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hyginn 2021-09-18 12:54:46 -04:00
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BIN-MYSPE.R
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@ -3,28 +3,13 @@
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-MYSPE unit
#
# ==============================================================================
#
# S T O P :
# =========
# Version: 1.3
#
# 2021
# UPDATE WARNING!
# ---------------
#
# This file has not yet been updated for coursework. You may inspect it, but
# do NOT use it for actual coursework as long as this warning is here. Parts
# of the code and data will change, and if you use this outdated code it will
# break your setup and workflow.
#
# ==============================================================================
#
#
# Version: 1.2
#
# Date: 2020-10-01
# Date: 2017-09 - 2021-09
# Author: Boris Steipe (boris.steipe@utoronto.ca)
#
# V 1.3 2021 update of MYSPE mechanics; fix a bug no one had complained about
# V 1.2 Reorganized proportional plot section into a "further reading"
# section, added nested-box, and sankey plot visualization of
# proportions. Introduced plotly.
@ -33,7 +18,7 @@
# V 1.0 Final code, after rewriting BLAST parser and updating MYSPElist
# V 0.1 First code copied from BCH441_A03_makeMYSPElist.R
#
# TODO:
# TODO: Sample solution for sankey plot function.
#
#
# == HOW TO WORK WITH LEARNING UNIT FILES ======================================
@ -51,14 +36,14 @@
#TOC>
#TOC> Section Title Line
#TOC> -----------------------------------------------------------------
#TOC> 1 PREPARATIONS 49
#TOC> 2 SUITABLE MYSPE SPECIES 61
#TOC> 3 ADOPT "MYSPE" 85
#TOC> 4 FURTHER READING: PLOTTING PROPORTIONS 124
#TOC> 4.1 Percentages 142
#TOC> 4.2 Visualizing proportions: Pie chart 161
#TOC> 4.3 Visualizing proportions: Nested squares 238
#TOC> 4.4 Visualizing proportions: Sankey diagrams 275
#TOC> 1 PREPARATIONS 51
#TOC> 2 SUITABLE MYSPE SPECIES 63
#TOC> 3 ADOPT "MYSPE" 87
#TOC> 4 FURTHER READING: PLOTTING PROPORTIONS 126
#TOC> 4.1 Percentages 144
#TOC> 4.2 Visualizing proportions: Pie chart 163
#TOC> 4.3 Visualizing proportions: Nested squares 241
#TOC> 4.4 Visualizing proportions: Sankey diagrams 278
#TOC>
#TOC> ==========================================================================
@ -112,8 +97,8 @@ if (! exists("myStudentNumber")) {
# If this produced NA, your Student Number may not be correct, or you are not in
# my class-list. Contact me. Otherwise, this should have printed a species name,
# and the taxonomy ID of its genome-sequenced strain. This is your unique species
# for this course. Note it in your journal ...
# and the taxonomy ID of its genome-sequenced strain. This is your unique
# speciesfor this course. Note it in your journal ...
biCode(MYSPE) # and also note it's "BiCode" ...
( myTaxID <- names(MYSPE) ) # and its taxID
@ -177,7 +162,8 @@ cat(sprintf("\n%s comprise %5.2f%% of fungi (%d of %d).",
# == 4.2 Visualizing proportions: Pie chart ================================
# Often, we will use a pie chart instead. Pie charts are rather informal types of plots, not well suited for analysis. But easy to do:
# Often, we will use a pie chart instead. Pie charts are rather informal types
# of plots, not well suited for analysis. But easy to do:
# Define four colors to identify the four categories
pCol <- c("#ed394e", "#ff9582", "#ffd5c4", "#f2f2f0")
@ -227,7 +213,7 @@ pie(myTbl)
# ... we can improve this quickly with a bit of tweaking:
N <- length(myTbl)
sel <- myOrder == names(myTbl) # TRUE for the MYSPE order, FALSE elsewhere
sel <- myOr == names(myTbl) # TRUE for the MYSPE order, FALSE elsewhere
myCol <- rep(pCol[4], N) # N elements of pCol[1]
myCol[sel] <- pCol[1] # replace this one color
@ -305,7 +291,14 @@ if (! requireNamespace("plotly")) {
# browseVignettes("plotly") # available vignettes
# data(package = "plotly") # available datasets
# Here, we use the plotly package that wraps a very well developed javascript library with many options for interactive plots.
# Here, we use the plotly package that wraps a very well developed javascript
# library with many options for interactive plots. I am producing this plot
# hard-coded for the sample organism "Sporothrix schenkii"; you would need
# to change the code to adapt it to your own MYSPE - or even build a function
# for this. Do try this if you have a bit of coding experience, sankey diagrams
# are a good way to show hierarchical data relations - and if you get this
# working for your own organism you can be proud that you have understood
# how preparing the data works.
myNodes <- list(label = c("Fungi (1014)", # 0 <- node ID