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2021 minimal maintenance

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hyginn 2021-09-16 01:16:22 -04:00
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BIN-Storing_data.R
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@ -3,11 +3,12 @@
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-Storing_data unit
#
# Version: 1.3.1
# Version: 1.3.2
#
# Date: 2017-10 - 2020-09
# Date: 2017-10 - 2021-09
# Author: Boris Steipe (boris.steipe@utoronto.ca)
#
# V 1.3.2 2021 minimal maintenance
# V 1.3.1 add overlooked jsonlite:: prefix to fromJson()
# V 1.3 Made file locations more consistent. All student-edited files
# go into the myScripts directory
@ -32,30 +33,30 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> -----------------------------------------------------------------------
#TOC> 1 A Relational Datamodel in R: review 62
#TOC> 1.1 Building a sample database structure 102
#TOC> 1.1.1 completing the database 208
#TOC> 1.2 Querying the database 241
#TOC> 1.3 Task: submit for credit (part 1/2) 272
#TOC> 2 Implementing the protein datamodel 294
#TOC> 2.1 JSON formatted source data 320
#TOC> 2.2 "Sanitizing" sequence data 361
#TOC> 2.3 Create a protein table for our data model 383
#TOC> 2.3.1 Initialize the database 385
#TOC> 2.3.2 Add data 397
#TOC> 2.4 Complete the database 417
#TOC> 2.4.1 Examples of navigating the database 444
#TOC> 2.5 Updating the database 476
#TOC> 3 Add your own data 488
#TOC> 3.1 Find a protein 496
#TOC> 3.2 Put the information into JSON files 526
#TOC> 3.3 Create an R script to create your own database 568
#TOC> 3.3.1 Check and validate 596
#TOC> 3.4 Task: submit for credit (part 2/2) 641
#TOC>
#TOC> 1 A Relational Datamodel in R: review 63
#TOC> 1.1 Building a sample database structure 103
#TOC> 1.1.1 completing the database 209
#TOC> 1.2 Querying the database 242
#TOC> 1.3 Task: submit for credit (part 1/2) 273
#TOC> 2 Implementing the protein datamodel 297
#TOC> 2.1 JSON formatted source data 323
#TOC> 2.2 "Sanitizing" sequence data 364
#TOC> 2.3 Create a protein table for our data model 386
#TOC> 2.3.1 Initialize the database 388
#TOC> 2.3.2 Add data 400
#TOC> 2.4 Complete the database 420
#TOC> 2.4.1 Examples of navigating the database 447
#TOC> 2.5 Updating the database 479
#TOC> 3 Add your own data 491
#TOC> 3.1 Find a protein 499
#TOC> 3.2 Put the information into JSON files 530
#TOC> 3.3 Create an R script to create your own database 572
#TOC> 3.3.1 Check and validate 600
#TOC> 3.4 Task: submit for credit (part 2/2) 645
#TOC>
#TOC> ==========================================================================
@ -205,7 +206,7 @@ str(philDB)
# go back, re-read, play with it, and ask for help. These are the foundations.
# === 1.1.1 completing the database
# === 1.1.1 completing the database
# Next I'll add one more person, and create the other two tables:
@ -384,7 +385,7 @@ dbSanitizeSequence(x)
# == 2.3 Create a protein table for our data model =========================
# === 2.3.1 Initialize the database
# === 2.3.1 Initialize the database
# The function dbInit contains all the code to return a list of empty
@ -396,7 +397,7 @@ myDB <- dbInit()
str(myDB)
# === 2.3.2 Add data
# === 2.3.2 Add data
# fromJSON() returns a dataframe that we can readily process to add data
@ -443,7 +444,7 @@ source("./scripts/ABC-createRefDB.R")
str(myDB)
# === 2.4.1 Examples of navigating the database
# === 2.4.1 Examples of navigating the database
# You can look at the contents of the tables in the usual way we access
@ -596,7 +597,7 @@ if (file.exists(sprintf("./myScripts/%staxonomy.json", biCode(MYSPE)))) {
# "break" them with a code experiment. But always have a script with
# which you can create what you need.
# === 3.3.1 Check and validate
# === 3.3.1 Check and validate
# Is your protein named according to the pattern "MBP1_MYSPE"? It should be.