I have tried to look up previous answers, if anyone thinks this has been answered before please direct me to link.
I have a dataframe (Tickets) with 4 Date time values - and in another column I have timezone values.
sample dataset as below.
TIMEZONE,Creation_Datetime
US/Mountain,2013-07-09 10:08:00
US/Central,2014-03-24 05:37:13
US/Pacific,2013-01-26 04:30:57
US/Eastern,2013-01-21 02:59:18
US/Eastern,2014-02-24 08:39:17
US/Eastern,2013-02-05 02:30:36
I need to convert creation_Datetime in all rows as per the timezone in corresponding row.
Tickets$Creation_Datetime= with_tz(
Tickets$Creation_Datetime,
as.character(Tickets$TIMEZONE)
)
...throws an error "invalid tz value".
It works with a single tz Tickets$TIMEZONE[1] value or character value 'US/Mountain' passed as timezone.
Help with applying 'apply' function to all rows.
Related
This question already has answers here:
How to prevent ifelse() from turning Date objects into numeric objects
(7 answers)
Closed 4 years ago.
I have a data frame that contains one column that is a series of dates, collected via a Google form. The date and time were collected separately. The data was entered by selecting a day from a calendar, and the date was entered manually - should have been a 24-hour clock, but the field appears to have just checked that the hour and minute were in the correct range.
I've read the file in from .csv . I converted the date time character field (as read in from the .csv) to a date time format in a new variable by using as.POSIXct(foo$When, tz="NZ", format="%Y-%m-%d %H:%M"). The dates and times were correctly constructed.
Except: I have some incorrect date/time entries in the original data. These have all been set to NA in the new field, as you expect. For those that do include a time, I have been trying to fix them while still retaining a POSIXct format.
I have been unsuccessful.
Here is an example of the data I have, and what I have tried to do:
TestDataForHelp <- data.frame(OldDateTime =
c("2013-12-04 21:10", "2013-12-15 09:07", "2014-01-01 06:27",
"2014-11-02 21:15", "2014-11-07 23:00", "2015-01-04 21:42",
"201508-11-02 20:15", "201508-11-02 20:15", "2017-11-02"))
TestDataForHelp$ActualDateTime <-
as.POSIXct(TestDataForHelp$OldDateTime, tz="NZ", format="%Y-%m-%d %H:%M")
TestDataForHelp$FixedDateTime <-
ifelse(TestDataForHelp$OldDateTime=="201508-11-02 20:15",
as.POSIXct("2015-11-02 20:15", tz="NZ", format="%Y-%m-%d %H:%M"),
TestDataForHelp$ActualDateTime)
The new variable, FixedDateTime, does not have a POSIXct type. It has been implicitly converted to a numeric type. How can I retain the POSIXct format from ActualDateTime and not have the implicit type conversion?
I would like to not have FixedDateTime but, rather, put the corrected data into ActualDateTime. The ifelse() seems to be the part of the code causing the format to shift from POSIXct to numeric. If I do:
TestDataForHelp$CopiedDateTime <- TestDataForHelp$ActualDateTime
The new variable, that is simply a copy of the original, retains the POSIXct type.
The previous question linked in the comments relates to date values only, not date time values. The data manipulation becomes more complicated with dealing with date time values, given that mine also do not include seconds. The other difference is that the original variable contains a mix of date, date-time, and incorrect date-time values, whereas that previous question had values that were all the same. It was unclear whether the non-uniform content of the variable was causing the problem.
Edit: I fixed the problem by fixing the strings before I converted them to dates. This removed the need to try to loop through the dates.
I can replicate the numeric answer, but not explain it. It is however calculating the results correctly for you. I'm not sure why it's returning as a numeric. However, the conversion from numeric to date is easy enough if you know the origin, which should be 1970-01-01. So I believe the following does the trick:
(Note, the first block is just what you already have)
TestDataForHelp$FixedDateTime <- ifelse(TestDataForHelp$OldDateTime=="201508-11-02 20:15",
as.POSIXct("2015-11-02 20:15", tz="NZ", format="%Y-%m-%d %H:%M"),
TestDataForHelp$ActualDateTime)
TestDataForHelp$FixedDateTime <- as.POSIXct(TestDataForHelp$FixedDateTime,
origin = as.POSIXct("1970-01-01", tz="NZ"))
I am attempting to convert a column in my data set from fctr to date format. The current column has data formatted as follows: "01/01/14. 01:00 Am." Ideally I would like to create a column for day and then a column for time as well. There are periods following the day and the time which is another issue I am facing. So far I have attempted to use lubridate to create a new column of data but I get the error "All formats failed to parse. No formats found." Any help would be greatly appreciated, thank you.
test <- fourteen %>%
mutate(When = mdy_hms(V3))
View(test)
If your date factor literally has levels that look like 01/01/14. 01:00 Am. including two periods and a space between the first period and the first hour digits and a space between the minutes and the am/pm designation, and all the dates are in this format, then the following should work:
... mutate(When = as.POSIXct(V3, format="%m/%d/%y. %H:%M %p.")) ...
In particular, the following standalone testcase works fine:
as.POSIXct(factor("01/01/14. 01:00 Am."), format="%m/%d/%y. %H:%M %p.")
For more information on the format argument being used here, see the R help page for the function strftime.
I have a data set in R that contains a column of dates in the format yyyy/mm/dd. I am trying to use as.Date to convert these dates to date objects in R. However, I cannot seem to find the correct argument for origin to input into as.Date. The following code is an example of what I have been trying. I am using a CSV file from Excel, so I used origin="1899/12/30 based on other sites I have looked at.
> as.Date(2001/04/26, origin="1899/12/30")
[1] "1900-01-18"
However, this is not working since the input date 2001/04/26 is returned as "1900-01-18". I need to convert the dates into date objects so I can then convert the dates into julian dates.
You can either is as.Date with a numeric value, or with a character value. When you type just 2001/04/26 into R, that's doing division and getting 19.24 (a numeric value). And numeric values require an origin and the number you supply is the offset from that origin. So you're getting 19 days away from your origin, ie "1900-01-18". A date like Apr 26 2001 would be
as.Date(40659, origin="1899-12-30")
# [1] "2011-04-26"
If your dates from Excel "look like" dates chances are they are character values (or factors). To convert a character value to a Date with as.Date() you want so specify a format. Here
as.Date("2001/04/26", format="%Y/%m/%d")
# [1] "2001-04-26"
see ?strptime for details on the special % variables. Now if you're read your data into a data.frame with read.table or something, there's a chance your variable may be a factor. If that's the case, you'll want do convert to character with'
as.Date(as.character(mydf$datecol), format="%Y/%m/%d")
The r code that I am working on is supposed to use the data collected in every five minute intervals.
The data is saved in csv format. However, due to inconsistency in the data collected, the time column in the data sometimes represent timestamp instead of just time.(dd/mm/yyyy HH:MM, instead of HH:MM)
This causes an error to my system as the system reads the data as having multiple different values for the same time value. Therefore, I would like to omit the date format from the timestamp such that the code would only read the time value.
My failed attempt was:
as.Date(data[[1]],"%H:%M")
which gave me all NA values for the time column.
I have searched for similar questions in SO, but I did not manage to find a clear answer to my question. Can anyone suggest me some possible functions to use?
I appreciate your help.
You could just strip the date portion of the text and then use as.POSIXct to convert them all to a %H:%M timestamp, e.g.:
x <- c("10:25","01/01/2014 10:30")
x <- gsub("^.+(\\d{2}:\\d{2})$","\\1",x)
as.POSIXct(x,format="%H:%M",tz="UTC")
#[1] "2014-06-02 10:25:00 UTC" "2014-06-02 10:30:00 UTC"
I've been trying to do a time series on my dataframe, and I need to strip times from my csv. This is what I've got:
campbell <-read.csv("campbell.csv")
campbell$date = strptime(campbell$date, "%m/%d")
campbell.ts <- xts(campbell[,-1],order.by=campbell[,1])
First, what I'm trying to do is just get xts to strip the dates as "xx/xx" meaning just the month and day. I have no year for my data. When I try that second line of code and call upon the date column, it converts it to "2013-xx-xx." These months and days have no year associated with them, and I can't figure out how to get rid of the 2013. (The csv file I'm calling on has the dates in the format "9/30,10/1...etc.)
Secondly, once I try and make a time series (the third line), I am unsure what the "order.by" command is calling on. What am I indexing?
Any help??
Thanks!
For strptime, you need to provide the full date, i.e. day, month and year. In case, any of these is not provided, current ones are assumed from the system's time and appended to the incomplete date. So, if you want to retain your date format as you have read it, first make a copy of that and store in a temporary variable and then use strptime over campbell$date to convert into R readable date format. Since, year is not a concern to you, you need not bother about it even though it is automatically appended by strptime.
campbell <-read.csv("campbell.csv")
date <- campbell$date
campbell$date <- strptime(campbell$date, "%m/%d")
Secondly, what you are doing by 'the third line' (xts(campbell[,-1],order.by=campbell[,1])) command is that, your are telling to order all the data of campbell except the first column (campbell[,-1]) according to the index provided by the time data in the first column of campbell (campbell[,1]). So, it would only work given the date is in the first column.
After ordering the data according to time-series, you can replace back the campbell$date column with date to get back the date format you wanted (although here, first you have to order date also like shown below)
date <- xts(date, order.by=campbell[,1]) # assuming campbell$date is campbell[,1]
campbell.ts <- xts(campbell[,-1], order.by=campbell[,1])
campbell.ts <- cbind(date, campbell.ts)
format(as.Date(campbell$dat, "%m/%d/%Y"), "%m/%d")