README.Rmd

---
output: github_document
---

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```{r, include = FALSE}
knitr::opts_chunk$set(
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  fig.path = "man/figures/README-",
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# HPBdata

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HPBdata aims to provide access to historical meteorological measurements at 
various temporal resolutions (1 min, 10 min, 1 day) obtained at 'Hohenpeißenberg' 
site (Bavaria, Germany). Data was acquired from the [Climate Data Center](https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate) 
hosted by the German Weather Service and has been coverted to `{xts}` objects 
for subsequent applications in need of representative meteorological measurements.

Since I found myself in the need of a meteorological measurement sample dataset 
for climatological evaluations, meteorological calculations, extreme value 
statistics, etc quite often, I decided to assemble needed data in order to 
simplify package development in the future.

## Installation

You can install the development version of HPBdata with:

``` r
# install.packages("devtools")
devtools::install_github("dimfalk/HPBdata")
```

and load the package via

```{r}
library(HPBdata)
```

## Getting started

There is not really much to say about the use. HPBdata is a data-only package 
without any functions provided consisting of three objects: 

`obs_1min`, `obs_10min` and `obs_1d`.

These are named lists of different length containing xts objects of different 
length at specified interval width for various meteorological parameters.

For details, it is strongly encouraged to study the dataset documentation 
provided, e.g. `?obs_10min`, and the official dataset description linked there.

```{r example}
# inspect superordinate list
class(obs_10min)

length(obs_10min)

names(obs_10min)

# inspect xts objects contained
class(obs_10min[["TT_10"]])

length(obs_10min[["TT_10"]])
```

As a little remark worth noting, the xts objects contained are provided with 
some additional metadata appended in form of attributes based on a data model 
implemented in `{timeseriesIO}` to ensure I/O compatibility: 

```{r}
attributes(obs_10min[["TT_10"]])[5:22]
```

Other than that, you can now just leverage all the sweet xts functionality:

```{r, warning = FALSE}
library(xts)

# subsetting
x <- obs_10min[["TT_10"]]["2022"]
x

# aggregating
xts::apply.monthly(x, "mean", na.rm = TRUE) |> round(1)

# plotting
plot(x, ylab = "air temperature at 2 m [°C]", col = "red", main = "02290 Hohenpeißenberg")
```