R: Tidyverse methods for tsibble

tidyverse {tsibble}

R Documentation

Tidyverse methods for tsibble

Description

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() will not collapse on the index variable.
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.
unnest() requires argument key = id() to get back to a tsibble.

Usage

## S3 method for class 'tbl_ts'
arrange(.data, ...)

## S3 method for class 'grouped_ts'
arrange(.data, ..., .by_group = FALSE)

## S3 method for class 'tbl_ts'
filter(.data, ...)

## S3 method for class 'tbl_ts'
slice(.data, ...)

## S3 method for class 'tbl_ts'
select(.data, ..., .drop = FALSE)

## S3 method for class 'tbl_ts'
rename(.data, ...)

## S3 method for class 'tbl_ts'
mutate(.data, ..., .drop = FALSE)

## S3 method for class 'tbl_ts'
transmute(.data, ..., .drop = FALSE)

## S3 method for class 'tbl_ts'
summarise(.data, ..., .drop = FALSE)

## S3 method for class 'tbl_ts'
summarize(.data, ..., .drop = FALSE)

## S3 method for class 'tbl_ts'
group_by(.data, ..., add = FALSE)

## S3 method for class 'grouped_ts'
ungroup(x, ...)

## S3 method for class 'tbl_ts'
left_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ...)

## S3 method for class 'tbl_ts'
right_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ...)

## S3 method for class 'tbl_ts'
inner_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ...)

## S3 method for class 'tbl_ts'
full_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ...)

## S3 method for class 'tbl_ts'
semi_join(x, y, by = NULL, copy = FALSE, ...)

## S3 method for class 'tbl_ts'
anti_join(x, y, by = NULL, copy = FALSE, ...)

## 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, fill = NA, convert = FALSE,
  drop = TRUE, sep = NULL)

## S3 method for class 'tbl_ts'
nest(data, ..., .key = "data")

## S3 method for class 'lst_ts'
unnest(data, ..., key = id(), .drop = NA,
  .id = NULL, .sep = NULL, .preserve = NULL)

## S3 method for class 'tbl_ts'
unnest(data, ..., key = id(), .drop = NA,
  .id = NULL, .sep = NULL, .preserve = NULL)

## S3 method for class 'grouped_ts'
fill(data, ..., .direction = c("down", "up"))

Arguments

`.data`	A `tbl_ts`.
`...`	same arguments accepted as its dplyr generic.
`.by_group`	If `TRUE`, will sort first by grouping variable. Applies to grouped data frames only.
`.drop`	Deprecated, please use `as_tibble()` for `.drop = TRUE` instead. `FALSE` returns a tsibble object as the input. `TRUE` drops a tsibble and returns a tibble.
`add`	When `add = FALSE`, the default, `group_by()` will override existing groups. To add to the existing groups, use `add = TRUE`.
`x`	A `tbl()`
`y`	tbls to join
`by`	a character vector of variables to join by. If `NULL`, the default, `*_join()` will do a natural join, using all variables with common names across the two tables. A message lists the variables so that you can check they're right (to suppress the message, simply explicitly list the variables that you want to join). To join by different variables on x and y use a named vector. For example, `by = c("a" = "b")` will match `x.a` to `y.b`.
`copy`	If `x` and `y` are not from the same data source, and `copy` is `TRUE`, then `y` will be copied into the same src as `x`. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.
`suffix`	If there are non-joined duplicate variables in `x` and `y`, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
`data`	A data frame.
`key`	Unquoted variables to create the key (via id) after unnesting.
`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 `rlang::ensym()` (note that this kind of interface where symbols do not represent actual objects is now discouraged in the tidyverse; we support it here for backward compatibility).
`na.rm`	If `TRUE`, will remove rows from output where the value column in `NA`.
`convert`	If `TRUE` will automatically run `type.convert()` on the key column. This is useful if the column types are actually numeric, integer, or logical.
`factor_key`	If `FALSE`, the default, the key values will be stored as a character vector. If `TRUE`, will be stored as a factor, which preserves the original ordering of the columns.
`fill`	If set, missing values will be replaced with this value. Note that there are two types of missingness in the input: explicit missing values (i.e. `NA`), and implicit missings, rows that simply aren't present. Both types of missing value will be replaced by `fill`.
`drop`	If `FALSE`, will keep factor levels that don't appear in the data, filling in missing combinations with `fill`.
`sep`	If `NULL`, the column names will be taken from the values of `key` variable. If non-`NULL`, the column names will be given by "<key_name><sep><key_value>".
`.key`	The name of the new column, as a string or symbol. This argument is passed by expression and supports quasiquotation (you can unquote strings and symbols). The name is captured from the expression with `rlang::ensym()` (note that this kind of interface where symbols do not represent actual objects is now discouraged in the tidyverse; we support it here for backward compatibility).
`.id`	Data frame identifier - if supplied, will create a new column with name `.id`, giving a unique identifier. This is most useful if the list column is named.
`.sep`	If non-`NULL`, the names of unnested data frame columns will combine the name of the original list-col with the names from nested data frame, separated by `.sep`.
`.preserve`	Optionally, list-columns to preserve in the output. These will be duplicated in the same way as atomic vectors. This has dplyr::select semantics so you can preserve multiple variables with `.preserve = c(x, y)` or `.preserve = starts_with("list")`.
`.direction`	Direction in which to fill missing values. Currently either "down" (the default) or "up".

Examples

# Sum over sensors ----
pedestrian %>%
  summarise(Total = sum(Count))
# Back to tibble
pedestrian %>%
  as_tibble() %>%
  summarise(Total = sum(Count))
# 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)
)
stocks %>% gather(stock, price, -time)
# 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()
nested_stock %>% 
  unnest(key = id(stock))
stock_qtl <- stocksm %>% 
  group_by(stock) %>% 
  index_by(day3 = lubridate::floor_date(time, unit = "3 day")) %>% 
  summarise(
    value = list(quantile(price)), 
    qtl = list(c("0%", "25%", "50%", "75%", "100%"))
  )
unnest(stock_qtl, key = id(qtl))

[Package tsibble version 0.6.2 Index]