The tsibble package provides a data class of tbl_ts to manage temporal data frames in a tidy and modern way. A tsibble consists of a time index, keys and other measured variables in a data-centric format, which is built on top of the tibble.
You could install the development version from Github using
# install.packages("devtools")
devtools::install_github("earowang/tsibble", build_vignettes = TRUE)The weather data included in the package nycflights13 is used as an example to illustrate. The "index" variable refers to the time_hour containing the date-times, and the "key" is the origin as weather stations. The key together with the index uniquely identifies each observation, which defines a valid tsibble. Others can be considered as measured variables.
library(tsibble)
weather_ts <- as_tsibble(nycflights13::weather, origin, index = time_hour)
weather_ts
#> # A tsibble: 26,130 x 15 [1HOUR]
#> # Keys: origin
#> origin year month day hour temp dewp humid wind_dir wind_speed
#> * <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 EWR 2013 1 1 0 37.04 21.92 53.97 230 10.35702
#> 2 EWR 2013 1 1 1 37.04 21.92 53.97 230 13.80936
#> 3 EWR 2013 1 1 2 37.94 21.92 52.09 230 12.65858
#> 4 EWR 2013 1 1 3 37.94 23.00 54.51 230 13.80936
#> 5 EWR 2013 1 1 4 37.94 24.08 57.04 240 14.96014
#> 6 EWR 2013 1 1 6 39.02 26.06 59.37 270 10.35702
#> 7 EWR 2013 1 1 7 39.02 26.96 61.63 250 8.05546
#> 8 EWR 2013 1 1 8 39.02 28.04 64.43 240 11.50780
#> 9 EWR 2013 1 1 9 39.92 28.04 62.21 250 12.65858
#> 10 EWR 2013 1 1 10 39.02 28.04 64.43 260 12.65858
#> # ... with 26,120 more rows, and 5 more variables: wind_gust <dbl>,
#> # precip <dbl>, pressure <dbl>, visib <dbl>, time_hour <dttm>The key is not constrained to a single variable, but expressive for nested and crossed data structures. See vignette() for details.
The common dplyr verbs, such as summarise(), mutate(), select(), filter(), and arrange(), work with the tsibble. We also have a new verb tsummarise() to aggregate interested variables over calendar periods.
weather_ts %>%
group_by(origin) %>%
tsummarise(
year_month = yearmth(time_hour), # monthly aggregates
avg_temp = mean(temp, na.rm = TRUE),
ttl_precip = sum(precip, na.rm = TRUE)
)
#> # A tsibble: 36 x 4 [1MONTH]
#> # Keys: origin
#> # Groups: origin
#> origin year_month avg_temp ttl_precip
#> * <chr> <mth> <dbl> <dbl>
#> 1 EWR 2013 Jan 35.45787 2.70
#> 2 EWR 2013 Feb 34.09193 2.76
#> 3 EWR 2013 Mar 39.98992 1.92
#> 4 EWR 2013 Apr 52.75792 1.07
#> 5 EWR 2013 May 62.75508 2.76
#> 6 EWR 2013 Jun 73.38200 5.12
#> 7 EWR 2013 Jul 80.65563 1.66
#> 8 EWR 2013 Aug 74.40011 2.65
#> 9 EWR 2013 Sep 67.52960 0.94
#> 10 EWR 2013 Oct 59.77984 0.14
#> # ... with 26 more rowsThe tsummarise goes hand in hand with the index functions including as.Date(), yearmth(), yearqtr(), and year().