16 2 1 2 3 3 3 3 2 3 1 2 16 2 2 2 4 3 4 3 4 4 I am performing t-tests on each variable for group 1 vs. all the other groups to identify significantly different attributes with this group

I want to subset a data.table using a variable which has the same name as the column which leeds to some problems: dt <- data.table(a=sample(c('a', 'b', 'c'), 20, replace=TRUE), b=sample(c('a', 'b', 'c'), 20, replace=TRUE), c=sample(20), key=c('a', 'b')) evn <- environment() a <- 'b' dt[a == a] #Expected Result dt[a == 'b'] I came across this possible solution : env <- environment() dt[a == get('a',env)] But it is as unhandy as: this.a = a dt[a == this.a] So is there another elegant solution? For now, a temporary solution could be, `..` <- function (..., .env = globalenv()) { get(deparse

This question already has an answer here: Split data.frame based on levels of a factor into new data.frames 1 answer My data is like this (for example): ID Rate State 1 24 AL 2 35 MN 3 46 FL 4 34 AL 5 78 MN 6 99 FL Data: structure(list(ID = 1:6, Rate = c(24L, 35L, 46L, 34L, 78L, 99L), State = structure(c(1L, 3L, 2L, 1L, 3L, 2L), .Label = c("AL","FL", "MN"), class = "factor")), .Names = c("ID", "Rate", "State"), class = "data.frame", row.names = c(NA, -6L)) I want to split the data by state and I want to get 3 data sets like below: data set 1 ID Rate State 1 24 AL 4 34 AL data set 2 ID Rate

There are very nifty ways of subsetting xts objects. For example, one can get all the data for all years, months, days but being strictly between 9:30 AM and 4 PM by doing: my_xts["T09:30/T16:00"] Or you can get all the observations between two dates by doing: my_xts["2012-01-01/2012-03-31"] Or all the dates before/after a certain date by doing: my_xts["/2011"] # from start of data until end of 2011 my_xts["2011/"] # from 2011 until the end of the data How can I get all the data for only certain months for all years or only certain days for all months and years? Do any other subsetting tricks

I have some data that I am looking at in R. One particular column, titled "Height", contains a few rows of NA. I am looking to subset my data-frame so that all Heights above a certain value are excluded from my analysis. df2 <- subset ( df1 , Height < 40 ) However whenever I do this, R automatically removes all rows that contain NA values for Height. I do not want this. I have tried including arguments for na.rm f1 <- function ( x , na.rm = FALSE ) { df2 <- subset ( x , Height < 40 ) } f1 ( df1 , na.rm = FALSE ) but this does not seem to do anything; the rows with NA still end up disappearing