README.md

In zhangcandrew/rdataretriever: R Interface to the Data Retriever

rdataretriever

+ (old package name)

R interface to the Data Retriever.

The Data Retriever automates the tasks of finding, downloading, and cleaning up publicly available data, and then stores them in a local database or csv files. This lets data analysts spend less time cleaning up and managing data, and more time analyzing it.

This package lets you access the Retriever using R, so that the Retriever's data handling can easily be integrated into R workflows.

Installation

rdataretriever is an R wrapper for the Python based Data Retriever. This means that Python and the retriever package need to be installed first.

Basic installation

Use this if you are new to Python or don't have a local Python installation

1. Install the Python 3.7 version of the miniconda Python distribution from https://docs.conda.io/en/latest/miniconda.html
2. In R install the reticulate package:

coffee install.packages("reticulate")

1. In R run the following to install the retriever Python package:

coffee library(reticulate) py_available(initialize = TRUE) py_install("retriever")

1. Install the rdataretriever R package:

coffee devtools::install_github("ropensci/rdataretriever")

Advanced installation

Use this if you are already familiar with Python and have a local Python installation

1. Check that your local Python installation is Python 3
2. In R install the reticulate package:

coffee install.packages("reticulate")

1. In R run the following (replacing "/path/to/python" with the path to you Python executeable) to install the retriever Python package:

coffee library(reticulate) use_python("/path/to/python") py_install("retriever")

1. Install the rdataretriever R package:

coffee devtools::install_github("ropensci/rdataretriever")

Examples

library(rdataretriever)

# List the datasets available via the Retriever
rdataretriever::datasets()

# Install the portal into csv files in your working directory
rdataretriever::install_csv('portal')

# Download the raw portal dataset files without any processing to the
# subdirectory named data
rdataretriever::download('portal', './data/')

# Install and load a dataset as a list
portal = rdataretriever::fetch('portal')
names(portal)
head(portal$species)

New Spatial data Installation

Set-up and Requirements

Tools

• PostgreSQL with PostGis, psql(client), raster2pgsql, shp2pgsql, gdal,

The rdataretriever supports installation of spatial data into Postgres DBMS.

1. Install PostgreSQL and PostGis

   To install PostgreSQL with PostGis for use with spatial data please refer to the OSGeo Postgres installation instructions.

   We recommend storing your PostgreSQL login information in a .pgpass file to avoid supplying the password every time. See the .pgpass documentation for more details.

   After installation, Make sure you have the paths to these tools added to your system's PATHS. Please consult an operating system expert for help on how to change or add the PATH variables.

   For example, this could be a sample of paths exported on Mac:

   ```shell

   ~/.bash_profile file, Postgres PATHS and tools .

   export PATH="/Applications/Postgres.app/Contents/MacOS/bin:${PATH}" export PATH="$PATH:/Applications/Postgres.app/Contents/Versions/10/bin"

   ```

2. Enable PostGIS extensions

   If you have Postgres set up, enable PostGIS extensions. This is done by using either Postgres CLI or GUI(PgAdmin) and run

   For psql CLI shell psql -d yourdatabase -c "CREATE EXTENSION postgis;" psql -d yourdatabase -c "CREATE EXTENSION postgis_topology;"

   For GUI(PgAdmin)

   sql CREATE EXTENSION postgis; CREATE EXTENSION postgis_topology For more details refer to the PostGIS docs.

Sample commands

rdataretriever::install_postgres('harvard-forest') # Vector data
rdataretriever::install_postgres('bioclim') # Raster data

# Install only the data of USGS elevation in the given extent
rdataretriever::install_postgres('usgs-elevation', list(-94.98704597353938, 39.027001800158615, -94.3599408119917, 40.69577051867074))

Using Dockers

To run the image interactively

docker-compose run --service-ports rdata /bin/bash

To run tests

docker-compose run rdata Rscript load_and_test.R

To get citation information for the rdataretriever in R use citation(package = 'rdataretriever')

Acknowledgements

A big thanks to Ben Morris for helping to develop the Data Retriever. Thanks to the rOpenSci team with special thanks to Gavin Simpson, Scott Chamberlain, and Karthik Ram who gave helpful advice and fostered the development of this R package. Development of this software was funded by the National Science Foundation as part of a CAREER award to Ethan White.

zhangcandrew/rdataretriever documentation built on May 28, 2019, 5:57 p.m.