I have several (ice-)core section samples in a dataset (ID
in the example below). Some cores have missing sections (i.e. gaps), but I do not know which ones. How to find this out using R?
Example:
dt <- structure(list(ID = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 3L), .Label = c("a", "b", "c"), class = "factor"), Sec.start = c(0,
5, 10, 20, 50, 100, 200, 0, 5, 10, 30), Sec.end = c(5, 10, 20,
30, 100, 200, 400, 5, 10, 20, 50), Section = c("0-5", "5-10",
"10-20", "20-30", "50-100", "100-200", "200-400", "0-5", "5-10",
"10-20", "30-50")), .Names = c("ID", "Sec.start", "Sec.end",
"Section"), row.names = c(NA, -11L), class = "data.frame")
dt
ID Sec.start Sec.end Section
1 a 0 5 0-5
2 a 5 10 5-10
3 a 10 20 10-20
4 a 20 30 20-30
5 b 50 100 50-100
6 b 100 200 100-200
7 b 200 400 200-400
8 c 0 5 0-5
9 c 5 10 5-10
10 c 10 20 10-20
11 c 30 50 30-50
"a" and "b" do not have gaps, whereas "c" does (missing piece between 20 and 30), so I am after a following result:
$a
[1] TRUE
$b
[1] TRUE
$c
[1] FALSE
You can try:
lapply(split(dt,dt$ID),function(x) all(x[-1,2]==x[-nrow(x),3]))
#$a
#[1] TRUE
#$b
#[1] TRUE
#$c
#[1] FALSE
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With