TRAINING
Course Focused on an Introduction to Statistics in R
September–November 2026
The R programming language is gaining popularity, particularly for data analysis data common statistical methods, mainly due to its power and ease of use. This course will provide students with the knowledge needed to independently perform data analysis this programming environment. We start at a very basic level, explaining thefundamentals of programming in Rand basic statistical techniques. We then address the most common scenarios in data analysis: datapresentation , hypothesis testing, and model fitting.
No prior knowledge of R is required. However,a knowledge of statistics is necessary, as the theoretical concepts that will be used are not strictly part of the course content.
The classes will be primarilyhands-on: students are expected to review the provided materials in advance, and the in-person sessions will be largely devoted to developing skill data analysis.
Thecourse is intended for doctorate students doctorate area sciences, faculty and staff at the University of Navarra and CIMA, and professionals from outside the University of Navarra.
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Introduction.Review of statistics. Programming languages. R and RStudio. Basic commands.
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R syntax. Types of variables and objects. Functions. Operators. Vectors, factors, data.frames, lists. Functions and arguments in R. RStudio environment. R packages.
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Reading and writing data:Text and binary files. Reading data using different packages. Encoding. Importing and exporting files from other applications. Connections. Reading data the internet.
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Tidy data and management of data. Split-apply-combine strategy for handling data. Pipes. Reordering of instructions from data.
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Descriptive statistics. Calculation of descriptive statistics with R.
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Graphics. Principles of Tufte's Economics . Exploratory and expository graphs. Charts with R base: main chart types and options. Adding objects to a graph. Graph annotations. Composing several graphs. Exporting graphs.
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Package ggplot2. Grammar of graphs. Multivariate representation through aesthetic qualities of the graph. Geometries. Scales. Panels. Topics.
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Programming with R. Ramifications. Conditions. Loops: logical conditions, functions.
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Inference. Point and interval estimation. The concept of confidence interval. Parametric and non-parametric hypothesis tests for different statistics. Concept of p-value. Quantile-quantile plots to examine normality. Contingency table.
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Regression and correlation. Simple linear regression. Quadratic regression. Transformation of potential and exponential relationships to linear. Residuals and diagnostics of a model. Confidence and prediction bands. Linear regression with several variables.
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One-way and two-way Anova. Diagnostics. Kruskal-Wallis and Friedman test. Interaction. Introduction to design.
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Power and sample size. The pwr package. Concepts of design experimental: confounding variables.
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Multiple and logistic regression. Model selection, stepwise procedures.
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Generalised linear models.
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Resampling methods. Bootstrap and permutations. Power calculation with simulations.
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Advanced topics.Sensitivity, specificity, and ROC curves. Calculation of AUC and equivalence with the Wilcoxon test. Introduction to survival analysis.
The course is aimed at students of doctorate of area of Sciences, professionals from the University of Navarra and CIMA, and professionals from outside the University of Navarra.
To registration the program, you must sign up at the following link, where you can apply for admission; once your application has been processed, you will be able to enrollment.
Rates:
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Ordinary: 1,200 euros
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PIFs UN: 240 euros
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UN employees: 300 euros
Once you have passed the course you will obtain a certificate issued by the University of Navarra.
Admission dates: Application deadline: August 31, 2026.
Price: 1,200 euros.
