Publications by
Preparing VCF Data for Analysis: Transposition
NOTE - before you begin, make sure your WORKING DIRECTORY is set to the location of the .vcf file being used. Learning objectives All of this material will appear on the exam. Take notes on the workflow, functions, and concepts. Know what it means to reshape data Learn what transposition of data means Know what SNP data in VCF form needs to ...
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Portfolio: Working with VCF files 3 - Removing samples with many NAs using for() loops
Learning objectives Review the problem of missing data in SNP datasets Introduce the concept of researcher degrees of freedom Review how to locate NAs in R using is.na() and which() Outline the use of for() loops in R to carry out repetitive tasks. Review the use of regular expressions to clean text data. Introduction The data we are using t...
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Mean imputation of missing data in R
Learning objectives All of this material will appear on the exam. Take notes on the workflow, functions, and concepts. Main objectives By the end of this lesson you will know how to … Identify all of the missing values in a column of a dataframe or vector Replaces all the NAs in a column with a new value, such as the mean. Know how a for()...
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PCA Analysis Case Study - Bird Species Morphology
Learning objectives All of this material will appear on the exam. Take notes on the workflow, functions, and concepts. Main objectives Work through a full analysis of a dataset with PCA Understand the connection between scree plots and the amount of variation explained by each PC Learn how to make a scree plot in terms of explained variation ...
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Worked Example: PCA on SNPs data from a vcf file Part 1 - Data Preparation
Introduction In this worked example you will replicate a PCA on a published dataset. The example is split into 2 Parts: Part 1: Data Preparation (this file) Part 2: Data analysis with PCA In this Data Preparation phase, you will do the following things: Load the SNP genotypes in .vcf format (vcfR::read.vcfR()) Extract the genotypes into an R...
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Worked Example: PCA on SNPs data from a vcf file Part 2 - Data Analysis
Introduction The example is split into 2 Parts: Part 1: Data Preparation Part 2: Data analysis with PCA (this file) Part 1 must be completed first to create a file, SNPs_cleaned.csv, that has been completely prepared for analysis. Now in Part 2, you will analyze the data with PCA. The steps here will be: Center the data (scale()) Run a PCA ...
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Document
Functions Outline Loops Functions Loops - For Loops For loops iterate along an input vector, stores the current value of the vector as a variable, and repeatedly evaluates a code chunk until the vector is exhausted. for (i in 1:5) { print(i) } ## [1] 1 ## [1] 2 ## [1] 3 ## [1] 4 ## [1] 5 for (i in 1:8) { print(i) } ## [1] 1 ## ...
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Dashboard example
Dashboard_example Richard Cook — 2022-12-08 Intro This dashboard covers several topics: The marmap package The viridis package Miscellaneous material Marmap Package Column 1 Florida The marmap package provides tools and data for visualizing the ocean floor. Here is an example contour plot of marmap’s florida dataset. Column 2 H...
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Assignment_05
1 Visualization 1.1 univariate plot plot(lydia_ts[, "Prod_sales"], col = "red", lty = 2, lwd = 2, ylab = " ", ylim = c(min(lydia_ts), max(lydia_ts))) lines(lydia_ts[, "Advert_cost"], type = "l", col = "blue", lwd = 2) legend("topright", c("Advert_cost", "Prod_sales"), col = c("blue", "red"), lty = c(1, 2), lwd = c(2, 2), bty = "n")...
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Lab02
1 3a-1 Firing Brain Model(Effects of Eroder) Santana, Ramos, and Toom (2015) Cirillo, Nardi, and Spitoni (2018) Szaban (n.d.) Gacs and Torma (2018) In this part, it uses a two-dimensional CA, entailing a square grid of N × N cell with 3 possible states( ready(G), firing(F) and resting(R)). Initilaly, probability of firing status assumes to be 0...
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