Research Methods: Open Science and Reproducible Research in Linguistics

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.title[
# Research Methods: Open Science and Reproducible Research in Linguistics
]
.subtitle[
## Welcome to the tidyverse I: Standards and plots
]
.author[
### Joseph V. Casillas, PhD
]
.date[
### Rutgers University</br>Spring 2019</br>Last update: 2025-02-09
]

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# Coding standards

(and some other important stuff)

---

# Coding standards

### Comments

- You ~~should~~ have to comment your code

- While you are doing an analysis you feel like you know everything about your 
data. The logic of every step you take makes sense and is fresh in your mind.

- 6 months from now you will not remember any of that

- It is imperative that you obsessively comment your code

- Think of it as leaving little post-it notes in your script that help you 
remember your train of thought

- It provides a way to time stamp your progress

- It allows you to logically divide a script into sections

- It allows others to understand your code

---

# Coding standards

### Comments

- In R, comments are any characters that follow **\#** on the same line

.pull-left[

``` r
# This is a comment

# You can put comments above your code
x <- 2 # or next to it

x^2 # x squared
```

```
## [1] 4
```

]

.pull-right[

``` r
# Fit a model for mpg as a function of drat
mod <- lm(mpg ~ drat, data = mtcars)

# Plot it
ggplot(mtcars, aes(x = drat, y = mpg)) + 
    geom_point() + 
    geom_abline(intercept = coef(mod)[1], slope = coef(mod)[2])
```

<img src="index_files/figure-html/comments-example-2-1.png" width="504" />
]

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# Examples

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# Variable naming conventions

---

# Coding standards

### Variable naming conventions

.pull-left[

- Similar to project/file/folder naming conventions

- Use meaningful names

- Programming language-specific prescriptivism

- Avoid caps and periods

- Use \_ for separating meaningful words

]

.pull-right[

.large[Ex. from R4DS]

```
i_use_snake_case
otherPeopleUseCamelCase
some.people.use.periods
And_aFew.People_RENOUNCEconvention
```

.small[
**My suggestion**: Use `snake_case` for everything (i.e., naming objects in R, columns in excel, etc.). You'll see why later...
]

]

---

# Coding standards

### Space

- Unlike when naming projects, folders, files, and variables, it is advisable to leave white space in some places in your scripts to make them more readable

- In general it is good practice to leave a single space before and after arguments in functions and when assigning objects
  - .red[Meh]: `x<-2`
  - .blue[Better]: `x <- 2`

--
  - .red[Meh]: `lm(mpg~drat,data=mtcars)`
  - .blue[Better]: `lm(mpg ~ drat, data = mtcars)`

- This is also true for markdown syntax
  - .red[Meh]: `#Introduction`
  - .blue[Better]: `# Introduction`

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# Data visualization 101

.big[Plotting with `ggplot`]

---

# Data visualization 101

### Basic plot types

.large[

.pull-left[

- Scatterplots

- Bargraph

- Boxplots

- Point estimate + spread

]

.pull-right[

- Imagine we are interested in learning more about gas mileage.

- We have the `mtcars` dataset that will allow us to look at miles per gallon 
(**mpg**), engine displacement (**disp**), and transmission type (**am**)

- **mpg** and **disp** are continuous variables, **am** is categorical

]

---

# Data visualization 101

### Scatterplot

- When you have two continuous variables

???

```
mtcars %>%
  ggplot(., aes(x = disp, y = mpg, fill = factor(am))) + 
    geom_point(pch = 21, size = 2)  + 
    scale_fill_brewer(palette = "Set2", name = '', 
                      labels = c('Automatic', 'Manual')) + 
    theme_grey(base_size = 18, base_family = 'Times')
```

---

# Data visualization 101

### Bargraph

- Please never make a bargraph

???

```
mtcars %>%
  group_by(am) %>% 
  summarize(mean_mpg = mean(mpg)) %>% 
  ggplot(., aes(x = factor(am), y = mean_mpg, fill = factor(am))) + 
    geom_bar(stat = 'identity') + 
    ylim(0, 40) + 
    xlab('') + 
    scale_x_discrete(breaks = c(0, 1), labels = c('Automatic', 'Manual')) + 
    scale_fill_brewer(palette = "Set2", name = '', 
                      labels = c('Automatic', 'Manual')) + 
    theme_grey(base_size = 18, base_family = 'Times')
```

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# Data visualization 101

### Bargraph

---

# Data visualization 101

### Boxplot (box and whisker plot)

- Better option than bargraph

???

```
mtcars %>%
  ggplot(., aes(x = factor(am), y = mpg, fill = factor(am))) + 
    geom_boxplot() + 
    ylim(0, 40) + 
    xlab('') + 
    scale_x_discrete(breaks = c(0, 1), labels = c('Automatic', 'Manual')) + 
    scale_fill_brewer(palette = "Set2", name = '', 
                      labels = c('Automatic', 'Manual')) + 
    theme_grey(base_size = 18, base_family = 'Times')
```

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---

# Data visualization 101

### Point estimate + spread

- Point estimate can be mean, median, mode, 
- Spread can be SD, SE, 95% CI, etc.

???

```
mtcars %>%
  ggplot(., aes(x = factor(am), y = mpg, fill = factor(am))) + 
    stat_summary(fun.data = mean_sdl, geom = 'pointrange', pch = 21, 
                 size = 1.5) + 
    ylim(0, 40) + 
    xlab('') + 
    scale_x_discrete(breaks = c(0, 1), labels = c('Automatic', 'Manual')) + 
    scale_fill_brewer(palette = "Set2", name = '', 
                      labels = c('Automatic', 'Manual')) + 
    theme_grey(base_size = 18, base_family = 'Times')
```

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# What you need to know

.big[A grammar of graphics]

---

# ggplot2

### Overview

- Written by Hadley Wickham when he was a graduate student at Iowa State

- Automatically deals with spacings, text, titles but also allows you to annotate by "adding"

- Plots are built up in layers

- Plot the data, overlay a summary, meta data and annotation

---

# ggplot2

### Components

- Works with a dataframe

- aesthetic mappings: how data are mapped to color, size

- geoms: geometric objects like points, lines, shapes

- stats: statistical transformations

- facets: for conditional plots

---

# ggplot2

### It starts with data

- We will continue using the `mtcars` dataframe

- Fire up RStudio, load the `tidyverse` and take a look at `mtcars`

- Try using `glimpse(mtcars)`, , `head(mtcars)`, `dim(mtcars)`, and `summary(mtcars)`

- What kind of information did you learn about the dataframe?

---

# ggplot2

### It starts with data

``` r
glimpse(mtcars)
```

```
## Rows: 32
## Columns: 11
## $ mpg  <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,…
## $ cyl  <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8,…
## $ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 16…
## $ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180…
## $ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,…
## $ wt   <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.…
## $ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18…
## $ vs   <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,…
## $ am   <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,…
## $ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3,…
## $ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2,…
```

``` r
dim(mtcars)
```

```
## [1] 32 11
```

---

# ggplot2

### It starts with data

``` r
head(mtcars, 15)
```

```
##                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4          21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag      21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710         22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive     21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout  18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant            18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360         14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D          24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230           22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280           19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C          17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE         16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL         17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC        15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
```

---

# ggplot2

### It starts with data

``` r
summary(mtcars)
```

```
##       mpg             cyl             disp             hp       
##  Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
##  1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
##  Median :19.20   Median :6.000   Median :196.3   Median :123.0  
##  Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
##  3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
##  Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
##       drat             wt             qsec             vs        
##  Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
##  1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
##  Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
##  Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
##  3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
##  Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
##        am              gear            carb      
##  Min.   :0.0000   Min.   :3.000   Min.   :1.000  
##  1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
##  Median :0.0000   Median :4.000   Median :2.000  
##  Mean   :0.4062   Mean   :3.688   Mean   :2.812  
##  3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
##  Max.   :1.0000   Max.   :5.000   Max.   :8.000
```

---

# ggplot2

### It starts with data

- We will start all of our plots in the same way... using the dataframe and 
the pipe operator (**|>**) like this:

``` r
# Call dataframe and 'pipe' into ggplot function
*mtcars |>
  ggplot()
```

- Notice that I have commented my code

- The pipe operator sends whatever is on the left of the pipe (`mtcars`) to whatever is on the right side (`ggplot`)

- As is, this code won't do anything yet

---

# ggplot2

### Aesthetics (aes)

- aesthetic mappings: how data are mapped to color, size, and **axis**
- Let's update our code:

``` r
# Call dataframe and 'pipe' into ggplot function
# Add aesthetic mapping to x and y axis
mtcars |>  
  ggplot() + 
* aes(x = disp, y = mpg)
```

---

# ggplot2

### Geometric objects (geom_)

- Whenever you want to make a plot you have to think about what kind of geometric object makes sense for your needs. What do we need here?

``` r
# Call dataframe and 'pipe' into ggplot function
# Add aesthetic mapping to x and y axis
# Add geometric object (geom_point)
mtcars |>  
  ggplot() + 
  aes(x = disp, y = mpg) + # We 'add' a layer to the plot
* geom_point()
```

---

# ggplot2

### Geometric objects (geom_)

- There are many types of geoms
  - `geom_point()`
  - `geom_smooth()`
  - `geom_hist()`
  - `geom_bar()`
  - `geom_boxplot()`
  - etc.

- Exercise
  - Recall that `mtcars` has a lot of variables (see `glimpse(mtcars)`)
  - Try swapping other variables for x and y
  - Add a smoother to the plot (`geom_smooth()`)
  - Add more aesthetics (try `color` and `shape`). What happens?
  - Add a geom to make a boxplot

---

# ggplot2

### Statistical transformations (stat_)

- We can use the `stat_summary()` function to calculate a value and overlay it 
on the plot

.pull-left[

``` r
mtcars |>
  ggplot() +
  aes(x = am, y = mpg) + 
  stat_summary(
    fun.data = mean_sdl, 
    geom = 'pointrange'
  )
```

]

.pull-right[

]

---

# ggplot2

### Faceting

- What about when we want to see more factors at once?
- For example, what if we want to see **mpg** as a function of **am** and **cyl**?

#### Solution 1a

.pull-left[

``` r
mtcars |> 
  ggplot() +
  aes(x = am, y = mpg) + 
  stat_summary(
    fun.data = mean_sdl, 
    geom = 'pointrange'
  ) + 
* facet_grid(. ~ cyl)
```

]

.pull-right[

]

---

# ggplot2

### Faceting

- What about when we want to see more factors at once?
- For example, what if we want to see **mpg** as a function of **am** and **cyl**?

#### Solution 1b

.pull-left[

``` r
mtcars |> 
  ggplot() + 
  aes(x = am, y = mpg) + 
  stat_summary(
    fun.data = mean_sdl, 
    geom = 'pointrange'
  ) + 
* facet_grid(cyl ~ .)
```

]

.pull-right[

]

---

# ggplot2

### Faceting

- What about when we want to see more factors at once?
- For example, what if we want to see **mpg** as a function of **am** and **cyl**?

#### Solution 2

.pull-left[

``` r
mtcars |> 
  ggplot() + 
* aes(x = factor(am), y = mpg, shape = factor(cyl)) +
  stat_summary(
    fun.data = mean_sdl, 
    geom = 'pointrange', 
    position = position_dodge(0.5)
  )
```
]

.pull-right[

]

---

# More exercises

### Using `mtcars`, create the following

- a boxplot with a variable mapped to the `fill` aesthetic

- a boxplot with a variable mapped to the `color` aesthetic and a horizontal facet

- a scatterplot with a regression line (see `?geom_smooth`)

- a scatterplot with a regression line (see `?geom_smooth`) and a categorical factor (try the aesthetic `shape`)

- a histogram of `mpg`

- What does `geom_violin()` do? What geom can it replace?

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# Getting help

## If you have problems using ggplot2
## ask for help in the slack channel

### You can find some very basic tutorials related to 
### plotting in R [here][here]

[here]: http://www.jvcasillas.com/base_lattice_ggplot/