Module 2: Data Wrangling

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.title[
# Module 2: Data Wrangling
]
.subtitle[
## Introduction to Tools of the Trade in Data Analysis
]
.author[
### Dr. Christopher Kenaley
]
.institute[
### Boston College
]
.date[
### 2025/9/8
]

---

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# In class today

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Today we'll ....

## Tidy data

- Shape of data (long vs wide)

- Pivoting

- Joining and relational data

- Peak under the hood of Module Project 2

]

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## Shape of data (`%>%`)?

Wide: one row per sample; many columns for measurements

Iris ships in wide form (4 measurement columns + Species)

Pros:

- Easy to read raw values
 - Convenient for direct modeling/viz with specific columns

``` r
head(iris, 6) 
```

```
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa
```

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## Shape of data (`%>%`)?

Long: many rows per sample; fewer columns for measurements

Iris ships in long form (4 measurement in one column)

Pros:

- Tidy structure enables grouped summaries, faceting, and tidy verbs (functions)

``` r
iris_long <- iris %>%
  pivot_longer(
    cols =Sepal.Length:Petal.Width 
  )

iris_long %>% head(6)
```

```
## # A tibble: 6 × 3
##   Species name         value
##   <fct>   <chr>        <dbl>
## 1 setosa  Sepal.Length   5.1
## 2 setosa  Sepal.Width    3.5
## 3 setosa  Petal.Length   1.4
## 4 setosa  Petal.Width    0.2
## 5 setosa  Sepal.Length   4.9
## 6 setosa  Sepal.Width    3
```

---
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``` r
iris_long %>%
  ggplot(aes(x = name, y = value, fill = Species)) +
  geom_boxplot() +
  facet_grid(.~Species)+
  theme(axis.text.x = element_text(angle=60,hjust=1))
```

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## Merging/joining data

Let's make some pH data

``` r
pH <- iris_long %>% 
  group_by(Species,name) %>% 
  reframe(pH=runif(1,5,8))
```

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## Merging/joining data

join based on species

``` r
iris_long %>% 
  left_join(pH)
```

```
## Joining with `by = join_by(Species, name)`
```

```
## # A tibble: 600 × 4
##    Species name         value    pH
##    <fct>   <chr>        <dbl> <dbl>
##  1 setosa  Sepal.Length   5.1  5.52
##  2 setosa  Sepal.Width    3.5  5.54
##  3 setosa  Petal.Length   1.4  5.23
##  4 setosa  Petal.Width    0.2  5.46
##  5 setosa  Sepal.Length   4.9  5.52
##  6 setosa  Sepal.Width    3    5.54
##  7 setosa  Petal.Length   1.4  5.23
##  8 setosa  Petal.Width    0.2  5.46
##  9 setosa  Sepal.Length   4.7  5.52
## 10 setosa  Sepal.Width    3.2  5.54
## # ℹ 590 more rows
```
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## Merging/joining data

join based on species and plot

``` r
iris_long %>% 
  left_join(pH) %>%
  ggplot(aes(pH,value,col=name))+geom_point(alpha=0.1)+
  facet_grid(.~Species)
```

```
## Joining with `by = join_by(Species, name)`
```