Before you work through this walkthrough, you should make sure you've read (or at least understood) the contents of the beginner's tutorial.
In the beginner's tutorial, we showed how ProjectTemplate automatically loads data files from the data
and cache
directories. If you're working with plain text files or any of the supported binary file formats, this automatic data loading should work out of the box without any effort on your part. But if you have to retrieve data sets from more complex data sources, ProjectTemplate has advanced features that will let you set up ad hoc autoloading. In the rest of this document, we'll talk about working with SQL databases, remote resources available over HTTP and FTP, large data files stored on external drives and R files that contain code that generates data at runtime.
Let's start by working with an SQLite database. We'll use a database from the Analytics X competition in which contestants were trying to predict crimes that took place in Philadelphia. You can download the database file here.
The simplest way to access the database is to store the analyticsx.db
file in the data
directory of a new project. Let's set up a project using the standard ProjectTemplate invocation:
library('ProjectTemplate')
create.project('AnalyticsX')
Then we'll shift into the relevant directory and move our database over:
cd AnalyticsX
mv ~/Downloads/analyticsx.db data
Then we reload R and load the project. You'll see ProjectTemplate automatically load the five tables found in our SQLite database:
library('ProjectTemplate')
load.project()
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For most users, this automatic loading procedure is probably enough. But if you need more fine-grained control, you can use the .sql
ad hoc file type to load specific tables from an SQLite database. You can also specify an exact SQL query to run against the database. We'll go through all three cases below.
First, let's move the analyticsx.db
file out of the data
directory to prevent it from being autoloaded:
mv data/analyticsx.db .
After that, we'll create an .sql
file in the data
directory. We need to specify (a) that we're working with an SQLite database, (b) the path to the SQLite database and (c) the specific table we want to load:
type: sqlite
dbname: analytics.db
table: homicides
Running load.project
will then load only this table from our database.
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If we want to load all of the tables from a database file that we can't place inside of the data
directory, we can use a .sql
file to do this by replacing the name of a specific table with an asterisk:
type: sqlite
dbname: analytics.db
table: *
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You can load a subset of your data by specifying an SQL query instead of a table:
type: sqlite
dbname: analytics.db
query: SELECT * FROM homicides
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Working with a MySQL database is exactly as easy as using a .sql
file to access a SQLite database. All that changes is the use of the mysql
type instead of the sqlite
type:
type: mysql
dbname: analytics.db
table: *
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If you need to access a file that's available over HTTP or FTP, you can use a .url
file. Inside of the file, you'll specify the URL where your data set is available and the type of data set you're accessing:
url: http://www.johnmyleswhite.com/ProjectTemplate/sample_data.csv
separator: ,
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If you need to access a file that's stored outside of the project's main directory, you use a .file
file. Inside of the file, you'll specify the path of the data file and the extension of the data set you're accessing:
path: /usr/share/dict/words
extension: csv
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Sometimes you want to generate random data for your analysis: this, after all, is the heart of Monte Carlo analyses of statistical methods. You can do this by inserting R code into a file in the data
directory. We'll put this into the data/d.R
file:
set.seed(1)
d <- rnorm(1000, 0, 1)
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ProjectTemplate has been designed to make it easier to unit test the functions you've written for your analysis. To get started, you can call stub.tests()
, which will generate a file at tests/autogenerated.R
filled with sample tests for every one of the functions you defined inside of the lib
directory. You should edit these tests, as they are expected to fail by default.
After editing your tests, you can call test.project()
to run all of the unit tests in the tests
directory.
EXAMPLE
If you want to log your work, ProjectTemplate will automatically load a log4r logger object into the logger
variable that will write to a plain text stored at the logs/project.log
. To use this logger, you only need to change the configuration file to specify:
logging: on
After making this change, the logger
object will be created once you call load.project()
.
Coming soon
Coming soon