Intro to Visualizations & String Data

class: center, middle, inverse, title-slide

# Intro to Visualizations & String Data
### Daniel Anderson
### Week 2

---

layout: true

---
# Agenda

.pull-left[
* Visualizing distributions
  + histograms
  + density plots
  + Empirical cumulative density plots
  + QQ plots
* Visualizing amounts
  + bar plots
  + dot plots
  + heatmaps
]

.pull-right[
* Working textual data
  + Splitting into words, n-grams
  + Visualizing word frequencies
* Cleaning string data
  + Substrings, basic transformations
  + Substitutions and quick pattern matching
]

A promise: You will have **at least 30 minutes** for lab, even if we have to pause this lecture early

---
# Learning Objectives

* Understand various ways the same underlying data can be displayed

* Think through pros/cons of each

* Understand the basic structure of the code to produce the various plots

* Create structured data from text and be able to visualize word frequencies

* Be able to replace patterns in strings and understand which character need to be "escaped"

---
class: inverse-red center middle
# One continuous variable

---
# Histogram

![](w2_files/figure-html/histo-1.png)

---
# Density plot

![](w2_files/figure-html/dens-1.png)

---
# (Empirical) Cumulative Density

![](w2_files/figure-html/cum_dens-1.png)

---
# QQ Plot
Compare to theoretical quantiles (for normality)

![](w2_files/figure-html/qq-1.png)

---
# Empirical examples
I'll move fast, but if you want to (try to) follow along, feel free.

---
background-image:url(https://allisonhorst.github.io/palmerpenguins/reference/figures/lter_penguins.png)
background-size: contain

# Penguin data

---

```r
library(palmerpenguins)
penguins
```

```
## # A tibble: 344 × 8
##    species island    bill_length_mm bill_depth_mm
##    <fct>   <fct>              <dbl>         <dbl>
##  1 Adelie  Torgersen           39.1          18.7
##  2 Adelie  Torgersen           39.5          17.4
##  3 Adelie  Torgersen           40.3          18  
##  4 Adelie  Torgersen           NA            NA  
##  5 Adelie  Torgersen           36.7          19.3
##  6 Adelie  Torgersen           39.3          20.6
##  7 Adelie  Torgersen           38.9          17.8
##  8 Adelie  Torgersen           39.2          19.6
##  9 Adelie  Torgersen           34.1          18.1
## 10 Adelie  Torgersen           42            20.2
## # … with 334 more rows, and 4 more variables:
## #   flipper_length_mm <int>, body_mass_g <int>,
## #   sex <fct>, year <int>
```

---
# Basic histogram

```r
ggplot(penguins, aes(x = bill_length_mm)) +
  geom_histogram()
```

```
## `stat_bin()` using `bins = 30`. Pick better
## value with `binwidth`.
```

![](w2_files/figure-html/age_hist-1.png)

---
# Make it a little prettier

```r
ggplot(penguins, aes(x = bill_length_mm)) +
  geom_histogram(fill = "#56B4E9",
                 color = "white",
                 alpha = 0.9) 
```

```
## `stat_bin()` using `bins = 30`. Pick better
## value with `binwidth`.
```

![](w2_files/figure-html/age_hist2-1.png)

---
# Change the number of bins

```r
ggplot(penguins, aes(x = bill_length_mm)) +
  geom_histogram(fill = "#56B4E9",
                 color = "white",
                 alpha = 0.9,
*                bins = 50)
```

![](w2_files/figure-html/age_hist50-1.png)

---
# Vary the number of bins

![](w2_files/figure-html/bins-1.png)

---
# Denisty plot
### ugly 😫

```r
ggplot(penguins, aes(bill_length_mm)) +
  geom_density()
```

![](w2_files/figure-html/dens-titanic-1.png)

---
# Denisty plot
### Change the fill 😌

```r
ggplot(penguins, aes(bill_length_mm)) +
  geom_density(fill = "#56B4E9")
```

![](w2_files/figure-html/dens-titanic-blue-1.png)

---
# Density plot estimation
* Kernal density estimation
  + Different kernal shapes can be selected
  + Bandwidth matters most
  + Smaller bands = bend more to the data

* Approximation of the underlying continuous probability function
  + Integrates to 1.0 (y-axis is somewhat difficult to interpret)

---
# Denisty plot
### change the bandwidth

```r
ggplot(penguins, aes(bill_length_mm)) +
  geom_density(fill = "#56B4E9", 
               bw = 5)
```

![](w2_files/figure-html/dens-titanic5-1.png)

---
class: middle

![](w2_files/figure-html/vary-bw-1.png)

---
# Quickly
How well does it approximate a normal distribution?

```r
ggplot(penguins, aes(sample = bill_length_mm)) +
  stat_qq_line(color = "#56B4E9") +
  geom_qq(color = "gray40") 
```

![](w2_files/figure-html/qq-titanic-1.png)

---
class: inverse-red center middle
# Grouped data
### Distributions

How do we display more than one distribution at a time?

---
# Boxplots

![](w2_files/figure-html/boxplots-1.png)

---
# Violin plots

![](w2_files/figure-html/violin-1.png)

---
# Jittered points

![](w2_files/figure-html/jittered-1.png)

---
# Sina plots

![](w2_files/figure-html/sina-1.png)

---
# Stacked histograms

![](w2_files/figure-html/stacked-histo-1.png)

---
# Overlapping densities

![](w2_files/figure-html/overlap-dens-1.png)

---
# Ridgeline densities

![](w2_files/figure-html/ridgeline-1.png)

---
class: inverse-orange center middle
# Quick empirical examples

---
# Boxplots

```r
ggplot(penguins, aes(island, bill_length_mm)) +
  geom_boxplot(fill = "#A9E5C5")
```

![](w2_files/figure-html/boxplots-empirical-1.png)

---
# Violin plots

```r
ggplot(penguins, aes(island, bill_length_mm)) +
  geom_violin(fill = "#A9E5C5")
```

![](w2_files/figure-html/violin-empirical-1.png)

---
# Jittered point plots

```r
ggplot(penguins, aes(island, bill_length_mm)) +
  geom_jitter(width = 0.3, height = 0)
```

![](w2_files/figure-html/jittered-empirical-1.png)

---
# Sina plot

```r
ggplot(penguins, aes(island, bill_length_mm)) +
  ggforce::geom_sina()
```

![](w2_files/figure-html/sina-empirical-1.png)

---
# Stacked histogram

```r
ggplot(penguins, aes(bill_length_mm)) +
  geom_histogram(aes(fill = island))
```

![](w2_files/figure-html/stacked-histo-empirical-1.png)

.realbig[🤨]

---
# Dodged

```r
ggplot(penguins, aes(bill_length_mm)) +
  geom_histogram(aes(fill = island), 
                 position = "dodge")
```

![](w2_files/figure-html/dodged-histo-empirical-1.png)

Note `position = "dodge"` does not go into `aes` (not accessing a variable in
your dataset)

---
# Better

```r
ggplot(penguins, aes(bill_length_mm)) +
  geom_histogram(fill = "#A9E5C5",
                 color = "white",
                 alpha = 0.9,) +
* facet_wrap(~island)
```

![](w2_files/figure-html/wrapped-histo-empirical-1.png)

---
# Overlapping densities

```r
ggplot(penguins, aes(bill_length_mm)) +
  geom_density(aes(fill = island),
               color = "white",
               alpha = 0.4)
```

![](w2_files/figure-html/overlap-dens-empirical-1.png)

--
Note the default colors generally don't work great in most of these

---

```r
ggplot(penguins, aes(bill_length_mm)) +
  geom_density(aes(color = island, fill = island),
               alpha = 0.6) +
  scale_fill_manual(values = c("#003326", "#009973", "#d6fff5")) +
  scale_color_manual(
    values = darken(c("#003326", "#009973", "#d6fff5"), .1)
  )
```

![](w2_files/figure-html/overlap-dens-empirical2-1.png)

---
# Ridgeline densities

```r
ggplot(penguins, aes(bill_length_mm, island)) +
  ggridges::geom_density_ridges(color = "white",
                                fill = "#A9E5C5")
```

![](w2_files/figure-html/ridgeline-dens-empirical-1.png)

---
class: inverse-red center middle
# Visualizing amounts

---
# Bar plots

![](w2_files/figure-html/bars-1.png)

---
# Flipped bars

![](w2_files/figure-html/flipped_bars-1.png)

---
# Dotplot

![](w2_files/figure-html/dots-1.png)

---
# Heatmap

![](w2_files/figure-html/heatmap-1.png)

---
# Empirical examples
### How much does college cost?

```r
library(here)
library(rio)
tuition <- import(here("data", "us_avg_tuition.xlsx"),
                  setclass = "tbl_df")
head(tuition)
```

```
## # A tibble: 6 × 13
##   State      `2004-05` `2005-06` `2006-07`
##   <chr>          <dbl>     <dbl>     <dbl>
## 1 Alabama     5682.838  5840.550  5753.496
## 2 Alaska      4328.281  4632.623  4918.501
## 3 Arizona     5138.495  5415.516  5481.419
## 4 Arkansas    5772.302  6082.379  6231.977
## 5 California  5285.921  5527.881  5334.826
## 6 Colorado    4703.777  5406.967  5596.348
## # … with 9 more variables: `2007-08` <dbl>,
## #   `2008-09` <dbl>, `2009-10` <dbl>,
## #   `2010-11` <dbl>, `2011-12` <dbl>,
## #   `2012-13` <dbl>, `2013-14` <dbl>,
## #   `2014-15` <dbl>, `2015-16` <dbl>
```

---
# By state: 2015-16

```r
ggplot(tuition, aes(State, `2015-16`)) +
  geom_col()
```

![](w2_files/figure-html/state-tuition1-1.png)

.realbig[🤮🤮🤮]

---
# Two puke emoji version
.realbig[🤮🤮]

```r
ggplot(tuition, aes(State, `2015-16`)) +
  geom_col() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10))
```

![](w2_files/figure-html/state-tuition2-1.png)

---
# One puke emoji version
.realbig[🤮]

```r
ggplot(tuition, aes(State, `2015-16`)) +
  geom_col() +
  coord_flip()
```

---
![](w2_files/figure-html/state-tuition3-eval-1.png)

---
# Kinda smiley version
.realbig[😏]

```r
ggplot(tuition, aes(fct_reorder(State, `2015-16`), `2015-16`)) +
  geom_col() +
  coord_flip() 
```

---
![](w2_files/figure-html/state-tuition4-eval-1.png)

---
# Highlight Oregon
.realbig[🙂]

```r
ggplot(tuition, aes(fct_reorder(State, `2015-16`), `2015-16`)) +
  geom_col() +
  geom_col(fill = "cornflowerblue", 
           data = filter(tuition, State == "Oregon")) +
  coord_flip() 
```

---

![](w2_files/figure-html/oregon-highlight-eval-1.png)

---
# Not always good to sort

![](w2_files/figure-html/income_df-sorted-1.png)

---
# Much better

![](w2_files/figure-html/income_df-1.png)

---
# Average tuition by year
### How?

```r
head(tuition)
```

---
# Rearrange

```r
tuition %>%
  pivot_longer(`2004-05`:`2015-16`,
               names_to = "year", 
               values_to = "avg_tuition") 
```

```
## # A tibble: 600 × 3
##    State   year    avg_tuition
##    <chr>   <chr>         <dbl>
##  1 Alabama 2004-05    5682.838
##  2 Alabama 2005-06    5840.550
##  3 Alabama 2006-07    5753.496
##  4 Alabama 2007-08    6008.169
##  5 Alabama 2008-09    6475.092
##  6 Alabama 2009-10    7188.954
##  7 Alabama 2010-11    8071.134
##  8 Alabama 2011-12    8451.902
##  9 Alabama 2012-13    9098.069
## 10 Alabama 2013-14    9358.929
## # … with 590 more rows
```

---
# Compute summaries

```r
annual_means <- tuition %>%
  pivot_longer(`2004-05`:`2015-16`,
               names_to = "year", 
               values_to = "avg_tuition")  %>%
  group_by(year) %>%
  summarize(mean_tuition = mean(avg_tuition))

annual_means
```

```
## # A tibble: 12 × 2
##    year    mean_tuition
##    <chr>          <dbl>
##  1 2004-05     6409.564
##  2 2005-06     6654.177
##  3 2006-07     6809.914
##  4 2007-08     7085.881
##  5 2008-09     7156.560
##  6 2009-10     7761.810
##  7 2010-11     8228.834
##  8 2011-12     8539.115
##  9 2012-13     8842.357
## 10 2013-14     8947.938
## 11 2014-15     9037.357
## 12 2015-16     9317.633
```

---
# Good

```r
ggplot(annual_means, aes(year, mean_tuition)) +
  geom_col() 
```

![](w2_files/figure-html/avg-tuition1-eval-1.png)

---
# Better?

```r
ggplot(annual_means, aes(year, mean_tuition)) +
  geom_col() +
  coord_flip()
```

![](w2_files/figure-html/avg-tuition2-1.png)

---
# Better still?

```r
ggplot(annual_means, aes(year, mean_tuition)) +
  geom_point() +
  coord_flip()
```

![](w2_files/figure-html/tuition3-1.png)

---
# Even better

```r
annual_means %>%
  mutate(year = readr::parse_number(year)) %>%
  ggplot(aes(year, mean_tuition)) +
    geom_line(color = "cornflowerblue") +
    geom_point()
```

![](w2_files/figure-html/tuition4-1.png)

--
Treat time (year) as a continuous variable

---
# Grouped points
Show change in tuition from 05-06 to 2015-16

```r
tuition %>% 
  select(State, `2005-06`, `2015-16`)
```

```
## # A tibble: 50 × 3
##    State       `2005-06` `2015-16`
##    <chr>           <dbl>     <dbl>
##  1 Alabama      5840.550  9751.101
##  2 Alaska       4632.623  6571.340
##  3 Arizona      5415.516 10646.28 
##  4 Arkansas     6082.379  7867.297
##  5 California   5527.881  9269.844
##  6 Colorado     5406.967  9748.188
##  7 Connecticut  8249.074 11397.34 
##  8 Delaware     8610.597 11676.22 
##  9 Florida      3924.234  6360.159
## 10 Georgia      4492.167  8446.961
## # … with 40 more rows
```

---

```r
lt <- tuition %>% 
  select(State, `2005-06`, `2015-16`) %>% 
  pivot_longer(`2005-06`:`2015-16`,
               names_to = "Year", 
               values_to = "Tuition")
lt
```

```
## # A tibble: 100 × 3
##    State      Year      Tuition
##    <chr>      <chr>       <dbl>
##  1 Alabama    2005-06  5840.550
##  2 Alabama    2015-16  9751.101
##  3 Alaska     2005-06  4632.623
##  4 Alaska     2015-16  6571.340
##  5 Arizona    2005-06  5415.516
##  6 Arizona    2015-16 10646.28 
##  7 Arkansas   2005-06  6082.379
##  8 Arkansas   2015-16  7867.297
##  9 California 2005-06  5527.881
## 10 California 2015-16  9269.844
## # … with 90 more rows
```

---
class: middle

```r
ggplot(lt, aes(State, Tuition)) +
  geom_line(aes(group = State), color = "gray40") +
  geom_point(aes(color = Year)) +
  coord_flip()
```

---

![](w2_files/figure-html/grouped_points3-eval-1.png)

---
# Extensions
We need to move on to other things, but we definitely would want to keep going here:

+ Order states according to something more meaningful (starting tuition, ending tuition, or difference in tuition)

+ Meaningful title, e.g., "Change in average tuition over a decade"

+ Consider better color scheme for points

+ Potentially color the difference line by magnitude

---
# Let's back up a bit
* Lets go back to our full data, but in a format that we can have a `year`
variable.

```r
tuition_l <- tuition %>%
    pivot_longer(-State,
                 names_to = "year", 
                 values_to = "avg_tuition")

tuition_l
```

---
# Heatmap

```r
ggplot(tuition_l, aes(year, State)) +
  geom_tile(aes(fill = avg_tuition)) 
```

![](w2_files/figure-html/heatmap2-eval-1.png)

---
# Better heatmap

```r
ggplot(tuition_l, aes(year, fct_reorder(State, avg_tuition))) +
  geom_tile(aes(fill = avg_tuition)) 
```

![](w2_files/figure-html/heatmap3-eval-1.png)

---
# Even better heatmap

```r
ggplot(tuition_l, aes(year, fct_reorder(State, avg_tuition))) +
  geom_tile(aes(fill = avg_tuition)) +
  scale_fill_viridis_c(option = "magma")
```

![](w2_files/figure-html/heatmap4-eval-1.png)

---
background-image: url(img/heatmap.png)
class: inverse-blue bottom
background-size:contain

---
# Quick aside
* Think about the data you have
* Given that these are state-level data, they have a geographic component

```r
#install.packages("maps")
state_data <- map_data("state") %>% # ggplot2::map_data
  rename(State = region)
```

---
# Join it
Obviously we'll talk more about joins later

```r
tuition <- tuition %>% 
  mutate(State = tolower(State))
states <- left_join(state_data, tuition)
head(states)
```

```
##        long      lat group order   State
## 1 -87.46201 30.38968     1     1 alabama
## 2 -87.48493 30.37249     1     2 alabama
## 3 -87.52503 30.37249     1     3 alabama
## 4 -87.53076 30.33239     1     4 alabama
## 5 -87.57087 30.32665     1     5 alabama
## 6 -87.58806 30.32665     1     6 alabama
##   subregion  2004-05 2005-06  2006-07  2007-08
## 1      <NA> 5682.838 5840.55 5753.496 6008.169
## 2      <NA> 5682.838 5840.55 5753.496 6008.169
## 3      <NA> 5682.838 5840.55 5753.496 6008.169
## 4      <NA> 5682.838 5840.55 5753.496 6008.169
## 5      <NA> 5682.838 5840.55 5753.496 6008.169
## 6      <NA> 5682.838 5840.55 5753.496 6008.169
##    2008-09  2009-10  2010-11  2011-12  2012-13
## 1 6475.092 7188.954 8071.134 8451.902 9098.069
## 2 6475.092 7188.954 8071.134 8451.902 9098.069
## 3 6475.092 7188.954 8071.134 8451.902 9098.069
## 4 6475.092 7188.954 8071.134 8451.902 9098.069
## 5 6475.092 7188.954 8071.134 8451.902 9098.069
## 6 6475.092 7188.954 8071.134 8451.902 9098.069
##    2013-14  2014-15  2015-16
## 1 9358.929 9496.084 9751.101
## 2 9358.929 9496.084 9751.101
## 3 9358.929 9496.084 9751.101
## 4 9358.929 9496.084 9751.101
## 5 9358.929 9496.084 9751.101
## 6 9358.929 9496.084 9751.101
```

---
# Rearrange

```r
states <- states %>% 
  gather(year, tuition, `2004-05`:`2015-16`)
head(states)
```

---
# Plot

```r
ggplot(states) +
  geom_polygon(aes(long, lat, group = group, fill = tuition)) +
* coord_fixed(1.3) +
  scale_fill_viridis_c(option = "magma") +
  facet_wrap(~year) 
```

![](w2_files/figure-html/usa-plot-1.png)

---
background-image: url(img/states-heatmap.png)
class: inverse bottom
background-size:contain

---
class: inverse-red middle

# Intro to textual data

## Disclaimer: 
This is going to be relatively fast, with not much depth. It's an intro, and I can work with you more individually if you end up working with text data.

---
# Structured vs unstructured

* Most every dataset you've ever worked with is what is referred to as a **structured** dataset - it has rows and columns.

* But there is an incredible amount of data out there that is **unstructured** - it just sort of exists

--
* Most text data is unstructured. How would you analyze the contents of a book? No rows or columns there

---
# Getting text data

There are **many** ways to get text data. Any digital text could potentially be used as textual data.

--
How about Wikipedia?

--
Anything that lives on the web is a common use case. Social media data being perhaps primary among them.

---
# "Screen" scraping
Short foray into web scraping. It's not expected you fully follow this. More about "exposure" and less about building competencies.

Use the [rvest](https://rvest.tidyverse.org/) package to scrape the data you see "on the screen".

Let's read in the Wikipedia page on Eugene

```r
library(rvest)
eugene <- read_html("https://en.wikipedia.org/wiki/Eugene%2C_Oregon")
```

---
background-image:url(img/eugene-wiki.png)
background-size: contain

---
# Grab paragraphs
The `"#mw-content-text > div.mw-parser-output > p"` is the CSS selector that I pulled from the website

```r
paragraphs <- eugene %>% 
  html_elements("#mw-content-text > div.mw-parser-output > p") %>%
  html_text2()
```

The first paragraph is just an empty line, so they are numbered p + 1

Print the first paragraph

```r
cat(stringr::str_wrap(paragraphs[2], 50))
```

```
## Eugene (/juːˈdʒiːn/ yoo-JEEN) is a city in the
## U.S. state of Oregon, in the Pacific Northwest. It
## is at the southern end of the Willamette Valley,
## near the confluence of the McKenzie and Willamette
## rivers, about 50 miles (80 km) east of the Oregon
## Coast.[7]
```

---
# Print the fourth paragraph

```r
cat(stringr::str_wrap(paragraphs[5], 50))
```

```
## The first people to settle in the Eugene area were
## known as the Kalapuyans, also written Calapooia
## or Calapooya. They made "seasonal rounds," moving
## around the countryside to collect and preserve
## local foods, including acorns, the bulbs of the
## wapato and camas plants, and berries. They stored
## these foods in their permanent winter village.
## When crop activities waned, they returned to their
## winter villages and took up hunting, fishing, and
## trading.[19][20] They were known as the Chifin
## Kalapuyans and called the Eugene area where they
## lived "Chifin", sometimes recorded as "Chafin" or
## "Chiffin".[21][22]
```

---
# Analysis
How do we analyze the text? What we we even analyze?

--
First, let's structure it! Turn the text into a simple data frame.

```r
library(tidyverse)
eugene_df <- tibble(
  paragraph = seq_along(paragraphs),
  description = paragraphs
)
eugene_df
```

```
## # A tibble: 130 × 2
##    paragraph
##        <int>
##  1         1
##  2         2
##  3         3
##  4         4
##  5         5
##  6         6
##  7         7
##  8         8
##  9         9
## 10        10
## # … with 120 more rows, and 1 more variable:
## #   description <chr>
```

---
# Can we analyze it now?

Not really... what would we analyze?

--
Words!

Let's break it into words. This is where the [tidytext](https://juliasilge.github.io/tidytext/) package comes into play.

---
# The `unnest_tokens()` function

Just like most functions in the tidyverse, we pipe our data to `unnest_tokens()`

* First argument is the name of the new column we want in our data

* Second argument is the text data to process

* Third argument is how the text should processed. The default is `"words"`, meaning the text will be broken into words.

---
# Example

```r
library(tidytext)
eugene_tidy_words <- eugene_df %>% 
  unnest_tokens(word, description)
eugene_tidy_words
```

```
## # A tibble: 7,814 × 2
##    paragraph word     
##        <int> <chr>    
##  1         2 eugene   
##  2         2 juːˈdʒiːn
##  3         2 yoo      
##  4         2 jeen     
##  5         2 is       
##  6         2 a        
##  7         2 city     
##  8         2 in       
##  9         2 the      
## 10         2 u.s      
## # … with 7,804 more rows
```

Not perfect, but pretty good

---
# What to do now?
Let's count some words!

```r
eugene_tidy_words %>% 
  count(word, sort = TRUE)
```

```
## # A tibble: 2,478 × 2
##    word       n
##    <chr>  <int>
##  1 the      597
##  2 and      249
##  3 of       231
##  4 in       230
##  5 eugene   178
##  6 a        136
##  7 to       116
##  8 is        95
##  9 for       76
## 10 was       76
## # … with 2,468 more rows
```

---
# Plot the top 15 words

```r
eugene_tidy_words %>% 
  count(word, sort = TRUE) %>% 
  mutate(word = fct_reorder(word, n)) %>% 
  slice(1:15) %>% # select only the first 15 rows
  ggplot(aes(n, word)) +
    geom_col(fill = "cornflowerblue")
```

![](w2_files/figure-html/unnamed-chunk-9-1.png)

---
# Not very informative

## Why?

--
Most of the words are common words like "the", "and", "of" (top three words)

--
These are referred to as "stop words".

--
Luckily, **tidytext** provides us with a dictionary of stop words. We can use an `anti_join()` with this dictionary to remove these words.

---
# Quick refresher

A `semi_join()` works just like an `inner_join()`, but without adding any columns. A `semi_join()` works by **keeping** only rows that are in common with the two datasets.

--
An `anti_join()` does basically the opposite, by **removing** any rows that are in common between the two datasets.

---
# Look at the stop words
This dataset is available to you as soon as you load **tidytext**.

There are three lexicons - I usually use all three.

```r
stop_words 
```

```
## # A tibble: 1,149 × 2
##    word        lexicon
##    <chr>       <chr>  
##  1 a           SMART  
##  2 a's         SMART  
##  3 able        SMART  
##  4 about       SMART  
##  5 above       SMART  
##  6 according   SMART  
##  7 accordingly SMART  
##  8 across      SMART  
##  9 actually    SMART  
## 10 after       SMART  
## # … with 1,139 more rows
```

---
# Count
Let's try counting again without the stop words included.

.pull-left[

```r
eugene_tidy_words %>% 
* anti_join(stop_words) %>%
  count(word, sort = TRUE)
```

```
## # A tibble: 2,199 × 2
##    word           n
##    <chr>      <int>
##  1 eugene       178
##  2 city          54
##  3 oregon        50
##  4 university    40
##  5 community     27
##  6 lane          24
##  7 eugene's      23
##  8 college       20
##  9 home          20
## 10 center        19
## # … with 2,189 more rows
```

]

.pull-right[
## So much more informative!
]

---
# Plot the top 15 words

```r
eugene_tidy_words %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  mutate(word = reorder(word, n)) %>% # make y-axis ordered by n
  slice(1:15) %>% # select only the first 15 rows
  ggplot(aes(n, word)) +
    geom_col(fill = "cornflowerblue")
```

![](w2_files/figure-html/unnamed-chunk-12-1.png)

---
# Add the headers in 
I hid the code here because it's weird and inefficient but the HTML structure made it difficult. You can look at the source if you want. The new dataframe is called `eugene_tidy_words2`.

```
## # A tibble: 130 × 3
##    paragraph header 
##        <int> <chr>  
##  1         1 Intro  
##  2         2 Intro  
##  3         3 Intro  
##  4         4 Intro  
##  5         5 History
##  6         6 History
##  7         7 History
##  8         8 History
##  9         9 History
## 10        10 History
## # … with 120 more rows, and 1 more variable:
## #   description <chr>
```

---
# Count words by header

Not surprisingly, "eugene" appears to be the most common among multiple categories.

We might want to remove "eugene" as well.

```r
eugene_tidy_words2 %>% 
  unnest_tokens(word, description) %>% 
  count(header, word, sort = TRUE) %>% 
  anti_join(stop_words)
```

```
## # A tibble: 3,029 × 3
##    header           word       n
##    <chr>            <chr>  <int>
##  1 Arts and culture eugene    54
##  2 History          eugene    21
##  3 Infrastructure   eugene    20
##  4 Arts and culture church    19
##  5 Education        eugene    15
##  6 Geography        eugene    15
##  7 Economy          eugene    14
##  8 Education        school    14
##  9 Government       eugene    14
## 10 Arts and culture center    12
## # … with 3,019 more rows
```

---
# Plot
Top 10 words by header

```r
p <- eugene_tidy_words2 %>% 
  unnest_tokens(word, description) %>% 
  count(header, word, sort = TRUE) %>% 
  anti_join(stop_words) %>% 
  group_by(header) %>% 
  slice(1:10) %>% 
* mutate(word = reorder_within(word, n, header)) %>%
  ggplot(aes(n, word)) +
    geom_col(fill = "cornflowerblue") +
*   scale_y_reordered() +
    facet_wrap(~header, scales = "free_y", ncol = 3) 
```

---
class: full-size-fig

![](w2_files/figure-html/unnamed-chunk-16-1.png)

---
class: inverse-orange middle
# String manipulations

---
# Base vs tidyverse
The tidyverse package is [{stringr}](https://stringr.tidyverse.org/)

It is more consistent than base functions and occassionally faster

However, I tend to prefer the base functions, and they are still more commonly seen "in the wild" than **stringr**.

We'll therefore briefly cover both.

---
# Inconsistencies

Super common example

```r
set.seed(123)
ex <- tibble(
  gender = sample(
    c("f", "F", "Fem", "Female", "FEMALE", 
      "m", "M", "Male", "MALE",
      "nb", "NB", "non-binary", 
      "agender", "AGENDER", "Agender",
      "gender-fluid", "fluid",
      "No response"),
    500, 
    replace = TRUE),
  score = rnorm(500)
)
```

---
# Have vs want

.pull-left[
## Have

```
## # A tibble: 18 × 2
##    gender           n
##    <chr>        <int>
##  1 agender         25
##  2 Agender         26
##  3 AGENDER         29
##  4 f               24
##  5 F               24
##  6 Fem             27
##  7 Female          27
##  8 FEMALE          25
##  9 fluid           28
## 10 gender-fluid    28
## 11 m               29
## 12 M               34
## 13 Male            37
## 14 MALE            27
## 15 nb              36
## 16 NB              27
## 17 No response     21
## 18 non-binary      26
```
]

.pull-right[
## Want

```
## # A tibble: 6 × 2
##   gender          n
##   <chr>       <int>
## 1 agender        80
## 2 female        127
## 3 fluid          56
## 4 male          127
## 5 no response    21
## 6 non-binary     89
```

]

---
class: inverse-blue middle
# Walkthrough
Getting to what we want takes a few steps. Let's do it together!

---
# Consistent case
* The first thing we might want to do is change everything to uppercase or lowercase. This will fix many of our inconsistencies.

* Options are `stringr::str_to_upper()`, `stringr::str_to_lower()`, `base::toupper()` or `base::tolower()`

---
# Consistent case

.pull-left[
## Original

```r
ex %>% 
  count(gender)
```

.pull-right[
## Modified

```r
ex %>%
  mutate(gender = tolower(gender)) %>% 
  count(gender)
```

```
## # A tibble: 11 × 2
##    gender           n
##    <chr>        <int>
##  1 agender         80
##  2 f               48
##  3 fem             27
##  4 female          52
##  5 fluid           28
##  6 gender-fluid    28
##  7 m               63
##  8 male            64
##  9 nb              63
## 10 no response     21
## 11 non-binary      26
```

]

---
# What next?

--
Collapse the genders that have "fluid"?

--
Use `grepl()` (global regular expression parser **l**ogical) with `ifelse()` to replace *if* a pattern is found

--
You could also use `stringr::str_detect()` instead. The arguments are just in reversed order

```r
grepl("fluid", gender)
str_detect(gender, "fluid")
```

---

```r
ex %>%
  mutate(
    gender = tolower(gender),
    gender = ifelse(grepl("fluid", gender), "gender-fluid", gender)
  ) %>% 
  count(gender)
```

```
## # A tibble: 10 × 2
##    gender           n
##    <chr>        <int>
##  1 agender         80
##  2 f               48
##  3 fem             27
##  4 female          52
##  5 gender-fluid    56
##  6 m               63
##  7 male            64
##  8 nb              63
##  9 no response     21
## 10 non-binary      26
```

---
# stringr

```r
library(stringr)
ex %>%
  mutate(
    gender = tolower(gender),
    gender = ifelse(str_detect(gender, "fluid"), "gender-fluid", gender)
  ) %>% 
  count(gender)
```

---
# What next?

--
How about - if it starts with "m", then "Male"?

--
Use `"^"` to denote "starts with"

---

```r
ex %>%
  mutate(
    gender = tolower(gender),
    gender = ifelse(grepl("fluid", gender), "gender-fluid", gender),
    gender = ifelse(grepl("^m", gender), "male", gender)
  ) %>% 
  count(gender)
```

```
## # A tibble: 9 × 2
##   gender           n
##   <chr>        <int>
## 1 agender         80
## 2 f               48
## 3 fem             27
## 4 female          52
## 5 gender-fluid    56
## 6 male           127
## 7 nb              63
## 8 no response     21
## 9 non-binary      26
```

---
# Again
Replicate the same thing, but this time with "female". Note that we couldn't do this initially, but we can now because `"fluid"` has been collapsed with `"gender-fluid"`.

You try first

---

```r
ex %>%
  mutate(
    gender = tolower(gender),
    gender = ifelse(grepl("fluid", gender), "gender-fluid", gender),
    gender = ifelse(grepl("^m", gender), "male", gender),
    gender = ifelse(grepl("^f", gender), "female", gender)
  ) %>% 
  count(gender)
```

```
## # A tibble: 7 × 2
##   gender           n
##   <chr>        <int>
## 1 agender         80
## 2 female         127
## 3 gender-fluid    56
## 4 male           127
## 5 nb              63
## 6 no response     21
## 7 non-binary      26
```

---
# Again?
Can we do the same thing with `"^n"` for non-binary?

--
## NO!

--
Little bit more complicated - use Boolean logic.

---

```
## # A tibble: 6 × 2
##   gender           n
##   <chr>        <int>
## 1 agender         80
## 2 female         127
## 3 gender-fluid    56
## 4 male           127
## 5 no response     21
## 6 non-binary      89
```

---
# stringr version

```r
ex %>%
  mutate(
    gender = tolower(gender),
    gender = ifelse(str_detect(gender, "fluid"), "gender-fluid", gender),
    gender = ifelse(str_detect(gender, "^m"), "male", gender),
    gender = ifelse(str_detect(gender, "^f"), "female", gender),
    gender = ifelse(
      str_detect(gender, "^n") & gender != "no response",
      "non-binary",
      gender
    )
  ) %>% 
  count(gender)
```

---
# Special characters

* `^`: Anchor - matches the start of a string
* `$`: Anchor - matches the end of a string
* `*`: Matches the preceding character **zero or more** times
* `?`: Matches the preceding character **zero or one** times
* `+`: Matches the preceding character **one or more** times
* `{`: Used to specify number of matches, `a{n}`, `a{n,}`, and `a{n, m}`
* `.`: Wildcard - matches any character
* `|`: OR operator
* `[`: Alternates, also used for character matching (e.g., `[:digit:]`)
* `(`: Used for backreferencing, look aheads, or groups
* `\`: Used to escape special characters

---
# More detail
Both of the below are good places to get more comprehensive information

* [RStudio cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/strings.pdf)

* [Regular expression](https://stringr.tidyverse.org/articles/regular-expressions.html) vignette

---
# One more quick example

```r
library(edld652)
d <- get_data("EDFacts_acgr_lea_2011_2019")
```

```
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```

```r
d
```

```
## # A tibble: 11,326 × 29
##    ALL_COHORT ALL_RATE CWD_COHORT CWD_RATE
##         <dbl> <chr>         <dbl> <chr>   
##  1        252 80                3 PS      
##  2        398 75               47 70-79   
##  3       1020 89               51 40-49   
##  4        750 91               35 60-69   
##  5        128 55-59            15 LT50    
##  6        166 90-94             9 GE50    
##  7        336 90               30 60-79   
##  8        273 77               11 LT50    
##  9        134 70-74             4 PS      
## 10        266 58               33 50-59   
## # … with 11,316 more rows, and 25 more variables:
## #   DATE_CUR <chr>, ECD_COHORT <dbl>,
## #   ECD_RATE <chr>, FIPST <chr>, FILEURL <chr>,
## #   LEAID <chr>, LEANM <chr>, LEP_COHORT <dbl>,
## #   LEP_RATE <chr>, MAM_COHORT <dbl>,
## #   MAM_RATE <chr>, MAS_COHORT <dbl>,
## #   MAS_RATE <chr>, MBL_COHORT <dbl>, …
```

---
# Do three things:

* Create a new variable that identifies if the LEA is associated with a city or a county

* Drop "City" or "County" from the LEA name (e.g., "Albertville City" would be "Albertville")

* Replace all `.` with `--DOT--` in `FILEURL` (so they are not actual links)

---
# City or county

Ideas?

```r
d <- d %>% 
  mutate(county = grepl("county$", tolower(LEANM)))
```

---
# Quick Check

```r
d %>% 
  select(LEANM, county) %>% 
  print(n = 15)
```

```
## # A tibble: 11,326 × 2
##    LEANM            county
##    <chr>            <lgl> 
##  1 Albertville City FALSE 
##  2 Marshall County  TRUE  
##  3 Hoover City      FALSE 
##  4 Madison City     FALSE 
##  5 Leeds City       FALSE 
##  6 Boaz City        FALSE 
##  7 Trussville City  FALSE 
##  8 Alexander City   FALSE 
##  9 Andalusia City   FALSE 
## 10 Anniston City    FALSE 
## 11 Arab City        FALSE 
## 12 Athens City      FALSE 
## 13 Attalla City     FALSE 
## 14 Auburn City      FALSE 
## 15 Autauga County   TRUE  
## # … with 11,311 more rows
```

---
# Remove city/county

* Lots of ways to do this. Use `base::gsub()` or `stringr::str_replace_all()`

* Replace everything after the space with nothing

```r
d %>% 
  select(LEANM) %>% 
  mutate(new_name = gsub(" .+", "", LEANM))
```

```
## # A tibble: 11,326 × 2
##    LEANM            new_name   
##    <chr>            <chr>      
##  1 Albertville City Albertville
##  2 Marshall County  Marshall   
##  3 Hoover City      Hoover     
##  4 Madison City     Madison    
##  5 Leeds City       Leeds      
##  6 Boaz City        Boaz       
##  7 Trussville City  Trussville 
##  8 Alexander City   Alexander  
##  9 Andalusia City   Andalusia  
## 10 Anniston City    Anniston   
## # … with 11,316 more rows
```

---
# Another way
There are other ways too, of course

```r
d %>% 
  select(LEANM) %>% 
  mutate(new_name = gsub(" City$| County$", "", LEANM))
```

---
# Final step

Handling the URLs. This is a bit artificial, but it illustrates escaping, which is important.

* Remember `.` is a special character, so needs to be escaped

* `\` itself is a special character so it needs to be escaped. Functionally, then, you escape special characters with `\\`, not `\`.

---

```r
d %>% 
  select(FILEURL) 
```

```
## # A tibble: 11,326 × 1
## # … with 11,316 more rows, and 1 more variable:
## #   FILEURL <chr>
```

---

```r
d %>% 
  select(FILEURL) %>% 
  mutate(FILEURL = gsub("\\.", "--DOT--", FILEURL)) %>% 
  as.data.frame()
```

```
##                                                                                             FILEURL
## 1    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 2    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 3    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 4    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 5    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 6    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 7    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 8    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 9    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 10   https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 11   https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
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## 991  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 992  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 993  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 994  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 995  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 996  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 997  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 998  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 999  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 1000 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
##  [ reached 'max' / getOption("max.print") -- omitted 10326 rows ]
```

---
# Wrapping up
This was pretty quick, still a lot we didn't get to

I'll try to embed more opportunities for you to practice these skills throughout the term

---
class: inverse-green middle
# Next time

No class - MLK Jr. Day. Please do have your lab in before then.

---
class: inverse-orange middle
# Lab 1