I have a data frame with a date column and some other value columns. I would like to extract from the data frame those rows in which the date column matches any of the eleme
Both suggestions so far are definitely good, but if you are going to be doing a lot of work with dates, you may want to invest some time with the xts
package:
# Some sample data for 90 consecutive days
set.seed(1)
testdf <- data.frame(mydate = seq(as.Date('2012-01-01'),
length.out=90, by = 'day'),
col1 = rnorm(90), col2 = rnorm(90),
col3 = rnorm(90))
# Convert the data to an xts object
require(xts)
testdfx = xts(testdf, order.by=testdf$mydate)
# Take a random sample of dates
testdfx[sample(index(testdfx), 5)]
# col1 col2 col3
# 2012-01-17 -0.01619026 0.71670748 1.44115771
# 2012-01-29 -0.47815006 0.49418833 -0.01339952
# 2012-02-05 -0.41499456 0.71266631 1.51974503
# 2012-02-27 -1.04413463 0.01739562 -1.18645864
# 2012-03-26 0.33295037 -0.03472603 0.27005490
# Get specific dates
testdfx[c('2012-01-05', '2012-01-09')]
# col1 col2 col3
# 2012-01-05 0.3295078 1.586833 0.5210227
# 2012-01-09 0.5757814 -1.224613 -0.4302118
You can also get dates from another vector.
# Get dates from another vector
lookup = c("2012-01-12", "2012-01-31", "2012-03-05", "2012-03-19")
testdfx[lookup]
testdfx[lookup]
# col1 col2 col3
# 2012-01-12 0.38984324 0.04211587 0.4020118
# 2012-01-31 1.35867955 -0.50595746 -0.1643758
# 2012-03-05 -0.74327321 -1.48746031 1.1629646
# 2012-03-19 0.07434132 -0.14439960 0.3747244
The xts
package will give you intelligent subsetting options. For instance, testdfx["2012-03"]
will return all the data from March; testdfx["2012"]
will return for the year; testdfx["/2012-02-15"]
will return the data from the start of the dataset to February 15; and testdfx["2012-02-15/"]
will go from February 15 to the end of the dataset.