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- CRAN Task View [High-Performance and Parallel Computing with R](http://cran.r-project.org/web/views/HighPerformanceComputing.html)
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- Parallel [Computing with the R Language in a Supercomputing Environment](https://link.springer.com/chapter/10.1007/978-3-642-13872-0_64)
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tasks:
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- Write parallel for loops to speed up computation time.
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---
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```{r setup, include=FALSE, purl=F}
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source("functions.R")
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source("knitr_header.R")
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```
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# Reading
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```{r reading,results='asis',echo=F,purl=F}
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md_bullet(rmarkdown::metadata$reading)
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```
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# Tasks
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```{r tasks,results='asis',echo=F, purl=F}
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md_bullet(rmarkdown::metadata$tasks)
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```
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## Background
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```{r cache=F, message=F,warning=FALSE}
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library(tidyverse)
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library(spData)
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library(sf)
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## New Packages
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library(foreach)
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library(doParallel)
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registerDoParallel()
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getDoParWorkers() # check registered cores
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```
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Write an Rmd script that:
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* Loads the `world` dataset in the `spData` package
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* Runs a parallel `foreach()` to loop over countries (`name_long`) and:
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*`filter` the world object to include only on country at a time.
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* use `st_is_within_distance` to find the distance from that country to all other countries in the `world` object within 100000m Set `sparse=F` to return a simple array of `T` for countries within the distance.
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* set `.combine=rbind` to return a simple matrix.
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* Confirm that you get the same answer without using foreach:
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* imply use `st_is_within_distance` with the transformed `world` object as both `x` and `y` object.
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* compare the results with `identical()`
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* you can also check the time difference with `system.time()`.
- CRAN Task View [High-Performance and Parallel Computing with R](http://cran.r-project.org/web/views/HighPerformanceComputing.html)
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- Parallel [Computing with the R Language in a Supercomputing Environment](https://link.springer.com/chapter/10.1007/978-3-642-13872-0_64)
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tasks:
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- Write parallel for loops to speed up computation time.
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---
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# Reading
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- list(`CRAN [Task View` = "High-Performance and Parallel Computing with R](http://cran.r-project.org/web/views/HighPerformanceComputing.html)")
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- Parallel [Computing with the R Language in a Supercomputing Environment](https://link.springer.com/chapter/10.1007/978-3-642-13872-0_64)
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# Tasks
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- Write parallel for loops to speed up computation time.
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## Background
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```r
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library(tidyverse)
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library(spData)
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library(sf)
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## New Packages
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library(foreach)
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library(doParallel)
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registerDoParallel()
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getDoParWorkers() # check registered cores
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```
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```
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## [1] 2
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```
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Write an Rmd script that:
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* Loads the `world` dataset in the `spData` package
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* Runs a parallel `foreach()` to loop over countries (`name_long`) and:
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*`filter` the world object to include only on country at a time.
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* use `st_is_within_distance` to find the distance from that country to all other countries in the `world` object within 100000m Set `sparse=F` to return a simple array of `T` for countries within the distance.
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* set `.combine=rbind` to return a simple matrix.
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* Confirm that you get the same answer without using foreach:
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* imply use `st_is_within_distance` with the transformed `world` object as both `x` and `y` object.
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* compare the results with `identical()`
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* you can also check the time difference with `system.time()`.
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This approach could be used to identify which countries were 'close' to others. For example, these countries are within 10^{5}m of Costa Rica:
- Documentation for [RMarkdown Websites](https://rmarkdown.rstudio.com/rmarkdown_websites.htm)
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tasks:
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- Commit your first draft of your project to GitHub
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---
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md_bullet(rmarkdown::metadata$reading)
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```
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# Tasks
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```{r reading,results='asis',echo=F}
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md_bullet(rmarkdown::metadata$tasks)
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```
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### First Draft
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The first draft of your project will be assessed by your peers in GitHub. The objectives of the peer evaluation are:
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* Provide an opportunity to share your knowledge to improve their project
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You should use the project website template (or similar) to generate a html version of your project report. If your project requires any data not available in public repositories, you should put it in a folder called `/data` in your project's home directory and then import it into R with `read.csv('data/filname.csv')` or similar so that anyone with a copy of the repository can re-create the HTML output.
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## Required components of first draft
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1)**Introduction**[~ 200 words]: Clearly stated background and questions / hypotheses / problems being addressed. Sets up the analysis in an interesting and compelling way.
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2)**Data**: Script downloads at least one dataset automatically through the internet or loads the data from the `data/` folder. This could use a direct download (e.g. download.file()) or an API (e.g. anything from ROpenSci).
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3)**Figure**: The HTML file includes at least one figure of the data.
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2)**Reproducibility**: The .Rmd should generate the HTML output when "Build Website" is clicked.
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### Confirming 'reproducibility'
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After pushing the files to GitHub, try downloading it as a zip file, opening in RStudio, and clicking build website - it should work.
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## Common issues
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1) Importing data from somewhere on your computer. You should not have any commands such as `read.csv("~/projects/inputdata.csv")` that read any data from your computer other than the `data/` folder in your repository.
- Commit your first draft of your project to GitHub
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### First Draft
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The first draft of your project will be assessed by your peers in GitHub. The objectives of the peer evaluation are:
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* Provide an opportunity to share your knowledge to improve their project
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You should use the project website template (or similar) to generate a html version of your project report. If your project requires any data not available in public repositories, you should put it in a folder called `/data` in your project's home directory and then import it into R with `read.csv('data/filname.csv')` or similar so that anyone with a copy of the repository can re-create the HTML output.
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## Required components of first draft
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1)**Introduction**[~ 200 words]: Clearly stated background and questions / hypotheses / problems being addressed. Sets up the analysis in an interesting and compelling way.
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2)**Data**: Script downloads at least one dataset automatically through the internet or loads the data from the `data/` folder. This could use a direct download (e.g. download.file()) or an API (e.g. anything from ROpenSci).
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3)**Figure**: The HTML file includes at least one figure of the data.
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2)**Reproducibility**: The .Rmd should generate the HTML output when "Build Website" is clicked.
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### Confirming 'reproducibility'
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After pushing the files to GitHub, try downloading it as a zip file, opening in RStudio, and clicking build website - it should work.
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## Common issues
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1) Importing data from somewhere on your computer. You should not have any commands such as `read.csv("~/projects/inputdata.csv")` that read any data from your computer other than the `data/` folder in your repository.
- Review at least two other students' projects and make comments via a _pull request_ in GitHub before next class next week.
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- Review at least two other students' projects and make comments via a _pull request_ in GitHub.
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- Browse the [Leaflet website](http://rstudio.github.io/leaflet/) and take notes in your readme.md about potential uses in your project. What data could you use? How would you display it?
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- Browse the [HTML Widgets page](http://gallery.htmlwidgets.org/) for many more examples. Take notes in your readme.md about potential uses in your project.
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---
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```{r reading,results='asis',echo=F}
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md_bullet(rmarkdown::metadata$reading)
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```
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# Tasks
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```{rreading,results='asis',echo=F}
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```{rtasks,results='asis',echo=F}
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md_bullet(rmarkdown::metadata$tasks)
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```
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## Evaluation Instructions
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# Project Peer Evaluation
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## Instructions
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Select two repositories and evaluate them according to the instructions listed in the [Project First Draft task](TK_11.html)
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Select two repositories and evaluate them according to the instructions and rubric below.
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### Download and reproduce the project
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1) Explore the final projects in the [class repositor](https://github.com/AdamWilsonLabEDU)
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2)Open the repository and check if there have already been two reviews by checking if there are 2 (or more) "Pull Requests". For example, in the image below, there are 0 pull requests, so this repository would be available for you to review. If there are already 2 pull requests, select another repository.
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2) Go to the github page linked in the assignment and download the repository as a zip file (click on the <imgsrc='assets/download.png'width=100> button).
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1) Explore the final projects in the [class repository](https://github.com/AdamWilsonLabEDU?q=finalproject)
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2)Select two projects that do not already have two evaluations (pull requests). For example, in the image above, there are 0 pull requests, so this repository would be available for you to review. If there are already 2 pull requests, select another repository.
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2) Go to the github page linked in the assignment and download the repository as a zip file (click on the <imgsrc='project_assets/download.png'width=100> button).
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3) Unzip the file after it downloads
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4) Open the project or `index.Rmd` in RStudio and click `knit` or `Build Website` in the `Build` tab in the upper right.
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Evaluate the following provide any feedback via pull request.
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1) Website
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1)**Introduction**[~ 200 words]: Clearly stated background and questions / hypotheses / problems being addressed. Sets up the analysis in an interesting and compelling way.
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2)**Data**: Script downloads at least one dataset automatically through the internet. This could use a direct download (e.g. download.file()) or an API (anything from ROpenSci).
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3)**Figure**: The HTML file includes at least one figure of the data.
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2)**Output:** The .Rmd produces HTML output with
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1) section headers for all the major sections of the paper
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2) a draft of the complete introduction.
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4) Open the project or `index.Rmd` in RStudio and click `Build Website` in the `Build` tab in the upper right.
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5) Evaluate whether the project meets the specifications listed in the [Project First Draft task](TK_11.html)
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### Provide feedback and evaluation via pull request
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After you reproduce the project, you will provide feedback via pull request.
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The following video will walk you through the steps of providing feedback via a pull request.
1) In the "Code" tab of the github page for the project, click on the file you want to provide feedback on (typically this will be `index.Rmd`)
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2) Click the pencil icon on the right side to edit the file
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3) You can make changes or comment on the code
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* To make changes, simply edit the text
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* To comment, you must still make some sort of change on the lines where you want to cmment. The easiest thing is simply to add a space at the end of the line (as I do in the video above).
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4) At the bottom of the file, there is a section called "Commit Changes", select the button for **Create a new branch for this commit and start a pull request.** and name the new branch `project_feedback_githubusername`
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5) Click "Propose File change"
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6) Click on the button "Files Changed #1" near the middle of the next page
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7) Hover over lines you would like to comment on and click the little blue plus button. Then enter your comment and select "Add single comment"
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6) Repeat steps 2-6 for any additional files you want to comment on
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Be sure to install any required libraries (do not complain if it fails because you don't have a library installed).
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