1 Introduction

Since the beginning of the century, we have been bombarded with amazing advancements and inventions, especially in the field of statistics, information technology, computer science, or a new emerging filed - data science. However, I believe the downside of this introduction is that we use big and trendy words too often (i.e., big data, machine learning, deep learning).

Each substantive field will have a metric subfield:

Econometrics in economics
Psychometrics in psychology
Chemometrics in chemistry
Sabermetrics in sports
Biostatistics in public health and medicine

But to laymen, these are known as:

Data Science
Applied Statistics
Computational Social Science

It’s all fun and exciting when I learned these new tools. But I have to admit that I hardly retain any of these new ideas. However, writing down from the beginning till the end of a data analysis process is the solution that I came up with. Accordingly, let’s dive right in.

Some general recommendations:

The more you practice/habituate/condition, more line of codes that you write, more function that you memorize, I think the more you will like this journey.
Readers can follow this book several ways:
- If you are interested in particular methods/tools, you can jump to that section by clicking the section name.
- If you want to follow a traditional path of data analysis, read the Linear Regression section.
- If you want to create your experiment and test your hypothesis, read the Analysis of Variance (ANOVA) section.
Alternatively, if you rather see the application of models, and disregard any theory or underlying mechanisms, you can skip to summary and application portion of each section.
If you don’t understand a part, search the title of that part of that part on Google, and read more into that subject. This is just a general guide.
If you want to customize your code beyond the ones provided in this book, run in the console help(code) or ?code. For example, I want more information on hist function, I’ll type in the console ?hist or help(hist).
Another way is that you can search on Google. Different people will use different packages to achieve the same result in R. Accordingly, if you want to create a histogram, search on Google histogram in R, then you should be able to find multiple ways to create histogram in R.

Tools of statistics

Probability Theory
Mathematical Analysis
Computer Science
Numerical Analysis
Database Management

Code Replication

This book was built with R version 4.2.3 (2023-03-15 ucrt) and the following packages:

package	version	source
abind	1.4-5	CRAN (R 4.2.0)
agridat	1.21	CRAN (R 4.2.3)
ape	5.7-1	CRAN (R 4.2.3)
assertthat	0.2.1	CRAN (R 4.2.3)
backports	1.4.1	CRAN (R 4.2.0)
bookdown	0.35	CRAN (R 4.2.3)
boot	1.3-28.1	CRAN (R 4.2.3)
broom	1.0.5	CRAN (R 4.2.3)
bslib	0.6.1	CRAN (R 4.2.3)
cachem	1.0.8	CRAN (R 4.2.3)
callr	3.7.3	CRAN (R 4.2.3)
car	3.1-2	CRAN (R 4.2.3)
carData	3.0-5	CRAN (R 4.2.3)
cellranger	1.1.0	CRAN (R 4.2.3)
cli	3.6.1	CRAN (R 4.2.3)
coda	0.19-4	CRAN (R 4.2.3)
colorspace	2.1-0	CRAN (R 4.2.3)
corpcor	1.6.10	CRAN (R 4.2.0)
crayon	1.5.2	CRAN (R 4.2.3)
cubature	2.1.0	CRAN (R 4.2.3)
curl	5.1.0	CRAN (R 4.2.3)
data.table	1.14.8	CRAN (R 4.2.3)
DBI	1.2.0	CRAN (R 4.2.3)
dbplyr	2.4.0	CRAN (R 4.2.3)
desc	1.4.3	CRAN (R 4.2.3)
devtools	2.4.5	CRAN (R 4.2.3)
digest	0.6.31	CRAN (R 4.2.3)
dplyr	1.1.2	CRAN (R 4.2.3)
ellipsis	0.3.2	CRAN (R 4.2.3)
evaluate	0.23	CRAN (R 4.2.3)
extrafont	0.19	CRAN (R 4.2.2)
extrafontdb	1.0	CRAN (R 4.2.0)
fansi	1.0.4	CRAN (R 4.2.3)
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fastmap	1.1.1	CRAN (R 4.2.3)
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fs	1.6.3	CRAN (R 4.2.3)
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ggplot2	3.4.4	CRAN (R 4.2.3)
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gtable	0.3.4	CRAN (R 4.2.3)
haven	2.5.3	CRAN (R 4.2.3)
Hmisc	5.1-0	CRAN (R 4.2.3)
hms	1.1.3	CRAN (R 4.2.3)
htmltools	0.5.7	CRAN (R 4.2.3)
htmlwidgets	1.6.2	CRAN (R 4.2.3)
httr	1.4.7	CRAN (R 4.2.3)
investr	1.4.2	CRAN (R 4.2.3)
jpeg	0.1-10	CRAN (R 4.2.2)
jquerylib	0.1.4	CRAN (R 4.2.3)
jsonlite	1.8.8	CRAN (R 4.2.3)
kableExtra	1.3.4	CRAN (R 4.2.3)
knitr	1.45	CRAN (R 4.2.3)
lattice	0.21-8	CRAN (R 4.2.3)
latticeExtra	0.6-30	CRAN (R 4.2.3)
lifecycle	1.0.4	CRAN (R 4.2.3)
lme4	1.1-35.1	CRAN (R 4.2.3)
lmerTest	3.1-3	CRAN (R 4.2.3)
lsr	0.5.2	CRAN (R 4.2.3)
ltm	1.2-0	CRAN (R 4.2.3)
lubridate	1.9.2	CRAN (R 4.2.3)
magrittr	2.0.3	CRAN (R 4.2.3)
MASS	7.3-60	CRAN (R 4.2.3)
matlib	0.9.6	CRAN (R 4.2.3)
Matrix	1.6-1	CRAN (R 4.2.3)
MCMCglmm	2.35	CRAN (R 4.2.3)
memoise	2.0.1	CRAN (R 4.2.3)
mgcv	1.9-0	CRAN (R 4.2.3)
minqa	1.2.6	CRAN (R 4.2.3)
modelr	0.1.11	CRAN (R 4.2.3)
munsell	0.5.0	CRAN (R 4.2.3)
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nloptr	2.0.3	CRAN (R 4.2.3)
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nnet	7.3-19	CRAN (R 4.2.3)
numDeriv	2016.8-1.1	CRAN (R 4.2.0)
openxlsx	4.2.5.2	CRAN (R 4.2.3)
pbkrtest	0.5.2	CRAN (R 4.2.3)
pillar	1.9.0	CRAN (R 4.2.3)
pkgbuild	1.4.3	CRAN (R 4.2.3)
pkgconfig	2.0.3	CRAN (R 4.2.3)
pkgload	1.3.3	CRAN (R 4.2.3)
png	0.1-8	CRAN (R 4.2.2)
ppsr	0.0.2	CRAN (R 4.2.3)
prettyunits	1.2.0	CRAN (R 4.2.3)
processx	3.8.2	CRAN (R 4.2.3)
ps	1.7.5	CRAN (R 4.2.3)
pscl	1.5.5.1	CRAN (R 4.2.3)
purrr	1.0.2	CRAN (R 4.2.3)
R6	2.5.1	CRAN (R 4.2.3)
RColorBrewer	1.1-3	CRAN (R 4.2.0)
Rcpp	1.0.11	CRAN (R 4.2.3)
readr	2.1.4	CRAN (R 4.2.3)
readxl	1.4.3	CRAN (R 4.2.3)
remotes	2.4.2.1	CRAN (R 4.2.3)
reprex	2.0.2	CRAN (R 4.2.3)
rgl	1.2.1	CRAN (R 4.2.3)
rio	1.0.1	CRAN (R 4.2.3)
rlang	1.1.1	CRAN (R 4.2.3)
RLRsim	3.1-8	CRAN (R 4.2.3)
rmarkdown	2.25	CRAN (R 4.2.3)
rprojroot	2.0.4	CRAN (R 4.2.3)
rstudioapi	0.15.0	CRAN (R 4.2.3)
Rttf2pt1	1.3.12	CRAN (R 4.2.2)
rvest	1.0.3	CRAN (R 4.2.3)
sass	0.4.8	CRAN (R 4.2.3)
scales	1.3.0	CRAN (R 4.2.3)
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stringi	1.7.12	CRAN (R 4.2.2)
stringr	1.5.1	CRAN (R 4.2.3)
svglite	2.1.1	CRAN (R 4.2.3)
systemfonts	1.0.5	CRAN (R 4.2.3)
tensorA	0.36.2	CRAN (R 4.2.0)
testthat	3.1.10	CRAN (R 4.2.3)
tibble	3.2.1	CRAN (R 4.2.3)
tidyr	1.3.0	CRAN (R 4.2.3)
tidyselect	1.2.0	CRAN (R 4.2.3)
tidyverse	2.0.0	CRAN (R 4.2.3)
tzdb	0.4.0	CRAN (R 4.2.3)
usethis	2.2.2	CRAN (R 4.2.3)
utf8	1.2.3	CRAN (R 4.2.3)
vctrs	0.6.3	CRAN (R 4.2.3)
viridisLite	0.4.2	CRAN (R 4.2.3)
webshot	0.5.5	CRAN (R 4.2.3)
withr	2.5.2	CRAN (R 4.2.3)
xfun	0.39	CRAN (R 4.2.3)
xml2	1.3.6	CRAN (R 4.2.3)
xtable	1.8-4	CRAN (R 4.2.3)
yaml	2.3.7	CRAN (R 4.2.3)
zip	2.3.0	CRAN (R 4.2.3)

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Preface

2 Prerequisites