| tsibble-tidyverse {tsibble} | R Documentation |
arrange(): if not arranging key and index in past-to-future order, a warning is
likely to be issued.
slice(): if row numbers are not in ascending order, a warning is likely to
be issued.
select(): keeps the variables you mention as well as the index.
transmute(): keeps the variable you operate on, as well as the index and key.
summarise() reduces a sequence of values over time instead of a single summary,
as well as dropping empty keys/groups.
## S3 method for class 'tbl_ts' arrange(.data, ...) ## S3 method for class 'tbl_ts' filter(.data, ..., .preserve = FALSE) ## S3 method for class 'tbl_ts' slice(.data, ..., .preserve = FALSE) ## S3 method for class 'tbl_ts' select(.data, ...) ## S3 method for class 'tbl_ts' rename(.data, ...) ## S3 method for class 'tbl_ts' mutate(.data, ...) ## S3 method for class 'tbl_ts' transmute(.data, ...) ## S3 method for class 'tbl_ts' summarise(.data, ...) ## S3 method for class 'tbl_ts' gather(data, key = "key", value = "value", ..., na.rm = FALSE, convert = FALSE, factor_key = FALSE) ## S3 method for class 'tbl_ts' spread(data, key, value, ...) ## S3 method for class 'tbl_ts' nest(.data, ...)
.data |
A |
... |
Same arguments accepted as its tidyverse generic. |
.preserve |
when |
data |
A data frame. |
key |
Names of new key and value columns, as strings or symbols. This argument is passed by expression and supports
quasiquotation (you can unquote strings
and symbols). The name is captured from the expression with
|
value |
Names of new key and value columns, as strings or symbols. This argument is passed by expression and supports
quasiquotation (you can unquote strings
and symbols). The name is captured from the expression with
|
na.rm |
If |
convert |
If |
factor_key |
If |
Column-wise verbs, including select(), transmute(), summarise(),
mutate() & transmute(), keep the time context hanging around. That is,
the index variable cannot be dropped for a tsibble. If any key variable
is changed, it will validate whether it's a tsibble internally. Use as_tibble()
to leave off the time context.
library(dplyr, warn.conflicts = FALSE)
# Sum over sensors
pedestrian %>%
index_by() %>%
summarise(Total = sum(Count))
# shortcut
pedestrian %>%
summarise(Total = sum(Count))
# Back to tibble
pedestrian %>%
as_tibble() %>%
summarise(Total = sum(Count))
library(tidyr)
# example from 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)
)
(stocksm <- stocks %>% gather(stock, price, -time))
stocksm %>% spread(stock, price)
nested_stock <- stocksm %>%
nest(-stock)
stocksm %>%
group_by(stock) %>%
nest()