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---
title: "A Guide on Data Analysis"
author: "Mike Nguyen"
date: "`r Sys.Date()`"
header-includes:
- \usepackage{titling}
- \pretitle{\begin{center}
\includegraphics[width=2in,height=2in]{logo.png}\LARGE\\}
- \posttitle{\end{center}}
documentclass: book
bibliography:
- book.bib
- packages.bib
- references.bib
biblio-style: apalike
link-citations: true
link-as-notes: true
description: This is a guide on how to conduct data analysis in the field of data science, statistics, or machine learning.
github-repo: mikenguyen13/data_analysis
url: https\://bookdown.org/mike/data_analysis/
favicon: "favicon.ico"
cover-image: "images/cover.jpg"
apple-touch-icon: "logo.png"
apple-touch-icon-size: 120
knit: "bookdown::render_book"
site: bookdown::bookdown_site
always_allow_html: yes
---
# Preface {#preface .unnumbered}
```{r setup, include = FALSE}
knitr::opts_chunk$set(warning = FALSE,
message = FALSE,
fig.width = 6,
fig.height = 4,
out.width = '\\textwidth',
echo = FALSE,
cache = TRUE)
```
```{r, include = F}
knitr::write_bib(c(.packages(), "bookdown", "knitr", "rmarkdown"), "packages.bib")
```
<img src="images/cover.jpg" class="cover" width="250" height="328"/> This book is an effort to simplify and demystify the complex process of data analysis, making it accessible to a wide audience. While I do not claim to be a professional statistician, econometrician, or data scientist, I firmly believe in the value of learning through teaching and practical application. Writing this book has been as much a learning journey for me as I hope it will be for you.
The intended audience includes those with little to no experience in statistics, econometrics, or data science, as well as individuals with a budding interest in these fields who are eager to deepen their knowledge. While my primary domain of interest is marketing, the principles and methods discussed in this book are universally applicable to any discipline that employs scientific methods or data analysis.
I hope this book provides a valuable starting point for aspiring statisticians, econometricians, and data scientists, empowering you to navigate the fascinating world of causal inference and data analysis with confidence.
::: {style="text-align:center"}
{width="300" height="300"}
:::
```{r, out.width='25%', fig.align='center', echo=FALSE, eval = FALSE}
knitr::include_graphics('logo.png')
```
## How to cite this book {.unnumbered}
> **1. APA (7th edition):**
>
> Nguyen, M. (2020). *A Guide on Data Analysis*. Bookdown.
>
> [**https://bookdown.org/mike/data_analysis/**](https://bookdown.org/mike/data_analysis/)
>
> **2. MLA (8th edition):**
>
> Nguyen, Mike. *A Guide on Data Analysis*. Bookdown, 2020. [**https://bookdown.org/mike/data_analysis/**](https://bookdown.org/mike/data_analysis/)
>
> **3. Chicago (17th edition):**
>
> Nguyen, Mike. 2020. *A Guide on Data Analysis*. Bookdown. [**https://bookdown.org/mike/data_analysis/**](https://bookdown.org/mike/data_analysis/)
>
> **4. Harvard:**
>
> Nguyen, M. (2020) *A Guide on Data Analysis*. Bookdown. Available at: [**https://bookdown.org/mike/data_analysis/**](https://bookdown.org/mike/data_analysis/)
```{r, eval = FALSE}
@book{nguyen2020guide,
title={A Guide on Data Analysis},
author={Nguyen, Mike},
year={2020},
publisher={Bookdown},
url={https://bookdown.org/mike/data_analysis/}
}
```
## More books {.unnumbered}
More books by the author can be found [here](https://mikenguyen.netlify.app/books/written_books/):
- [Advanced Data Analysis](https://bookdown.org/mike/advanced_data_analysis/): the second book in the data analysis series, which covers machine learning models (with a focus on prediction)
- [Marketing Research](https://bookdown.org/mike/marketing_research/)
- [Communication Theory](https://bookdown.org/mike/comm_theory/)
# Introduction
Since the turn of the century, we have witnessed remarkable advancements and innovations, particularly in statistics, information technology, computer science, and the rapidly emerging field of data science. However, one challenge of these developments is the overuse of **buzzwords** like *big data*, *machine learning*, and *deep learning*. While these terms are powerful in context, they can sometimes obscure the foundational principles underlying their application.
Every substantive field often has its own specialized metric subfield, such as:
- **Econometrics** in economics\
- **Psychometrics** in psychology\
- **Chemometrics** in chemistry\
- **Sabermetrics** in sports analytics\
- **Biostatistics** in public health and medicine
To the layperson, these disciplines are often grouped under broader terms like:
- **Data Science**\
- **Applied Statistics**\
- **Computational Social Science**
As exciting as it is to explore these new tools and techniques, I must admit that retaining these concepts can be challenging. For me, the most effective way to internalize and apply these ideas has been to document the data analysis process from start to finish.
With that in mind, let's dive in and explore the fascinating world of data analysis together.
::: {style="text-align:center"}
{width="450" height="350"}
:::
```{r fig.align='center', echo=FALSE, include = FALSE}
library("jpeg")
knitr::include_graphics('images/meme.jpg')
```
## General Recommendations
- The journey of mastering data analysis is fueled by practice and repetition. The more lines of code you write, the more functions you familiarize yourself with, and the more you experiment, the more enjoyable and rewarding this process becomes.
- Readers can approach this book in several ways:
- **Focused Learning**: If you are interested in specific methods or tools, you can jump directly to the relevant section by navigating through the table of contents.
- **Sequential Learning**: To follow a traditional path of data analysis, start with the [Linear Regression] section.
- **Experimental Approach**: If you are interested in designing experiments and testing hypotheses, explore the [Analysis of Variance (ANOVA)] section.
- For those primarily interested in applications and less concerned with theoretical foundations, focus on the summary and application sections of each chapter.
- If a concept is unclear, consider researching the topic online. This book serves as a guide, and external resources like tutorials or articles can provide additional insights.
- To customize the code examples provided in this book, use R's built-in help functions. For instance:
- To learn more about a specific function, type `help(function_name)` or `?function_name` in the R console.
- For example, to find details about the `hist` function, type `?hist` or `help(hist)` in the console.
- Additionally, searching online is a powerful resource (e.g., Google, ChatGPT, etc.). Different practitioners often use various R packages to achieve similar results. For instance, if you need to create a histogram in R, a simple search like *"histogram in R"* will provide multiple approaches and examples.
By adopting these strategies, you can tailor your learning experience and maximize the value of this book.
**Tools of statistics**
- Probability Theory
- Mathematical Analysis
- Computer Science
- Numerical Analysis
- Database Management
```{r warning=FALSE, eval=FALSE, include=FALSE}
if (!require("pacman"))
install.packages("pacman")
if (!require("devtools"))
install.packages("devtools")
library("pacman")
library("devtools")
```
```{r include=FALSE}
# automatically create a bib database for R packages
knitr::write_bib(c(
.packages(), 'bookdown', 'knitr', 'rmarkdown'
), 'packages.bib')
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```
**Code Replication**
This book was built with `r R.version.string` and the following packages:
```{r, echo = FALSE, results="asis"}
# usethis::use_description_defaults()
# usethis::use_description(package = c("tidyverse","usethis"),check_name = F)
#
# # .libPaths()
# deps <- desc::desc_get_deps("C:/Program Files/R/R-4.0.4/library")
# deps <- desc::desc_get_deps("C:/Users/tn9k4/OneDrive - University of Missouri/Documents/R/win-library/4.0")
# if you want to make it beautiful for markdown
deps <- desc::desc_get_deps()
pkgs <- sort(deps$package[deps$type == "Imports"])
pkgs <- sessioninfo::package_info(pkgs, dependencies = FALSE)
df <- tibble::tibble(
package = pkgs$package,
version = pkgs$ondiskversion,
source = gsub("@", "\\\\@", pkgs$source)
)
knitr::kable(df, format = "markdown")
```
```{r, echo=FALSE}
devtools::session_info()
# usethis::use_package("dplyr") # Default is "Imports"
# usethis::use_package("ggplot2", "Suggests")` # Suggests
```