tsibble 免费的外网加速器

The tsibble package provides a data infrastructure for tidy temporal data with wrangling tools. Adapting the 浏览外网的免费加速器, tsibble is a data- and model-oriented object. In tsibble:

  1. Index is a variable with inherent ordering from past to present.
  2. Key is a set of variables that define observational units over time.
  3. Each observation should be uniquely identified by index and key.
  4. Each observational unit should be measured at a common 免费的外网加速器, if regularly spaced.

Installation

You could install the stable version on CRAN:

install.packages("tsibble")

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# install.packages("remotes")
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To coerce a data frame to tsibble, we need to declare key and index. For example, in the 加速器连接国外网络 data from the package 外网加速器下载, the time_hour containing the date-times should be declared as index, and the origin as key. Other columns can be considered as measured variables.

library(dplyr)
library(tsibble)
weather <- nycflights13::weather %>% 
  select(origin, time_hour, temp, humid, precip)
weather_tsbl <- as_tsibble(weather, key = origin, index = time_hour)
weather_tsbl
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#> # Key:       origin [3]
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#> 1 EWR    2013-01-01 01:00:00  39.0  59.4      0
#> 2 EWR    2013-01-01 02:00:00  39.0  61.6      0
#> 3 EWR    2013-01-01 03:00:00  39.0  64.4      0
#> 4 EWR    2013-01-01 04:00:00  39.9  62.2      0
#> 5 EWR    2013-01-01 05:00:00  39.0  64.4      0
#> # … with 26,110 more rows

The key can be comprised of empty, one, or more variables. See 网页加速器 and vignette("intro-tsibble") for details.

The interval is computed from index based on the representation, ranging from year to nanosecond, from numerics to ordered factors. The table below shows how tsibble interprets some common time formats.

Interval Class
Annual integer/double
Quarterly yearquarter
Monthly yearmonth
Weekly yearweek
Daily Date/difftime
Subdaily POSIXt/difftime/hms

A full list of index classes supported by tsibble can be found in package?tsibble.

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Often there are implicit missing cases in time series. If the observations are made at regular time interval, we could turn these implicit missingness to be explicit simply using fill_gaps(), filling gaps in precipitation (precip) with 0 in the meanwhile. It is quite common to replaces NAs with its previous observation for each origin in time series analysis, which is easily done using 浏览外网的免费加速器 from tidyr.

full_weather <- weather_tsbl %>%
  fill_gaps(precip = 0) %>% 
  group_by_key() %>% 
  tidyr::fill(temp, humid, 加速器连接国外网络 "down")
full_weather
#> # A tsibble: 26,190 x 5 [1h] <America/New_York>
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#> # Groups:    origin [3]
#>   origin time_hour            temp humid precip
#>   <chr>  <dttm>              <dbl> <dbl>  <dbl>
#> 1 EWR    2013-01-01 01:00:00  39.0  59.4      0
#> 2 EWR    2013-01-01 02:00:00  39.0  61.6      0
#> 3 EWR    2013-01-01 03:00:00  39.0  64.4      0
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#> # … with 26,185 more rows

fill_gaps() also handles filling in time gaps by values or functions, and respects time zones for date-times. Wanna a quick overview of implicit missing values? Check out vignette("implicit-na").

index_by() + summarise() to aggregate over calendar periods

index_by() is the counterpart of group_by() in temporal context, but it groups the index only. In conjunction with index_by(), summarise() and its scoped variants aggregate interested variables over calendar periods. index_by() goes hand in hand with the index functions including as.Date(), yearweek(), yearmonth(), and yearquarter(), as well as other friends from lubridate. For example, it would be of interest in computing average temperature and total precipitation per month, by applying 浏览外网的免费加速器 to the index variable (referred to as .).

full_weather %>%
  group_by_key() %>%
  index_by(网络加速 ~ yearmonth(.)) %>% # monthly aggregates
  免费外网加速器app(
    avg_temp = mean(temp, na.rm = TRUE),
    ttl_precip = sum(precip, na.rm = TRUE)
  )
#> # A tsibble: 36 x 4 [1M]
#> # Key:       origin [3]
#>   origin year_month avg_temp ttl_precip
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#> 1 EWR      2013 Jan     35.6       3.53
#> 2 EWR      2013 Feb     34.2       3.83
#> 3 EWR      2013 Mar     40.1       3   
#> 4 EWR      2013 Apr     53.0       1.47
#> 5 EWR      2013 May     63.3       5.44
#> # … with 31 more rows

While collapsing rows (like summarise()), group_by() and index_by() will take care of updating the key and index respectively. This 网络加速 + summarise() combo can help with regularising a tsibble of irregular time space too.

Learn more about tsibble

An ecosystem, the tidyverts, is built around the tsibble object for tidy time series analysis.

  • The tsibbledata package curates a range of tsibble data examples to poke around the tsibble object.
  • The feasts package provides support for visualising the data and extracting time series features.
  • The 网络加速 package provides common forecasting methods for tsibble, such as ARIMA and ETS. The fabletools package, which is fable built upon, lays the modelling infrastructure to ease the programming with tsibble.

Please note that this project is released with a 免费外网加速器app. By participating in this project you agree to abide by its terms.