-
Notifications
You must be signed in to change notification settings - Fork 0
/
learning.qmd
135 lines (113 loc) · 5.59 KB
/
learning.qmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
# Continued learning
## Free online books:
### Beginner
- [R for Data Science](https://r4ds.hadley.nz): Excellent open and
online resource for using R for data analysis and data science.
- [Fundamentals of Data
Visualization](https://serialmentor.com/dataviz/): Excellent online
resource for using ggplot2 and R graphics. The book mostly focuses
on concepts and theory of how to visualize, rather than the
practicalities (i.e. no coding involved).
- [ModernDive: Statistical Inference via Data
Science](https://moderndive.com/): Great book on using statistics
and data science methods in R.
- [Happy Git and GitHub for the useR](https://happygitwithr.com/)
(**highly recommended**): Specifically useful is the chapter on
[Daily Workflows](https://happygitwithr.com/workflows-intro.html)
using Git.
- [Data Visualization: A practical
introduction](https://socviz.co/index.html#preface): A book that
goes into practical as well as conceptual detail on how and why to
make certain graphs, given your data.
- [Course material for a statistics class](https://stat545.com/):
Excellent course material for teaching statistics and R.
- [ModernDive: Statistical Inference via Data
Science](https://moderndive.com/): Great book on using statistics
and data science methods in R
- [Data Skills for Reproducible
Research](https://psyteachr.github.io/reprores-v3/): A book-format
resource for learning about reproducible research practices, mainly
aimed at psychology students.
- [Data wrangling, exploration, and analysis with
R](https://stat545.com/): A foundational course, originally designed
by Jenny Bryan (of Posit and tidyverse) for students in the life
sciences at UBC, but now used by students in many fields.
### Intermediate and above
- [Efficient R
Programming](https://csgillespie.github.io/efficientR/): Excellent
book on being efficient when writing R code.
- [Advanced R](https://adv-r.hadley.nz/): Detailed book on advanced
features of R.
- [R Packages](https://r-pkgs.org/): Learn how to create R Packages
from the basics.
- [R Programming for Data
Science](https://bookdown.org/rdpeng/rprogdatascience/): Great
overview of using R for Data Science, with more of a focus on the
programming side of things
## Quick references:
- [RStudio
cheatsheets](https://www.rstudio.com/resources/cheatsheets/):
Multiple, high-quality cheatsheets you can print off to use as a
handy reference.
- [Tidyverse style guide](https://style.tidyverse.org/): To learn
about how to write well-styled code in R.
- [Tidyverse design philosophy of writing
code](https://design.tidyverse.org/)
## Articles:
- [Good enough practicies in scientific
computing](https://doi.org/10.1371/journal.pcbi.1005510): An article
listing and describing some practices to use when writing code.
- [Best practices in scientific
computing](https://doi.org/10.1371/journal.pbio.1001745).
- Case study of reproducible methodds in Bioinformatics: [@Kim2018a].
- [*Our path to better science in less time using open data science
tools* article](https://www.nature.com/articles/s41559-017-0160)
## General sites:
- [Organizing R Source
Code](https://www.r-bloggers.com/r-best-practices-r-you-writing-the-r-way/).
- [Hands-on tutorial for learning Git, in a web-based
terminal](https://try.github.io/levels/1/challenges/1).
- [Simpler, first-steps guide to using
Git](https://rogerdudler.github.io/git-guide/).
- [RStudio tutorial on using R
Markdown](https://rmarkdown.rstudio.com/lesson-1.html).
- [Markdown syntax
guide](https://rmarkdown.rstudio.com/authoring_basics.html).
- [Pandoc Markdown
Manual](https://pandoc.org/MANUAL.html#pandocs-markdown) (R Markdown
uses Pandoc).
- [Adding citations in R
Markdown](https://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html).
- [Case studies](https://www.practicereproducibleresearch.org/) and
lessons for doing reproducibility
## Interactive sites or resources for hands-on learning:
- [Interactive tutorials for using R, within
R](https://swirlstats.com/).
- [RStudio's Learning Primers](https://rstudio.cloud/learn/primers).
## Videos:
- Video on using [Git in
RStudio](https://rstudio.com/resources/webinars/managing-part-2-github-and-rstudio/).
## Getting help:
- [StackOverflow for
tidyr](https://stackoverflow.com/questions/tagged/tidyr).
- [StackOverflow for
dplyr](https://stackoverflow.com/questions/tagged/dplyr).
- [StackOverflow for
ggplot2](https://stackoverflow.com/questions/tagged/ggplot2?sort=frequent&pageSize=50).
- Tip: Combine auto-completion with `::` to find new functions and
documentation on the functions (e.g. try typing `base::` and then
hitting Tab to show a list of all functions found in base R).
- [Oh Shit Git!](https://ohshitgit.com/): A resource for dealing with
Git issues.
<!-- ## Useful R packages -->
<!-- TODO: create csv and insert here? or create a script to make qmd to include here? -->
<!-- knitr::kable(r3::useful_packages_list, caption = "Useful and common packages to use in data analysis.") -->
## Teaching:
- [Openscapes Champions Lesson
Series](https://openscapes.github.io/series/): Learning materials
for being a teacher.
- [Framework for Open and Reproducible Research
Training](https://forrt.org/clusters/): A great set of resources for
learning about how and why to teach open and reproducible research.
- Post: [Why beginners should
teach](https://www.tatianamac.com/posts/why-beginners-should-teach)