Richard,
This response was awe-inspiring. Thank you.
-----Original Message-----
From: R-help <r-help-boun...@r-project.org> On Behalf Of Richard O'Keefe
Sent: Sunday, November 21, 2021 8:55 PM
To: Philip Monk <prm...@gmail.com>
Cc: R Project Help <r-help@r-project.org>
Subject: Re: [R] Date read correctly from CSV, then reformatted incorrectly
by R
CSV data is very often strangely laid out. For analysis, Buffer Date
Reading
100 ... ...
100 ... ...
and so on is more like what a data frame should be. I get quite annoyed
when I finally manage to extract data from a government agency only to find
that my tax money has been spent on making it harder to access than it
needed to be.
(1) You do NOT need any additional library to convert dates.
?strptime is quite capable.
(2) Just because reshaping CAN be done in R doesn't mean it
SHOULD be. Instead of reading data in as the wrong format
and then hacking it into shape every time, it makes sense
to convert the data once and only once, then load the
converted data. It took just a couple of minutes to write
(CSVDecoder read: 'transpose-in.csv') bindOwn: [:source |
(CSVEncoder write: 'transpose-out.csv') bindOwn: [:target |
source next bind: [:header | "Label date-1 ... date-n"
target nextPut: {header first. 'Date'. 'Reading'}.
[source atEnd] whileFalse: [
source next bind: [:group |
group with: header keysAndValuesDo: [:index :reading :date |
1 < index ifTrue: [
(date subStrings: '/') bind: [:dmy |
(dmy third,'-',dmy second,'-',dmy first) bind: [:iso |
target nextPut: {group first. iso.
reading}]]]]]]]]]]].
in another programming language, run it, and turn your example into
Buffer,Date,Reading
100,2016-10-28,2.437110889
100,2016-11-19,-8.69674895
100,2016-12-31,3.239299816
100,2017-01-16,2.443183304
100,2017-03-05,2.346743827
200,2016-10-28,2.524329899
200,2016-11-19,-7.688862068
...
You could do the same kind of thing easily in Perl, Python, F#, ...
Then just read the table in using
read.csv("transpose-out.csv", colClasses = c("integer","Date","numeric"))
and you're away laughing.
(3) Of course you can do the whole thing in base R.
h <- read.csv("transpose-in.csv", header=FALSE, nrows=1,
stringsAsFactors=FALSE)
d <- strptime(h[1,-1], format="%d/%m/%Y")
b <- read.csv("transpose-in.csv", header=FALSE, skip=1)
r <- expand.grid(Date=d, Buffer=b[,1])
r$Result <- as.vector(t(as.matrix(b[,-1])))
Lessons:
(A) You don't have to read a CSV file (or any other) all in one piece.
This pays off when the structure is irregular.
(B) You don't HAVE to accept or convert column names.
(C) strptime is your friend.
(D) expand.grid is particularly handy for "matrix form" CSV data.
(E) Someone who suggests doing something in another language because
it is easier can end up with egg on his face when doing the whole
thing in R turns out to be easier, simpler, and far more obvious.
(A) really is an important lesson.
(F) It's *amazing* what you can do in base R. It is useful to
familiarise yourself with its capabilities before considering other
packages. Compositional data? Not in base R. Correspondence
analysis? Not in base R. Data reshaping? Very much there.
On Sun, 21 Nov 2021 at 06:09, Philip Monk <prm...@gmail.com> wrote:
> Hello,
>
> Simple but infuriating problem.
>
> Reading in CSV of data using :
>
> ```
> # CSV file has column headers with date of scene capture in format
> dd/mm/yyyy # check.names = FALSE averts R incorrectly processing dates
> due to '/'
> data <- read.csv("C:/R_data/Bungala (b2000) julian.csv", check.names =
> FALSE)
>
> # Converts data table from wide (many columns) to long (many rows) and
> creates the new object 'data_long'
> # Column 1 is the 'Buffer' number (100-2000), Columns 2-25 contain
> monthly data covering 2 years (the header row being the date, and rows
> 2-21 being a value for each buffer).
> # Column headers for columns 2:25 are mutated into a column called
> 'Date', values for each buffer and each date into the column 'LST'
> data_long <- data %>% pivot_longer(cols = 2:25, names_to = "Date",
> values_to = "LST")
>
> # Instructs R to treat the 'Date' column data as a date data_long$Date
> <- as.Date(data_long$Date) ```
>
> Using str(data), I can see that R has correctly read the dates in the
> format %d/%m/%y (e.g. 15/12/2015) though has the data type as chr.
>
> Once changing the type to 'Date', however, the date is reconfigured.
> For instance, 15/01/2010 (15 January 2010), becomes 0015-01-20.
>
> I've tried ```data_long$Date <- as.Date(data_long$Date, format =
> "%d/%m.%y")```, and also ```tryformat c("%d/%m%y")```, but either the
> error persists or I get ```NA```.
>
> How do I make R change Date from 'chr' to 'date' without it going wrong?
>
> Suggestions/hints/solutions would be most welcome. :)
>
> Thanks for your time,
>
> Philip
>
> Part-time PhD Student (Environmental Science) Lancaster University,
> UK.
>
> ~~~~~
>
> I asked a question a few weeks ago and put together the answer I
> needed from the responses but didn't know how to say thanks on this
> list. So, thanks Andrew Simmons, Bert Gunter, Jeff Newmiller and Daniel
Nordlund!
>
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>
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>
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______________________________________________
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______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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