Converting Monthly Data to Daily in R

Question

I have a data.frame df that has monthly data:

Date           Value 
2008-01-01      3.5          
2008-02-01      9.5          
2008-03-01      0.1                   


        

Answer 1

Not sure if i understood perfectly but i think something like this may work.

First, i define the monthly data table

library(data.table)

DT_month=data.table(Date=as.Date(c("2008-01-01","2008-02-01","2008-03-01","2008-05-01","2008-07-01"))
              ,Value=c(3.5,9.5,0.1,5,8))

Then, you have to do the following

DT_month[,Month:=month(Date)]
DT_month[,Year:=year(Date)]

start_date=min(DT_month$Date)
end_date=max(DT_month$Date)

DT_daily=data.table(Date=seq.Date(start_date,end_date,by="day"))
DT_daily[,Month:=month(Date)]
DT_daily[,Year:=year(Date)]
DT_daily[,Value:=-100]

for( i in unique(DT_daily$Year)){
  for( j in unique(DT_daily$Month)){
    if(length(DT_month[Year==i & Month== j,Value])!=0){
      DT_daily[Year==i & Month== j,Value:=DT_month[Year==i & Month== j,Value]]
    }
  }
}

Basically, the code will define the month and year of each monthly value in separate columns.

Then, it will create a vector of daily data using the minimum and maximum dates in your monthly data, and will create two separate columns for year and month for the daily data as well.

Finally, it goes through every combination of year and months of data filling the daily values with the monthly ones. In case there is no data for certain combination of month and year, it will show a -100.

Please let me know if it works.

Answer 2

Another way:

library(lubridate)

d <- read.table(text = "Date           Value 
                2008-01-01      3.5          
                2008-02-01      9.5          
                2008-03-01      0.1",
                stringsAsFactors = FALSE, header = TRUE)

Dates <- seq(from = min(as.Date(d$Date)),
             to = ceiling_date(max(as.Date(d$Date)), "month") - days(1),
             by = "1 days")

data.frame(Date = Dates,
           Value = setNames(d$Value, d$Date)[format(Dates, format = "%Y-%m-01")])

Answer 3

to.daily can only be applied to xts/zooobjects and can only convert to a LOWER frequency. i.e. from daily to monthly, but not the other way round. One easy way to accomplish what you want is converting df to an xts object:

df.xts <- xts(df$Value,order.by = df$Date)

And merge, like so:

na.locf(merge(df.xts, foo=zoo(NA, order.by=seq(start(df.xts), end(df.xts),
  "day",drop=F)))[, 1])
               df.xts
2018-01-01    3.5
2018-01-02    3.5
2018-01-03    3.5
2018-01-04    3.5
2018-01-05    3.5
2018-01-06    3.5
2018-01-07    3.5
….
2018-01-27    3.5
2018-01-28    3.5
2018-01-29    3.5
2018-01-30    3.5
2018-01-31    3.5
2018-02-01    9.5
2018-02-02    9.5
2018-02-03    9.5
2018-02-04    9.5
2018-02-05    9.5
2018-02-06    9.5
2018-02-07    9.5
2018-02-08    9.5
….
2018-02-27    9.5
2018-02-28    9.5
2018-03-01    0.1

If you want to adjust the price continuously over the course of a month use na.spline in place of na.locf.

Answer 4

Maybe not an efficient one but with base R we can do

do.call("rbind", lapply(1:nrow(df), function(i) 
data.frame(Date = seq(df$Date[i], 
                  (seq(df$Date[i],length=2,by="months") - 1)[2], by = "1 days"), 
                  value = df$Value[i])))

We basically generate a sequence of dates from start_date to the last day of that month which is calculated by

seq(df$Date[i],length=2,by="months") - 1)[2]

and repeat the same value for all the dates and put them in the data frame.

We get a list of dataframe and then we can rbind them using do.call.

Answer 5

An option using tidyr::expand expand a row between 1st day of month to last day of month. The lubridate::floor_date can provide 1st day of month and lubridate::ceiling_date() - days(1) will provide last day of month.

library(tidyverse)
library(lubridate)

df %>% mutate(Date = ymd(Date)) %>%
group_by(Date) %>%
expand(Date = seq(floor_date(Date, unit = "month"),
       ceiling_date(Date, unit="month")-days(1), by="day"), Value) %>%
as.data.frame()

#          Date Value
# 1  2008-01-01   3.5
# 2  2008-01-02   3.5
# 3  2008-01-03   3.5
# 4  2008-01-04   3.5
# 5  2008-01-05   3.5
#.....so on
# 32 2008-02-01   9.5
# 33 2008-02-02   9.5
# 34 2008-02-03   9.5
# 35 2008-02-04   9.5
# 36 2008-02-05   9.5
#.....so on

# 85 2008-03-25   0.1
# 86 2008-03-26   0.1
# 87 2008-03-27   0.1
# 88 2008-03-28   0.1
# 89 2008-03-29   0.1
# 90 2008-03-30   0.1
# 91 2008-03-31   0.1

Data:

df <- read.table(text = 
"Date           Value 
2008-01-01      3.5          
2008-02-01      9.5          
2008-03-01      0.1",
header = TRUE, stringsAsFactors = FALSE)