similar to expand grid - but in expand.grid the algo does not infer missing dates from the timeseries our function does !!

complete_time(df)

complete_time_factors(df)

Arguments

df: The dataframe or tibble to operate on

Value

An expanded data.frame of all time, and optionally all factor permutations

Functions

complete_time_factors: Apply every combination of variable factors for complete time series.

Examples


require(ggplot2)
#> Loading required package: ggplot2
#> 
#> Attaching package: ‘ggplot2’
#> The following object is masked from ‘package:crayon’:
#> 
#>     %+%
require(tibble)
#> Loading required package: tibble
require(dplyr)
#> Loading required package: dplyr
#> Warning: package ‘dplyr’ was built under R version 4.1.2
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

# time series
 ts <-  as.Date("2022-01-03"):as.Date(Sys.Date())
 ts <- sort(
   as.Date(
     ts[sample(c(TRUE,FALSE),size = 101,replace = TRUE,prob = c(0.7,0.3))],
     origin='1970-01-01')
   )

 df <- tibble::tibble(time=as.Date(ts),
              col1=sample(replace=TRUE,letters[c(1:5)],size=length(ts)),
              col2=sample(replace=TRUE,1:26,size=length(ts)),
              )

 time_name <- sapply(df,class)[sapply(df,class)=='Date']%>%names()
 
 new_df <- complete_time_factors(df)

 
#similar to expand grid - but in expand.grid the 
#algo does not infer missing dates from the timeseries
# our function does !!
 
 df%>%
   dplyr::count(time,wt=col2)%>%
   dplyr::mutate(n-lag(n,1))%>%head(10)
#> # A tibble: 10 × 3
#>    time           n `n - lag(n, 1)`
#>    <date>     <int>           <int>
#>  1 2022-01-06    19              NA
#>  2 2022-01-07     3             -16
#>  3 2022-01-09    25              22
#>  4 2022-01-10    25               0
#>  5 2022-01-11    17              -8
#>  6 2022-01-12    18               1
#>  7 2022-01-14    18               0
#>  8 2022-01-15     1             -17
#>  9 2022-01-16     7               6
#> 10 2022-01-17    15               8
 
 new_df%>%
   dplyr::count(time,wt=col2)%>%
   dplyr::mutate(n-lag(n,1))%>%head(10)
#>          time  n n - lag(n, 1)
#> 1  2022-01-06 19            NA
#> 2  2022-01-07  3           -16
#> 3  2022-01-08  0            -3
#> 4  2022-01-09 25            25
#> 5  2022-01-10 25             0
#> 6  2022-01-11 17            -8
#> 7  2022-01-12 18             1
#> 8  2022-01-13  0           -18
#> 9  2022-01-14 18            18
#> 10 2022-01-15  1           -17
 
 #compare BEFORE and
 
 ggplot2::ggplot(df)+
 ggplot2::geom_line(ggplot2::aes(time,col2,col=col1))+
 ggplot2::facet_wrap(~col1)+
 ggplot2::theme_minimal()

 
 #... and after
 
 ggplot2::ggplot(new_df)+
 ggplot2::geom_line(ggplot2::aes(time,col2,col=col1))+
 ggplot2::facet_wrap(~col1)+
 ggplot2::theme_minimal()