Creating a ts time series with missing values from a data frame - r

Question

I have a data frame containing a time series of monthly data, with some missing values.
dates <- seq(
as.Date("2010-01-01"), as.Date("2017-12-01"), "1 month"
)
n_dates <- length(dates)
dates <- dates[runif(n_dates) < 0.5]
time_data <- data.frame(
date = dates,
value = rnorm(length(dates))
)
## date value
## 1 2010-02-01 1.3625419
## 2 2010-06-01 0.1512481
## etc.
In order do be able to make use of time series forecasting functionality in, e.g., forecast, I'd like to convert this to a ts object.
The dumb way to do this is to create a regular set of monthly dates over the whole time period, then left join back to the original data.
library(dplyr)
first_date <- min(time_data$date)
last_date <- max(time_data$date)
full_dates <- data.frame(
date = seq(first_date, last_date, "1 month")
)
extended_time_data <- left_join(full_dates, time_data, by = "date")
## date value
## 1 2010-02-01 1.3625419
## 2 2010-03-01 NA
## etc.
Now I can create the time series using ts().
library(lubridate)
time_series <- ts(
extended_time_data$value,
start = c(year(first_date), month(first_date)),
frequency = 12
)
For such a simple task, this is long-winded and pretty gross.
I also looked into first converting to xts, and using a convertor from the timetk package, but nothing jumped out at me as an easier way.
This question is a dupe of How to create time series with missing datetime values, but the answer there was even fuzzier.
How do I create a ts object from a time series with missing values?

Answer 1

Using the input data frame defined in the Note at the end, convert it to a zoo object with index of class yearmon. Then as.ts will convert it to ts.
library(zoo)
z <- read.zoo(DF, FUN = as.yearmon)
as.ts(z)
## Jan Feb Mar Apr May Jun Jul Aug
## 2000 1 NA NA 2 3 NA 4 5
If you prefer to express it in terms of pipes:
library(magrittr)
library(zoo)
DF %>% read.zoo(FUN = as.yearmon) %>% as.ts
If desired, interpolate the values in the time series using na.locf (last occurrence carried forward), na.approx (linear interpolation), na.spline, na.StructTS (seasonal Kalman filter) or other zoo NA filling function. e.g.
library(forecast)
DF %>% read.zoo(FUN = as.yearmon) %>% as.ts %>% na.spline %>% forecast
Note
The data in the question is not reproducible because random numbers are used without set.seed and n_dates is undefined. Below we define a data frame DF reproducibly for purposes of example.
library(zoo)
dates <- as.Date(as.yearmon("2000-01") + c(0, 3, 4, 6, 7)/12)
DF <- data.frame(dates, values = seq_along(dates))
giving:
> DF
dates values
1 2000-01-01 1
2 2000-04-01 2
3 2000-05-01 3
4 2000-07-01 4
5 2000-08-01 5

Answer 2

Instead of using the left_join an easier option is complete, convert it to a tsibble object which is now compatible with the forecast package functions
library(tidyverse)
library(tsibble)
time_data %>%
complete(date = seq(min(date), max(date), by = "1 month"),
fill = list(value = NA)) %>%
as_tsibble(index = date)
# A tsibble: 94 x 2 [1D]
# date value
# <date> <dbl>
# 1 2010-02-01 1.02
# 2 2010-03-01 NA
# 3 2010-04-01 NA
# 4 2010-05-01 1.75
# 5 2010-06-01 NA
# 6 2010-07-01 NA
# 7 2010-08-01 -0.233
# 8 2010-09-01 NA
# 9 2010-10-01 NA
#10 2010-11-01 -0.987
# ... with 84 more rows
As mentioned above, it is compatible withe forecast functions
library(fable)
time_data %>%
complete(date = seq(min(date), max(date), by = "1 month"),
fill = list(value = 0)) %>%
as_tsibble(index = date) %>%
ETS(value) %>%
forecast %>%
autoplot
NOTE: Here, the missing values are imputed as 0.
It can be imputed with the previous non-NA value with fill
time_data %>%
complete(date = seq(min(date), max(date), by = "1 month")) %>%
fill(value) %>%
as_tsibble(index = date) %>%
ETS(value) %>%
forecast %>%
autoplot
data
n_dates <- 3

Answer 3

A base option and using set.seed(789) before running your data generation
temp <- which(full_dates$date%in%time_data$date)
full_dates$new[temp] <- time_data$value
head(full_dates, 20)
date new
1 2010-02-01 0.62589399
2 2010-03-01 0.98117664
3 2010-04-01 NA
4 2010-05-01 -0.04770986
5 2010-06-01 -1.51961483
6 2010-07-01 NA
7 2010-08-01 0.79493644
8 2010-09-01 -0.14423251
9 2010-10-01 -0.70649791
10 2010-11-01 0.61071247
11 2010-12-01 NA
12 2011-01-01 1.08506164
13 2011-02-01 -0.71134925
14 2011-03-01 1.15628805
15 2011-04-01 1.23556280
16 2011-05-01 -0.32245531
17 2011-06-01 NA
18 2011-07-01 NA
19 2011-08-01 0.73277540
20 2011-09-01 -0.28752883
or same result but using data.table
setDT(full_dates)[temp, new:= time_data$value]
Now to xts
xts::xts(full_dates[,-1], order.by = full_dates$date, frequency = 12 )