The wiod package is now available on CRAN. The package contains the complete WIOD data sets, in a format compatible with the decompr and gvc package. Installation The package can be installed using: {% highlight r %} install.packages(‘wiod’) # case sensitive! {% endhighlight %} Usage Following installation, the package can be loaded using: {% highlight r %} library(wiod) {% endhighlight %} Data can be loaded using the the data() function, using wiod followed by the last two digits of the required year, as the argument, e.g.

When it comes to econometrics, the main take aways from the workshops are primarily in terms of the syntax of yet another computer program. The Linear Model Then using the Ordinary Least Squares approach to solving a model, we start with the following equation of the OLS model for a univariate regression. $$ y_i = \beta_0 + \beta_1 x_1 + \epsilon $$ This can be solver for the following (hat denotes the estimator, bar denotes the mean):

A new R package diagonals is now available on CRAN. The package implements several tools for dealing with fat diagonals on matrices, such as this one: {% highlight text %} [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] 1 1 1 1 0 0 0 0 0 0 0 0 [2,] 1 1 1 1 0 0 0 0 0 0 0 0 [3,] 1 1 1 1 0 0 0 0 0 0 0 0 [4,] 1 1 1 1 0 0 0 0 0 0 0 0 [5,] 0 0 0 0 1 1 1 1 0 0 0 0 [6,] 0 0 0 0 1 1 1 1 0 0 0 0 [7,] 0 0 0 0 1 1 1 1 0 0 0 0 [8,] 0 0 0 0 1 1 1 1 0 0 0 0 [9,] 0 0 0 0 0 0 0 0 1 1 1 1 [10,] 0 0 0 0 0 0 0 0 1 1 1 1 [11,] 0 0 0 0 0 0 0 0 1 1 1 1 [12,] 0 0 0 0 0 0 0 0 1 1 1 1 {% endhighlight %}

Quick experiment on embedding plot.ly graphics. {% highlight r %} library(ggplot2) library(plotly) {% endhighlight %} Basic plotly {% highlight r %} plot_ly(iris, x = Petal.Length, y = Petal.Width, color = Species, mode = “markers”) {% endhighlight %} {% highlight text %} Error in html_screenshot(x): Please install the webshot package (if not on CRAN, try devtools::install_github(“wch/webshot”)) {% endhighlight %} Now using ggplot2 {% highlight r %} ggiris <- qplot(Petal.Width, Sepal.Length, data = iris, color = Species) ggplotly(ggiris) {% endhighlight %}

A typical example in which diagonals can be helpful is Social Network Analysis. For example, if we use matrices to represent friendship perceptions between individuals, then we need a dyadic matrix. {% highlight r %} generate a dyadic matrix for 3 individuals m <- matrix(sample(0:1, 9, replace=TRUE), nrow=3, ncol=3) m {% endhighlight %} {% highlight text %} [,1] [,2] [,3] [1,] 1 1 1 [2,] 1 1 0 [3,] 0 1 0 {% endhighlight %}