如何选择满足特定条件的R数据帧中的第一行？

Question

How do I select the first row of an R data frame that meets certain criteria?

Here is the context:

I have a data frame with five columns:

"pixel", "year","propvar", "component", "cumsum." 

There are 1,225 combinations of pixel and year , because the data was computed from the annual time series of 49 geographic pixels for each of 25 study years. Within each pixel-year, I have computed propvar , the proportion of total variance explained by a given component of the fast Fourier transform for the time series of a given pixel-year. I then computed cumsum , which is the cumulative sum of propvar for each frequency component within a pixel-year. The component column just gives you an index for the Fourier series component (plus 1) from which propvar was calculated.

I want to determine the number of components required to explain greater than 99% of the variance. I figure one way to do this is to find the first row within each pixel-year where cumsum > 0.99, and create a data frame from it with three columns, pixel , year , and numbercomps , where numbercomps is the number of components required within a given pixel-year to explain greater than 99% of the variance. I do not know how to do this in R. Does anyone have a solution?

Answer 1

Sure. Something like this should do the trick:

# CREATE A REPRODUCIBLE EXAMPLE!
df <- data.frame(year = c("2001", "2003", "2001", "2003", "2003"),
                 pixel = c("a", "b", "a", "b", "a"), 
                 cumsum = c(99, 99, 98, 99, 99),
                 numbercomps=1:5)
df
#   year pixel cumsum numbercomps
# 1 2001     a     99           1
# 2 2003     b     99           2 
# 3 2001     a     98           3
# 4 2003     b     99           4
# 5 2003     a     99           5

# EXTRACT THE SUBSET YOU'D LIKE.
res <- subset(df, cumsum>=99)
res <- subset(res, 
              subset = !duplicated(res[c("year", "pixel")]),
              select = c("pixel", "year", "numbercomps"))
#   pixel year numbercomps
# 1     a 2001           1
# 2     b 2003           2
# 5     a 2003           5

EDIT Also, for those interested in data.table , there is this:

library(data.table)
dt <- data.table(df, key="pixel, year")    
dt[cumsum>=99, .SD[1], by=key(dt)]