Code and Data Slides Template

class: center, middle, inverse, title-slide

.title[
# Code and Data Slides Template
]
.author[
### S. Mason Garrison
]

---

# Simple Code Chunk

Here's a simple R code chunk:

``` r
x <- 3 * 4
y <- 10 - 2
z <- x + y
print(z)
```

```
## [1] 20
```

---

# Code with Output

This code chunk will show both the code and its output:

``` r
# Create a vector
numbers <- c(1, 2, 3, 4, 5)

# Calculate mean and standard deviation
mean_value <- mean(numbers)
sd_value <- sd(numbers)

# Print results
cat("Mean:", mean_value, "\n")
```

```
## Mean: 3
```

``` r
cat("Standard Deviation:", sd_value)
```

```
## Standard Deviation: 1.581139
```

---

# Code with Highlighted Lines

In this example, we'll highlight important lines:

``` r
# Load the tidyverse package
library(tidyverse)

# Create a simple dataframe
df <- tibble(
  x = 1:5,
  y = c(2, 4, 6, 8, 10)
)

# Plot the data
ggplot(df, aes(x, y)) +
  geom_point() +
  geom_line()
```

![](d00_code_files/figure-html/highlight-lines-1.png)

---

# Data Visualization Code

Here's an example of creating a data visualization with code:

``` r
conflicts_prefer(palmerpenguins::penguins,.quiet = TRUE)
ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g, color = species)) +
  geom_point(alpha = 0.7,na.rm=TRUE ) +
  labs(title = "Penguin Size",
       x = "Flipper Length (mm)",
       y = "Body Mass (g)",
       color = "Species") +
  theme_minimal()
```

![](d00_code_files/figure-html/data-viz-1.png)

---

# Data Manipulation

This slide demonstrates data manipulation with dplyr:

``` r
penguins %>%
  group_by(species, island) %>%
  summarize(
    mean_body_mass = mean(body_mass_g, na.rm = TRUE),
    mean_flipper_length = mean(flipper_length_mm, na.rm = TRUE)
  ) %>%
  arrange(desc(mean_body_mass))
```

```
## # A tibble: 5 × 4
## # Groups:   species [3]
##   species   island    mean_body_mass mean_flipper_length
##   <fct>     <fct>              <dbl>               <dbl>
## 1 Gentoo    Biscoe             5076.                217.
## 2 Chinstrap Dream              3733.                196.
## 3 Adelie    Biscoe             3710.                189.
## 4 Adelie    Torgersen          3706.                191.
## 5 Adelie    Dream              3688.                190.
```

---

# Interactive Code Output

This slide shows how to create an interactive table:

``` r
library(DT)

penguins %>%
  select(species, island, body_mass_g, flipper_length_mm) %>%
  datatable()
```

<div class="datatables html-widget html-fill-item" id="htmlwidget-c2e3fede5c798441fdae" style="width:100%;height:auto;"></div>
<script type="application/json" 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---

# Code and Plot Side by Side

.pull-left[

``` r
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE,formula='y~x') +
  labs(
    title = "Car Weight vs. Miles Per Gallon",
    x = "Weight (1000 lbs)",
    y = "Miles Per Gallon"
  )
```
]

.pull-right[
<img src="d00_code_files/figure-html/unnamed-chunk-1-1.png" width="100%" />
]

---

# Statistical Analysis

This slide demonstrates a simple statistical test:

``` r
# Perform a t-test
t_test_result <- t.test(flipper_length_mm ~  sex, data = penguins)

# Print the results
print(t_test_result)
```

```
## 
## 	Welch Two Sample t-test
## 
## data:  flipper_length_mm by sex
## t = -4.8079, df = 325.28, p-value = 2.336e-06
## alternative hypothesis: true difference in means between group female and group male is not equal to 0
## 95 percent confidence interval:
##  -10.064811  -4.219821
## sample estimates:
## mean in group female   mean in group male 
##             197.3636             204.5060
```

---

## Residuals Plot

.pull-left[

``` r
depth_length_fit <- linear_reg() %>%
  set_engine("lm") %>%
  fit(bill_depth_mm ~ bill_length_mm, data = penguins)

depth_length_fit_aug <- augment(depth_length_fit$fit)

ggplot(depth_length_fit_aug, mapping = aes(x = .fitted, y = .resid)) +
  geom_point(alpha = 0.5) +
  geom_hline(yintercept = 0, color = "#bf2986ff", lty = "dashed") +
  labs(x = "Predicted Height", y = "Residuals")
```
]
.pull-right[
<img src="d00_code_files/figure-html/unnamed-chunk-2-1.png" width="100%" />
]

---

## Model Output

``` r
linear_reg() %>%
  set_engine("lm") %>%
  fit(bill_depth_mm ~ bill_length_mm, data = penguins) %>%
  tidy()
```

```
## # A tibble: 2 × 5
##   term           estimate std.error statistic  p.value
##   <chr>             <dbl>     <dbl>     <dbl>    <dbl>
## 1 (Intercept)     20.9       0.844      24.7  4.72e-78
## 2 bill_length_mm  -0.0850    0.0191     -4.46 1.12e- 5
```

---

## Data Exploration

.pull-left[

``` r
ggplot(penguins, aes(x = bill_depth_mm)) +
  geom_histogram(binwidth = 5) +
  labs(x = "Bill Depth (mm)", y = NULL)
```

```
## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_bin()`).
```
]
.pull-right[

```
## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_bin()`).
```

<img src="d00_code_files/figure-html/unnamed-chunk-3-1.png" width="100%" />
]