# Using the Data Retriever from R¶

## rdataretriever¶

The Data Retriever provides an R interface to the Data Retriever so that the retriever’s data handling can easily be integrated into R workflows.

## Installation¶

To use the R package rdataretriever, you first need to install the retriever.

The rdataretriever can then be installed using install.packages("rdataretriever")

To install the development version, use devtools

# install.packages("devtools")
library(devtools)
install_github("ropensci/rdataretriever")


Note: The R package takes advantage of the Data Retriever’s command line interface, which must be available in the path. This path is given to the rdataretriever using the function use_RetrieverPath(). The location of retriever is dependent on the Python installation (Python.exe, Anaconda, Miniconda), the operating system and the presence of virtual environments in the system. The following instances exemplify this reliance and how to find retriever’s path.

### Ubuntu OS with default Python:¶

If retriever is installed in default Python, it can be found out in the system with the help of which command in the terminal. For example:

$which retriever /home/<system_name>/.local/bin/retriever  The path to be given as input to use_RetrieverPath() function is /home/<system_name>/.local/bin/ as shown below: library(rdataretriever) use_RetrieverPath("/home/<system_name>/.local/bin/")  The which command in the terminal finds the location of retriever including the name of the program, but the path required by the function is the directory that contains retriever. Therefore, the retriever needs to be removed from the path before using it. ### Ubuntu OS with Anaconda environment:¶ When retriever is installed in an virtual environment, the user can track its location only when that particular environment is activated. To illustrate, assume the virtual environment is py27: $ conda activate py27
(py27) $which retriever /home/<system_name>/anaconda2/envs/py27/bin/retriever  This path can be used for rdataretriever after removing retriever as follows: library(rdataretriever) use_RetrieverPath("/home/<system_name>/anaconda2/envs/py27/bin/")  Note: rdataretriever will be able to locate retriever even if the virtual environment is deactivated. ## rdataretriever functions:¶ ### datasets()¶ Description : The function returns a list of available datasets. Arguments : No arguments needed. Example : rdataretriever::datasets()  ### fetch()¶ Description : Each datafile in a given dataset is downloaded to a temporary directory and then imported as a data.frame as a member of a named list. Arguments : • dataset (String): Name of dataset to be downloaded • quiet (Bool): The argument decides if warnings need to be displayed (TRUE/FALSE) • data_name (String): Name assigned to dataset once it is downloaded Example : rdataretriever :: fetch(dataset = 'portal')  ### download()¶ Description : Used to download datasets directly without cleaning them and when user does not have a specific preference for the format of the data and the kind of database. Arguments : • dataset (String): Name of the dataset to be downloaded. • path (String): Specify dataset download path. • quiet (Bool): Setting TRUE minimizes the console output. • sub_dir (Bool): Setting TRUE keeps the subdirectories for archived files. • debug (Bool): Setting TRUE helps in debugging in case of errors. Example : rdataretriever :: download("iris","/Users/username/Desktop")  ### Installation functions¶ #### Format specific installation¶ Description : rdataretriever supports installation of datasets in three file formats through different functions: • csv (install_csv) • json (install_json) • xml (install_xml) Arguments : These functions require same arguments. • dataset (String): Name of the dataset to install. • table_name (String): Specify the table name to install. • debug (Bool): Setting TRUE helps in debugging in case of errors. • use_cache (Bool): Setting FALSE reinstalls scripts even if they are already installed. Example : rdataretriever :: install_csv("bird-size",table_name = "Bird_Size",debug = TRUE)  #### Database specific installation¶ Description : rdataretriever supports installation of datasets in four different databses through different functions: • MySQL (install_mysql) • PostgreSQL (install_postgres) • SQLite (install_sqlite) • MSAccess (install_msaccess) Arguments for PostgreSQL and MySQL : • database_name (String): Specify database name. • debug (Bool): Setting True helps in debugging in case of errors. • host (String): Specify host name for database. • password (String): Specify password for database. • port (Int): Specify the port number for installation. • quiet (Bool): Setting True minimizes the console output. • table_name (String): Specify the table name to install. • use_cache (Bool): Setting False reinstalls scripts even if they are already installed. • user (String): Specify the username. Example : rdataretriever :: install_postgres(dataset = 'portal', user='postgres', password='abcdef')  Arguments for MSAccess and SQLite : • file (String): Enter file_name for database. • table_name (String): Specify the table name to install. • debug (Bool): Setting True helps in debugging in case of errors. • use_cache (Bool): Setting False reinstalls scripts even if they are already installed. Example : rdataretriever :: install_sqlite(dataset = 'iris', file = 'sqlite.db',debug=FALSE, use_cache=TRUE)  ### get_updates()¶ Description : This function will check if the version of the retriever’s scripts in your local directory ‘ ~/.retriever/scripts/’ is up-to-date with the most recent official retriever release. Example : rdataretriever :: get_updates()  ### reset()¶ Description : The function will Reset the components of rdataretriever using scope [ all, scripts, data, connection] Arguments : • scope : Specifies what components to reset. Options include: ’scripts’, ’data’, ’connection’ and ’all’, where ’all’ is the default setting that resets all components. Example : rdataretriever :: reset(scope = 'data')  ## Examples¶ library(rdataretriever) # List the datasets available via the retriever rdataretriever::datasets() # Install the Gentry forest transects dataset into csv files in your working directory rdataretriever::install('gentry-forest-transects', 'csv') # Download the raw Gentry dataset files without any processing to the # subdirectory named data rdataretriever::download('gentry-forest-transects', './data/') # Install and load a dataset as a list Gentry = rdataretriever::fetch('gentry-forest-transects') names(gentry-forest-transects) head(gentry-forest-transects$counts)


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