The ggtime package provides tools for graphically analysing time series, with exploration of trend and seasonality. It utilises the tsibble data format for time series and produces plots with ggplot2.
You can install the development version of ggtime from GitHub with:
# install.packages("remotes")
remotes::install_github("tidyverts/ggtime")library(ggtime)
#> Registered S3 method overwritten by 'tsibble':
#> method from
#> as_tibble.grouped_df dplyr
library(tsibble)
#>
#> Attaching package: 'tsibble'
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, union
library(ggplot2)
tsibbledata::aus_production %>%
autoplot(Bricks)
#> Warning: Removed 20 rows containing missing values or values outside the scale range
#> (`geom_line()`).
cal_trans_x <- function() {
scales::trans_new(
name = "calendar",
transform = ggtime:::calendar_wrap,
inverse = identity,
breaks = scales::breaks_pretty(),
domain = c(0, 60*60*24*7)
)
}
pedestrian[with(pedestrian, Sensor == "Southern Cross Station" & Date < "2015-03-01"),] |>
autoplot(Count) +
# coord_calendar(xlim = c(Sys.time(), Sys.Date() + lubridate::days(1)))
ggplot2::coord_trans(x = cal_trans_x(), xlim = as.POSIXct(c("2024-03-25 00:00:00", "2024-03-31 23:59:59"))) +
scale_x_datetime(date_breaks = "day", date_labels = "%a")
#> Warning in max(x): no non-missing arguments to max; returning -Inf
#> Warning in min.default(structure(numeric(0), class = "Date"), na.rm = FALSE):
#> no non-missing arguments to min; returning Inf
#> Warning in max.default(structure(numeric(0), class = "Date"), na.rm = FALSE):
#> no non-missing arguments to max; returning -Inf