将季度数据分解为R保持值的每日数据？

Question

How does one easily disaggregate quarterly data to daily data? In this case I'm using 10 years of US GDP data which have quarterly observations, and I want to expand the data frame to daily level, carrying over the GDP value each day until the next observation.

Reprex table:

structure(list(thedate = structure(c(14426, 14518, 14610, 14700, 
14791, 14883, 14975, 15065, 15156, 15248, 15340, 15431, 15522, 
15614, 15706, 15796, 15887, 15979, 16071, 16161, 16252, 16344, 
16436, 16526, 16617, 16709, 16801, 16892, 16983, 17075, 17167, 
17257, 17348, 17440, 17532, 17622, 17713, 17805, 17897, 17987
), class = "Date"), gdp = c(1.5, 4.5, 1.5, 3.7, 3, 2, -1, 2.9, 
-0.1, 4.7, 3.2, 1.7, 0.5, 0.5, 3.6, 0.5, 3.2, 3.2, -1.1, 5.5, 
5, 2.3, 3.2, 3, 1.3, 0.1, 2, 1.9, 2.2, 2, 2.3, 2.2, 3.2, 3.5, 
2.5, 3.5, 2.9, 1.1, 3.1, 2.1)), class = "data.frame", row.names = c(NA, 
-40L))

We see above:

2009-07-01 | 1.5
2009-10-01 | 4.5

The intended output would look like:

2009-07-01 | 1.5
2009-07-02 | 1.5
2009-07-03 | 1.5
etc.
2009-10-01 | 4.5
2009-10-02 | 4.5
2009-10-03 | 4.5

Answer 1

Here is a tidyr and zoo package answer that uses 'last observation carried forward' after inserting a sequence of dates with NA:

library(tidyverse)
library(zoo)

data %>%
  complete(thedate = seq.Date(min(thedate), max(thedate), by="day")) %>%
  do(na.locf(.))

Edit : Thanks to Shree for reminding that tidyr::fill would eliminate need for zoo:

library(tidyverse)

data %>%
  complete(thedate = seq.Date(min(thedate), max(thedate), by="day")) %>%
  fill(gdp)

Answer 2

library(lubridate)
d2 = do.call(rbind, lapply(2:NROW(d), function(i){
    data.frame(date = head(seq.Date(d$thedate[i-1], d$thedate[i], "days"), -1),
               gdp = d$gdp[i - 1])
}))
head(d2)
        date gdp
1 2009-07-01 1.5
2 2009-07-02 1.5
3 2009-07-03 1.5
4 2009-07-04 1.5
5 2009-07-05 1.5
6 2009-07-06 1.5
tail(d2)
           date gdp
3556 2019-03-26 3.1
3557 2019-03-27 3.1
3558 2019-03-28 3.1
3559 2019-03-29 3.1
3560 2019-03-30 3.1
3561 2019-03-31 3.1

Answer 3

Here's a base solution:

last_quarter_end_date <- seq.Date(df$thedate[nrow(df)], by = 'quarter', length.out = 2)[-1]-1
seqs <- diff(c(df$thedate, last_quarter_end_date))

data.frame(thedate = rep(df$thedate, seqs) + sequence(seqs)-1
           , gdp = rep(df$gdp, seqs))

Basically, the difference between dates is how many times you need to repeat a GDP column. Also, I can do seq_len() for each difference to add back to the original date.

Performance This approach is efficient although I'll note that 0.6 ms isn't really much different than 15 ms in the big picture.

Unit: microseconds
      expr     min       lq      mean  median       uq     max neval
 cole_base   528.1   554.15   690.379   644.9   663.75  3225.7   100
  d_b_base 15735.0 15994.40 17395.754 16243.9 18108.30 38761.8   100
 Ben_tidyr  2808.7  2936.40  3356.324  3076.6  3149.65  8065.1   100

Complete code for reference:

DF <- structure(list(thedate = structure(c(14426, 14518, 14610, 14700, 
                                           14791, 14883, 14975, 15065, 15156, 15248, 15340, 15431, 15522, 
                                           15614, 15706, 15796, 15887, 15979, 16071, 16161, 16252, 16344, 
                                           16436, 16526, 16617, 16709, 16801, 16892, 16983, 17075, 17167, 
                                           17257, 17348, 17440, 17532, 17622, 17713, 17805, 17897, 17987
), class = "Date"), gdp = c(1.5, 4.5, 1.5, 3.7, 3, 2, -1, 2.9, 
                            -0.1, 4.7, 3.2, 1.7, 0.5, 0.5, 3.6, 0.5, 3.2, 3.2, -1.1, 5.5, 
                            5, 2.3, 3.2, 3, 1.3, 0.1, 2, 1.9, 2.2, 2, 2.3, 2.2, 3.2, 3.5, 
                            2.5, 3.5, 2.9, 1.1, 3.1, 2.1)), class = "data.frame", row.names = c(NA, 
                                                                                                -40L))

library(microbenchmark)
library(tidyr)

microbenchmark(cole_base = {
  last_quarter_end_date <- seq.Date(DF$thedate[nrow(DF)], by = 'quarter', length.out = 2)[-1]-1
  seqs <- diff(c(DF$thedate, last_quarter_end_date))

  data.frame(thedate = rep(DF$thedate, seqs) + sequence(seqs)-1
             , gdp = rep(DF$gdp, seqs))
}
, d_b_base = {
  do.call(rbind, lapply(2:NROW(DF), function(i){
    data.frame(date = head(seq.Date(DF$thedate[i-1], DF$thedate[i], "days"), -1),
               gdp = DF$gdp[i - 1])
     }))
}
, Ben_tidyr = {
  DF %>%
    complete(thedate = seq.Date(min(thedate), max(thedate), by="day")) %>%
    fill(gdp)
}
)