I am trying to calculate the day differences between the two columns of Last.Start and Last.Stop. What I did is first convert the column of Last.Start to Character variable, then convert it to a Date type variable (as the last column of LastStart).
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SMFull$Last.Start <- as.character(SMFull$Last.Start)
SMFull$LastStart <- as.Date(SMFull$Last.Start, format="%Y/%m/%d")
Can someone advise me Why the format of LastStart was off?
Thanks in advance!
Related
I have loaded in time series data of creek depth and am needing to calculate total annual values (Oct-April) for each year. The following is what I have tried thus far:
depth <- read_csv("Depth.Water_Depth-_All_Instruments#GCNP_-_Robber's_Roost_Spring.EntireRecord .csv")
The following is a screenshot of the resulting data frame
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These are my attempts at making the timestamp column (ISO_UTC) into a date class. Although, each attempt makes all values for ISO_UTC into N/A values instead of dates / times.
ymd_hm(depth$ISO_UTC)
depth$ISO_UTC<- as.Date(depth$ISO_UTC, format = "%Y-%m-%dT%H:%M")
depth$ISO_UTC<- as.POSIXct(depth$ISO_UTC, format = "%Y-%m-%d %H:%M", tz = "US/Pacific")
Please help me to put these data into usable datetime values.
Please see the above details.
My data contains several measurements in one day. It is stored in CSV-file and looks like this:
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The V1 column is factor type, so I'm adding a extra column which is date-time -type: vd$Vdate <- as_datetime(vd$V1) :
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Then I'm trying to convert the vd-data into time series: vd.ts<- ts(vd, frequency = 365)
But then the dates are gone:
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I just cannot get it what I am doing wrong! Could someone help me, please.
Your dates are gone because you need to build the ts dataframe from your variables (V1, ... V7) disregarding the date field and your ts command will order R to structure the dates.
Also, I noticed that you have what is seems like hourly data, so you need to provide the frequency that is appropriate to your time not 365. Considering what you posted your frequency seems to be a bit odd. I recommend finding a way to establish the frequency correctly. For example, if I have hourly data for 365 days of the year then I have a frequency of 365.25*24 (0.25 for the leap years).
So the following is just as an example, it still won't work properly with what I see (it is limited view of your dataset so I am not sure 100%)
# Build ts data (univariate)
vs.ts <- ts(vd$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 have a vector of date values:
dates=c("43018","43343","42272","06/27/17","01/10/18","10/11/18")
This is a mixture of actual dates and the Excel number-value of dates (ie: number of days since January 1, 1900). I want to convert all of these values to the Excel format of dates, so we would have an output that looks like the following:
dates
[1] "43018" "43343" "42272" "42913" "43110" "43384"
My goal is to take these values and subtract them from another vector with an equal number of date values that are all the same to get an age of each observation.
Can anyone help point me in the right direction? Thank you!
Figured it out - use the "janitor" library and the excel_numeric_to_date function.
Badda bing badda boom.
I'm trying to convert a yyyy-mm-dd data in a data frame to the total number of days from some date to put in my survival function.
I've already tried as_date() and grepl(), but I can't seem to get it to work since there are either too many NA values in my data frame or I'm doing something wrong.
Ref.date <- ymd("1941-08-24")
Date.MI <- ymd("Date.MI")
Day <- as.numeric(difftime(Date.MI, Ref.date))
I expect just the total number of days since 1941-08-24.
How do I solve the problem?
difftime() gives you the option to specify the units for the resulting output. So maybe try something like this
as.numeric(difftime(as.POSIXct("1941-08-25"), as.POSIXct("1941-08-24"), units = c("days")))
The way to solve it:
as.numeric(difftime(as.POSIXct(Date.MI[[1]]), as.POSIXct("1941-08-24"), units = c("days")))
There were square brackets needed since that refers to the first column.
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")