If you working from Rstudio (Cloud), since this might be your first communication attempt with Github, please update your email address and your name and enter these two lines to terminal:
git config --global user.email "your@email"
git config --global user.name "Your Name"
After you have forked rstats-tartu/classes-2019 to your own GH account, you can set rstats-tartu/classes-2019 as an upstream remote repo from where you can pull updates to your computer (local).
git remote add upstream https://github.com/rstats-tartu/classes-2019.git
git pull upstream master
Why visualisation matters slides: https://taltech19-visualisation-matters.netlify.com/#1
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components and layers of ggplot plot, scatterplots, aesthetics: color, size, shape.
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facetting, combining different types of geoms, plotting statistical summaries.
You will learn how to read plain-text rectangular files and excel into R.
After import, you might need to wrangle your dataset into tidy format. There are three interrelated rules which make a dataset tidy:
- Each variable must have its own column.
- Each observation must have its own row.
- Each value must have its own cell.
You need to transform your data during analysis, because in real life you rarely start with a dataset that is in the right form for visualization and modeling.
So, often you will need to:
- summarise your data or to
- create new variables,
- rename variables, or
- reorder the observations.
We are going to use the dplyr library from tidyverse to learn how to carry out these tasks.
Again, we are follow closely R4DS book, chapter "Data transformation", available from http://r4ds.had.co.nz/transform.html. More examples from https://rstats-tartu.github.io/lectures/tidyverse.html#dplyr-ja-selle-viis-verbi
Please read hf-data/README.md.
viruses.csv virus genome summary dataset was downloaded from NCBI ftp site ftp://ftp.ncbi.nlm.nih.gov/genomes/GENOME_REPORTS/viruses.txt and cleaned up with R/download_virus_genomes_info.R script.
> viruses
# A tibble: 28,740 x 15
organism_name tax_id bioproject_acce~ bioproject_id group subgroup size_kb gc host
<chr> <int> <chr> <int> <chr> <chr> <dbl> <dbl> <chr>
1 Escherichia ~ 1.44e6 PRJNA485481 485481 dsDN~ Podovir~ 76.2 42.4 bact~
2 Enterovirus J 1.33e6 PRJNA485481 485481 ssRN~ Picorna~ 7.35 45.3 vert~
3 Bacilladnavi~ 2.27e6 PRJNA393166 393166 ssDN~ Bacilla~ 5.27 45.9 diat~
4 Invertebrate~ 1.30e6 PRJNA485481 485481 dsDN~ Iridovi~ 205. 30.3 inve~
5 Invertebrate~ 3.46e5 PRJNA485481 485481 dsDN~ Iridovi~ 199. 28.1 inve~
6 Bacillus pha~ 1.41e6 PRJNA485481 485481 dsDN~ Siphovi~ 80.4 35.2 bact~
7 Salmonella p~ 1.41e6 PRJNA485481 485481 dsDN~ Ackerma~ 155. 45.6 bact~
8 Brevibacillu~ 1.30e6 PRJNA485481 485481 dsDN~ Myoviri~ 45.8 39.1 bact~
9 Mycobacteriu~ 1.08e6 PRJNA485481 485481 dsDN~ Siphovi~ 59.7 66.6 bact~
10 Mycobacteriu~ 1.07e6 PRJNA485481 485481 dsDN~ Siphovi~ 57.4 61.4 bact~
# ... with 28,730 more rows, and 6 more variables: segments <chr>, genes <chr>, proteins <chr>,
# release_date <date>, modify_date <date>, status <chr>
virus_genome_tables.csv is cleaned up virus and host database from https://www.genome.jp/viptree.
> genome_tables
# A tibble: 9,274 x 13
ID taxid taxid2 length name nuctype Htaxid Hname Vfamily Hgroup hosttype seq_ids order
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 KF981~ 14533~ 145333~ 95702 Mycobac~ dsDNA 1762 - Siphov~ - Prokary~ KF9818~ 1
2 NC_02~ 13279~ 132795~ 95705 Mycobac~ dsDNA 1762 - Siphov~ - Prokary~ NC_023~ 2
3 NC_00~ 173824 173824~ 31007 Methano~ dsDNA 145261 Methano~ Siphov~ Euryar~ Prokary~ NC_002~ 3
4 NC_00~ 77048 77048-1 26111 Methano~ dsDNA 2159 - Siphov~ - Prokary~ NC_001~ 4
5 NC_03~ 16472~ 164728~ 83324 Gordoni~ dsDNA 410332 Gordoni~ Siphov~ Actino~ Prokary~ NC_030~ 5
6 NC_02~ 15669~ 156699~ 71200 Arthrob~ dsDNA 1667 Arthrob~ Myovir~ Actino~ Prokary~ NC_026~ 6
7 NC_03~ 17969~ 179699~ 51290 Arthrob~ dsDNA 17719~ Arthrob~ Myovir~ Actino~ Prokary~ NC_031~ 7
8 NC_01~ 11832~ 118323~ 43788 Salisae~ dsDNA 10897~ Salisae~ - Bacter~ Prokary~ NC_017~ 8
9 NC_01~ 491893 491893~ 34952 Abalone~ dsDNA 37770 Halioti~ - Mollus~ Eukaryo~ NC_011~ 9
10 NC_03~ 18159~ 181596~ 103445 Salmone~ dsDNA 286783 Salmone~ Myovir~ Gammap~ Prokary~ NC_031~ 10
# ... with 9,264 more rows
virushostdb.tsv is the original source for the virus_genome_tables.csv data, downloaded from ftp://ftp.genome.jp.
virus_genome_tables.csv and virushostdb.tsv were generated with R/download_virus_genome_tables.R script.
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