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 0.497 #> 2 2009-01-01 Y -0.168 #> 3 2009-01-01 Z -3.74 #> 4 2009-01-02 X 0.779 #> 5 2009-01-02 Y -4.02 #> 6 2009-01-02 Z 1.17 #> 7 2009-01-03 X 1.51 #> 8 2009-01-03 Y -1.03 #> 9 2009-01-03 Z 0.191 #> 10 2009-01-04 X -1.82 #> # … 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 0.497 -0.168 -3.74 #> 2 2009-01-02 0.779 -4.02 1.17 #> 3 2009-01-03 1.51 -1.03 0.191 #> 4 2009-01-04 -1.82 0.203 1.78 #> 5 2009-01-05 0.788 -0.112 2.67 #> 6 2009-01-06 -0.973 -4.86 -1.25 #> 7 2009-01-07 0.893 -1.25 10.1 #> 8 2009-01-08 0.763 -1.75 -1.68 #> 9 2009-01-09 0.651 0.571 5.35 #> 10 2009-01-10 -0.536 0.688 -0.893