Nothing
test_that("examples from README.md work properly", {
skip_on_cran()
x.ts <- ts_c(mdeaths, fdeaths)
x.xts <- ts_xts(x.ts)
x.df <- ts_df(x.xts)
x.dt <- ts_dt(x.df)
x.tbl <- ts_tbl(x.dt)
ts_scale(x.ts) # normalization
ts_scale(x.xts)
ts_scale(x.df)
ts_scale(x.dt)
ts_scale(x.tbl)
ts_trend(x.ts) # loess trend line
ts_pc(x.xts)
ts_pcy(x.df)
ts_lag(x.dt)
# with external packages
ts_forecast(x.tbl) # ets forecast
# collect time series as multiple time series
ts_c(ts_dt(EuStockMarkets), AirPassengers)
ts_c(EuStockMarkets, mdeaths)
# combine time series to a new, single time series
ts_bind(ts_dt(mdeaths), AirPassengers)
ts_bind(ts_xts(AirPassengers), ts_tbl(mdeaths))
ts_df(ts_c(fdeaths, mdeaths))
# ts_plot(ts_scale(ts_c(
# mdeaths,
# austres,
# AirPassengers,
# DAX = EuStockMarkets[, "DAX"]
# )))
p <- ts_ggplot(ts_scale(ts_c(
mdeaths,
austres,
AirPassengers,
DAX = EuStockMarkets[, "DAX"]
)))
expect_true(ggplot2::is.ggplot(p))
ts_(diff)(AirPassengers)
ts_(rowSums)(ts_c(mdeaths, fdeaths))
ts_prcomp <- ts_(function(x) predict(prcomp(x, scale = TRUE)))
ts_prcomp(ts_c(mdeaths, fdeaths))
ts_dygraphs <- ts_(dygraphs::dygraph, class = "xts")
ts_forecast <- ts_(function(x) forecast::forecast(x)$mean, vectorize = TRUE)
ts_seas <- ts_(
function(x) seasonal::final(seasonal::seas(x)),
vectorize = TRUE
)
ans <- ts_dygraphs(ts_c(mdeaths, EuStockMarkets))
ts_forecast(ts_c(mdeaths, fdeaths))
ts_seas(ts_c(mdeaths, fdeaths))
library(dplyr)
library(tsbox)
ts_tbl(ts_c(mdeaths, fdeaths)) %>%
ts_seas()
dta <- ts_df(ts_c(mdeaths, fdeaths))
expect_s3_class(dta, "data.frame")
})
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