Instructors: Francisco Villavicencio / Tim Riffe

Session 1 (Monday)
1) Introduction to R and RStudio
2) Using the editor: main characteristics of RStudio, packages
3) Data handling: import/export data to/from R
4) Basic operations: assigning
5) Using functions

Session 2 (Tuesday)
1) Common data types
2) Data structures overview
3) Vectors and matrices
4) Data frames
5) Reshaping, sorting and grouping

Session 3 (Wednesday)
1) Descriptive statistics in R
2) Contingency tables
3) Introduction to R plotting

Session 4 (Thursday)
1) Conditional execution: the ‘if’ command
2) Introduction to for-loops
3) Writing your own functions in R

Session 5 (Friday)
1) The apply() family functions
2) Using loops and custom functions in base plotting
3) Saving plots
4) Review of module

Instructor: Francisco Villavicencio

Session 1 (Monday)
1) Review of descriptive statistics
2) The normal distribution and QQ-plots
3) The t-distribution
4) Other distributions

Session 2 (Tuesday)
1) Linear models
2) Least square estimation
3) Residuals
4) Standard errors and confidence intervals
5) Diagnostic plots

Session 3 (Wednesday)
1) Hypothesis testing
2) The t-test and p-values
3) Comparison of groups: analysis of variance (ANOVA)
4) The F-test

Session 4 (Thursday)
1) Analysis of count data: the chi-square test
2) The Poisson distribution
3) The binomial distribution
4) Logistic regression

Session 5 (Friday)
1) Maximum likelihood estimation
2) Manual optimization
3) Non-linear regression
4) Review of the module

Instructor: Marie-Pier Bergeron-Boucher

Session 1 (Monday)
1) Basic demographic measures
2) The Lexis diagram
3) Rates, probabilities and proportions

Session 2 (Tuesday)
1) Life expectancy
2) Life table calculations
3) Building a life table in R
4) The Human Mortality Database (HMD)

Session 3 (Wednesday)
1) Standardization of demographic measures
2) Rate decomposition (Kitagawa method)
3) Life expectancy decomposition (Arriaga method)

Session 4 (Thursday)
1) Review of matrix algebra
2) Matrix population models
3) The Leslie matrix

Session 5 (Friday)
1) Population forecast principles
2) The Lee-Carter model
3) Review of the module

Instructor: Tim Riffe

Session 1 (Monday)
1) Review of intro
2) Base plotting approach
3) Base plot types
4) Plot device control
5) Theory I | visual vocabulary
Session 2 (Tuesday)
1) Color specification
2) Color palettes
3) Figure layering & composition
4) Theory II | design
Session 3 (Wednesday)
1) R figures in documents & presentations
2) Panel graphics
3) Text and symbols in base plots
4) Theory III | visualization in social sciences
Session 4 (Thursday)
1) Making custom functions for plot elements
2) Geometric transformations
3) Coordinate spaces
4) Post processing figures in Inkscape
5) Participant choice topic

Session 5 (Friday)
​1) Animation​
​2) Review of module
​3) Participant project presentations

Instructor: Jonas Schöley

Session 1 (Monday): Introduction to the tidyverse
1) R programming paradigms
2) The tidy approach to data analysis
3) The `tidyverse`
4) The tidy workflow
5) Getting started with `ggplot2` and `dplyr` and `rmarkdown`
6) Basic exploratory data analysis with `dplyr` and `ggplot2`

Session 2 (Tuesday): Data wrangling
1) Tidy data
2) Data pipelines
3) Long versus wide format data
4) Data reshaping
5) Data tidying
6) Making sense of messy data (`tidyr`, `dplyr`, `ggplot2`)

Session 3 (Wednesday): Tidy iteration
1) The split-apply-combine paradigm
2) Transforming/summarising data group by group
3) Fitting and summarising many models
4) Visualizing data group by group

Session 4 (Thursday): Data Visualization
1) Visualization as a design process
2) Marks, channels and perception
3) Best practices of data viz
4) `ggplot2`: working with color
5) `ggplot2`: making your plot ready for publication

Session 5 (Friday): The grand finale

Instructor: Juan Galeano

Session 1 (Monday)
1) Basic data manipulation using dplyr
2) %>% the pipe function
3) Group your data and summarise
4) Tidy your data
5) Plot your data: ggplot2

Session 2 (Tuesday)
1) Read shapefiles into R
2) General manipulation of spatial objects.
3) Univariate Class Intervals
4) Color palettes.
5) Thematic maps (I).

Session 3 (Wednesday)
1) Conversion between projection systems.
2) The ggmap package.
3) Thematic maps (II).

Session 4 (Thursday)
1) Spatial Statistics
2) Measures of spatial segregation and population diversity: The OasisR package.
3) Neighborhood Matrix.
4) Spatial autocorrelation: Global and Local Indicators of Spatial Autocorrelation (LISA).

Session 5 (Friday)
1) Plot Raster Data.
2) Web-mapping: Leaflet and ggiraph.
3) Review of module.

fvillavicencio@health.sdu.dk
University of Southern Denmark, Odense, Denmark

Francisco Villavicencio is a post-doctoral researcher at the Department of Public Health, University of Southern Denmark. He has a background in Mathematics and Geography, and holds a PhD in Demography. His research interests include the development of methods to deal with sparse demographic data (Bayesian inference, agent-based modeling), mortality, and formal demography. In the last years he has been teaching several courses in R, statistics and mathematics at the University of Southern Denmark and at the European Doctoral School of Demography.

mpbergeron@health.sdu.dk
University of Southern Denmark, Odense, Denmark

Marie-Pier Bergeron Boucher is a postdoctoral researcher at the Department of Public Health, University of Southern Denmark. She holds a master in Demography and a PhD in Public Health. Her main research interests include the study of human mortality, longevity and ageing, with a particular interest in developing new demographic methods to help understand and forecast population health and mortality dynamics in industrialized societies.

riffe@demogr.mpg.de
Max Planck Institute for Demographic Research, Rostock, Germany

Tim Riffe is a research scientist at the Max Planck Institute for Demographic Research. His theoretical work focuses on population renewal and temporal relationships over the life course. His empirical work uses original methodological approaches to study relationships between longevity and health in ageing populations, based on both administrative and survey data.

jschoeley@health.sdu.dk
University of Southern Denmark, Odense, Denmar

Jonas Schöley is a PhD student at the Biodemography Unit of the University of Southern Denmark. He develops tools and techniques for the analysis and visualization of demographic data such as the Human Mortality Explorer, Compositional Lexis Surfaces or the tricolore R package. His PhD research is on ontogenescence -- the mortality decline in utero and in young ages.

jgaleano@ced.uab.es
Center for Demographic Studies (CED), Autonomous University of Barcelona

Juan Galeano holds a PhD in Demography from the Center for Demographic Studies (CED) and the Autonomous University of Barcelona (UAB). Master in Demography from the European Doctoral School of Demography (EDSD), Master in Territorial and Population Studies from CED and UAB, and BA in Sociology from the University of Barcelona (UB). His current research focuses on the spatial and demographic consequences of the settlement of migrants in Spain since the beginning of the XXI century, mainly increasing population diversity and residential segregation