I have split my large data set by date like so to create a large list of several elements:
days <- split(df, df$Date)
My data has columns including time of sunrise, sunset etc. for each day. I now want to use a for loop to do further work on each day separately like this:
for(i in 1:length(days){
sunrisetime <- as.character(df$Sunrise[1])
# Further similar work (using time of sunrise & sunset for each date to split
into daytime hours and nighttime hours)
}
My question is about the df$Sunrise on the second line - I don't think this is the right code to use when trying to access the sunrise time of each day on the days list. I have tried all sorts of variations but am an R newbie so must just be hitting the wrong terms.
Thanks in advance.
sunrisetime<-rep(NA,length(days))
for(i in 1:length(days){
sunrisetime[i] <- as.character(df$Sunrise[i])
}
Related
This question already has answers here:
Extracting time from POSIXct
(7 answers)
Closed 8 months ago.
I have a dataset that I’m working with and I’m trying to change the format of my time column. The current format reads like this, example: “2022-05-23 23:06:58”, I’m trying to change this to only show me the hour times and erase the dates.
Other info: I want to make this change within my data frame, not just random times. I want to change over 100,000 rows so I need a function or solution that will do so. Tidyverse, Lubridate, Format, etc. Thank you guys.
Edit: There was one thing I may not have articulated fully, I wanted to keep the exact time and nothing else. so ‘23:48:07 would’ be how I’m looking for it not just the our. I need it so I can eventually subtract the time passed between two columns. You get me?
Try this
for the first question here is the code to convert to the hour of the day
your_time<-format(as.POSIXct(your_time), format = "%H:%M:%S")
#which gives "23" hours of the day
Since you want to apply on a large dataset we use this below
large_df%>%
mutate(Hour = format(as.POSIXct(Datetime), format ="%H:%M:%S"))
where the large_df is your large dataset worth over 100,000 records
The mutate will open another column for the result which is named the Hour column
and the Datetime is the DateTime column in your large_df dataset
Is the time as a string ok? Cause then you can use substr to extract the hour and minutes like so:
time <- c("2022-05-23 23:02:58", "2022-05-23 13:52:58", "2022-05-23 03:31:58", "2022-05-23 09:09:58")
n <- nchar(time)
hour <- substr(time, n - 7, n - 3)
Just time with your 100.000 row time column
library(data.table)
hour("2022-05-23 23:06:58") # 23
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 trying to take a column of my data that is in factor format and change it to time in the format
hours:minutes:seconds:milliseconds
I tried:
start.times <- as.POSIXct(as.character(start.times), format="%H:%M:%OS")
but it returned values with todays date and left out the milliseconds in them and that is not what I want.
I also tried downloading chron and running the code:
start.times <- times(start.times)
but this just returned NA's.....
Please help!
My data is all about start times and end times of dolphin vocalizations and I am trying to find the mean whistle duration and the inter whistle interval. Anyways, I don't really know how to get my data into the format I need it in. Thank you!
Assuming you have a factor that looks like:
start.time <- c("0:13:45.9", "3:09:44.9")
Then what you wrote should work if you change the last colon to a period
as.POSIXct(start.time, format ="%H:%M:%S.%OS")
I want to create a single column with a sequence of date/time increasing every hour for one year or one month (for example). I was using a code like this to generate this sequence:
start.date<-"2012-01-15"
start.time<-"00:00:00"
interval<-60 # 60 minutes
increment.mins<-interval*60
x<-paste(start.date,start.time)
for(i in 1:365){
print(strptime(x, "%Y-%m-%d %H:%M:%S")+i*increment.mins)
}
However, I am not sure how to specify the range of the sequence of dates and hours. Also, I have been having problems dealing with the first hour "00:00:00"? Not sure what is the best way to specify the length of the date/time sequence for a month, year, etc? Any suggestion will be appreciated.
I would strongly recommend you to use the POSIXct datatype. This way you can use seq without any problems and use those data however you want.
start <- as.POSIXct("2012-01-15")
interval <- 60
end <- start + as.difftime(1, units="days")
seq(from=start, by=interval*60, to=end)
Now you can do whatever you want with your vector of timestamps.
Try this. mondate is very clever about advancing by a month. For example, it will advance the last day of Jan to last day of Feb whereas other date/time classes tend to overshoot into Mar. chron does not use time zones so you can't get the time zone bugs that code as you can using POSIXct. Here x is from the question.
library(chron)
library(mondate)
start.time.num <- as.numeric(as.chron(x))
# +1 means one month. Use +12 if you want one year.
end.time.num <- as.numeric(as.chron(paste(mondate(x)+1, start.time)))
# 1/24 means one hour. Change as needed.
hours <- as.chron(seq(start.time.num, end.time.num, 1/24))
I would like to subset out the first 5 minutes of time series data for each day from minutely data, however the first 5 minutes do not occur at the same time each day thus using something like xtsobj["T09:00/T09:05"] would not work since the beginning of the first 5 minutes changes. i.e. sometimes it starts at 9:20am or some other random time in the morning instead of 9am.
So far, I have been able to subset out the first minute for each day using a function like:
k <- diff(index(xtsobj))> 10000
xtsobj[c(1, which(k)+1)]
i.e. finding gaps in the data that are larger than 10000 seconds, but going from that to finding the first 5 minutes of each day is proving more difficult as the data is not always evenly spaced out. I.e. between first minute and 5th minute there could be from 2 row to 5 rows and thus using something like:
xtsobj[c(1, which(k)+6)]
and then binding the results together
is not always accurate. I was hoping that a function like 'first' could be used, but wasn't sure how to do this for multiple days, perhaps this might be the optimal solution. Is there a better way of obtaining this information?
Many thanks for the stackoverflow community in advance.
split(xtsobj, "days") will create a list with an xts object for each day.
Then you can apply head to the each day
lapply(split(xtsobj, "days"), head, 5)
or more generally
lapply(split(xtsobj, "days"), function(x) {
x[1:5, ]
})
Finally, you can rbind the days back together if you want.
do.call(rbind, lapply(split(xtsobj, "days"), function(x) x[1:5, ]))
What about you use the package lubridate, first find out the starting point each day that according to you changes sort of randomly, and then use the function minutes
So it would be something like:
five_minutes_after = starting_point_each_day + minutes(5)
Then you can use the usual subset of xts doing something like:
5_min_period = paste(starting_point_each_day,five_minutes_after,sep='/')
xtsobj[5_min_period]
Edit:
#Joshua
I think this works, look at this example:
library(lubridate)
x <- xts(cumsum(rnorm(20, 0, 0.1)), Sys.time() - seq(60,1200,60))
starting_point_each_day= index(x[1])
five_minutes_after = index(x[1]) + minutes(5)
five_min_period = paste(starting_point_each_day,five_minutes_after,sep='/')
x[five_min_period]
In my previous example I made a mistake, I put the five_min_period between quotes.
Was that what you were pointing out Joshua? Also maybe the starting point is not necessary, just:
until5min=paste('/',five_minutes_after,sep="")
x[until5min]