I have a time series dataset for several meteorological variables. The time data is logged in three separate columns:
Year (e.g. 2012)
Day of year (e.g. 261 representing 17-September in a Leap Year)
Hrs:Mins (e.g. 1610)
Is there a way I can merge the three columns to create a single timestamp in R? I'm not very familiar with how R deals with the Day of Year variable.
Thanks for any help with this!
It looks like the timeDate package can handle gregorian time frames. I haven't used it personally but it looks straightforward. There is a shift argument in some methods that allow you to set the offset from your data.
http://cran.r-project.org/web/packages/timeDate/timeDate.pdf
Because you mentioned it, I thought I'd show the actual code to merge together separate columns. When you have the values you need in separate columns you can use paste to bring them together and lubridate::mdy to parse them.
library(lubridate)
col.month <- "Jan"
col.year <- "2012"
col.day <- "23"
date <- mdy(paste(col.month, col.day, col.year, sep = "-"))
Lubridate is a great package, here's the official page: https://github.com/hadley/lubridate
And here is a nice set of examples: http://www.r-statistics.com/2012/03/do-more-with-dates-and-times-in-r-with-lubridate-1-1-0/
You should get quite far using ISOdatetime. This function takes vectors of year, day, hour, and minute as input and outputs an POSIXct object which represents time. You just have to split the third column into two separate hour minute columns and you can use the function.
Related
so I am lost with the following problem:
I have a dataframe, in which one column contains (STARTED) the starting time of a survey, and several others information of the survey schedule of that survey participant (D5 to D10: only the planned survey dates, D17 to D50: planned send-out times of measurement per day). I'd like to create to columns that indicate now which survey day (1-6) and which measurement per day (1-6) this survey corresponds to.
First problem is the format (!)...
STARTED has the format %Y-%m-%d %H:%M:%S, D5 to D10 %d.%m.%Y and D17 to D50 %d.%m.%Y %H:%M.
I tried dmy_hms() from lubridate, parse_date_time(), and simply as.POSIXct(), but I always fail to get STARTED and the D17 to D50 section into a comparable format. Any solutions on this one?
After just separating STARTED into date & time columns, I was able to compare using ifelse() with D5 to D10 and to create the column of day running from 1 to 6.
This might be already more elegant with something like which(), but I was not able to create a vectorized version of this, as which(<<D5:D10>> == STARTED) would need to compare that per row. Does anyone have a solution for this?
And lastly, how on earth can I set up the second column indicating the measurement time? The first and last survey of the is easy, as there are also uniquely labelled, but for the other four ones I would need to compare per day whether the starting time is before the planned survey time of the following survey. I could imagine just checking whether STARTED falls in between two planned survey times just next to each other - as a POSIXct object that might work, if I can parse the different formats.
Help is greatly appreciated, thanks!
A screenshot from the beginning of the data:
Screenshot from R data using View()
For these first few rows, the intended variable day would need to be c(1,2,1,1,1,2,2) and measurement c(3,2,4,2,1,2,3).
Your other columns are not formatted with %d.%m.%Y, instead either %d.%m.%t (date only) or %d.%m.%y %H:%M. Note the change from %Y to %y.
Try:
as.Date("20.05.22", format = "%d.%m.%y")
# [1] "2022-05-20"
as.POSIXct("20.05.22 06:00", format = "%d.%m.%y %H:%M")
# [1] "2022-05-20 06:00:00 EDT"
My data contains several measurements in one day. It is stored in CSV-file and looks like this:
enter image description here
The V1 column is factor type, so I'm adding a extra column which is date-time -type: vd$Vdate <- as_datetime(vd$V1) :
enter image description here
Then I'm trying to convert the vd-data into time series: vd.ts<- ts(vd, frequency = 365)
But then the dates are gone:
enter image description here
I just cannot get it what I am doing wrong! Could someone help me, please.
Your dates are gone because you need to build the ts dataframe from your variables (V1, ... V7) disregarding the date field and your ts command will order R to structure the dates.
Also, I noticed that you have what is seems like hourly data, so you need to provide the frequency that is appropriate to your time not 365. Considering what you posted your frequency seems to be a bit odd. I recommend finding a way to establish the frequency correctly. For example, if I have hourly data for 365 days of the year then I have a frequency of 365.25*24 (0.25 for the leap years).
So the following is just as an example, it still won't work properly with what I see (it is limited view of your dataset so I am not sure 100%)
# Build ts data (univariate)
vs.ts <- ts(vd$V1, frequency = 365, start = c(2019, 4)
# check to see if it is structured correctly
print(vd.ts, calendar = T)
Finally my time series is working properly. I used
ts <- zoo(measurements, date_times)
and I found out that the date_times was supposed to be converted with as_datetime() as otherwise they were character type. The measurements are converted into data.frame type.
I am having trouble with my R assignment I am working on this semester.
Here is the part that I am tasked with doing that I am confused about:
iv. Download 3 month TBill rate from Fred for the same sample period 01/01/1993 to 12/31/2013.
Useful Hints: You may have to chop the data to match the sample period.
v. Construct a matrix of return series combining Stock, S&P500, and TBill for the sample period.
Useful Hints:
Note that the rownames for the TBill may not match with the other two return series, as the dates do not match, although the month and year matches
You have to construct the row names for each of the series as Year – Month format (e.g. 1993-01) or delete the rownames from T-bill before you can combine all three series into one Return matrix.
You have to convert the Return matrix to a dataframe before you use the lm() function.
I tried this below like I have used getSymbols before for SPY and AAPL but it pulls an entire data set rather than the specific date range. How can I chop the data so it fits the desired date range?
getSymbols('TB3MS', src = 'FRED', from = "1993-01-01", to = "2013-12-31")
Next, how would I go about constructing the matrix of return series combining all of the stocks? Can anyone point me in the right direction?
Filtering an xts object: see examples in the xts documentation ?xts.
# filter 1993 until 2013
TB3MS["1993/2013"]
But these dates are of, because tbills are at the first day of the month, the stock dates are the last day of the month. With the coredata you can extract the tbill data and stick it into the other timeseries if the rows match.
Taking the data example from your previous question, you could do something like this (and I'm creating more steps than needed, you could combine a few statements into one):
# create monthly returns of the spy data and give the column a better name than monthly.returns
spy_returns <- monthlyReturn(SPY)
colnames(spy_returns) <- "SPY_returns"
# filter the tbill data
TB3MS_1993_2013 <- TB3MS["1993/2013"]
# add tbill data to spy data
spy_returns$TB3MS <- coredata(TB3MS_1993_2013)
Merging xts objects can just be done with merge. They will be merged on the dates.
merge(spy_returns, aapl_returns) would combine these two. If you have a lot of tickers, use Reduce (check help and SO on how to use Reduce with merge) but better would be to use the tidyquant package if allowed.
There is a data frame like this:
The first two columns in the df describe the start date (month and year) and the end date (month and year). Column names describe every single month and year of a certain time period.
I need a function/loop that insterts "1" or "0" in each cell - "1" when the date from given column name is within the period described by the two first columns, and "0" if not.
I would appreciate any help.
You want to do two different things. (a) create a dummy variable and (b) see if a particular date is in an interval.
Making a dummy variable is the easiest one, in base R you can use ifelse. For example in the iris data frame:
iris$dummy <- ifelse(iris$Sepal.Width > 2.5, 1, 0)
Now working with dates is more complicated. In this answer we will use the library lubridate. First you need to convert all those dates to a format 'Month Year' to something that R can understand. For example for February you could do:
new_format_february_2016 <- interval(ymd('2016-02-01'), ymd('2016-03-01') - dseconds(1))
#[1] 2016-02-01 UTC--2016-02-29 23:59:59 UTC
This is February, the interval of time from the 1 of February to one second before the 1 of March. You can do the same with your start date column and you end date column.
To compare two intevals of time (so, to see if a particular month fall into your other intervals) you can do:
int_overlaps(new_format_february_2016, other_interval)
If this returns true, the two intervals (one particular month and another one) overlaps. This is not the same as one being inside another, but in your case it will work. Using this you can iterate over different columns and rows and build your dummy variable.
But before doing so, I would recommend to clean your data, as your current format is complicate to work with. To get all the power that vector types in R provides ideally you would want to have one row per observation and one variable per column. This does not seem to be the case with your data frame. Take a look to the chapter 'Tidy data' of 'R for Data Science' specially the spreading and gathering subsection:
Tidy data
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")