calculate rolling percent change missing or uneven data

thermophile 0
Viewed 44

I need to calculate 2 week percent change for a dataset that may not have samples spaced exactly 14 days apart. This forloop gives me % change for days that are exactly 14 days apart, but can't handle the sampling frequency wobble. I.e. 2022-06-14 % change is NA because there was no sample 2022-05-31 but there is one 2022-05-30. I would like a % change based either on the value of 2022-05-30 or an imputation of 2022-05-31 based on 2022-05-30 and 2022-06-02.

    library(dplyr)
    library(tidyr)
    library(lubridate)
    dat.N1 <- structure(list(date = c("2022-04-27", "2022-04-29", "2022-05-02", 
        "2022-05-04", "2022-05-06", "2022-05-17", "2022-05-19", "2022-05-24", 
        "2022-05-26", "2022-05-30", "2022-06-02", "2022-06-07", "2022-06-09", 
        "2022-06-14", "2022-06-17", "2022-06-21", "2022-06-28", "2022-06-30", 
        "2022-07-05", "2022-07-07", "2022-07-12"), copies_liter = c(168649.864, 
        62449.256, 464682.88, 127620.624, 2110.27168, 20384.6968, 6817.724, 
        145.2679712, 0.3792992, 51.4470568, 0.01, 30094.404, 42225.784, 
        37688.632, 30730.0368, 8108.9016, 6142.6856, 7411.6464, 77131.912, 
        23668.7056, 11973.198)), row.names = 210:230, class = "data.frame")
    
    dat.N1$date <- as.Date(dat.N1$date)
    
    dat.N1$date_min2 <- dat.N1$date-14
dat.N1$prop <-1:21

for (i in 1:21){

  copies_d_current <- dat.N1[i, "copies_liter"]
  copies_d_past <- dat.N1[dat.N1[, "date"]==dat.N1[i, "date_min2"],
                          "copies_liter"] 
  dat.N1$prop[i] <- ifelse(length(copies_d_current/copies_d_past)==0, 
                    NA, 
                    copies_d_current/copies_d_past %>% as.numeric())
  dat.N1$perc <- 100-dat.N1$prop*100
#print(i)
}
missing-data time-series r
2 Answers

Convert dat.N1 to a zoo series z and merge it with all days. Then use na.approx to fill in the NA days with interpolated values and finally use diff.zoo with arith=FALSE so that it takes ratios rather than differences. This gives a zoo series zz and we subset it to just get the ratios associated with the original data. Use fortify.zoo(ratios) if you need a data frame.

library(zoo)
z <- read.zoo(dat.N1)
m <- na.approx(merge(z, zoo(, seq(start(z), end(z), "day"))))
zz <- diff(m, 14, arith = FALSE, na.pad = TRUE)
ratios <- zz[time(z)]
G. Grothendieck0

I'm not sure what type of imputation you might want, but here is simple linear interpoloation that gives you the percent change 14 days prior.

dates = seq(min(dat.N1$date), max(dat.N1$date), by="day")
dat.N1 %>% 
  left_join(
    data.frame(
      date=dates, imp_14d_prior = approxfun(dat.N1$date,dat.N1$copies_liter)(dates)
    ), by=c("date_min2"="date")
  ) %>% 
  mutate(perc_ch = 100-(copies_liter/imp_14d_prior)*100)

Output:

         date copies_liter  date_min2 imp_14d_prior       perc_ch
1  2022-04-27 1.686499e+05 2022-04-13            NA            NA
2  2022-04-29 6.244926e+04 2022-04-15            NA            NA
3  2022-05-02 4.646829e+05 2022-04-18            NA            NA
4  2022-05-04 1.276206e+05 2022-04-20            NA            NA
5  2022-05-06 2.110272e+03 2022-04-22            NA            NA
6  2022-05-17 2.038470e+04 2022-05-03  2.961518e+05  9.311681e+01
7  2022-05-19 6.817724e+03 2022-05-05  6.486545e+04  8.948944e+01
8  2022-05-24 1.452680e+02 2022-05-10  8.755517e+03  9.834084e+01
9  2022-05-26 3.792992e-01 2022-05-12  1.207814e+04  9.999686e+01
10 2022-05-30 5.144706e+01 2022-05-16  1.872339e+04  9.972523e+01
11 2022-06-02 1.000000e-02 2022-05-19  6.817724e+03  9.999985e+01
12 2022-06-07 3.009440e+04 2022-05-24  1.452680e+02 -2.061648e+04
13 2022-06-09 4.222578e+04 2022-05-26  3.792992e-01 -1.113248e+07
14 2022-06-14 3.768863e+04 2022-05-31  3.430137e+01 -1.097750e+05
15 2022-06-17 3.073004e+04 2022-06-03  6.018889e+03 -4.105600e+02
16 2022-06-21 8.108902e+03 2022-06-07  3.009440e+04  7.305512e+01
17 2022-06-28 6.142686e+03 2022-06-14  3.768863e+04  8.370149e+01
18 2022-06-30 7.411646e+03 2022-06-16  3.304957e+04  7.757415e+01
19 2022-07-05 7.713191e+04 2022-06-21  8.108902e+03 -8.512005e+02
20 2022-07-07 2.366871e+04 2022-06-23  7.547126e+03 -2.136122e+02
21 2022-07-12 1.197320e+04 2022-06-28  6.142686e+03 -9.491797e+01
langtang0
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