This seems to be a topic that comes up periodically. The various ways in R
and other packages for reading in data often come with methods that simply
guess wrong or encounter one or more data items in a column that really do
not fit so fields may just by default become a more common denominator of
character or perhaps floating point.
There are ways that some such programs can be given a hint of what you
expect or even be supplied with a way to coerce them into what you want
while being read in. But realistically, often a more practical method might
be to take the data.frame variety you read in and before using it for other
purposes, check it for validity and make any needed changes. Simplistic ones
might be to see how many columns were read in to see if it matches
expectations or generate an error. Or you may trim columns (or rows) that
are not wanted.
In that vein, are there existing functions available that will accept what
types you want one or more columns to be in and that validate if the current
type is something else and then convert if needed? I mean we have functions
like as.integer(df$x ) or more flexibly as(df$x, "integer") and you may
simply build on a set of those and create others to suit any special needs.
Of course a good method carefully checks the results before over-writing as
sometimes the result may not be the same length (as shown below) or may
violate some other ideas or rules:
as(c(NULL, NA, 3, 3.1, "3.1", list(1,2,"a")), "character")
[1] "NA" "3" "3.1" "3.1" "1" "2" "a"
So if you have dates in some format, or sometimes an unknown format, there
are ways, including some others have shown, to make them into some other
date format or even make multiple columns that together embody the format.
What people sometimes do is assume software is perfect and should do
anything they want. It is the other way around and the programmer or data
creator has some responsibilities to use the right software on the right
data and that may also mean sanity checks along the way to see if the data
is what you expect or alter it to be what you need.
-----Original Message-----
From: R-help <r-help-boun...@r-project.org> On Behalf Of Philip Monk
Sent: Saturday, November 20, 2021 3:28 PM
To: Jeff Newmiller <jdnew...@dcn.davis.ca.us>
Cc: R-help Mailing List <r-help@r-project.org>
Subject: Re: [R] Date read correctly from CSV, then reformatted incorrectly
by R
Thanks, Jeff.
I follow what you're doing below, but know I need to read up on Date /
POSIXct. Helpful direction! :)
On Sat, 20 Nov 2021 at 18:41, Jeff Newmiller <jdnew...@dcn.davis.ca.us>
wrote:
Beat me to it! But it is also worth noting that once converted to Date or
POSIXct, timestamps should be treated as data without regard to how that
data is displayed. When you choose to output that data you will have options
as to the display format associated with the function you are using for
output.
My take:
dta <- read.table( text=
"Buffer 28/10/2016 19/11/2016 31/12/2016 16/01/2017
05/03/2017
100 2.437110889 -8.69674895 3.239299816 2.443183304
2.346743827
200 2.524329899 -7.688862068 3.386811734 2.680347706
2.253885237
300 2.100784256 -8.059855835 3.143786507 2.615152896
2.015645973
400 1.985608385 -10.6707206 2.894572791 2.591925038
2.057913137
500 1.824982163 -9.122519736 2.560350727 2.372226799
1.995863839
", header=TRUE, check.names=FALSE, as.is=TRUE)
dta
library(dplyr)
library(tidyr)
dt_fmt <- "%d/%m/%Y"
dta_long <- ( dta
%>% pivot_longer( cols = -Buffer
, names_to = "dt_chr"
, values_to = "LST"
)
%>% mutate( dt_date = as.Date( dt_chr, format = dt_fmt )
, dt_POSIXct = as.POSIXct( dt_chr, format = dt_fmt,
tz = "Etc/GMT+8" )
)
)
dta_long
On November 20, 2021 10:01:56 AM PST, Andrew Simmons <akwsi...@gmail.com>
wrote:
The as.Date function for a character class argument will try reading
in two formats (%Y-%m-%d and %Y/%m/%d).
This does not look like the format you have provided, which is why it
doesn't work. Try something like:
x <- c("28/10/2016", "19/11/2016", "31/12/2016", "16/01/2016",
"05/03/2017") as.Date(x, format = "%d/%m/%Y")
which produces this output:
x <- c("28/10/2016", "19/11/2016", "31/12/2016", "16/01/2016",
"05/03/2017")
as.Date(x, format = "%d/%m/%Y")
[1] "2016-10-28" "2016-11-19" "2016-12-31" "2016-01-16" "2017-03-05"
much better than before! I hope this helps
On Sat, Nov 20, 2021 at 12:49 PM Philip Monk <prm...@gmail.com> wrote:
Thanks Eric & Jeff.
I'll certainly read up on lubridate, and the posting guide (again)
(this should be in plain text).
CSV extract below...
Philip
Buffer 28/10/2016 19/11/2016 31/12/2016 16/01/2017
05/03/2017
100 2.437110889 -8.69674895 3.239299816 2.443183304
2.346743827
200 2.524329899 -7.688862068 3.386811734 2.680347706
2.253885237
300 2.100784256 -8.059855835 3.143786507 2.615152896
2.015645973
400 1.985608385 -10.6707206 2.894572791 2.591925038
2.057913137
500 1.824982163 -9.122519736 2.560350727 2.372226799
1.995863839
On Sat, 20 Nov 2021 at 17:08, 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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PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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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
and provide commented, minimal, self-contained, reproducible code.
--
Sent from my phone. Please excuse my brevity.
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
