r4ds_week24.Rmd

---
title: "R4DS Study Group - Week 24"
author: Pierrette Lo
date: 9/18/2020
output: 
  github_document:
    toc: true
    toc_depth: 2
    html_preview: true
editor_options: 
  chunk_output_type: inline
---

```{r setup, include=FALSE, cache=FALSE}
# allow code to show errors
knitr::opts_chunk$set(error = TRUE, rows.print=20)
```

## This week's assignment

* Chapter 11

```{r, warning=FALSE, message=FALSE}
library(tidyverse)
```

## Ch 11:2 Getting Started

### Notes

Off-topic: I recently discovered how to get a multi-line cursor in RStudio! Super useful if you need to do the same thing to multiple lines of text/code. 

`Ctrl/Cmd + Alt + Up/Down arrow` to select lines. 

`Esc` to cancel.

E.g. add bullets to this list:

```
My list:

Item 1
Item 2
Item 3
```

Also off-topic: use `Ctrl/Cmd + I` to get nicely formatted code indentation. This is good practice as it will help you keep track of hierarchies in your code.


On topic: 

* This is briefly mentioned at the end of the chapter - the [{readxl}](https://readxl.tidyverse.org/) package is very useful for reading in Excel files 
* However, it's best to use CSV format where possible. Excel files can encode a lot of information in formatting (fonts, colors, multiple headers, comments, etc.) that will be lost when imported into R. Best practice is to embed that information in the data itself, or in a separate file, and keep the data as a simple flat text file.

* Reminder to use `read_csv()` (with underscore - the tidyverse version) and not `read.csv` (with period - the base R version) for reasons explained in the text.

* You can also use the RStudio shortcut to import a dataset (by clicking on a CSV file in the Files pane), but be sure to copy the generated code into your script/notebook so you can remember later where your data came from!

### Exercises

>1. What function would you use to read a file where fields were separated with “|”?

```
read_delim("myfile.txt", delim = "|")
```

>2. Apart from `file`, `skip`, and `comment`, what other arguments do `read_csv()` and `read_tsv()` have in common?

Trick question! All of the arguments are the same - just apply to different types of files.

>3. What are the most important arguments to `read_fwf()`?

A fixed-width file is one where each column is delimited by a prespecified max width (as opposed to commas, tabs, etc.) 

Per the help (`?read_fwf`), the two arguments that you have to specify (no defaults provided) are `file` and `col_positions` (where each column starts and ends). There are a few helper functions that help determine what the column positions are - see `?read_fwf` for more details.

>4. Sometimes strings in a CSV file contain commas. To prevent them from causing problems they need to be surrounded by a quoting character, like " or '. By default, `read_csv()` assumes that the quoting character will be ". What argument to `read_csv()` do you need to specify to read the following text into a data frame?

```
"x,y\n1,'a,b'"
```

This method of creating a dataframe "de novo" is a little weird. 

First let's parse the desired output: 

* This is a CSV input, so columns are separated by commas
* Remember that \n means "new line" - i.e. next row
* Since this is text, the entire input must be surrounded by double quotes ("") before you provide it to `read_csv()`
* One of the cells is a character string 'a,b' which is surrounded by single quotes (since it is inside the double quotes) 

So we want the dataframe to look like this:

```   
x y

1 a,b
```

The `quote` argument to `read_csv` is where you specify what your strings are surrounded by - in this case, single quotes.

```{r}
read_csv("x,y\n1,'a,b'", quote = "'")
```

Note that in the help `?read_csv`, the default for `quote` is shown as 

```
"\""
```

Putting a backslash before a special character is known as "escaping" - it tells R that you're referring to the character " literally, not using it for its usual function of surrounding strings.

So the above means that the default method of quoting strings is using a double quote.

This will become useful later when you start using "regular expressions", which are sequences of characters used to search for string patterns - more about this in [Chapter 14](https://r4ds.had.co.nz/strings.html#matching-patterns-with-regular-expressions).

>5. Identify what is wrong with each of the following inline CSV files. What happens when you run the code?

```{r}
read_csv("a,b\n1,2,3\n4,5,6")
```

As indicated in the error message, the header (a, b) indicates 2 columns, but rows 1 and 2 (1, 2, 3 and 4, 5, 6) have 3 columns, so the 3rd column (3 and 6) is dropped.

```{r}
read_csv("a,b,c\n1,2\n1,2,3,4")
```

Header shows 3 columns, but row 1 only has 2 (so the last column is filled with NA), and row 2 has 4 columns, so the last value (4) is dropped.

```{r}
read_csv("a,b\n\"1")
```

I think they intended for "1" to be a character, not a number, but they didn't escape the double quotes correctly.

I couldn't get it to work inline, but there are better ways to convert columns to different data types that will be mentioned later.

```{r}
read_csv("a,b\n1,2\na,b")
```

Not sure exactly what they're looking for here, but the fact that 1 and 2 have been coerced to characters could be a problem later if you're expecting numbers.

```{r}
read_csv("a;b\n1;3")
```

Here it looks like the columns are separated by semicolons, not commas, so you should use `read_csv2` instead:

```{r}
read_csv2("a;b\n1;3")
```