Subset data by month in R

Question

I am working with a time series of meteorological data and want to extract just the summer months. The data frame looks like this:

FECHA;H_SOLAR;DIR_M;DIR_S;VEL_M;VEL_S;VEL_X;U;V;TEMP_M;HR;BAT;PRECIP;RAD;UVA;UVB;FOG;GRID;
00/01/01;23:50:00;203.5;6.6;2.0;0.5;-99.9;-99.9;-99.9;6.0;-99.9;9.0;-99.9;-99.9;-99.9;-99.9;-99.9;-99.9
00/01/02;23:50:00;235.5;7.5;1.8;0.5;-99.9;-99.9;-99.9;6.1;-99.9;8.9;-99.9;-99.9;-99.9;-99.9;-99.9;-99.9
00/01/03;23:50:00;217.4;6.1;1.4;0.5;-99.9;-99.9;-99.9;7.0;-99.9;8.9;-99.9;-99.9;-99.9;-99.9;-99.9;-99.9
00/01/04;23:50:00;202.5;8.6;1.8;0.5;-99.9;-99.9;-99.9;6.4;-99.9;8.8;-99.9;-99.9;-99.9;-99.9;-99.9;-99.9
00/01/05;23:50:00;198.5;7.1;1.8;0.5;-99.9;-99.9;-99.9;5.4;-99.9;8.8;-99.9;-99.9;-99.9;-99.9;-99.9;-99.9

I have found some examples of time subsetting in R but only between an starting and end date. What I want is to extract all data from a month for all years to create a new data frame to work with. I can create a zoo time series from the data but how do I subset? zoo aggregate?

Thanks in advance for your help.

Answer 1

You could use xts::.indexmon. Creating an xts object is similar to creating a zoo object. Assuming xData is your xts object, something like xData[.indexmon(xData) %in% c(5,6,7)] should do what you want.

Answer 2

One option is to use the cycle() function which gives the position in the cycle of each observation. For example:

gnp <- ts(cumsum(1 + round(rnorm(100), 2)),
          start = c(1954, 7), frequency = 12)

> cycle(gnp)
     Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1954                           7   8   9  10  11  12
1955   1   2   3   4   5   6   7   8   9  10  11  12
1956   1   2   3   4   5   6   7   8   9  10  11  12
..............

Then to subset a particular month, use the index of that month:

#Get all data from July
subset(gnp, cycle(gnp) == 7)

I should note that this returns a numeric vector, which may or may not be an issue for you depending on what you want to do from there. I'm curious to see other solutions as well.

Answer 3

You could extract the months out your day/month/year column to a new column called 'month' using substring() e.g.,

mydata$month <- as.numeric( substring( mydata$dayMonthYear, first = 4, last = 5) )

This should create a column that just contains the months.

then subset based on the values of $month

summer.months <- subset(mydata, month > 5 & month < 9)

or

summer.months <- subset(mydata, month %in% c(5:9) )

Answer 4

Thank you very much for your help. Let me write the three solutions working for me.

The first one is the answer form Joshua Ulrich:

Read the data, create an xts object and use indexmon

data=read.csv("peira.dat",sep=";",header=T,na.strings="-99.9")
dia=as.Date(data[,1],"%y/%m/%d")
xdata=xts(data[,c("PRECIP")],dia)
mdat=xdata[.indexmon(xdata) %in% c(5,6,7)]

Here is a plot of mdat

The second solution came from the spanish R mailing list and uses lubridate package:

library(lubridate)

data=read.csv("peira.dat",sep=";",header=T,na.strings="-99.9")

f.dat<-parse_date(data$FECHA, c("%y", "%m", "%d"), seps="/")
data$m.dat<-month(f.dat, label=F, abbr=F)
dat.gd<-data[data$m.dat>5 & data$m.dat<9,]
dia=as.Date(dat.gd[,1],"%y/%m/%d")
dataz=zoo(dat.gd[,c("PRECIP")],dia)

The plot of dataz:

And the last one also from the spanish mailing list it uses POSIXct to take into account both date and time

tt=as.POSIXct(paste(data$FECHA,data$H_SOLAR), format="%y/%m/%d %H:%M:%S") 

datZoo <- zoo(data[,-c(1,2)], tt)

month <- function (x) as.numeric(format(x, "%m"))
veranoIdx <- which(month(tt) %in% 6:8)
veranoZoo <- datZoo[veranoIdx]
veranoZoo