Publications by Yunting Chiu
APIs and Scraping
Instructions Write your solutions in this starter file. Modify the “author” field in the YAML header. Commit R Markdown and HTML files (no PDF files). Push both .Rmd and HTML files to GitHub. Make sure you have knitted to HTML for your final submission. Only include necessary code and data to answer the questions. Most of the functions y...
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PCA in the tidyverse Framework
We will in this lab explore how principal components are calculated and analyzed. We will be using the concrete data set from the {modeldata} package which is loaded with {tidymodels}. Compressive Strength of Concrete Mixtures Data library(ggbiplot) library(tidymodels) data(concrete) head(concrete) ## # A tibble: 6 x 9 ## cement blast_furnace_...
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Bootstrapping
In this assignment, I will be using Tidymodels instead of base R. 1 Exercise 1 (10 points) Review of k-fold cross-validation. Explain how k-fold cross-validation is implemented. This method involves randomly splitting the set of observations into k groups, or folds, of roughly similar size using a k-fold CV. The first fold serves as a validati...
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LDA, QDA, KNN models
We will be using the add-on package discrim to access functions to perform discriminant analysis models with parsnip and the klaR package to perform the QDA calculations. if you haven’t already got it installed run Create a test-train rsplit object of mlc_churn using initial_split(). Use the arguments to set the proportions of the training data...
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training and testing datasets
These sets of labs will introduce you to logistic regression. This will also be your first introduction to the rsample package which we will use to perform train-test split. Exercise 1 In this exercise we will explore the mlc_churn data set included in tidymodels. suppressPackageStartupMessages(library(tidyverse)) suppressPackageStartupMessages(...
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Hello RStudio
Learning goals Get acquainted with R and RStudio, which we will be using throughout the course to analyze data as well as to learn the statistical concepts discussed in the course. Appreciate the value of visualization in exploring the relationship between variables. Start using R for building plots and calculating summary statistics. Terminolo...
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Linear Model Selection
(9.1) A speaker stated: “In well-designed experiments involving quantitative explanatory variables, a procedure for reducing the number of explanatory variables after the data are obtained is not necessary.” Do you agree? Discuss. An explanatory variable is a type of independent variable. Assume that these variables have met the assumptions ...
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Predicting COVID Death Determinants
Introduction Here we have Covid-19 dataset included 12 variables and 51 observations. We will use regression trees methods to predict COVID death determinants. Covid Data library(tidyverse) library(rpart) library(rpart.plot) covid_df_train <- read_csv("./data/covidForRegression.csv") covid_df_train Decision Trees To Classify: Classification Tr...
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forcats and lubridate
suppressPackageStartupMessages(library(tidyverse)) suppressPackageStartupMessages(library(lubridate)) Question 1: Capital Bikeshare Data Load in the data containing trip information from the Capital Bikeshare program. Also load in the station information. Rename variables that have spaces in the names trip data, station data Note: These data we...
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readr and tidyr
suppressMessages(library(tidyverse)) Exercises 1. Baltimore City Crime Data: a. Import the data from https://dcgerard.github.io/stat_412_612/data/BPD_Part_1_Victim_Based_Crime_Data.zip. Baltimore_crime <- read_csv(file = "./data/BPD_Part_1_Victim_Based_Crime_Data.csv") ## ## ── Column specification ─────────────...
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