Conditional Statements: selecting/assigning a variable per row - r

Question

I have a data set with 2 VPs and 350 interval values for each. I am writing an if loop to select when a minimum value of VP1 overlaps with the maximum value of VP2.
The data usually sorts by VP, but I arranged to sort by minimum since it is a timeframe.
I ran the following code that worked to assign 0 or 1 when the values overlap the previous item, but it does not account for what the previous item is (ie. whether the previous item is VP1 or VP2).
for (i in 2:length(df$newvariable)) {
if (df$minimum[i] < df$maximum[i-1]){
df$newvariable[i] <- 0
} else {
df$newvariable[i] <- 1
}
}
I want to say if df$minimum[i] of VP1 < df$maximum[i] of VP2, then df$newvariable = 0. Otherwise, df$newvariable = 1.
I have not been able to find how to make it conditional per each row and loop again. Does anyone have any recommendations?
Many thanks.
Sample Data:
VP xmin xmax
1 0 6
2 0 2
2 6 14
1 14 24
2 20 30
1 30 36
... And so on for 600 or so rows.
Desired Output:
VP xmin xmax newvariable
1 0 6 -
2 0 2 0
2 6 14 1
1 14 24 1
2 20 30 0
1 30 36 1
If I have a dataframe that had another variable and I subsetted to only look at one part of the variable. For example, variable = talking and the assignments are 1 (yes) or 0 (no). I originally subsetted to just look at 0 and create new variables, like quiet_together. However, I want to put these dataframes back together but have added columns in the separate dataframes. If I want the same exact thing as described above but with the dataframe together (instead of 2 separate ones), how would I specify for the each assigned variable? I want to end up with two new columns based on xmin and xmax values while accounting for the value in the talking variable. The new columns would be talk_together (for the 1 value of the talking variable) and quiet_together (for the 0 value of the talking variable, when xmin <= xmax for the previous line.
For example:
Sample Data:
VP xmin xmax talking
1 0 6 0
2 0 2 0
2 2 6 1
2 6 14 0
1 6 14 1
2 14 24 1
1 14 20 0
1 20 30 1
2 24 32 0
1 30 32 0
... And so on for 600 or so rows.
Desired Output:
VP xmin xmax talking talk_together quiet_together
1 0 6 0 0 0
2 0 2 0 0 0
2 2 6 1 0 0
2 6 14 0 0 0
1 6 14 1 0 0
1 14 20 0 0 0
2 14 24 1 1 0
1 20 30 1 1 0
2 24 32 0 0 1
1 30 32 0 0 1

Answer 1

You could use lag from dplyr to compare with previous xmax value.
library(dplyr)
df %>% mutate(newvariable = as.integer(xmin >= lag(xmax)))
# VP xmin xmax newvariable
#1 1 0 6 NA
#2 2 0 2 0
#3 2 6 14 1
#4 1 14 24 1
#5 2 20 30 0
#6 1 30 36 1
Or shift with data.table
library(data.table)
setDT(df)[, newvariable := +(xmin >= shift(xmax))]
Base R alternatives are :
df$newvariable <- as.integer(c(NA, df$xmin[-1] >= df$xmax[-nrow(df)]))
and
df$newvariable <- +c(NA, tail(df$xmin, -1) >= head(df$xmax, -1))

Answer 2

With data.table, we can do
library(data.table)
setDT(df)[, newvariable := as.integer(xmin >= shift(xmax))]