https://github.com/DavisVaughan/warp
Group Dates
https://github.com/DavisVaughan/warp
Last synced: 5 months ago
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Group Dates
- Host: GitHub
- URL: https://github.com/DavisVaughan/warp
- Owner: DavisVaughan
- License: other
- Created: 2019-11-29T13:06:18.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2023-11-02T18:21:48.000Z (over 1 year ago)
- Last Synced: 2024-06-11T20:02:19.929Z (10 months ago)
- Language: R
- Homepage: https://davisvaughan.github.io/warp/
- Size: 847 KB
- Stars: 23
- Watchers: 4
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
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README
---
output: github_document
editor_options:
chunk_output_type: console
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# warp
[](https://github.com/DavisVaughan/warp/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/DavisVaughan/warp?branch=main)
The goal of warp is to provide tooling to group dates by a variety of periods, such as: yearly, monthly, by second, by week of the month, and more.
```{r}
library(warp)
```
## Installation
You can install the release version from CRAN with:
```r
install.package("warp")
```
You can install the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("pak")
pak::pak("DavisVaughan/warp")
```
## Example
One of the core functions in warp is `warp_distance()`, which allows you to provide a date time vector and compute the "distance" from an `origin`. For example, this computes the number of months from the unix epoch.
```{r}
x <- as.Date("1970-01-01") + -2:2
x
warp_distance(x, period = "month")
```
The values that `warp_distance()` returns correspond to the distance from `x` to the `origin`, in units defined by the `period` and the width defined by `every`. The `origin` defaults to the unix epoch of `1970-01-01 00:00:00` in the time zone of `x`, but you can change that. In this case the distances are saying that, for example, `"1970-01-02"` is in the same month as the origin, and `"1969-12-31"` is 1 month group away.
You can also compute daily distances. Rather than grouping by 1 day, let's lump every 2 days together, starting from the default `origin`.
```{r}
# Groups 1970-01-01 and 1970-01-02 together
warp_distance(x, period = "day", every = 2)
```
You will often want to set your own `origin` date. Let's shift it forward 1 to `1970-01-02`.
```{r}
origin <- as.Date("1970-01-02")
origin
# Groups 1970-01-02 and 1970-01-03 together
warp_distance(x, period = "day", every = 2, origin = origin)
```
Another interesting period to group by is the `"mweek"`, i.e. the week of the month. Notice that days 1-7 of January 1970 are grouped into the same bucket. Also note that days 29-31 of December 1969 fell at the end of their corresponding month. This irregular week of size 3 is treated as the 5th week of that month, but the offset value of `-1` is still the number of week buckets from the `origin` of `1970-01-01`.
```{r}
y <- as.Date("1969-12-28") + 0:14
tibble::tibble(
y = y,
mweek = warp_distance(y, "mweek")
)
```
## Inspiration
The algorithm for `warp_distance()` was inspired by `xts::endpoints()`.