Module 1: The Basics

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.title[
# Module 1: The Basics
]
.subtitle[
## Gitting and Subsetting
]
.author[
### Dr. Christopher Kenaley
]
.institute[
### Boston College
]
.date[
### 2025/8/29
]

---

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# Today we'll .....

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- Review Gitting, clone a repo

- Subsetting data

- `for` loops

- Peak under the hood of Module Project 1

]

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# Pleas for help

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Please accept team and repo invites!

]

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![](pending.png)

]

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# Git and RStudio
## Important steps

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1a: [If repo exists] Go to repo on github, copy URL

1b: [If repo doesn't exists] Make a repot on github, copy

]

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![](giturl.png)
]

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# Git and RStudio
## Important steps

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2: Create an R project
  + Go to File -> New Project....
  + Choose Version Control:
  + In the "New Project" dialog, select "Version Control".
]

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![](http://www.rstudio.com/images/docs/projects_new.png)
]

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# Git and RStudio
## Important steps

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3: Select "Git" as the system
- Click **Git**.
]

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![](versioncontrol.png)
]

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# Git and RStudio
## Important steps

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4: Paste the Repository URL
- In **Clone Git Repository**, paste the repo **URL** (copied from GitHub).
- Example: `https://github.com/YOUR-ORG/YOUR-REPO.git`
]

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![](gitpaste.png)

]

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# Git and RStudio
## Important steps

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  5: Name the repo locally
  - Give your local project a short, clear **Name**.
  - This becomes the folder name.
 6: Choose a destination folder
  - Click **Browse…** and pick where to put the project.
  - Tip: keep a top-level folder/directory like `~/bc-3140/` or `~/courses/`.
]

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![](clonedir.png)
]

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# Git and RStudio
## Important steps

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7: Click "Create Project"
- RStudio will clone the repo and open the project.
- Look for the **Git** pane (upper right: "Commit", "Pull", "Push").
]

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![](gittab.png)
]

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# Git and RStudio
# You're ready to code 🎉

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- Use the **Git** tab to manage workflow.
- *Always* Use **Pull** first to sync download
- **Commit** with message to stage make changes.
- **Push** to publish.
]

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![](gittab.png)

]

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# Subsetting

## Goals

- Compare **row** and **column** subsetting in **base R** 
- Show common patterns: conditions, positions, name patterns, etc. 
- Use the built-in **`iris`** dataset for reproducible examples

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# Data & Setup

``` r
library(tidyverse) #Always!
class(iris)
```

```
## [1] "data.frame"
```

``` r
iris <- as_tibble(iris) #converts data frame to tibble
class(iris)
```

```
## [1] "tbl_df"     "tbl"        "data.frame"
```

``` r
head(iris)
```

```
## # A tibble: 6 × 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
## 1          5.1         3.5          1.4         0.2 setosa 
## 2          4.9         3            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           3.6          1.4         0.2 setosa 
## 6          5.4         3.9          1.7         0.4 setosa
```

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# data frames and tibbles

- data frame (`?data.frame`): 2D table, rows x column

- tibble (`?tibble`): super-charges 2D table, rows x column

## Isolating rows, columns in 2D classes

``` r
# rows
iris[1,]
```

```
## # A tibble: 1 × 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
## 1          5.1         3.5          1.4         0.2 setosa
```
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## Isolating rows, columns in 2D classes

``` r
# multiple rows
iris[c(1,3,5),]
```

```
## # A tibble: 3 × 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
## 1          5.1         3.5          1.4         0.2 setosa 
## 2          4.7         3.2          1.3         0.2 setosa 
## 3          5           3.6          1.4         0.2 setosa
```

---
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## Isolating rows, columns in 2D classes

``` r
#columns
head(iris[,1])
```

```
## # A tibble: 6 × 1
##   Sepal.Length
##          <dbl>
## 1          5.1
## 2          4.9
## 3          4.7
## 4          4.6
## 5          5  
## 6          5.4
```

``` r
#columns with $
head(iris$Sepal.Length)
```

```
## [1] 5.1 4.9 4.7 4.6 5.0 5.4
```

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## Isolating rows, columns in 2D classes

``` r
#columns with names
head(iris[c("Sepal.Length","Sepal.Width")])
```

```
## # A tibble: 6 × 2
##   Sepal.Length Sepal.Width
##          <dbl>       <dbl>
## 1          5.1         3.5
## 2          4.9         3  
## 3          4.7         3.2
## 4          4.6         3.1
## 5          5           3.6
## 6          5.4         3.9
```

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# Row Filtering by Condition

### With `[]` and conditions

``` r
# Versicolor flowers with Sepal.Length > 6
subset_rows <- iris[iris$Sepal.Length > 6 & 
                      iris$Species == "versicolor", ]
head(subset_rows)
```

```
## # A tibble: 6 × 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species   
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>     
## 1          7           3.2          4.7         1.4 versicolor
## 2          6.4         3.2          4.5         1.5 versicolor
## 3          6.9         3.1          4.9         1.5 versicolor
## 4          6.5         2.8          4.6         1.5 versicolor
## 5          6.3         3.3          4.7         1.6 versicolor
## 6          6.6         2.9          4.6         1.3 versicolor
```

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# Row Filtering by Condition

### With `subset()` and conditions

``` r
# Using subset() (convenience wrapper)
subset_rows2 <- subset(iris, Sepal.Length > 6 
                       & Species == "versicolor")
head(subset_rows2)
```

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# Row Filtering by Condition

### Allows operations on data of interest

``` r
# means
vers_sepalL <- iris[iris$Species == "versicolor",]$Sepal.Length
set_sepalL <- iris[iris$Species == "setosa",]$Sepal.Length

mean(vers_sepalL) 
```

```
## [1] 5.936
```

``` r
mean(set_sepalL)
```

```
## [1] 5.006
```

``` r
#same using sapply
sapply(list(vers_sepalL,set_sepalL),mean)
```

```
## [1] 5.936 5.006
```