reproducible research tutorials

Basic R — Start learning R in RStudio.
Basic Python — Start learning Python with Pandas and Scikit-learn.
Basic git — Learn to use git with GitHub in RStudio.
Basic SQL — Speak to a database in its native language.
Tidy Data in R — Get your data in shape with tidyr & dplyr.
Maps with R — Tour R packages that make static and interactive maps.
Tidy Data in Python — Get your data in shape with pandas.
Plots in R — Craft publication-quality graphics with ggplot2.
Model Formulas — Write formulas for linear and GLM regression models in R
Text Mine — Carve your texts into structured data.
Data APIs in R — Acquire data from websites and APIs using R
Data APIs in Python — Acquire data from websites and APIs using Python.
RMarkdown — Extend your data pipeline with RMarkdown and Shiny.
Leaflet Maps — Make interactive maps in R using the leaflet package.
Shiny Apps — Get interactive with the Shiny R package.
Git in the Shell — Perform version control from the command line.
Advanced git — Learn advanced git techniques with GitHub and RStudio
Advanced Tidyverse — Use piped workflows for efficient data cleaning and visualization.
Spatial R Packages — Manipulate geospatial data with open source tools.
Vector Operations in R — Manipulate vector data.
Raster Operations in R — Efficiently analyze raster upon raster.
Open Source Geospatial tools — Meet the open source stack underlying geospatial data.
Raster Classification in R — Classify your remotely-sensed data.
OPeNDAP with Python — Access Land Data Assimilation System models with OPeNDAP.
Relational Databases using SQLite — Leverage relational models for organizing and querying data
Relational Databases — Make your data safe, scalable and relational.
Basic NetLogo — Build agent-based models with a simple graphical interface.
NetLogo Scripting — Implement open agent-based models.
Spatial NetLogo — Use spatial data in NetLogo ABMs.
Data Documentation & Publishing — Package your data and metadata for publication.

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