[R] Looping Through DataFrames with Differing Lenghts
David L Carlson
dcarlson at tamu.edu
Tue Mar 28 16:35:49 CEST 2017
We did not get the file on the list. You need to rename your file to "Container.txt" or the mailing list will strip it from your message. The read.csv() function returns a data frame so Data is already a data frame. The command DataFrame<-data.frame(Data) just makes a copy of Data.
Without the file, it is difficult to be certain, but your dates are probably stored as character strings and read.csv() will turn those to factors unless you tell it not to do that. Try
Data<-read.csv("Container.csv", stringsAsFactors=FALSE)
str(Data) # To see how the dates are stored
and see if things work better. If not, rename the file or use dput(Data) and copy the result into your email message. If the data is very long, use dput(head(Data, 15)).
-------------------------------------
David L Carlson
Department of Anthropology
Texas A&M University
College Station, TX 77840-4352
-----Original Message-----
From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Paul Bernal
Sent: Tuesday, March 28, 2017 9:12 AM
To: Ng Bo Lin <ngbolin91 at gmail.com>
Cc: r-help at r-project.org
Subject: Re: [R] Looping Through DataFrames with Differing Lenghts
Dear friends Ng Bo Lin, Mark and Ulrik, thank you all for your kind and
valuable replies,
I am trying to reformat a date as follows:
Data<-read.csv("Container.csv")
DataFrame<-data.frame(Data)
DataFrame$TransitDate<-as.Date(DataFrame$TransitDate, "%Y-%m-%d")
#trying to put it in YYYY-MM-DD format
However, when I do this, I get a bunch of NAs for the dates.
I am providing a sample dataset as a reference.
Any help will be greatly appreciated,
Best regards,
Paul
2017-03-28 8:15 GMT-05:00 Ng Bo Lin <ngbolin91 at gmail.com>:
> Hi Paul,
>
> Using the example provided by Ulrik, where
>
> > exdf1 <- data.frame(Date = c("1985-10-01", "1985-11-01", "1985-12-01”,
> "1986-01-01"), Transits = c(NA, NA, NA, NA))
> > exdf2 <- data.frame(Date = c("1985-10-01", "1986-01-01"), Transits =
> c(15,20)),
>
> You could also try the following function:
>
> for (i in 1:dim(exdf1)[1]){
> if (!exdf1[i, 1] %in% exdf2[, 1]){
> exdf2 <- rbind(exdf2, exdf1[i,])
> }
> }
>
> Basically, what the function does is that it runs through the number of
> rows in exdf1, and checks if the Date of the exdf1 row already exists in
> Date column of exdf2. If so, it skips it. Otherwise, it binds the row to
> df2.
>
> Hope this helps!
>
>
> Side note.: Computational efficiency wise, think Ulrik’s answer is
> probably better. Presentation wise, his is also much better.
>
> Regards,
> Bo Lin
>
> > On 28 Mar 2017, at 5:22 PM, Ulrik Stervbo <ulrik.stervbo at gmail.com>
> wrote:
> >
> > Hi Paul,
> >
> > does this do what you want?
> >
> > exdf1 <- data.frame(Date = c("1985-10-01", "1985-11-01", "1985-12-01",
> > "1986-01-01"), Transits = c(NA, NA, NA, NA))
> > exdf2 <- data.frame(Date = c("1985-10-01", "1986-01-01"), Transits =
> c(15,
> > 20))
> >
> > tmpdf <- subset(exdf1, !Date %in% exdf2$Date)
> >
> > rbind(exdf2, tmpdf)
> >
> > HTH,
> > Ulrik
> >
> > On Tue, 28 Mar 2017 at 10:50 Paul Bernal <paulbernal07 at gmail.com> wrote:
> >
> > Dear friend Mark,
> >
> > Great suggestion! Thank you for replying.
> >
> > I have two dataframes, dataframe1 and dataframe2.
> >
> > dataframe1 has two columns, one with the dates in YYYY-MM-DD format and
> the
> > other colum with number of transits (all of which were set to NA values).
> > dataframe1 starts in 1985-10-01 (october 1st 1985) and ends in 2017-03-01
> > (march 1 2017).
> >
> > dataframe2 has the same two columns, one with the dates in YYYY-MM-DD
> > format, and the other column with number of transits. dataframe2 starts
> > have the same start and end dates, however, dataframe2 has missing dates
> > between the start and end dates, so it has fewer observations.
> >
> > dataframe1 has a total of 378 observations and dataframe2 has a total of
> > 362 observations.
> >
> > I would like to come up with a code that could do the following:
> >
> > Get the dates of dataframe1 that are missing in dataframe2 and add them
> as
> > records to dataframe 2 but with NA values.
> >
> > <dataframe1 <dataframe2
> >
> > Date Transits Date
> > Transits
> > 1985-10-01 NA 1985-10-01 15
> > 1985-11-01 NA 1986-01-01 20
> > 1985-12-01 NA 1986-02-01 5
> > 1986-01-01 NA
> > 1986-02-01 NA
> > 2017-03-01 NA
> >
> > I would like to fill in the missing dates in dataframe2, with NA as value
> > for the missing transits, so that I could end up with a dataframe3
> looking
> > as follows:
> >
> > <dataframe3
> > Date Transits
> > 1985-10-01 15
> > 1985-11-01 NA
> > 1985-12-01 NA
> > 1986-01-01 20
> > 1986-02-01 5
> > 2017-03-01 NA
> >
> > This is what I want to accomplish.
> >
> > Thanks, beforehand for your help,
> >
> > Best regards,
> >
> > Paul
> >
> >
> > 2017-03-27 15:15 GMT-05:00 Mark Sharp <msharp at txbiomed.org>:
> >
> >> Make some small dataframes of just a few rows that illustrate the
> problem
> >> structure. Make a third that has the result you want. You will get an
> >> answer very quickly. Without a self-contained reproducible problem,
> > results
> >> vary.
> >>
> >> Mark
> >> R. Mark Sharp, Ph.D.
> >> msharp at TxBiomed.org
> >>
> >>
> >>
> >>
> >>
> >>> On Mar 27, 2017, at 3:09 PM, Paul Bernal <paulbernal07 at gmail.com>
> wrote:
> >>>
> >>> Dear friends,
> >>>
> >>> I have one dataframe which contains 378 observations, and another one,
> >>> containing 362 observations.
> >>>
> >>> Both dataframes have two columns, one date column and another one with
> >> the
> >>> number of transits.
> >>>
> >>> I wanted to come up with a code so that I could fill in the dates that
> >> are
> >>> missing in one of the dataframes and replace the column of transits
> with
> >>> the value NA.
> >>>
> >>> I have tried several things but R obviously complains that the length
> of
> >>> the dataframes are different.
> >>>
> >>> How can I solve this?
> >>>
> >>> Any guidance will be greatly appreciated,
> >>>
> >>> Best regards,
> >>>
> >>> Paul
> >>>
> >>> [[alternative HTML version deleted]]
> >>>
> >>> ______________________________________________
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> >> posting-guide.html
> >>> and provide commented, minimal, self-contained, reproducible code.
> >>
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