Column-wise verbs

  • The index variable cannot be dropped for a tsibble object.

  • When any key variable is modified, a check on the validity of the resulting tsibble will be performed internally.

  • Use as_tibble() to convert tsibble to a general data frame.

Row-wise verbs

A warning is likely to be issued, if observations are not arranged in past-to-future order.

Join verbs

Joining with other data sources triggers the check on the validity of the resulting tsibble.

Examples

library(dplyr, warn.conflicts = FALSE) # `summarise()` a tsibble always aggregates over time # Sum over sensors pedestrian %>% index_by() %>% summarise(Total = sum(Count))
#> # A tsibble: 17,542 x 2 [1h] <Australia/Melbourne> #> Date_Time Total #> <dttm> <int> #> 1 2015-01-01 00:00:00 2866 #> 2 2015-01-01 01:00:00 1535 #> 3 2015-01-01 02:00:00 994 #> 4 2015-01-01 03:00:00 569 #> 5 2015-01-01 04:00:00 311 #> 6 2015-01-01 05:00:00 159 #> 7 2015-01-01 06:00:00 129 #> 8 2015-01-01 07:00:00 146 #> 9 2015-01-01 08:00:00 258 #> 10 2015-01-01 09:00:00 419 #> # … with 17,532 more rows
# shortcut pedestrian %>% summarise(Total = sum(Count))
#> # A tsibble: 17,542 x 2 [1h] <Australia/Melbourne> #> Date_Time Total #> <dttm> <int> #> 1 2015-01-01 00:00:00 2866 #> 2 2015-01-01 01:00:00 1535 #> 3 2015-01-01 02:00:00 994 #> 4 2015-01-01 03:00:00 569 #> 5 2015-01-01 04:00:00 311 #> 6 2015-01-01 05:00:00 159 #> 7 2015-01-01 06:00:00 129 #> 8 2015-01-01 07:00:00 146 #> 9 2015-01-01 08:00:00 258 #> 10 2015-01-01 09:00:00 419 #> # … with 17,532 more rows
# Back to tibble pedestrian %>% as_tibble() %>% summarise(Total = sum(Count))
#> # A tibble: 1 x 1 #> Total #> <int> #> 1 45483871
library(tidyr) stocks <- tsibble( time = as.Date("2009-01-01") + 0:9, X = rnorm(10, 0, 1), Y = rnorm(10, 0, 2), Z = rnorm(10, 0, 4) )
#> Using `time` as index variable.
(stocksm <- stocks %>% pivot_longer(-time, names_to = "stock", values_to = "price"))
#> # A tsibble: 30 x 3 [1D] #> # Key: stock [3] #> time stock price #> <date> <chr> <dbl> #> 1 2009-01-01 X 1.49 #> 2 2009-01-01 Y -1.01 #> 3 2009-01-01 Z -4.81 #> 4 2009-01-02 X -0.659 #> 5 2009-01-02 Y -0.540 #> 6 2009-01-02 Z -0.159 #> 7 2009-01-03 X 0.537 #> 8 2009-01-03 Y -2.17 #> 9 2009-01-03 Z 2.75 #> 10 2009-01-04 X 0.747 #> # … with 20 more rows
stocksm %>% pivot_wider(names_from = stock, values_from = price)
#> # A tsibble: 10 x 4 [1D] #> time X Y Z #> <date> <dbl> <dbl> <dbl> #> 1 2009-01-01 1.49 -1.01 -4.81 #> 2 2009-01-02 -0.659 -0.540 -0.159 #> 3 2009-01-03 0.537 -2.17 2.75 #> 4 2009-01-04 0.747 0.724 2.82 #> 5 2009-01-05 1.90 -0.671 3.97 #> 6 2009-01-06 -2.06 2.73 4.58 #> 7 2009-01-07 0.0645 -1.42 -4.96 #> 8 2009-01-08 -0.265 1.32 10.6 #> 9 2009-01-09 -0.447 0.582 -0.628 #> 10 2009-01-10 -1.41 0.396 -1.69