GIS Fundamentals – (Map visualization – II)

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This workshop will further develop the material covered on mapping in the first workshop a few weeks back. We will focus on basic cartography principles for map-making and explore the functionalities of R and QGIS for making production-quality bi-variate static and dynamic choropleth maps. We will also explore the functionalities of leaflet, a powerful library, to create web maps and add extra information about more than two variables.

Participants should have some familiarity with R, but exposure to QGIS is not required.

GIS Fundamentals – (PostGIS-II)

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This workshop will further develop the material covered on spatial databases in the first workshop a few weeks back. PostGIS is the most widely used spatial database and is built on top of PostgreSQL, a powerful open source relational database. The focus of this workshop is to exploit PostGIS to deal with big vector data, and use familiar tools in R to reduce the burden to learn SQL.

This is a hands-on workshop. Please make sure that you have PostgreSQL installed along with pgAdmin.

Participants should be familiar with vector data processing in R.

GIS Fundamentals – VI (Map visualization)

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This is the sixth workshop in a series of workshops we are offering this semester on the fundamentals of GIS. Each workshop covers one or two key elements of GIS and is self-contained. The focus is on conceptual details that can provide sufficient preparation for applications, but we will also touch upon the technical aspects.

This workshop will cover basic concepts and tools available in QGIS and R for visualizing vector spatial data as single and bi-variate static choropleth maps. We will focus on basic cartography principles for map-making and explore the functionalities of R and QGIS for making production-quality maps.

Participants should have some familiarity with R, but exposure to QGIS is not required.

Geostatistics – II

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In many situations, we observe a process over a limited number of locations and are interested in estimating its values at nearby unsampled locations. Geostatistical methods provide a powerful tool to do this. In this session, we will build upon the material and intuitions covered in the first workshop. We will focus on the principles, application, and interpretation of results using R, and will cover topics such as variogram modeling, empirical variogram, estimation techniques, and prediction uncertainty.

You should be familiar with R and the basic concepts of GIS data, should have taken an introductory course in statistics, and know the basics of linear algebra.

Mapping tools for COVID-19 data – II

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This is the second workshop in this series and will build on the material and the techniques covered in the first workshop. We will use COVID-19 data available here https://github.com/nytimes/covid-19-data, combine it with other data such as from Census and will learn about creating maps, including web maps, that involve more than one variable.

We will use R and QGIS. You should know the basics of vector data GIS and introductory techniques of classification and clustering, be a regular R user, and should have some familiarity with QGIS.

R IV: Visualization

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This session will be held online, and presenters will be in touch with more information after you register.
Visualizing the results of research is a key aspect in facilitating scientific communication to a broad audience. The focus of this workshop will be on using common tools in R for visualization, general concerns in producing visual information, and demonstration of approaches that allow for interactivity.
Prereq: Some experience using R is required (R I, preferably R II workshops), as well as exposure to basic statistical analysis would be beneficial.

R by Example: Analyzing RECS using data.table

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In the R by Example series of workshops, we’ll discuss example analyses in R as a vehicle for learning  commonly used tools and programming patterns.  The “Analyzing RECS using data.table” workshop will focus on analyzing winter home temperatures in the US using data from the Residential Energy Consumption Survey (https://www.eia.gov/consumption/residential/).  We’ll use the data.table package for data manipulations and ggplot2 for plotting.  The workshop will be organized in a parallel fashion, with participants given time to build an analysis from scratch by adapting presented examples step by step. In the process, participants will become familiar with core data.table functionality including its pivot methods.  This workshop is geared towards beginner to intermediate R users or those new to data.table.

R by Example: Analyzing RECS using tidyverse

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In the R by Example series of workshops, we’ll discuss example analyses in R as a vehicle for learning  commonly used tools and programming patterns.  The “Analyzing RECS using tidyverse” workshop will focus on analyzing winter home temperatures in the US using data from the Residential Energy Consumption Survey (https://www.eia.gov/consumption/residential/).  We’ll use the tidyverse (tidyverse.org) throughout, relying on the dplyr package for data manipulations and ggplot2 for plotting.  The workshop will be organized in a parallel fashion, with participants given time to build an analysis from scratch by adapting presented examples step by step. In the process, participants will become familiar with core dplyr functions, pivoting using tidyr, and a basic ggplot2 example.  This workshop is geared towards beginner to intermediate R users.

Rcpp: Integrating C++ into R

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The Rcpp package for R provides “seamless R and C++ integration”.  In this workshop, we will discuss the use of Rcpp to speed up existing R code by rewriting slow functions in C++.  

The workshop will be centered around a couple of case studies with an opportunity provided for participants to implement a few of their own C++ functions, compile, and call them from R.  Participants should be comfortable programming in R, but need not have any prior exposure to C++.

Programming with R

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People using R for applied research are often not taught basic programming practices such as writing functions, efficient iterative processing, vectorization, and other practices that would make their research far more efficient and reproducible.  Understandably, focus is on basic data manipulation and getting model results.  Unfortunately, this can mean the data isn’t as explored as it should be, or other opportunities are lost (e.g. feature engineering), because of the presumed effort that would be required to deal with the data more fully.

This workshop will help you get more out of R so that you can take your efforts to the next level.
Prereq: Some basic experience using R is required.  You should know how to create and manipulate objects, run basic analyses, etc.  This could also be useful to anyone with programming experience in another language like Python.