I have several data frames containing 18 columns with approx. 50000 rows. Each row entry represents a measurement at a specific site (= column), and the data contain NA values.
I need to subtract the consecutive rows per column (e.g. row(i+1)-row(i)) to detect threshold values, but I need to ignore (and retain) the NAs, so that only the entries with numeric values are subtracted from each other.
I found very helpful posts with data.table solutions for a single column Iterate over a column ignoring but retaining NA values in R, and for multiple column operations (e.g. Summarizing multiple columns with dplyr?).
However, I haven't managed to combine the approaches suggested in SO (i.e. apply diff over multiple columns and ignore the NAs)
Here's an example df for illustration and a solution I tried:
library(data.table)
df <- data.frame(x=c(1:3,NA,NA,9:7),y=c(NA,4:6, NA,15:13), z=c(6,2,7,14,20, NA, NA, 2))
that's how it works for a single column
diff_x <- df[!is.na(x), lag_diff := x - shift(x)] # actually what I want, but for more columns at once
and that's how I apply a diff function over several columns with lapply
diff_all <- setDT(df)[,lapply(.SD, diff)] # not exactly what I want because NAs are not ignored and the difference between numeric values is not calculated
I'd appreciate any suggestion (base, data.table, dplyr ,... solutions) on how to implement a valid !is.na or similar statement into this second line of code very much.
So you are looking for:
library("data.table")
df <- data.frame(x=c(1:3,NA,NA,9:7),y=c(NA,4:6, NA,15:13), z=c(6,2,7,14,20, NA, NA, 2))
setDT(df)
# diff_x <- df[!is.na(x), lag_diff := x - shift(x)] # actually what I want, but
lag_d <- function(x) { y <- x[!is.na(x)]; x[!is.na(x)] <- y - shift(y); x }
df[, lapply(.SD, lag_d)]
or
library("data.table")
df <- data.frame(x=c(1:3,NA,NA,9:7),y=c(NA,4:6, NA,15:13), z=c(6,2,7,14,20, NA, NA, 2))
lag_d <- function(x) { y <- x[!is.na(x)]; x[!is.na(x)] <- y - shift(y); x }
as.data.frame(lapply(df, lag_d))