The examples and datasets in this Lab session follow very closely two sources:
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R ENVIRONMENT SET UP & DATA
Needed R Packages
We will use functions from packages base, utils, and stats (pre-installed and pre-loaded)
We may also use the packages below (specifying package::function for clarity).
# Load pckgs for this R session# --- General library(here) # tools find your project's files, based on working directorylibrary(dplyr) # A Grammar of Data Manipulationlibrary(skimr) # Compact and Flexible Summaries of Datalibrary(magrittr) # A Forward-Pipe Operator for R library(readr) # A Forward-Pipe Operator for R # Plotting & data visualizationlibrary(ggplot2) # Create Elegant Data Visualisations Using the Grammar of Graphicslibrary(ggfortify) # Data Visualization Tools for Statistical Analysis Resultslibrary(scatterplot3d) # 3D Scatter Plot# --- Statisticslibrary(MASS) # Support Functions and Datasets for Venables and Ripley's MASSlibrary(factoextra) # Extract and Visualize the Results of Multivariate Data Analyseslibrary(FactoMineR) # Multivariate Exploratory Data Analysis and Data Mininglibrary(rstatix) # Pipe-Friendly Framework for Basic Statistical Tests# --- Tidymodels (meta package)library(rsample) # General Resampling Infrastructure library(broom) # Convert Statistical Objects into Tidy Tibbles
TO DO
RIMESCOLARE LA LEZ 3 CON QUESTO https://gabors-data-analysis.com/images/slides-public/da-public-slides-ch19-v3-2023.pdf
DATASETS for today
In this tutorial, we will use:
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Dataset on ….
Name: …. Documentation: …. Sampling details: ….
Importing Dataset ....
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The data can be interactively obtained form the MASS R package
# (after loading pckg)# library(MASS) # I can call # utils::data(biopsy)