Create R Package

In this tutorial, we will show you how this can be done. The htmlwidgets package provides a framework for creating R bindings to JavaScript libraries. frame or CSV file in R, the data must all fit in memory. The standard online documents associated with R tend to be reference manuals targeting someone who already knows quite a bit. A package is a group of classes that belong together. Here is an example. If the version of R under which the package was compiled does not match your installed version of R you will get the message above. Call a function from an open-source R package and return the results to TIBCO Enterprise Runtime for R (see REvaluate)). After writing the DESCRIPTION file, I did not want to do this ever again. Package Penguin was founded by the idea that not one-size-fits-all for care packages. Analyses can either be created using R, or, using Q's own internal calculation engine, which is designed for producing tables from survey data (i. If it’s awesome, submit it to CRAN. Here is a step by step guide to this. You will get two attempts at each quiz and your best score for each quiz will count toward your final score. It may be difficult to find another package that will do what shiny does, i'd suggest just coding it in javascript, html and css. To create packages: To create a Package Diagram, select Diagram > New from the toolbar. Warning message: package ‘ncdf’ was built under R version 3. I use the following directory/file structure in my package: \examples \man \R DESCRIPTION LICENSE The bold are the directories while the DESCRIPTION and LICENSE files are required files. The Bioconductor repository has over 1000 packages, which are focused specifically on bioinformatics related. The goal of the article to set. Without packages, the entire universe of Java classes would be a huge, unorganized mess. This is a package in the recommended list, if you downloaded the binary when installing R, most likely it is included with the base package. How to Create a Python Module. There are packages that enables a direct connection between the an open-source database and R. A much earlier version (2. These are the lowest-level tools for managing Python packages and are recommended if higher-level tools do not suit your needs. Learn Building R Packages from Johns Hopkins University. RStudio automatically creates several subdirectories and files. To find out more about a package once you’ve installed it, type help. Introduction. If you are going to create code and put it into production, it's always a good idea to create a package with version control, examples, and other features. Released by Marek Hlavac on March. Simpler R coding with pipes > the present and future of the magrittr package Share Tweet Subscribe This is a guest post by Stefan Milton , the author of the magrittr package which introduces the %>% operator to R programming. To put your package on GitHub, you’ll need to get a GitHub account. Stu- dents that are not familiar with command line operations may feel intimidated by the way a user interacts with R, but this tutorial series should alleviate these. R has extensive facilities for analyzing time series data. Hopefully this recollection of random tips, tricks, and my thoughts of my package building experience can be helpful to would-be novice package developers in the future. The lubridate package for the R statistical computing environment was designed to help us deal with these kinds of data. Through the power of the R-ArcGIS Bridge, you can easily transfer data from ArcGIS to R to gain access to the wealth. You can do this very quickly by summarizing the attributes with data visualizations. One way to output a csv from R is with the command write. The areas in bold indicate new text that was added to the previous example. GSP's guide to netCDF format data and the 'R' package 'ncdf'. The contents are at a very approachable level throughout. Contributing to the continuously evolving open source community. The different ways to connect R and Excel has been already discussed in our previous article [R Excel essentials : Read, write and format Excel files using R]. In my last tutorial I’ve created the heapmap in Tableau. Installation. Promote best practices. The __init__. 'timevis' includes an extensive API to manipulate a timeline after creation, and supports getting data out of the visualization into R. Another common use of. The reason for this, is that data visualization is so critical to almost every part of getting things done as a data scientist: reporting, analysis, exploratory analysis (e. Install R package rmeta via CRAN. R has extensive facilities for analyzing time series data. Use the plumber R package to create HTTP APIs in R. Most hypertext documents on the web are written in HTML. First, note that in general, SCF staff will install an R package on the system on consult [at] stat [dot] berkeley [dot] edu (request). The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. 0 or later). Creating a package. Packages are the fundamental units of reproducible R code. The aim of this R tutorial is to show you how to easily and quickly export outputs (including data table, plots, paragraphs of text and R scripts) from R statistical software to a Microsoft Word document (. In addition to purrr, which provides very consistent and natural methods for iterating on R objects, there are two additional tidyverse packages that help with general programming challenges: magrittr provides the pipe, %>% used throughout the tidyverse. Creating a personal R package library on Enigma. It will show you how to add the necessary files and structure to create the package, how to build the package, and how to upload it to the Python Package Index. Edit the help File •Step 5. To customise your site, create _pkgdown. Create the folds given the features matrix Arguments features is the features matrix that has to be divided in folds for performing cross validation. In order to do this you need to create a personal R package library. If you’ve just used usethis to create a new package or project, that will be the active project. Therefore, we aim to promote efficient practices by package developers and write several implementations of our own to push the capabilities of statistical computing in R. rJava is a simple R-to-Java interface. Let's create two vectors, and then fit a linear model:. How to Create a Data Frame from Scratch in R. For example, if you are usually working with data frames, probably you will have heard about dplyr or data. 8, and chapters 7 and 8. R is a free software environment for statistical computing and graphics. yml and modify it as described in the documentation. soil type. let R_wait_reply = 5 ----- *R_nvim_wd* 6. General problem installing any R package. An R package includes a set of functions and datasets. For example, in using R to manage grades for a course, 'total score' for homework may be calculated by summing scores over 5 homework assignments. Stu- dents that are not familiar with command line operations may feel intimidated by the way a user interacts with R, but this tutorial series should alleviate these. The Bible -- Writing R Extensions is the most comprehensive reference for R package development. TO LEARN MORE. The psych package includes about 350 additional functions that I have created in the last 9 years. The new R package, manhattanly, creates interactive manhattan plots using the plotly. o" to create the. Plus, with RStudio, it is easy to do. Demo: phyloseq – A Bioconductor package for handling and analysis of high-throughput phylogenetic sequence data. packages("rmarkdown") R Markdown files are the source code for rich, reproducible documents. DESCRIPTION file. skeleton() creates a directory tree which is, well, a package skeleton, including the data which appears as. let R_wait_reply = 5 ----- *R_nvim_wd* 6. Create the Heatmap. py file is necessary because with this file, Python will know that this directory is a Python package directory other than an ordinary directory (or folder – whatever you want to call it). Learn Building R Packages from Johns Hopkins University. Developers only need to write very little code to make it work. Its logic is loosely modeled after base R graphics, but in three dimensions rather than two. How to create Radar Charts in R with Plotly. Caret Package is a comprehensive framework for building machine learning models in R. into the create_sunset_cal() function , and it will output a. Slidify uses a modular design which encourages a plug and play infrastructure. io Find an R package R language docs Run R in your browser R Notebooks R Package Documentation A comprehensive index of R packages and documentation from CRAN, Bioconductor, GitHub and R-Forge. You can use RStudio: Click File | New Project. Registering your package allows others to install it with a short name, like bower install. This section describes the creation of a time series, seasonal decomposition, modeling with exponential and ARIMA models, and forecasting with the forecast package. That is what the new package is all about. ) Examine the Contents file and give keywords that match those found in the KEYWORDS. packages("rmarkdown") R Markdown files are the source code for rich, reproducible documents. You could include the full price of the service within the package, or offer the service at a discount. The list shows that the package r-base is installed and r is listed in the build string of the other R packages in. table, two of the most popular R packages. I’ll also walk through the process of creating a data package in R and some of the vignettes for the package that have been created by my students and others from throughout the world. For example, if you are usually working with data frames, probably you will have heard about dplyr or data. knitr will run each chunk of R code in the document and append the results of the code to the document next to the. However, creating an R package in Unix is not hard, it IS in Windows, as R is designed in a Unix environment which includes a set of compilers, programming utilities, and text-formatting routines while Windows lacks those. Build and/or document how to use packages such as: dplyr, DBI, odbc, keyring and pool. Leaflet for R - Introduction is great package for generating maps. Once you’ve come up with a name, there are two ways to create the package. If you’re using Windows, you’ll want to install Rtools. Create the folds given the features matrix Arguments features is the features matrix that has to be divided in folds for performing cross validation. So, I used this opportunity to create my personal R package and put this in as the first function. Take a moment to ensure that it is installed, and that we have attached the ggplot2 package. This article describes how to install R packages offline by using miniCRAN to create a local repository of packages and dependencies. Select creating R package and name the package, for example, as mytoolbox and place it in the directory of our choice. A quick but detailed demo on creating your own R Package using RStudio 3. Check it out! So, briefly, developing R packages is done by running:. Use best-in-class packages. The caret package in R is designed to streamline the process of applied machine learning. Primitive functions are only found in the base package, and since they operate at a low level, they can be more efficient (primitive replacement functions don’t have to make copies), and can have different rules for argument matching (e. Getting Started If you’re new to R, like myself, R is a programming language for statistical data analysis. Open a terminal window. 8, and chapters 7 and 8. If we look down at the Console in the left pane, we can see what our current working directory is. Otherwise, usethis verifies that current working directory is or is below a valid project directory and that becomes the active project. 4 Creating new variables in R Many research studies involve some data management before the data are ready for statistical analysis. lm() is a linear model function, such like linear regression analysis. You can set up Plotly to work in online or offline mode. In the introduction to support vector machine classifier article, we learned about the key aspects as well as the mathematical foundation behind SVM classifier. js' Timeline module and the 'htmlwidgets' R package. As of version 2. When accessing data stored in a data. For example, in using R to manage grades for a course, 'total score' for homework may be calculated by summing scores over 5 homework assignments. pkgKitten provides a simple alternative to the R function package. R packages contain a variety of resources including R code, native (C/C++/Fortran) code, datasets, documentation, as well as other supporting files. urbnmapr is the first R package developed by and for Urban staff. Caret Package is a comprehensive framework for building machine learning models in R. If a Pandas DataFrame is provided, the index/column information will be used to label the columns and rows. TO LEARN MORE. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. py file is necessary because with this file, Python will know that this directory is a Python package directory other than an ordinary directory (or folder - whatever you want to call it). When install. A while back, I started to create an R package that would help me and my collegues at STATWORX with our daily work. However, you may want to install a non-CRAN/Bioconductor package or perhaps an updated version of a package on CRAN/Bioconductor. The objective of this section is: Create an R package that contains data (and associated documentation) 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. Kolaczyk and Gábor Csárdi’s, Statistical Analysis of Network Data with R (2014). 5 Data Within a Package. venv will usually install the most recent version of Python that you have available. Knn classifier implementation in R with caret package. Creating a new R package. skeleton automates some of the setup for a new source package. To create a runtime-only package (a package that cannot be installed), select the Runtime only radio button. To register a new package: The package name must adhere to the bower. For instance the MASS package was developed by Venables and Ripley for their book Modern Applied Statistics with S and the car package was developed by John Fox for his book An R and S plus Companion to Applied Regression. gz file from the package website). My favourite R package for: summarising data - Dabbling with Data says: January 2, 2018 at 9:00 am […] on the heels of delving into the world of R frequency table tools, it's now time to expand the scope and think about data summary functions in general. This implies that when you want to install a new pacakge, you need administrative access to this directory. Then I discovered the superheat package, which attracted me because of the side plots. 2 caret: Building Predictive Models in R The package contains functionality useful in the beginning stages of a project (e. I recently reached that point and wanted to learn how to build my own R package – as simple as possible. To create your own packages look at Writing R Extensions (the definitive guide), Leisch's Creating R Packages: A Tutorial, and Rossi's Making R packages Under Windows: A Tutorial. A line or two of R code is all it takes to produce a D3 graphic or Leaflet map. For example, in using R to manage grades for a course, 'total score' for homework may be calculated by summing scores over 5 homework assignments. R Tutorial: Data. you can end up with a collection of many packages. This file will passed dynamically to the Excel Destination connection in the Data Flow, to save the output. R packages contain a variety of resources including R code, native (C/C++/Fortran) code, datasets, documentation, as well as other supporting files. Creating a Simple package The following article uses options that are available starting with the Freeware edition and project type. (What does this new package do? Find out here. Installation. This section deals with the basic structures R uses to store data and how to assemble them, as well as how to get data into and out of R. gz that you can then install (so that you can load it and use it within R) or give to a friend to install. Warning in install. However, creating an R package in Unix is not hard, it IS in Windows, as R is designed in a Unix environment which includes a set of compilers, programming utilities, and text-formatting routines while Windows lacks those. The rmarkdown package will call the knitr package. , switch and call). RStudio automatically creates several subdirectories and files. Writing an R package from scratch Step 1: Create your package directory. The OfficeR package allows you to access and manipulate Word documents and PowerPoint presentations, while rvg allows us to insert the R visualizations as Office objects that we can manipulate from the document. Use stuff from here: Getting Started page on the old math wiki. This book is written in a way that most novice R users (i. A package is basically multiple objects grouped together, to form a single object. 1 Put all R functions to bundle in a le 2 We then structure the pieces into the form of an R package 3 Build an R package and packs everything into an archive le 4 Install the zipped archive le to use the functions Yujin Chung Lec14: R Packages Fall 2016 20/33. •Check the R package using R CMD check pkgName. Edit the help File •Step 5. Based on the 'vis. rda files in the data/ directory. If you've just used usethis to create a new package or project, that will be the active project. For example I use R CMD build cum. Each example builds on the previous one. This article describes how to install R packages offline by using miniCRAN to create a local repository of packages and dependencies. One way to output a csv from R is with the command write. With the powerful rmarkdown package, we could easily create nice HTML document by adding some meta information in the header, for example. py file is necessary because with this file, Python will know that this directory is a Python package directory other than an ordinary directory (or folder – whatever you want to call it). table, two of the most popular R packages. Chambers, Springer, 1998), in particular sections 1. R-devel: R-devel, to be R-3. Open the environment with the R package using the Open with Jupyter Notebook option. miniCRAN identifies and downloads packages and dependencies into a single folder that you copy to other computers for offline R package installation. The plots are usable from the R console, the RStudio viewer pane, R Markdown documents, in Shiny apps, embeddable in websites and can be exported as. Writing R Extensions is the exhaustive, canonical reference for writing R packages, maintained by the R core developers. The package names in the table are clickable if you want more information. Warning message: package ‘ncdf’ was built under R version 3. They include reusable R functions, the documentation that describes how to use them, and sample data. This website is the main channel to provide support in this area. This section describes the creation of a time series, seasonal decomposition, modeling with exponential and ARIMA models, and forecasting with the forecast package. Select creating R package and name the package, for example, as mytoolbox and place it in the directory of our choice. I would like to create a local R package repository such that users in my company can install packages from it and the system admins can update the local repo periodically. While the R FAQ offer guidelines, some users may prefer to simply run a command in order to upgrade their R to the latest version. Each version of Python on your system has it’s own set of packages and reticulate will automatically find a version of Python that contains the first package that you import from R. This post is part of the "Guide to Creating Your Own R-Based Macro" series. You will learn about the structure of R packages, set up a package, and write a function and include it in your package. The rgl R package is needed for rglplot to work. If you currently use RODBC to connect to Oracle, you’ll like ODBC more!. Let's create two vectors, and then fit a linear model:. gganimate is an extension of the ggplot2 package for creating animated ggplots. (2005) and Eugster et al (2008). Create a directory for the package. To create your own packages look at Writing R Extensions (the definitive guide), Leisch's Creating R Packages: A Tutorial, and Rossi's Making R packages Under Windows: A Tutorial. by Andrie deVries One of the reasons that R is so popular is the CRAN archive of useful packages. R Packages written by Hadley Wickham (a prolific R package developer and creator of devtools). class: center, middle, inverse, title-slide # Everything you need to know. But, writing codes for plotting graphs in R time & again can get very tiring. Writing good code for data science is only part of the job. devtools has a slew of functions aimed at helping you create your own R packages, such as automatically running all example code in your help files to make sure everything works. Beyond efficiency, and repeatability, programmatic access enables you to do things that just aren't possible with point and click. The package sf tries to fill this gap, and aims at succeeding sp in the long term. create R package include Fortran source code. Open a terminal window. What is a Package. New to Plotly? Plotly's R library is free and open source! Get started by downloading the client and reading the primer. We use cookies to offer you a better experience, personalize content, tailor advertising, provide social media features, and better understand the use of our services. The R Base Package Documentation for package 'base' version 4. 2 Documents to read 1. Chapter 1 Introduction. Analyses can either be created using R, or, using Q's own internal calculation engine, which is designed for producing tables from survey data (i. How to create an API wrapper package? I stumbled across this tutorial about how to build an API wrapper package in R, but I am having trouble because for my use case I need to use a token when making calls to the API. General problem installing any R package. The contents are at a very approachable level throughout. Knn classifier implementation in R with caret package. When you create a package, the Create Package process creates a ConflictingProcessList. 'timevis' includes an extensive API to manipulate a timeline after creation, and supports getting data out of the visualization into R. OTHER USEFUL PACKAGES. In the case of Sankey diagrams, the trick is to get the data into the tidy data format. Introduction to the data. R) to the foo. Even if you don't. As of version 2. venv will usually install the most recent version of Python that you have available. After adding a new function (foo. The Package Diagram also serves as a catalog for you to jump to the diagram that you want to look at. Hello stevenworthington, I am not used to R, i am unix administrator. lm() is a linear model function, such like linear regression analysis. Use proj_get() or proj_sitrep() to manually query the project and read more in the docs. Simulating The Network. So, I used this opportunity to create my personal R package and put this in as the first function. DESCRIPTION file. Use a productive notebook interface to weave together narrative text and code to produce elegantly formatted output. R packages are an ideal way to package and distribute R code and data for re-use by others. The sparklyr package provides a complete dplyr backend. gganimate is an extension of the ggplot2 package for creating animated ggplots. Packages are the fundamental units of reproducible R code. The R Programming Language (R Core Team 2013) has become one of the most dominant programming languages for data analysis and visualization in recent years. Take a moment to ensure that it is installed, and that we have attached the ggplot2 package. Using C++, calling C++ from R, and Creating R packages A standard approach to implementing computationally-intensive statistical analyses and methods (including in R packages) is to code the intensive portion of the computation in C++ and call this from R. If you find yourself often repeating the same scripts in R, you might come to the point where you want to turn them into reusable functions and create your own R package. This tutorial will look at the open source statistical software package R. Then I discovered the superheat package, which attracted me because of the side plots. RStudio automatically creates several subdirectories and files. Create the folds given the features matrix Arguments features is the features matrix that has to be divided in folds for performing cross validation. Stu- dents that are not familiar with command line operations may feel intimidated by the way a user interacts with R, but this tutorial series should alleviate these. You will then need to create the package projects together with the EPP team. But relatively few have created packages themselves. This page include a list of packages that people contributed : Contributed Packages This doc from RSTUDIO describes. Analyses can either be created using R, or, using Q's own internal calculation engine, which is designed for producing tables from survey data (i. New to Plotly? Plotly's R library is free and open source! Get started by downloading the client and reading the primer. py in order for Python to consider it as a package. We will use dplyr to read and manipulate Fisher’s Iris multivariate data set in this tutorial. Open R studio or R Package (CRAN) & install ggplot2 library first. Create a Data Frame from All Combinations of Factor Variables: expm1:. To find out more about a package once you’ve installed it, type help. This section deals with the basic structures R uses to store data and how to assemble them, as well as how to get data into and out of R. Here I’ve used the same data downloaded from Kaggle. Which eliminates pretty much all reasons for procrastination. skeleton automates some of the setup for a new source package. R) to create the foo. To create a runtime-only package (a package that cannot be installed), select the Runtime only radio button. The R Programming Language (R Core Team 2013) has become one of the most dominant programming languages for data analysis and visualization in recent years. The installation of R on Enigma and Usher comes installed with all of the packages froom CRAN and Bioconductor. The table below show my favorite go-to R packages for data import, wrangling, visualization and analysis — plus a few miscellaneous tasks tossed in. The current version is 3. Call a function from an open-source R package and return the results to TIBCO Enterprise Runtime for R (see REvaluate)). Build an R Package -- Step 6. Conscious uncoupling devtools started off as a lean-and-mean package to facilitate local package development, but over the years it accumulated more and more functionality. This article describes how to create animation in R using the gganimate R package. R package, show how to document the code properly and nally discuss the tools for package validation and distribution. General problem installing any R package. R Packages written by Hadley Wickham (a prolific R package developer and creator of devtools). This article describes how to install R packages offline by using miniCRAN to create a local repository of packages and dependencies. Most travel packages include a lodging component and an activity component, but don't forget the in-house add-ons. Creating R Packages Rory Winston Outline Basics Creating a Simple Package Interfacing With Native Code Creating R Packages Rory Winston February 17, 2011 Rory Winston Melbourne R User Group. Read the “R. devtools has undergone a conscious uncoupling to. Graphics in R Powerful environment for visualizing scienti c data Integrated graphics and statistics infrastructure Publication quality graphics Fully programmable Highly reproducible Full LATEX Link& Sweave support Vast number of R packages with graphics utilities Graphics and Data Visualization in R Overview Slide 4/121. Run the SE80 T-code in the R/3 system. It is comparable to the. Once installed, go to the Package menu in R and select all the packages, and hit Enter. The first two approaches shown here use packages that depend on the sp package, which defines a set of spatial classes that have become the de facto standard spatial data types in R. This page tells you how to create a package, with a focus on the most common kind of package, library packages. The R software is free and runs on all common operating systems. If the version of R under which the package was compiled does not match your installed version of R you will get the message above. If you’ve just used usethis to create a new package or project, that will be the active project. However it is really too detailed for most of what we. 1 and roxygen2. R has extensive facilities for analyzing time series data. In this tutorial, we will look at the process of creating your own R package. For information on the R Commander GUI, see John Fox, Using the R Commander (Chapman & Hall/CRC Press, 2017) and the introductory manual distributed with the package (accessible via the Help -> Introduction to the R. How to create transport package in R/3?---Steps for Creating Package: 1. Whenever one wishes to be specific about where the symbol should be looked for (which should be most of the time), it possible to wrap R packages in Python namespace objects (see R packages). 1 and roxygen2 package. Here is an example. What is a Package. Today we’ll be seeing to create the Heatmap in R. Released by Marek Hlavac on March. So if you need a package quickly or on a one-time basis, or if the package is particularly specialized, you might install it locally. The new odbc package solves that problem by providing a DBI backend to any ODBC driver connection. It features an easy-to-use package system that allows code to easily be shared. Use the plumber R package to create HTTP APIs in R. This article describes how to install R packages offline by using miniCRAN to create a local repository of packages and dependencies. The htmlwidgets package provides a framework for creating R bindings to JavaScript libraries. OTHER USEFUL PACKAGES. This was an extremely simple version of making a package. table package in R Revised: October 2, 2014 (A later revision may be available on thehomepage) Introduction This vignette is aimed at those who are already familiar with creating and subsetting data. packages() inside an R session is the simplest way to ensure that all the packages in your local R library are up to date. Any Python package you install from PyPI or Conda can be used from R with reticulate. Building, Testing, and Distributing Packages Build and Reload. Open a terminal window. You have to invoke the "Create App Package" wizard to build an app package. The official reference documentation, Writing R Extensions, can be daunting, but creating a package is actually easier than you might think. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: