na

Is there a single function in R that determines if a value is NA , NaN , Inf , -Inf , or otherwise not a well-formed number? You want is.finite > is.finite(NA) [1] FALSE > is.finite(NaN) [1] FALSE > is.finite(Inf) [1] FALSE > is.finite(1L) [1] TRUE > is.finite(1.0) [1] TRUE > is.finite("A") [1] FALSE > is.finite(pi) [1] TRUE > is.finite(1+0i) [1] TRUE 来源： https://stackoverflow.com/questions/7518245/one-function-to-detect-nan-na-inf-inf-etc

How do I select all the rows that have a missing value in the primary key in a data table. DT = data.table(x=rep(c("a","b",NA),each=3), y=c(1,3,6), v=1:9) setkey(DT,x) Selecting for a particular value is easy DT["a",] Selecting for the missing values seems to require a vector search. One cannot use binary search. Am I correct? DT[NA,]# does not work DT[is.na(x),] #does work Fortunately, DT[is.na(x),] is nearly as fast as (e.g.) DT["a",] , so in practice, this may not really matter much: library(data.table) library(rbenchmark) DT = data.table(x=rep(c("a","b",NA),each=3e6), y=c(1,3,6), v=1:9)

After learning about the options for working with sparse matrices in R , I want to use the Matrix package to create a sparse matrix from the following data frame and have all other elements be NA . s r d 1 1089 3772 1 2 1109 190 1 3 1109 2460 1 4 1109 3071 2 5 1109 3618 1 6 1109 38 7 I know I can create a sparse matrix with the following, accessing elements as usual: > library(Matrix) > Y <- sparseMatrix(s,r,x=d) > Y[1089,3772] [1] 1 > Y[1,1] [1] 0 but if I want to have the default value to be NA, I tried the following: M <- Matrix(NA,max(s),max(r),sparse=TRUE) for (i in 1:nrow(X)) M[s[i],r[i]

I have a data.frame containing some columns with all NA values, how can I delete them from the data.frame. Can I use the function na.omit(...) specifying some additional arguments? MadScone One way of doing it: df[, colSums(is.na(df)) != nrow(df)] If the count of NAs in a column is equal to the number of rows, it must be entirely NA. Or similarly df[colSums(!is.na(df)) > 0] Here is a dplyr solution: df %>% select_if(~sum(!is.na(.)) > 0) It seeems like you want to remove ONLY columns with ALL NA s, leaving columns with some rows that do have NA s. I would do this (but I am sure there is an

I want to know how to omit NA values in a data frame, but only in some columns I am interested in. For example, DF <- data.frame(x = c(1, 2, 3), y = c(0, 10, NA), z=c(NA, 33, 22)) but I only want to omit the data where y is NA , therefore the result should be x y z 1 1 0 NA 2 2 10 33 na.omit seems delete all rows contain any NA . Can somebody help me out of this simple question? But if now I change the question like: DF <- data.frame(x = c(1, 2, 3,NA), y = c(1,0, 10, NA), z=c(43,NA, 33, NA)) If I want to omit only x=na or z=na , where can I put the | in function? You could use the complete