index.Rmd

---
title: 'Regular Expressions and stringr'
subtitle: 'Pavitra Chakravarty'
author: 'R-Ladies Cologne, R-Ladies Gaborone'
output:
  xaringan::moon_reader:
    lib_dir: libs
    css: xaringan-themer.css
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(
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)
```

```{r xaringan-themer, include = FALSE}
library(xaringanthemer)
style_mono_light(
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  header_font_google = google_font("Josefin Sans"),
  text_font_google   = google_font("Montserrat", "300", "300i"),
  code_font_google   = google_font("Droid Mono"),
)
```

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### What are regular expressions?

+ Regular expression is a pattern that describes a specific set of strings with a common structure
+ Heavily used for string matching / replacing in all programming languages
+ Heart and soul for string operations

---

### Regular expression syntax 

6 basic canonical characteristics of regular expressions

+ __basic pattern matching__: Using functions from stringr package with exact sequence of characters
  
  + `str_detect()`, `str_subset()`, `str_view()`, `str_view_all()`

+ __anchors__: Indicate start and stop of sentence

  + `^: indicating start of sentence`, `$: indicating end of sentence`
 
+ __escape characters__: special characters cannot be directly coded in string
 
  + `\`: if you want to find strings with single quote `'`, "escape" single quote by preceding it with `\`
  
---

+ __character classes__: specify entire classes of characters, such as numbers, letters, etc using either `[:` and `:]` around        predefined name or  `\` and a special character
  
  + `[:digit:]` or `\d`: digits, 0 1 2 3 4 5 6 7 8 9, equivalent to `[0-9]` 
  + `\D`: non-digits, equivalent to `[^0-9]`  
  + `[:lower:]`: lower-case letters, equivalent to `[a-z]`  
  + `[:upper:]`: upper-case letters, equivalent to `[A-Z]` 
  + `[:alpha:]`: alphabetic characters, equivalent to `[[:lower:][:upper:]]` or `[A-z]` 
  + `[:alnum:]`: alphanumeric characters, equivalent to `[[:alpha:][:digit:]]` or `[A-z0-9]`   
  + `\w`: word characters, equivalent to `[[:alnum:]_]` or `[A-z0-9_]` 
  + `\W`: not word, equivalent to `[^A-z0-9_]`  
  + `[:blank:]`: blank characters, i.e. space and tab 
  * `[:space:]`: space characters: tab, newline, vertical tab, form feed, carriage return, space
  * `\s`: space, ` `  
  * `\S`: not space  
  
+ __quantifiers__: Quantifiers specify how many repetitions of the pattern

  + `*`: matches at least 0 times   
  + `+`: matches at least 1 times     
  + `?`: matches at most 1 times   
  + `{n}`: matches exactly n times   
  + `{n,}`: matches at least n times
  + `{n,m}`: matches between n and m times

+ __character clusters__: Use of paranthesis to keep pattern together

  + `()`: use with pattern-matching characters to create groups
  
---
  
### Dataset being used today

```{r dataset, eval=TRUE}
library(tidyverse)

enron <- read_csv("https://raw.githubusercontent.com/UBC-STAT/stat545.stat.ubc.ca/master/content/data/enron/enron.csv") %>% drop_na()

glimpse(enron)
head(enron, n=50)

```

---

### Canonical principle #1: Basic pattern-matching


```{r str_detect, eval=TRUE}
enron %>% filter(str_detect(enron$person, "Allen"))

```


```{r str_subset, eval=TRUE}
str_subset(enron$email, "tracy.ngo")

```


```{r str_view_all, eval=FALSE}
str_view_all(enron$email, "tracy.ngo")

```

---

### Canonical principle #2: Anchors

  + `^`: matches the start of the string.   
  + `$`: matches the end of the string.   
  + `\b`: matches the empty string at either edge of a _word_. Don't confuse it with `^ $` which marks the edge of a _string_.   
  + `\B`: matches the empty string provided it is not at an edge of a word.   

```{r str_start_anchor, eval=TRUE}
enron %>% filter(str_detect(enron$email, "@ECT")) %>% select 

```


```{r str_end_anchor, eval=TRUE}
enron %>% filter(str_detect(enron$email, "weekend$"))

```
---

### Canonical principle #3: Escape characters

```{r escape_back, eval=TRUE}
x <- c("123-456-7890", "(123)456-7890", "(123) 456-7890", "1235-2351")
str_view(x, "(\\d\\d\\d)\\d\\d\\d-\\d\\d\\d\\d")


```
---

```{r escape_dollar, eval=TRUE}
str_view("so it goes $^$ here", "\\$\\^\\$")

```


---

### Canonical principle #4: Character Classes

```{r chr_class_1, eval=TRUE}
str_view(stringr::words, "^[yx]", match=TRUE)

```

---

```{r chr_class_2, eval=TRUE}
str_view(stringr::words, "[^e]ed$", match = TRUE)

```

---

```{r chr_class_3, eval=TRUE}
str_view(c("red", "reed"), "[^e]ed$", match = FALSE)

```

---

```{r chr_class_4, eval=TRUE}
str_view(stringr::words, "^(thr)*", match = TRUE)
```


### Canonical principle #5: Quantifiers


  + `*`: matches at least 0 times.   
  + `+`: matches at least 1 times.     
  + `?`: matches at most 1 times.    
  + `{n}`: matches exactly n times.    
  + `{n,}`: matches at least n times.    
  + `{n,m}`: matches between n and m times. 

```{r quant_1, eval=TRUE}
x <- c("dkl kls. klk. _", "(425) 591-6020", "her number is (581) 434-3242", "442", "  dsi")
str_view(x, "^[dkh]*$")

```

---

```{r quant_2, eval=TRUE}
x <- c("123-456-7890", "(123)456-7890", "(123) 456-7890", "1235-2351")
str_view(x, "\\([0-9][0-9][0-9]\\)[ ]*[0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]")

```

```{r quant_3, eval=TRUE}
x <- c("123-456-7890", "(123)456-7890", "(123) 456-7890", "1235-2351")
str_view(x, "\\([0-9][0-9][0-9]\\)[ ]+[0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]")

```

---

```{r quant_4, eval=TRUE}
x <- c("123456-7890", "(123) 456-7890", "(123)456-7890", "1235-2351")
str_view(x, "\\([0-9][0-9][0-9]\\)[ ]?[0-9][0-9][0-9]-[0-9][0-9][0-9][0-9]")

```
---

```{r quant_5, eval=TRUE}
x <- c("4444-22-22", "test", "333-4444-22")
str_view(x, "\\d{4}-\\d{2}-\\d{2}")
```

---

### Canonical principle #6: Character Clusters

```{r cc_1, eval=TRUE}
enron %>% filter(str_detect(email, "@.*\\.(edu|net)")) %>% select(email)

```

```{r cc_2, eval=TRUE}
enron %>% filter(str_detect(email, "@.*(ns)\\.(net)")) %>% select(email)

```
---

### Lets Play!

https://regexcrossword.com/challenges/beginner/puzzles/1

---

### Acknowledgements



Material has been borrowed heavily from the STAT 545 course. This course was started by Jenny Bryan: https://stat545.stat.ubc.ca/notes/notes-b05/

More STAT 545 resources: https://stat545.com/character-vectors.html, https://youtu.be/I0dJ1zpxAtU

R for Data Science chapter on Strings: https://r4ds.had.co.nz/strings.html

Solution set for R4DS on Strings: https://brshallo.github.io/r4ds_solutions/14-strings.html#matching-patterns-w-regex

Regex Puzzle Builder: https://regexcrossword.com/puzzlebuilder