How to increase time series granularity in R Dataframe? [duplicate] - r

Question

This question already has answers here:
Insert rows for missing dates/times
(9 answers)
Closed 5 years ago.
I have a dataframe that contains hourly weather information. I would like to increase the granularity of the time measurements (5 minute intervals instead of 60 minute intervals) while copying the other columns data into the new rows created:
Current Dataframe Structure:
Date Temperature Humidity
2015-01-01 00:00:00 25 0.67
2015-01-01 01:00:00 26 0.69
Target Dataframe Structure:
Date Temperature Humidity
2015-01-01 00:00:00 25 0.67
2015-01-01 00:05:00 25 0.67
2015-01-01 00:10:00 25 0.67
.
.
.
2015-01-01 00:55:00 25 0.67
2015-01-01 01:00:00 26 0.69
2015-01-01 01:05:00 26 0.69
2015-01-01 01:10:00 26 0.69
.
.
.
What I've Tried:
for(i in 1:nrow(df)) {
five.minutes <- seq(df$date[i], length = 12, by = "5 mins")
for(j in 1:length(five.minutes)) {
df$date[i]<-rbind(five.minutes[j])
}
}
Error I'm getting:
Error in as.POSIXct.numeric(value) : 'origin' must be supplied

Answer 1

The one possible solution can be using fill from tidyr and right_join from dplyr.
The approach is to create date/time series between min and max+55mins times from dataframe. Left join dataframe with timeseries which will provide you all desired rows but NA for Temperature and Humidity. Now use fill to populated NA values with previous valid values.
# Data
df <- read.table(text = "Date Temperature Humidity
'2015-01-01 00:00:00' 25 0.67
'2015-01-01 01:00:00' 26 0.69
'2015-01-01 02:00:00' 28 0.69
'2015-01-01 03:00:00' 25 0.69", header = T, stringsAsFactors = F)
df$Date <- as.POSIXct(df$Date, format = "%Y-%m-%d %H:%M:%S")
# Create a dataframe with all possible date/time at intervale of 5 mins
Dates <- data.frame(Date = seq(min(df$Date), max(df$Date)+3540, by = 5*60))
result <- df %>%
right_join(Dates, by="Date") %>%
fill(Temperature, Humidity)
result
# Date Temperature Humidity
#1 2015-01-01 00:00:00 25 0.67
#2 2015-01-01 00:05:00 25 0.67
#3 2015-01-01 00:10:00 25 0.67
#4 2015-01-01 00:15:00 25 0.67
#5 2015-01-01 00:20:00 25 0.67
#6 2015-01-01 00:25:00 25 0.67
#7 2015-01-01 00:30:00 25 0.67
#8 2015-01-01 00:35:00 25 0.67
#9 2015-01-01 00:40:00 25 0.67
#10 2015-01-01 00:45:00 25 0.67
#11 2015-01-01 00:50:00 25 0.67
#12 2015-01-01 00:55:00 25 0.67
#13 2015-01-01 01:00:00 26 0.69
#14 2015-01-01 01:05:00 26 0.69
#.....
#.....
#44 2015-01-01 03:35:00 25 0.69
#45 2015-01-01 03:40:00 25 0.69
#46 2015-01-01 03:45:00 25 0.69
#47 2015-01-01 03:50:00 25 0.69
#48 2015-01-01 03:55:00 25 0.69

Answer 2

I think this might do:
df=tibble(DateTime=c("2015-01-01 00:00:00","2015-01-01 01:00:00"),Temperature=c(25,26),Humidity=c(.67,.69))
df$DateTime<-ymd_hms(df$DateTime)
DateTime=as.POSIXct((sapply(1:(nrow(df)-1),function(x) seq(from=df$DateTime[x],to=df$DateTime[x+1],by="5 min"))),
origin="1970-01-01", tz="UTC")
Temperature=c(sapply(1:(nrow(df)-1),function(x) rep(df$Temperature[x],12)),df$Temperature[nrow(df)])
Humidity=c(sapply(1:(nrow(df)-1),function(x) rep(df$Humidity[x],12)),df$Humidity[nrow(df)])
tibble(as.character(DateTime),Temperature,Humidity)
<chr> <dbl> <dbl>
1 2015-01-01 00:00:00 25.0 0.670
2 2015-01-01 00:05:00 25.0 0.670
3 2015-01-01 00:10:00 25.0 0.670
4 2015-01-01 00:15:00 25.0 0.670
5 2015-01-01 00:20:00 25.0 0.670
6 2015-01-01 00:25:00 25.0 0.670
7 2015-01-01 00:30:00 25.0 0.670
8 2015-01-01 00:35:00 25.0 0.670
9 2015-01-01 00:40:00 25.0 0.670
10 2015-01-01 00:45:00 25.0 0.670
11 2015-01-01 00:50:00 25.0 0.670
12 2015-01-01 00:55:00 25.0 0.670
13 2015-01-01 01:00:00 26.0 0.690