Data in r package

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This is the 2nd post in my series on exploring R packages in which I share my findings. My habit has been to utilize one or two functions in a package without investigating other functionality. Each post will include how I was using a package integrated with a case-study that illustrates newly discovered functions. However, the EDA process could be a hassle at times.

This R package aims to automate most of data handling and visualization, so that users could focus on studying the data and extracting insights. While researching the package I was excited to discover functionality that has become core to my EDA process. For our case-study we are using data from the Tidy Tuesday Project archive.

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Read This Post to learn more about my approach to preprocessing data. Once the data is assessed, we can decide on steps that might be added to a preprocessing data pipeline. The skim function gives an incredible amount of detail to help guide data preprocessing.

This visual allows rapid assessment of features that may need to be dropped or have their values estimated via imputation.

To leave a comment for the author, please follow the link and comment on their blog: Exploring Data. Want to share your content on R-bloggers? Gains insights that help preprocess data. We can begin by removing a few columns and so lets add that step to our preprocessing.

New Insights Breakout by data-type: 20 categorical and 19 numeric features Substantial missing values within some features Many features with skewed distributions Large number of features that appear unnecessary Categorical features with large number of unique values. New Insights Most features have complete data. Many features if kept need imputation estimate and replace missing data.

Plot Quakers vs. If kept, it would be worth updating to a categorical variable. Join me on the journey. Never miss an update! Subscribe to R-bloggers to receive e-mails with the latest R posts. You will not see this message again.Many useful R function come in packages, free libraries of code written by R's active user community. To install an R package, open an R session and type at the command line.

R will download the package from CRAN, so you'll need to be connected to the internet. Once you have a package installed, you can make its contents available to use in your current R session by running. There are thousands of helpful R packages for you to use, but navigating them all can be a challenge.

To help you out, we've compiled this guide to some of the best.

Put your data in an R package

We've used each of these, and found them to be outstanding — we've even written some of them. But you don't have to take our word for it, these packages are also some of the top most downloaded R packages. DBI - The standard for for communication between R and relational database management systems. Packages that connect R to databases depend on the DBI package.

Note: RStudio professional products come with professional drivers for some of the most popular databases. Choose the package that fits your type of database.

You can also just export your spreadsheets from Excel as. Or an SPSS data set? Foreign provides functions that help you load data files from other programs into R. R can handle plain text files — no package required. Just use the functions read. If you have even more exotic data, consult the CRAN guide to data import and export.

For more information about using R with databases see db. This collection includes all the packages in this section, plus many more for data import, tidying, and visualization listed here. Packages that implement htmlwidgets include:. This collection includes rsample, parsnip, recipes, broom, and many other general and specialized packages listed here. A perfect way to explore data and share findings with non-programmers. The result? Automated reporting. Copy and paste, or pair up with R Markdown.

Useful for big data. You may also ask for help from R and RStudio users on community. Be sure to include a reproducible example of your issue. Click here to start a new community discussion. Recommended Packages Many useful R function come in packages, free libraries of code written by R's active user community.

data in r package

To install an R package, open an R session and type at the command line install. To load data DBI - The standard for for communication between R and relational database management systems. To visualize data ggplot2 - R's famous package for making beautiful graphics. Packages that implement htmlwidgets include: leaflet maps dygraphs time series DT tables diagrammeR diagrams network3D network graphs threeJS 3D scatterplots and globes.

For Spatial data spmaptools - Tools for loading and using spatial data including shapefiles. For Time Series and Financial data zoo - Provides the most popular format for saving time series objects in R.R has a number of quick, elegant ways to join data frames by a common column.

Bureau of Transportation Statistics. Or, download these two data sets — plus my R code in a single file and a PowerPoint explaining different types of data merges — here:. The mydf delay data frame only has airline information by code. One base R way to do this is with the merge function, using the basic syntax merge df1, df2.

You can also tell merge whether you want all rows, including ones without a match, or just rows that match, with the arguments all. So, all. Full code:. The new joined data frame includes a column called Description with the name of the airline based on the carrier code. A left join keeps all rows in the left data frame and only matching rows from the right data frame. Take a look at the syntax: In this case, order matters. And, because I need to join by two differently named columns, I included a by argument.

This joined data set now has a new column with the name of the airline. Here are the latest Insider stories. More Insider Sign Out. Sign In Register. Sign Out Sign In Register. Latest Insider. Check out the latest Insider stories here. More from the IDG Network. How to do spatial analysis in R with sf. R data. How to create tables in R with expandable rows. How to create drill-down graphs with highcharter in R.

The 10 Most Important Packages in R for Data Science

Sharon Machlis. Related: R Language Analytics. Get expert insights from our member-only Insider articles.By default, all packages in the search path are used, then the data subdirectory if present of the current working directory. The default value of NULL corresponds to all libraries currently known. RData or. TXT are read using read. CSV are read using read.

If more than one matching file name is found, the first on this list is used. Files with extensions. The data sets to be loaded can be specified as a set of character strings or names, or as the character vector listor as both. For each given data set, the first two types. The third and fourth types will always result in the creation of a single variable with the same name without extension as the data set.

If no data sets are specified, data lists the available data sets. It looks for a new-style data index in the Meta or, if this is not found, an old-style 00Index file in the data directory of each specified package, and uses these files to prepare a listing. If there is a data area but no index, available data files for loading are computed and included in the listing, and a warning is given: such packages are incomplete. The information about available data sets is returned in an object of class "packageIQR".

The structure of this class is experimental.

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Where the datasets have a different name from the argument that should be used to retrieve them the index will have an entry like beaver1 beavers which tells us that dataset beaver1 can be retrieved by the call data beaver. If lib. A character vector of all data sets specified whether found or notor information about all available data sets in an object of class "packageIQR" if none were specified. One can take advantage of the search order and the fact that a.

R file will change directory. If raw data are stored in mydata. R to read mydata. For instance one can convert numeric vectors to factors with the appropriate labels. Thus, the.

Quick list of useful R packages

R file can effectively contain a metadata specification for the plaintext formats. There is no requirement for data foo to create an object named foo nor to create one objectalthough it much reduces confusion if this convention is followed and it is enforced if datasets are lazy-loaded. This avoided having large datasets in memory when not in use: that need has been almost entirely superseded by lazy-loading of datasets.

The ability to specify a dataset by name without quotes is a convenience: in programming the datasets should be specified by character strings with quotes. Use of data within a function without an envir argument has the almost always undesirable side-effect of putting an object in the user's workspace and indeed, of replacing any object of that name already there. A sometimes important distinction is that the second approach places objects in the namespace but the first does not.

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So if it is important that the function sees mytable as an object from the package, it is system data and the second approach should be used. In the unusual case that a package uses a lazy-loaded dataset as a default argument to a function, that needs to be specified by ::e.

This function creates objects in the envir environment by default the user's workspace replacing any which already existed.

Created by DataCamp. Data Sets Loads specified data sets, or list the available data sets.There are three main ways to include data in your package, depending on what you want to do with it and who should be able to use it:. This is the best place to put example datasets. This is the best place to put data that your functions need.

A simple alternative to these three options is to include it in the source of your package, either creating by hand, or using dput to serialise an existing data set into R code. The most common location for package data is surprise!

data in r package

Each file in this directory should be a. RData file created by save containing a single object with the same name as the file. RData files are already fast, small and explicit. Other options are described in data.


For larger datasets, you may want to experiment with the compression setting. The default is bzip2but sometimes gzip or xz can create smaller files.

R Package Development 1: Where to Start

The following example shows memory usage before and after loading the nycflights13 package. I highly recommend taking the time to include the code used to do this in the source version of your package. This will make it easy for you to update or reproduce your version of the data.

Do all this in one step with:. You can see this approach in practice in some of my recent data packages. This means that they must be documented. Documenting data is like documenting a function with a few minor differences. R and looks something like this:. For data frames, you should include a definition list that describes each variable.

data in r package

Sometimes functions need pre-computed data tables. Beware: by default, if the file does not exist, system. But remember unit tests are for testing correctness, not performance, so keep the size small.

Data for vignettes. You should also make sure that the data has been optimally compressed:.R is the most popular language for Data Science.

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There are many packages and libraries provided for doing different tasks. For example, there is dplyr and data. Also, there is a library like 'Shiny' to create a Web application and knitr for the Report generation where finally mlr3xgboostand caret are used in Machine Learning.

Graphs with one variable, two variables, and three variables, along with both categorical and numerical data, can be built. Also, grouping can be done through symbol, size, color, etc. The interactive graphics can be made with the help of plot. It is mostly used for health care domains for genomic data and fields like business for predictive analytics. Also, the data size ranges from more than 10 GB to GB. It can also work with computational backends like dplyrsparklyrand dtplyr.

You can install dplyr through using the tidyverse package, which will come with the package dplyr. The significant amount of work mostly goes on when cleaning and tidying the data.

Basically, tidy data consists of those datasets where every cell acts as a single value, where every row is an observation, and every column is variable. The following tutorial in DataCamp provides detailed knowledge in tidyr. Cleaning Data in R. Shiny can be used to build the web application without requiring JavaScript.

It can be used together with htmlwidgets, JavaScript actions, and CSS themes to have extended features. Also, it can be used to build dashboards along with the standalone web applications. You can visit the link mentioned below to learn more about Shiny. Shiny Fundamentals with R.

data in r package

You can visit the link mentioned below to learn more about plotly. Intermediate Interactive Data Visualization with plotly in R. It was inspired by Sweave and has extended the features by adding lots of packages like a weaver, animation, cacheSweave, etc.

You can visit the link mentioned below to learn more about knitr. Reporting with R Markdown.

R Packages: A Beginner's Guide

It is also efficient, which supports Object-Oriented programming where 'R6' objects are being provided along with machine learning workflow.

It is also seen as one of the extensible frameworks for clustering, regression, classification, and survival analysis. You can visit the link mentioned below to learn more about mlr3. XGBoost is an implementation of the gradient boosting framework. It also provides an interface for R where the model in R's caret package is also present. Its speed and performance are faster than the implementation in H20, Spark, and Python. This package's primary use case is for machine learning tasks like classification, ranking problems, and regression.

You can visit the link mentioned below to learn more about XGBoost. A caret package is a short form of Classification And Regression Training used for predictive modeling where it provides the tools for the following process. You can visit the link mentioned below to learn more about caret from the author "Max Kuhn". Machine Learning with caret in R. In this tutorial, you've learned about different packages in R used for the Data Science process.

This tutorial focused on installation, loading, and finally, getting the resources to DataCamp for learning about these packages.

Log in. Learn about different packages in R used for data science.Many R packages are designed to manipulate, visualize, and model data so it may be a good idea for you to include some data in your package. The primary reason most developers include data in their package is to demonstrate how to use the functions included in the package with the included data.

Creating a package as a means to distribute data is also a method that is gaining popularity. Additionally you may want to include data that your package uses internally, but is not available to somebody who is using your package. When including data in your package consider the fact that your compressed package file should be smaller than 5MB, which is the largest package size that CRAN allows.

If your package is larger than 5MB make sure to inform users in the instructions for downloading and installing your package. Including data in your package is easy thanks to the devtools package. To include datasets in a package, first create the objects that you would like to include in your package inside of the global environment.

You can include any R object in a package, not just data frames. This way package users can use common R help syntax like? You should create one R file called data. You can write the data documentation in the data. R file. Data frames that you include in your package should follow the general schema above where the documentation page has the following attributes:.

The minimap package also includes a few vectors. You should always include a title for a description of a vector or any other object. If you need to elaborate on the details of a vector you can include a description in the documentation or a source tag.

Just like with data frames the documentation for a vector should end with a string containing the name of the object. A common task for R packages is to take raw data from files and to import them into R objects so that they can be analyzed. You might want to include some sample raw data files so you can show different methods and options for importing the data.

If you stored a data file in this directory called response. Include that line of code in the documentation to your package so that your users know how to access the raw data file. There are several packages which were created for the sole purpose of distributing data including janeaustenrgapminderbabynamesand lego. Using an R package as a means of distributing data has advantages and disadvantages.

On one hand the data is extremely easy to load into R, as a user only needs to install and load the package. This can be useful for teaching folks who are new to R and may not be familiar with importing and cleaning data. Data packages also allow you document datasets using roxygen2which provides a much cleaner and more programmer-friendly kind of code book compared to including a file that describes the data.

If you decide to create a data package you should document the process that you used to obtain, clean, and save the data. Inside of this directory you should include any raw files that the data objects in your package are derived from. You should also include one or more R scripts which import, clean, and save those data objects in your R package. Theoretically if you needed to update the data package with new data files you should be able to just run these scripts again in order to rebuild your package.

Including data in a package is useful for showing new users how to use your package, using data internally, and sharing and documenting datasets.

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You can document data within your package just like you would document a function. Mastering Software Development in R.