instructions.Rmd

---
title: "Welcome to the DSI learnR resources"
output: html_notebook
editor_options: 
  chunk_output_type: inline
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk (or the green triangle on the top left of the grey chunk), or by placing your cursor inside the grey box (which is a code chunk) and pressing *Ctrl+Shift+Enter*. 

```{r}
library(learnr)

```


## Hello
You already did the tutorial contained in `ex-setup-r` to download and setup r and RStudio. 

To give you a simple idea of what these tutorials do on a local machine, let's run the following line. 

```{r learnr_hi}

runlearnr("hello/hello.Rmd","r/passr.Rmd")

```

## An important setup step do not skip over this heading!
We also need to install some packages...this is not very elegant, but it goes over all the files in this project and looks for Rmds. Then it reads each line of the Rmds, and extracts references to library calls. I've already done this, so I've pasted the output into one line for you to run.  You can modify this if you don't want to install all the packages at once (it may take a while). 

```{r setup}
# rmd <- list.files(path = ".", recursive = T) %>%
#   str_subset("\\.Rmd$") 
# rmd <- lapply(rmd, readLines)
# packs <- rm_between(rmd, c("library(", "require("), c(")", ")"), extract=TRUE)
# packs <- unique(unlist(packs))

# boot <- 'if (!require("pacman")) install.packages("pacman")'
# cat(paste0(boot, "\npacman::p_load(", paste(na.omit(unlist(packs)), collapse=", "), ")\n"))

source('r/runlearnr.R')
#rmarkdown::run("hello/hello.Rmd")


install.packages("pacman")

library(pacman)

pacman::p_load(tidyverse, openNLP, NLP, stringr, ggmap, WikidataR, ggplot2, psych, doBy, reshape2, knitr, lattice, learnr, dslabs, shiny, gradethis, textdata, wordcloud, ggwordcloud, wordcloud2, kableExtra, RSentiment, sentimentr, tidytext, corrplot, rsconnect, nycflights13, Lahman, qdapRegex)

```



## Data basics - week 4
Learn about how data works in `R` 
```{r learnr_basics}

runlearnr("ex-data-basics/ex-data-basics.Rmd")

```

## Filtering and mutating data - week 4
Learn about how to do basic manipulation in `R`

```{r learnr_explor}

runlearnr("ex-data-filters/ex-data-filter.Rmd")

```

## Learn some basic summarisation and visualisation approaches

```{r}

runlearnr("ex-eda/eda.Rmd")

```

## MatchR

This one is more interesting! In this one, you will have to address _questions_ using outputs from `R`. You don't have to write any code yourself.  Instead, you'll look at what the code outputs, and select the right code block to address the right question.

```{r learnr_matchr}
#You should have this directory locally, and so the first line will work.  If you want to check you have the most current version, uncomment the github lines and run that. 

if (interactive()) {
  runlearnr("DSI-matchR/snippet_matchR_clustered_quizy.Rmd")
  }

## to get from github
# if (interactive()) {
#   shiny::runGitHub(repo = "learnr", 
#           username = "sjgknight",
#           ref = "master",
#           subdir = "DSI-matchR/")
#   }
```

## Some more interactive tools to explore your data
You might find it useful to play with the expandar tool, which walks through some basic analysis functions (not all of which will be relevant for your data) and lets you export the bits you want.

The esquisse tool may also be a useful addon to use.

```{r exploratory}
pacman::p_load(ExPanDaR, esquisse)

ExPanDaR::ExPanD(mtcars)

esquisse::esquisser()

```



## CleanR

There are two resources you might like to explore here

Allison Horst's fantastic 'explore NA' interactive, which uses the `naniar` package to explore NAs in data, with a range of helpful visualisations (included with permission). 

```{r learnr_na}

runlearnr("DSI-cleanR/explore-na-master/explore_na_tutorial.Rmd")

```

I've also started building an example of other common wrangling issues, it will include:

1. string cleaning (whitespace, strings that should be NA, etc.)
1. removing extraneous strings in numeric (Total: 873)
1. tidying variable types
1. reconciling names using both more manual dplyr approach, and refinr
1. converting currencies e.g. https://cran.r-project.org/web/packages/priceR/priceR.pdf 
1. words-to-numbers
1. encoding-issue

... but it isn't finished yet. If you want to play with some of these issues, check the DSI-cleanR directory.

```{r learnr_cleanr}


```



## More!

If you like this style of tutorial, you might find some useful resources in:

1. http://milton-the-cat.rocks/home/adventr.html#Contents_of_adventr 
1. https://github.com/RohanAlexander/DoSStoolkit the DoSStoolkit, in particular git_outta_here (to learn git), r_marky_markdown (to learn more markdown), 
1. https://github.com/asteves/cinfeRence causal inference tutorials 
1. https://ppbds.github.io/primer.tutorials/ bayesian statistics


If you're inspired and want to build some, or have ideas for useful tutorials or edits to the existing ones, please let me know!

You might also be interested in exploring https://psyteachr.github.io/webexercises/