# Creating Vectors

How to create vectors in Julia and R? The following table translates the most common Julia commands into R language.

Continue reading Julia-R Cheatsheet – Creating Vectors

How to create vectors in Julia and R? The following table translates the most common Julia commands into R language.

Continue reading Julia-R Cheatsheet – Creating Vectors

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The package ‘Seasonal’ facilitates seasonal adjustment in R. The R package provides an easy-to-handle wrapper around the X-13ARIMA-SEATS Fortran libraries provided by the US Census Bureau. X-13ARIMA-SEATS is the state-of-the-art seasonal adjustment software produced, distributed, and maintained by the Census Bureau. Continue reading Seasonal Adjustment in R

Learning a new programming language is costly. Usually it takes a considerable amount of time to get acquainted with a new language. Especially the first phase can be painful and frustrating. The good thing is that with enough time and effort most of us will learn how to master a programming language eventually. However, note that, once we are comfortable with one language, we hardly want to change again. It turns out that the cost of abandoning on programming language and switch to another are even higher than at the beginning. Knowing this, we really want to make sure not to invest in the wrong language. There might be nothing worse than after finally mastering a programming language, recognizing that there is no use for this language anymore. While in a former post I highlighted reason why to use R, I concentrate on the Pros and Cons of R in this post.

There exists several reasons why one should start using R. During the last decade R has become the leading tool for statistics, data analysis, and machine learning. By now, R represents a viable alternative to traditional statistical programs such as Stata, SPSS, SAS, and Matlab. The reasons for R’s success are manifold. Continue reading Why R?

Seasonal adjustment refers to a statistical technique that tries to quantify and remove the influences of predictable seasonal patterns to reveal nonseasonal changes in data that would otherwise be overshadowed by the seasonal differences. Seasonal adjustments provide a Continue reading Seasonal adjustment

The easiest way to compute clustered standard errors in R is the modified ` summary()`

. I added an additional parameter, called `cluster,`

to the conventional ` summary() `

function. This parameter allows to specify a variable that defines the group / cluster in your data. The summary output will return clustered standard errors. Here is the syntax:

` summary(lm.object, cluster=c("variable")) `

Continue reading Clustered Standard Errors in R

The following R script creates an example dataset to illustrate the application of clustered standard errors. You can download the dataset here.

The script creates a dataset with a specific number of student test results. Individual students are identified via the variable ` student_id `

. The variable ` id_score `

comprises a student’s test score. In the test, students can score from 1 to 10 with 10 being the highest score possible. Continue reading Example data – Clustered Standard Errors

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