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.
unnest() requires argument key = id() to get back to a tsibble.
# S3 method for tbl_ts
arrange(.data, ...)# S3 method for grouped_ts
arrange(.data, ..., .by_group = FALSE)
# S3 method for tbl_ts
filter(.data, ..., .preserve = FALSE)
# S3 method for tbl_ts
slice(.data, ..., .preserve = FALSE)
# S3 method for tbl_ts
select(.data, ...)
# S3 method for grouped_ts
select(.data, ...)
# S3 method for tbl_ts
rename(.data, ...)
# S3 method for grouped_ts
rename(.data, ...)
# S3 method for tbl_ts
mutate(.data, ...)
# S3 method for tbl_ts
transmute(.data, ...)
# S3 method for grouped_ts
transmute(.data, ...)
# S3 method for tbl_ts
summarise(.data, ...)
# S3 method for tbl_ts
summarize(.data, ...)
# S3 method for tbl_ts
group_by(.data, ..., add = FALSE, .drop = FALSE)
# S3 method for grouped_ts
ungroup(x, ...)
# S3 method for tbl_ts
left_join(x, y, by = NULL, copy = FALSE,
suffix = c(".x", ".y"), ...)
# S3 method for tbl_ts
right_join(x, y, by = NULL, copy = FALSE,
suffix = c(".x", ".y"), ...)
# S3 method for tbl_ts
inner_join(x, y, by = NULL, copy = FALSE,
suffix = c(".x", ".y"), ...)
# S3 method for tbl_ts
full_join(x, y, by = NULL, copy = FALSE,
suffix = c(".x", ".y"), ...)
# S3 method for tbl_ts
semi_join(x, y, by = NULL, copy = FALSE, ...)
# S3 method for tbl_ts
anti_join(x, y, by = NULL, copy = FALSE, ...)
# S3 method for tbl_ts
gather(data, key = "key", value = "value", ...,
na.rm = FALSE, convert = FALSE, factor_key = FALSE)
# S3 method for tbl_ts
spread(data, key, value, fill = NA, convert = FALSE,
drop = TRUE, sep = NULL)
# S3 method for tbl_ts
nest(data, ..., .key = "data")
# S3 method for lst_ts
unnest(data, ..., key = id(), .drop = NA,
.id = NULL, .sep = NULL, .preserve = NULL)
# S3 method for tbl_ts
unnest(data, ..., key = id(), .drop = NA,
.id = NULL, .sep = NULL, .preserve = NULL)
# S3 method for tbl_ts
fill(data, ..., .direction = c("down", "up"))
# S3 method for grouped_ts
fill(data, ..., .direction = c("down", "up"))
A tbl_ts.
same arguments accepted as its dplyr generic.
If TRUE, will sort first by grouping variable. Applies to
grouped data frames only.
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").
When add = FALSE, the default, group_by() will
override existing groups. To add to the existing groups, use
add = TRUE.
When .drop = TRUE, empty groups are dropped.
A tbl()
tbls to join
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.
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.
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.
A data frame.
Unquoted variables to create the key (via id) after unnesting.
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).
If TRUE, will remove rows from output where the
value column in NA.
If TRUE will automatically run
type.convert() on the key column. This is useful if the column
types are actually numeric, integer, or logical.
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.
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.
If FALSE, will keep factor levels that don't appear in the
data, filling in missing combinations with fill.
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>".
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).
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.
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.
Direction in which to fill missing values. Currently either "down" (the default) or "up".
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.
# NOT RUN {
# 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)
)
(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))
# }
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