Computing for the Social Sciences
Computing for the Social Sciences
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This section contains lecture notes and exercises for the course.
Evaluate your model with resampling
library(tidyverse) library(tidymodels) library(ranger) library(rcfss) set.seed(123) theme_set(theme_minimal()) Introduction So far, we have built a model and preprocessed data with a recipe. We also introduced workflows as a way to bundle a parsnip model and recipe together.
Last updated on May 25, 2021
stat-learn
Preprocess your data
library(tidyverse) library(tidymodels) library(rcfss) library(naniar) # visualize missingness library(skimr) # summary statistics tables set.seed(123) theme_set(theme_minimal()) Introduction So far we have learned to build linear and logistic regression models, using the parsnip package to specify and train models with different engine.
Last updated on Jun 1, 2022
stat-learn
Tune model parameters
library(tidymodels) library(rpart) library(modeldata) library(kableExtra) library(vip) set.seed(123) doParallel::registerDoParallel() theme_set(theme_minimal()) Introduction Some model parameters cannot be learned directly from a data set during model training; these kinds of parameters are called hyperparameters. Some examples of hyperparameters include the number of predictors that are sampled at splits in a tree-based model (we call this mtry in tidymodels) or the learning rate in a boosted tree model (we call this learn_rate).
Last updated on Sep 1, 2021
stat-learn
A dive into R Markdown
Run the code below in your console to download this exercise as a set of R scripts. usethis::use_course("uc-cfss/a-deep-dive-into-r-markdown") Reproducibility in scientific research Reproducibility is “the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them.
Last updated on Jan 12, 2022
programming
,
project-management
Basic workflow for text analysis
Obtain your text sources Text data can come from lots of areas: Web sites Twitter Databases PDF documents Digital scans of printed materials The easier to convert your text data into digitally stored text, the cleaner your results and fewer transcription errors.
Last updated on May 25, 2021
text
Bugs and styling code
library(tidyverse) set.seed(1234) Admiral Grace Hopper discovered the first bug in a computer Run the code below in your console to download this exercise as a set of R scripts.
Last updated on Feb 9, 2022
programming
Build a linear model
library(tidymodels) library(tidyverse) library(rcfss) library(rstanarm) library(broom.mixed) set.seed(123) theme_set(theme_minimal()) Introduction There are several different approaches to fitting a linear model in R.^[See Tidy Modeling with R for an overview of how these approaches vary.
Last updated on May 25, 2021
stat-learn
Building Shiny applications
library(tidyverse) library(shiny) Shiny is a package from RStudio that can be used to build interactive web pages with R. While that may sound scary because of the words “web pages”, it’s geared to R users who have no experience with web development, and you do not need to know any HTML/CSS/JavaScript.
Last updated on May 25, 2021
shiny
Computer programming as a form of problem solving
library(tidyverse) library(palmerpenguins) Professor X from X-Men (the Patrick Stewart version, not James Mcavoy) Computer Problems. XKCD. Computers are not mind-reading machines. They are very efficient at certain tasks, and can perform calculations thousands of times faster than any human.
Last updated on May 25, 2021
datawrangle
Debugging and condition handling
library(tidyverse) set.seed(1234) theme_set(theme_minimal()) Run the code below in your console to download this exercise as a set of R scripts. usethis::use_course("uc-cfss/debugging-and-defensive-programming") Conditions are a method for communicating to the user when something unanticipated has occurred.
Last updated on May 25, 2021
programming
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