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resources.Rmd
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---
output:
html_document
---
```{r, include = FALSE}
library(dplyr)
library(kableExtra)
```
## Additional Resources
***
**Need to review?**
- [Day 1 - RStudio & Reproducibility Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-1.pdf)
- [Day 2 - Basic R & Data Input/Output Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-2.pdf)
- [Day 3 - Data Subsetting Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-3.pdf)
- [Day 4 - Data Summarization & Data Classes Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-4.pdf)
- [Day 5 - Data Cleaning Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-5.pdf)
- [Day 6 - Data Manipulation & Data Visualization `esquisse` Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-6.pdf)
- [Day 7 - Data Visualization `ggplot2` & Factors Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-7.pdf)
- [Day 8 - Statistics Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-8.pdf)
- [Day 9 - Functions Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-9.pdf)
If you need to check what packages are part of the `tidyverse`, you can do so at this [link](https://www.tidyverse.org/packages/).
RStudio shortcuts can be found [here](<http://www.rstudio.com/ide/docs/using/keyboard_shortcuts>).
Extra information about file paths can be found [here](https://docs.google.com/presentation/d/18u1Vhd3Uq-QprC0btpxS_-Ka-LKVUvncyoqdbGdb-g4/edit?usp=sharing).
**Need help?**
- [Various RStudio "Cheatsheets"](https://www.rstudio.com/resources/cheatsheets/)
- [R reference card](http://cran.r-project.org/doc/contrib/Short-refcard.pdf)
- [R jargon](https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf)
- [R terminology](https://cran.r-project.org/doc/manuals/r-release/R-lang.pdf)
- [What is the Tidyverse?](https://www.tidyverse.org/packages/)
- Animations of join functions:
[`full-join()`](https://github.com/gadenbuie/tidyexplain/blob/master/images/full-join.gif)
[`inner_join()`](https://github.com/gadenbuie/tidyexplain/blob/master/images/inner-join.gif)
[`left-join()`](https://github.com/gadenbuie/tidyexplain/blob/master/images/left-join.gif)
[`right-join()`](https://github.com/gadenbuie/tidyexplain/blob/master/images/right-join.gif)
- PC users who want to see how to move files around (especially from downloads), check out this video: https://youtu.be/we6vwB7DsNU
- Mac users who want to see how to move files around (especially from downloads), check out this video: https://www.youtube.com/watch?v=Ao9e0cDzMrE
**Want more?**
- [Tidyverse Skills for Data Science Book](https://jhudatascience.org/tidyversecourse/)
(more about the tidyverse, some modeling, and machine learning)
- [Tidyverse Skills for Data Science Course](https://www.coursera.org/specializations/tidyverse-data-science-r)
(same content with quizzes, can get certificate with $)
- [R for Data Science](http://r4ds.had.co.nz/)
(great general information)
- [R basics chapter of Introduction to Data Science by Rafael A. Irizarry](https://rafalab.github.io/dsbook/r-basics.html)
(great general information)
- [Open Case Studies](https://www.opencasestudies.org/)
(resource for specific public health cases with statistical implementation and interpretation)
- [Dataquest](https://www.dataquest.io/)
(general interactive resource)
- [Quick R Guide]( http://statmethods.net/)
(nice free general resource)
- [Building up a `ggplot2` figure](https://hopstat.wordpress.com/2016/02/18/how-i-build-up-a-ggplot2-figure/)
(guide to making plots)
**Interested in Reproducibility?**
Check out Candace's courses:
- [Introduction](https://jhudatascience.org/Reproducibility_in_Cancer_Informatics/)
- [Advanced](https://jhudatascience.org/Adv_Reproducibility_in_Cancer_Informatics/)
**R for Stata, SPSS, and SAS files**
- The [Haven](https://haven.tidyverse.org/) package
(This package is super useful for reading and writing files so that they are compatible across Stata, SPSS, SAS, and R)
- [R vs Stata](https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf)
(See page 505)
<br>
## Videos of Previous Online Lectures
***
### From Winter Institute 2022
```{r, echo = FALSE, message = FALSE, results='asis'}
mat <- matrix(c(
"RStudio", "https://youtu.be/zpAQrglIJb0",
"Basic R", "https://youtu.be/_8ZG0G1nNlA",
"Reproducibility", "https://youtu.be/ChFTiZ7Clo4",
"Data IO", "https://youtu.be/bnYN7AfYGNM",
"Subsetting Data", "https://youtu.be/kPU3dl25ox8",
"Data Summarization", "https://youtu.be/C_a8bGGdrIA",
"Data Classes", "https://youtu.be/ZCe30kIJ0Xc",
"Data Cleaning", "https://youtu.be/siFL49CCsJg",
"Data Manipulation", "https://youtu.be/7M81XUIY5SE",
"Intro to Data Visualization", "https://youtu.be/Md46muvSrYE",
"Data Visualization", "https://youtu.be/YjDT3ZUSiR4",
"Factors", "https://youtu.be/Co4l0YhuYSk",
"Statistics", "https://youtu.be/3nSIN1mu8uw",
"Functions", "https://youtu.be/XUKJtUYU1Ic"
), ncol = 2, byrow = TRUE)
mat <- data.frame(mat, stringsAsFactors = FALSE)
colnames(mat) <- c("Day", "Link to Video")
knitr::kable(mat, format = "html") %>%
kable_styling()
```
### From Summer Institute 2021
```{r, echo = FALSE, message = FALSE, results='asis'}
mat <- matrix(c(
"RStudio", "https://youtu.be/zpAQrglIJb0",
"Basic R", "https://youtu.be/md30mwFJ2_Q",
"Data IO", "https://youtu.be/6xNyETqIqZU",
"Subsetting Data", "https://youtu.be/YeBSc2YXr4U",
"Data Summarization Part 1 + 2", "https://youtu.be/yL3BGDWtVC4",
"Data Classes", "https://youtu.be/zBCvbikMTAc",
"Data Cleaning", "https://youtu.be/TaREvr5evwk",
"Data Manipulation", "https://youtu.be/-039V99I-PE",
"Reproducibility", "https://youtu.be/ui9dJLqtdCs",
"Statistics", "https://youtu.be/EJwlBoBHoz4",
"Data Visualization", "https://youtu.be/ngQX9khx7UQ",
"Functions", "https://youtu.be/epIujSKgNi4"
), ncol = 2, byrow = TRUE)
mat <- data.frame(mat, stringsAsFactors = FALSE)
colnames(mat) <- c("Day", "Link to Video")
knitr::kable(mat, format = "html") %>%
kable_styling()
```
### From Winter Institute 2020
```{r, echo = FALSE, message = FALSE, results='asis'}
mat <- matrix(c(
"RStudio and Data Classes", "https://youtu.be/vyIsDnsq5jY",
"Subsetting Data", "https://youtu.be/mT8lSagYbjM",
"Data Summarization Part 1", "https://youtu.be/SZYpzt9zy0g",
"Data Classes", "https://youtu.be/82zSA1N0mnA",
"Data Cleaning", "https://youtu.be/G3V2YPaQN34",
"Data Manipulation", "https://youtu.be/43MPdw5bf4o",
"Statistics 1", "https://youtu.be/Jr4ljyzrr4U",
"Statistics 2", "https://youtu.be/ub2BSbK9lMM"
), ncol = 2, byrow = TRUE)
mat <- data.frame(mat, stringsAsFactors = FALSE)
colnames(mat) <- c("Day", "Link to Video")
knitr::kable(mat, format = "html") %>%
kable_styling()
```
### From Summer Institute 2017
```{r videos, echo = FALSE, message = FALSE, results='asis'}
mat <- matrix(c(
"Day 1", "https://youtu.be/Xi-wsACc7p0",
"Day 2", "https://youtu.be/u1FQt9Hr8iw",
"Day 3", "https://youtu.be/woc7AGJTzZw",
"Day 4", "https://youtu.be/RZ7eVIMPIes",
"Day 5", "https://youtu.be/e8cFV8wluC0"
), ncol = 2, byrow = TRUE)
mat <- data.frame(mat, stringsAsFactors = FALSE)
colnames(mat) <- c("Day", "Link to Video")
knitr::kable(mat, format = "html") %>%
kable_styling()
```
<br>