50.1-Appendix.Rmd

# Appendix

## Git

[Cheat Sheet](https://training.github.com/downloads/github-git-cheat-sheet.pdf)

[Cheat Sheet in different languages](https://training.github.com/)

[Learn Git](http://try.github.io/)

[Interactive Cheat Sheet](http://ndpsoftware.com/git-cheatsheet.html#loc=remote_repo;)

[Ultimate Guide of Git and GitHub for R user](https://happygitwithr.com/)

-   Setting up Git: `git config` with `--global` option to configure user name, email, editor, etc.

-   Creating a repository: `git init` to initialize a repo. Git stores all of its repo data in the `.git` directory.

-   Tracking changes:

    -   `git status` shows the status of the repo

        -   File are stored in the project's working directory (which users see)

        -   The staging area (where the next commit is being built)

        -   local repo is where commits are permanently recorded

    -   `git add` put files in the staging area

    -   `git commit` saves the staged content as a new commit in the local repo.

        -   `git commit -m "your own message"` to give a messages for the purpose of your commit.

-   History

    -   `git diff` shows differences between commits

    -   `git checkout` recovers old version of fields

        -   `git checkout HEAD` to go to the last commit

        -   `git checkout <unique ID of your commit>` to go to such commit

-   Ignoring

    -   `.gitignore` file tells Git what files to ignore

    -   `cat . gitignore *.dat results/` ignore files ending with "dat" and folder "results".

-   Remotes in GitHub

    -   A local git repo can be connected to one or more remote repos.

    -   Use the HTTPS protocol to connect to remote repos

    -   `git push` copies changes from a local repo to a remote repo

    -   `git pull` copies changes from a remote repo to a local repo

-   Collaborating

    -   `git clone` copies remote repo to create a local repo with a remote called `origin` automatically set up

-   Branching

    -   `git check - b <new-branch-name`

    -   `git checkout master` to switch to master branch.

-   Conflicts

    -   occur when 2 or more people change the same lines of the same file

    -   the version control system does not allow to overwrite each other's changes blindly, but highlights conflicts so that they can be resolved.

-   Licensing

    -   People who incorporate General Public License (GPL'd) software into their won software must make their software also open under the GPL license; most other open licenses do not require this.

    -   The Creative Commons family of licenses allow people to mix and match requirements and restrictions on attribution, creation of derivative works, further sharing and commercialization.

-   Citation:

    -   Add a CITATION file to a repo to explain how you want others to cite your work.

-   Hosting

    -   Rules regarding intellectual property and storage of sensitive info apply no matter where code and data are hosted.

## Short-cut

These are shortcuts that you probably you remember when working with R. Even though it might take a bit of time to learn and use them as your second nature, but they will save you a lot of time.\
Just like learning another language, the more you speak and practice it, the more comfortable you are speaking it.\

| function                                         | short-cut                 |
|--------------------------------------------------|---------------------------|
| navigate folders in console                      | `" " + tab`               |
| pull up short-cut cheat sheet                    | `ctrl + shift + k`        |
| go to file/function (everything in your project) | `ctrl + .`                |
| search everything                                | `cmd + shift + f`         |
| navigate between tabs                            | `Crtl + shift + .`        |
| type function faster                             | `snip + shift + tab`      |
| type faster                                      | `use tab for fuzzy match` |
| `cmd + up`                                       |                           |
| `ctrl + .`                                       |                           |

Sometimes you can't stage a folder because it's too large. In such case, use `Terminal` pane in Rstudio then type `git add -A` to stage all changes then commit and push like usual.

## Function short-cut

apply one function to your data to create a new variable: `mutate(mod=map(data,function))`\
instead of using `i in 1:length(object)`: `for (i in seq_along(object))`\
apply multiple function: `map_dbl`\
apply multiple function to multiple variables:`map2`\
`autoplot(data)` plot times series data\
`mod_tidy = linear(reg) %>% set_engine('lm') %>% fit(price ~ ., data=data)` fit lm model. It could also fit other models (stan, spark, glmnet, keras)

-   Sometimes, data-masking will not be able to recognize whether you're calling from environment or data variables. To bypass this, we use `.data$variable` or `.env$variable`. For example `data %>% mutate(x=.env$variable/.data$variable`\
-   Problems with data-masking:\
    + Unexpected masking by data-var: Use `.data` and `.env` to disambiguate\
    + Data-var cant get through:\
    + Tunnel data-var with {{}} + Subset `.data` with [[]]
-   Passing Data-variables through arguments

```{r eval=FALSE}
library("dplyr")
mean_by <- function(data,by,var){
    data %>%
        group_by({{{by}}}) %>%
        summarise("{{var}}":=mean({{var}})) # new name for each var will be created by tunnel data-var inside strings
}

mean_by <- function(data,by,var){
    data %>%
        group_by({{{by}}}) %>%
        summarise("{var}":=mean({{var}})) # use single {} to glue the string, but hard to reuse code in functions
}
```

-   Trouble with selection:\

```{r eval=FALSE}
library("purrr")
name <- c("mass","height")
starwars %>% select(name) # Data-var. Here you are referring to variable named "name"

starwars %>% select(all_of((name))) # use all_of() to disambiguate when 

averages <- function(data,vars){ # take character vectors with all_of()
    data %>%
        select(all_of(vars)) %>%
        map_dbl(mean,na.rm=TRUE)
} 

x = c("Sepal.Length","Petal.Length")
iris %>% averages(x)


# Another way
averages <- function(data,vars){ # Tunnel selectiosn with {{}}
    data %>%
        select({{vars}}) %>%
        map_dbl(mean,na.rm=TRUE)
} 

x = c("Sepal.Length","Petal.Length")
iris %>% averages(x)
```

## Citation

include a citation by `[@Farjam_2015]`

cite packages used in this session

`package=ls(sessionInfo()$loadedOnly) for (i in package){print(toBibtex(citation(i)))}`

```{r, eval=FALSE}
package=ls(sessionInfo()$loadedOnly) 
for (i in package){
    print(toBibtex(citation(i)))
    }
```

## Install all necessary packages/libaries on your local machine

Get a list of packages you need to install from this book (or your local device)

```{r}
installed <- as.data.frame(installed.packages())

head(installed)

write.csv(installed, file.path(getwd(),'installed.csv'))
```
After having the `installed.csv` file on your new or local machine, you can just install the list of packages

```{r, eval = FALSE}
# import the list of packages
installed <- read.csv('installed.csv')

# get the list of packages that you have on your device
baseR <- as.data.frame(installed.packages())

# install only those that you don't have
install.packages(setdiff(installed, baseR))
```