hw4.qmd

---
title: "hw4"
format: html
knitr:
  opts_chunk:
    root.dir: "/Users/david/projects/ncsu/st558/homework/hw4/raw"
---

# Homework 4

```{r, message=FALSE, echo=FALSE}
library(tidyverse)
library(readxl)
```

# Task 1

## 1. If your working directory is myfolder/homework/, what relative path would you specify to get the file located at myfolder/MyData.csv?

> `../myfolder/MyData.csv`

## 2. What are the major benefits of using R projects
> - Customization of project specific settings
> - Ability to debug code using the debugger
> - addresses separation of concerns by providing isolation between projects
> - version control integration with R-Studio
> - reproducibility - all libraries, packages, dependencies are contained within the project

## 3. What is git and what is GitHub?
> Git is an open source, distributed version control system that tracks changes to files to ensure Good Programming Practice. It provides a programming interface to document changes in files and tracks them over the lifecycle of a project and provides a way to enable collaboration among people.

> GitHub provides a web-based platform that serves as a remote or "hub" for source code management. The remote repository can be accessed over ssh and https file protocols.

## 4. What are the two main differences between a tibble and a data.frame?

> The two main differences between a tibble and a data.frame are:
>
> 1. A tibble doesn't do coercion, preserving the data types and placing the responsibility for data types with the human.
> 2. Tibbles provide a consistent interface when working with the tidyverse that provides         efficiency and quality. Functions of the tidyverse take a tibble as input, and              return a tibble as output. Data frames 

## 5. Rewrite the following nested function call using BaseR’s chaining operator:

     
```{r, eval=FALSE, echo=TRUE}
arrange(filter(select(as_tibble(iris), starts_with("Petal"), Species),
         Petal.Length < 1.55), Species)
```

### Answer:
```{r}
df <- as_tibble(iris) |>
    select(starts_with("Petal"), Species) |>
            filter(Petal.Length < 1.55)
```



# Task 2 - Reading Delimited Data
## Glass Data
1. Read in from URL. Add colnames
2. Overwrite type variable using `mutate`
3. subset based on:
     - Fe < 0.2
     - type in('tableware','headlamp')
```{r}
glass_data <- read_csv("https://www4.stat.ncsu.edu/~online/datasets/glass.data",
    col_names = c("Id", "RI", "Na", "Mg", "Al", "Si", "K", "Ca", "Ba", "Fe")) |>
    mutate(glass_type_c = factor(X11, levels = 1:7, 
                                 labels = c("building_windows_float_processed",
                                            "building_windows_non_float_processed",
                                            "vehicle_windows_float_processed",
                                            "vehicle_windows_non_float_processed",
                                            "containers",
                                            "tableware",
                                            "headlamp"))) |>
    rename(glass_type_n = X11) |>
    filter(glass_type_c %in% c("tableware","headlamp") & Fe < 0.2) |>
    select(glass_type_c, everything())

print(glass_data)
```


## Yeast Data
1. Read in from URL. Add colnames
2. remove `seq_name` and `nuc`
3. add columns for mean and median across numeric vars, by class

```{r}
yeast_data <- read_table("https://www4.stat.ncsu.edu/~online/datasets/yeast.data",
                         col_names = c("seq_name","mcg","gvh","alm","mit","erl","pox","vac","nuc",
                                       "class")) |>
                select(-seq_name,-nuc) |>
                group_by(class) |>
                mutate(across(where(is.numeric),
                    .fns = list(mean = ~ mean(.x, na.rm = TRUE),
                                median = ~ median(.x, na.rm = TRUE)),
                    .names = "{.fn}_{.col}")) |>
                select(class, contains(c("mean","median")), everything())
    
yeast_data
```

# Task 2: Combining Excel and Delimited Data
## Red and White Wine
1. Download and import first sheet
2. Import second sheet containing variable names. Overwrite colnames
3. set `type` equal to "white"
4. Import red wine and overwrite colnames. set `type` equal to "red"
5. Combine data sets with `bind_rows()`
6. filter based on:
     - quality > 6.5
     - alcohol < 132
7. Sort descending `quality`
8. select vars that contain `acid`, `alcohol`, `type`, and `quality`
9. add mean and standard deviation of `alcohol` by `quality` category


```{r}

tibble(read_xlsx(path = "./raw/white-wine.xlsx", sheet="white-wine"))

col_names <- read_xlsx(path = "./raw/white-wine.xlsx", sheet="variables")

white_wine <- tibble(read_xlsx(path = "./raw/white-wine.xlsx", sheet="white-wine")) |>
                     setNames(col_names$Variables) |>
                     mutate(type = "white") |>
                     select(quality, alcohol, type, everything())

white_wine

red_wine <- read_delim("./raw/red-wine.csv", delim = ";") |>
    setNames(col_names$Variables) |>
    mutate(type = "red") |>
    select(quality, alcohol, type, everything())

red_wine

combined_wine <- bind_rows(white_wine, red_wine) |>
                filter(quality > 6.5 & alcohol <132) |>
                arrange(desc(quality)) |>
                select(contains("acid"), alcohol, type, quality) |>
                group_by(quality) |>
                mutate(
                    mean_alcohol = format(mean(alcohol, na.rm = TRUE),nsmall=2),
                    sd_alcohol = sd(alcohol, na.rm = TRUE)
                ) |>
                select(quality, alcohol, type, mean_alcohol, sd_alcohol, everything())
options(digits = 7)
print(combined_wine)
```