Note that this package is a work in progress! New data will be added as soon as they become available.
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Datasets with enriched metadata
The main goal of pRycollection is to provide datasets about Paraguay for research and teaching that are not easily found or accessible. pRycollection is a combination of the 3-letter code for Paraguay - PRY - and the word collection. The 3-letter code was chosen to avoid possible confusion with the programming language Python (py).
Installation
You can install the development version of pRycollection from GitHub with:
# install.packages("pak")
pak::pak("schneiderpy/pRycollection")
# load pRycollection
library(pRycollection)About the data
The pRycollection data package was build from the beginning with FAIR principles in mind. FAIR stands for Findable, Accessible, Interoperable, and Reusable. These principles are critical to maximizing the impact and value of data in research and practice.
The raw data is hosted on Zenodo
To see what datasets are included in the package load the pRycollection data package and the dataset package (to access metadata). Then type the following code line:
data(package = "pRycollection")This will open a new tab in your source pane listing all available datasets.
Examples
This is a basic example which shows you how to use pRycollection. Let’s use the py_temperature dataset.
A summary of the chosen dataset.
summary(py_temperature)
#> Schneider (2025): Summary of Weekly mean temperature data [dataset], https://doi.org/10.5281/zenodo.16729963
#>
#> Country name
#> Country ISO code
#> Mean temperature (degrees Celsius)
#> Holiday indicator
#> rowid country ISO city
#> Length:1565 Length:1565 Length:1565 Min. :1
#> Class :character Class :character Class :character 1st Qu.:2
#> Mode :character Mode :character Mode :character Median :3
#> Mean :3
#> 3rd Qu.:4
#> Max. :5
#> week avg_temp holiday
#> Min. :2016-01-04 Min. : 9.329 Min. :0.0000
#> 1st Qu.:2017-07-03 1st Qu.:20.043 1st Qu.:0.0000
#> Median :2018-12-31 Median :24.214 Median :0.0000
#> Mean :2018-12-31 Mean :23.280 Mean :0.1885
#> 3rd Qu.:2020-06-29 3rd Qu.:26.529 3rd Qu.:0.0000
#> Max. :2021-12-27 Max. :32.000 Max. :1.0000The first six rows of the dataset …
head(py_temperature)
#> Schneider (2025): Weekly mean temperature data [dataset], https://doi.org/10.5281/zenodo.16729963
#> rowid country ISO city week avg_temp holiday
#> <defined> <defined> <defined> <defined> <dttm_dfn> <defined> <defined>
#> 1 obs:1 Paraguay PY 1 [Asuncion] 2016-01-04 27.8 0
#> 2 obs:2 Paraguay PY 1 [Asuncion] 2016-01-11 30.3 0
#> 3 obs:3 Paraguay PY 1 [Asuncion] 2016-01-18 29.9 0
#> 4 obs:4 Paraguay PY 1 [Asuncion] 2016-01-25 27.3 1
#> 5 obs:5 Paraguay PY 1 [Asuncion] 2016-02-01 26.6 0
#> 6 obs:6 Paraguay PY 1 [Asuncion] 2016-02-08 30.1 0… the dimensions of the dataset …
dim(py_temperature)
#> [1] 1565 7Citation
To cite the pRycollection package or datasets, please use:
citation("pRycollection")
#> To cite pRycollection in publications please use:
#>
#> Schneider A (2025). _pRycollection: Diverse datasets from Paraguay_.
#> R package version 0.0.1,
#> <https://github.com/schneiderpy/pRycollection/>.
#>
#> Please also cite the related data package:
#>
#> Schneider A (2025). "pRydata: Diverse datasets from Paraguay."
#> doi:10.5281/zenodo.16729963
#> <https://doi.org/10.5281/zenodo.16729963>.
#>
#> To see these entries in BibTeX format, use 'print(<citation>,
#> bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.