Intro update (#1333)

* Update contributors * Trim intro * Update intro.qmd * Update intro.qmd --------- Co-authored-by: Mine Cetinkaya-Rundel <cetinkaya.mine@gmail.com>
2023-03-01 22:51:48 -06:00 · 2023-03-01 22:51:48 -06:00 · fc631a4509
parent 63cbf8a90d
commit fc631a4509
2 changed files with 49 additions and 55 deletions
--- a/contributors.csv
+++ b/contributors.csv
@ -1,15 +1,18 @@
 login,n,name,blog
 ALShum,1,Alex,www.ALShum.com
+Abinashbunty,1,Abinash Satapathy,https://www.abinash.nl/
 Adrianzo,1,A. s.,NA
 AlanFeder,1,NA,NA
-AlbertRapp,1,NA,
+AlbertRapp,1,NA,NA
 AnttiRask,1,Antti Rask,youcanbeapirate.com
 BB1464,1,Oluwafemi OYEDELE,statisticalinference.netlify.app
 BarkleyBG,1,Brian G. Barkley,BarkleyBG.netlify.com
 BinxiePeterson,1,Bianca Peterson,NA
 BirgerNi,1,Birger Niklas,NA
 DDClark,1,David Clark,NA
+DOH-RPS1303,1,Russell Shean,
 DSGeoff,1,NA,NA
+Divider85,3,NA,
 EdwinTh,4,Edwin Thoen,thats-so-random.com
 EricKit,1,Eric Kitaif,NA
 GeroVanMi,1,Gerome Meyer,https://astralibra.ch
@ -17,9 +20,12 @@ GoldbergData,1,Josh Goldberg,https://twitter.com/GoldbergData
 Iain-S,1,Iain,NA
 JeffreyRStevens,2,Jeffrey Stevens,https://decisionslab.unl.edu/
 JeldorPKU,1,蒋雨蒙,https://jeldorpku.github.io
+KittJonathan,10,Jonathan Kitt,
 MJMarshall,2,NA,NA
 MarckK,1,Kara de la Marck,https://www.linkedin.com/in/karadelamarck
 MattWittbrodt,1,Matt Wittbrodt,mattwittbrodt.com
+MatthiasLiew,3,Matthias Liew,
+NedJWestern,1,Ned Western,
 Nowosad,6,Jakub Nowosad,https://nowosad.github.io
 PursuitOfDataScience,14,Y. Yu,https://youzhi.netlify.app/
 RIngyao,1,Jajo,NA
@ -29,7 +35,7 @@ ReeceGoding,1,NA,NA
 RobinKohrs,1,Robin Kohrs,https://quarantino.netlify.app/
 Robinlovelace,2,Robin,http://robinlovelace.net
 RodAli,1,Rod Mazloomi,NA
-RohanAlexander,1,Rohan Alexander,https://www.rohanalexander.com/
+RohanAlexander,5,Rohan Alexander,https://www.rohanalexander.com/
 RomeroBarata,1,Romero Morais,NA
 ShanEllis,1,Shannon Ellis,shanellis.com
 Shurakai,2,Christian Heinrich,NA
@ -38,6 +44,7 @@ a-rosenberg,1,NA,NA
 a2800276,1,Tim Becker,NA
 adam-gruer,1,Adam Gruer,adamgruer.rbind.io
 adidoit,1,adi pradhan,http://adidoit.github.io
+aephidayatuloh,1,Aep Hidyatuloh,
 agila5,1,Andrea Gilardi,NA
 ajay-d,1,Ajay Deonarine,http://deonarine.com/
 aleloi,1,NA,NA
@ -63,8 +70,9 @@ bgreenwell,9,Brandon Greenwell,NA
 bklamer,11,Brett Klamer,NA
 boardtc,1,NA,NA
 c-hoh,1,Christian,hohenfeld.is
+caddycarine,1,Caddy,
 camillevleonard,1,Camille V Leonard,https://www.camillevleonard.com/
-canovasjm,1,NA,
+canovasjm,1,NA,NA
 cedricbatailler,1,Cedric Batailler,cedricbatailler.me
 chrMongeau,1,Christian Mongeau,http://mongeau.net
 coopermor,2,Cooper Morris,NA
@ -76,10 +84,12 @@ curtisalexander,1,Curtis Alexander,https://www.calex.org
 cwarden,2,Christian G. Warden,http://xn.pinkhamster.net/
 cwickham,1,Charlotte Wickham,http://cwick.co.nz
 darrkj,1,Kenny Darrell,http://darrkj.github.io/blogs
+davidrsch,4,David,
 davidrubinger,1,David Rubinger,NA
 derwinmcgeary,1,Derwin McGeary,http://derwinmcgeary.github.io
 dgromer,2,Daniel Gromer,NA
 djbirke,1,NA,NA
+djnavarro,1,Danielle Navarro,https://djnavarro.net
 dongzhuoer,5,Zhuoer Dong,https://dongzhuoer.github.io
 dpastoor,2,Devin Pastoor,NA
 duju211,13,Julian During,NA
@ -87,6 +97,7 @@ dylancashman,1,Dylan Cashman,https://www.eecs.tufts.edu/~dcashm01/
 eddelbuettel,1,Dirk Eddelbuettel,http://dirk.eddelbuettel.com
 elgabbas,1,Ahmed El-Gabbas,https://elgabbas.github.io
 enryH,1,Henry Webel,NA
+ercan7,1,Ercan Karadas,
 ericwatt,1,Eric Watt,www.ericdwatt.com
 erikerhardt,2,Erik Erhardt,StatAcumen.com
 etiennebr,2,Etienne B. Racine,NA
@ -98,9 +109,10 @@ funkybluehen,1,NA,NA
 gabrivera,1,NA,NA
 gadenbuie,1,Garrick Aden-Buie,https://garrickadenbuie.com
 garrettgman,103,Garrett Grolemund,NA
+gl-eb,1,Gleb Ebert,glebsite.ch
 gridgrad,1,bahadir cankardes,NA
 gustavdelius,2,Gustav W Delius,NA
-hadley,1085,Hadley Wickham,http://hadley.nz
+hadley,1151,Hadley Wickham,http://hadley.nz
 hao-trivago,2,Hao Chen,NA
 harrismcgehee,7,Harris McGehee,https://gist.github.com/harrismcgehee
 hendrikweisser,1,NA,NA
@ -112,27 +124,28 @@ jacobkap,1,Jacob Kaplan,http://crimedatatool.com/
 jazzlw,1,Jazz Weisman,NA
 jdblischak,1,John Blischak,https://jdblischak.com/
 jdstorey,1,John D. Storey,http://jdstorey.github.io/
-jeffboichuk,2,Jeff Boichuk,https://www.commerce.virginia.edu/faculty/boichuk
 jefferis,1,Gregory Jefferis,http://www2.mrc-lmb.cam.ac.uk/group-leaders/h-to-m/gregory-jefferis/
 jennybc,5,Jennifer (Jenny) Bryan,https://jennybryan.org
 jenren,1,Jen Ren,NA
 jeroenjanssens,1,Jeroen Janssens,http://jeroenjanssens.com
-jeromecholewa,1,NA,
+jeromecholewa,1,NA,NA
 jilmun,3,Janet Wesner,jilmun.github.io
 jimhester,2,Jim Hester,http://www.jimhester.com
 jjchern,6,JJ Chen,NA
 jkolacz,1,Jacek Kolacz,NA
 joannejang,2,Joanne Jang,joannejang.com
-johannes4998,1,NA,
+johannes4998,1,NA,NA
 johnsears,1,John Sears,NA
 jonathanflint,1,NA,NA
 jonmcalder,1,Jon Calder,http://joncalder.co.za
 jonpage,3,Jonathan Page,economistry.com
+jonthegeek,1,Jon Harmon,http://jonthegeek.com
 jooyoungseo,2,JooYoung Seo,https://jooyoungseo.github.io
 jpetuchovas,1,Justinas Petuchovas,NA
 jrdnbradford,1,Jordan,www.linkedin.com/in/jrdnbradford
 jrnold,4,Jeffrey Arnold,http://jrnold.me
 jroberayalas,7,Jose Roberto Ayala Solares,jroberayalas.netlify.com
+jtr13,1,Joyce Robbins,
 juandering,1,NA,NA
 jules32,1,Julia Stewart Lowndes,http://jules32.github.io
 kaetschap,1,Sonja,NA
@ -145,7 +158,7 @@ kirillseva,2,Kirill Sevastyanenko,NA
 koalabearski,1,NA,NA
 krlmlr,1,Kirill Müller,NA
 kucharsky,1,Rafał Kucharski,NA
-kwstat,1,Kevin Wright,
+kwstat,1,Kevin Wright,NA
 landesbergn,1,Noah Landesberg,noahlandesberg.com
 lawwu,1,Lawrence Wu,NA
 lindbrook,1,NA,NA
@ -155,9 +168,10 @@ matanhakim,1,Matan Hakim,NA
 maurolepore,2,Mauro Lepore,https://fgeo.netlify.com/
 mbeveridge,7,Mark Beveridge,https://twitter.com/mbeveridge
 mcewenkhundi,1,NA,NA
+mcsnowface,6,"mcsnowface, PhD",
 mfherman,1,Matt Herman,mattherman.info
 michaelboerman,1,Michael Boerman,https://michaelboerman.com
-mine-cetinkaya-rundel,66,Mine Cetinkaya-Rundel,https://stat.duke.edu/~mc301
+mine-cetinkaya-rundel,95,Mine Cetinkaya-Rundel,https://stat.duke.edu/~mc301
 mitsuoxv,5,Mitsuo Shiota,https://mitsuoxv.rbind.io/
 mjhendrickson,1,Matthew Hendrickson,https://about.me/matthew.j.hendrickson
 mmhamdy,1,Mohammed Hamdy,NA
@ -174,8 +188,10 @@ nirmalpatel,2,Nirmal Patel,http://playpowerlabs.com
 nischalshrestha,1,Nischal Shrestha,http://nischalshrestha.me
 njtierney,1,Nicholas Tierney,http://www.njtierney.com
 olivier6088,1,NA,NA
+p0bs,1,Robin Penfold,p0bs.com
 pabloedug,1,Pablo E. Garcia,NA
 padamson,1,Paul Adamson,padamson.github.io
+penelopeysm,1,Penelope Y,
 peterhurford,1,Peter Hurford,http://www.peterhurford.com
 pkq,4,Patrick Kennedy,NA
 pooyataher,1,Pooya Taherkhani,https://gitlab.com/pooyat
@ -201,9 +217,12 @@ sfirke,1,Sam Firke,samfirke.com
 shoili,1,NA,shoili.github.io
 sibusiso16,52,S'busiso Mkhondwane,NA
 sonicdoe,11,Jakob Krigovsky,https://sonicdoe.com
+stephan-koenig,3,Stephan Koenig,stephankoenig.me
+stephenbalogun,6,Stephen Balogun,https://stephenbalogun.github.io/stbalogun/
 stragu,4,Stéphane Guillou,https://stragu.github.io/
 svenski,1,Sergiusz Bleja,NA
 talgalili,1,Tal Galili,https://www.r-statistics.com
+tgerarden,1,Todd Gerarden,http://toddgerarden.com
 timbroderick,1,Tim Broderick,http://www.timbroderick.net
 timwaterhouse,1,Tim Waterhouse,NA
 tjmahr,1,TJ Mahr,tjmahr.com
@ -212,14 +231,16 @@ tomjamesprior,1,Tom Prior,NA
 tteo,4,Terence Teo,tteo.github.io
 twgardner2,1,NA,NA
 ulyngs,4,Ulrik Lyngs,www.ulriklyngs.com
+uribo,1,Shinya Uryu,https://uribo.hatenablog.com
 vanderlindenma,1,Martin Van der Linden,NA
 waltersom,1,Walter Somerville,NA
 werkstattcodes,1,NA,http://werk.statt.codes
 wibeasley,2,Will Beasley,http://scholar.google.com/citations?user=ffsJTC0AAAAJ&hl=en
 yihui,4,Yihui Xie,https://yihui.name
 yimingli,3,Yiming (Paul) Li,https://yimingli.net
+yingxingwu,1,NA,
 yutannihilation,1,Hiroaki Yutani,https://twitter.com/yutannihilation
 yuyu-aung,1,Yu Yu Aung,NA
 zachbogart,1,Zach Bogart,zachbogart.com
 zeal626,1,NA,NA
-zekiakyol,4,Zeki Akyol,zekiakyol.com
+zekiakyol,16,Zeki Akyol,zekiakyol.com
--- a/intro.qmd
+++ b/intro.qmd
@ -7,7 +7,7 @@ source("_common.R")
 ```

 Data science is an exciting discipline that allows you to transform raw data into understanding, insight, and knowledge.
-The goal of "R for Data Science" is to help you learn the most important tools in R that will allow you to do data science efficiently and reproducibly.
+The goal of "R for Data Science" is to help you learn the most important tools in R that will allow you to do data science efficiently and reproducibly, and to have some fun along the way 😃.
 After reading this book, you'll have the tools to tackle a wide variety of data science challenges using the best parts of R.

 ## What you will learn
@ -54,7 +54,7 @@ A good visualization will show you things you did not expect or raise new questi
 A good visualization might also hint that you're asking the wrong question or that you need to collect different data.
 Visualizations can surprise you, and they don't scale particularly well because they require a human to interpret them.

-**Models** are complementary tools to visualization.
+**Models** are complementary tools to visualization.
 Once you have made your questions sufficiently precise, you can use a model to answer them.
 Models are a fundamentally mathematical or computational tool, so they generally scale well.
 Even when they don't, it's usually cheaper to buy more computers than it is to buy more brains!
@ -92,32 +92,21 @@ That means this book can't cover every important topic.

 ### Modeling

-To learn more about modeling, we highly recommend [Tidy Modeling with R](https://www.tmwr.org) by our colleagues Max Kuhn and Julia Silge.
+Modelling is super important for data science, but it's a big topic and unfortunately we just don't have the space to give it the coverage it deserves here.
+To learn more modeling, we highly recommend [Tidy Modeling with R](https://www.tmwr.org) by our colleagues Max Kuhn and Julia Silge.
 This book will teach you the tidymodels family of packages, which, as you might guess from the name, share many conventions with the tidyverse packages we use in this book.

 ### Big data

 This book proudly and primarily focuses on small, in-memory datasets.
 This is the right place to start because you can't tackle big data unless you have experience with small data.
-The tools you learn in majority of this book will easily handle hundreds of megabytes of data, and with a bit of care, you can typically use them to work with 1-2 Gb of data.
+The tools you learn in majority of this book will easily handle hundreds of megabytes of data, and with a bit of care, you can typically use them to work a few gigabytes of data.
+We'll also show you how to get data out of databases and parquet files, both of which are often used to store big data.
+You won't necessarily be able to work with the entire dataset, but that's not a problem because you only need a subset or subsample to answer the question that you're interested in.

-That being said, the book also touches on getting data out of databases and out of parquet files, both of which are commonly used solutions for storing big data.
-
-However, if you're routinely working with larger data (10-100 Gb, say), you should learn more about [data.table](https://github.com/Rdatatable/data.table).
-This book doesn't teach data.table because it has a very concise interface that offers fewer linguistic cues, which makes it harder to learn.
-However, the performance payoff is well worth the effort required to learn it if you're working with large data.
-
-If your data is bigger than this, carefully consider whether your big data problem is actually a small data problem in disguise.
-While the complete dataset might be big, often, the data needed to answer a specific question is small.
-You might be able to find a subset, subsample, or summary that fits in memory and still allows you to answer the question that you're interested in.
-The challenge here is finding the right small data, which often requires a lot of iteration.
-
-Another possibility is that your big data problem is actually a large number of small data problems in disguise.
-Each individual problem might fit in memory, but you have millions of them.
-For example, you might want to fit a model to each person in your dataset.
-This would be trivial if you had just 10 or 100 people; instead, you have a million.
-Fortunately, each problem is independent of the others (a setup that is sometimes called embarrassingly parallel), so you just need a system (like [Hadoop](https://hadoop.apache.org/) or [Spark](https://spark.apache.org/)) that allows you to send different datasets to different computers for processing.
-Once you've figured out how to answer your question for a single subset using the tools described in this book, you can learn new tools like **sparklyr** to solve it for the full dataset.
+If you're routinely working with larger data (10-100 Gb, say), we recommend learning more about [data.table](https://github.com/Rdatatable/data.table).
+We don't teach it here because it uses a different interface to the tidyverse and requires you ot learn some different conventions.
+However, it is incredible faster and the performance payoff is worth investing some time learning it if you're working with large data.

 ### Python, Julia, and friends

@ -125,22 +114,12 @@ In this book, you won't learn anything about Python, Julia, or any other program
 This isn't because we think these tools are bad.
 They're not!
 And in practice, most data science teams use a mix of languages, often at least R and Python.
-
-However, we strongly believe that it's best to master one tool at a time.
-You will get better faster if you dive deep rather than spreading yourself thinly over many topics.
-This doesn't mean you should only know one thing, just that you'll generally learn faster if you stick to one thing at a time.
-You should strive to learn new things throughout your career, but make sure your understanding is solid before you move on to the next exciting thing.
-
-We think R is a great place to start your data science journey because it is an environment designed from the ground up to support data science.
-R is not just a programming language; it is also an interactive environment for doing data science.
-To support interaction, R is a much more flexible language than many of its peers.
-This flexibility has its downsides, but the big upside is how easy it is to have code that is structured like the problem you are trying to solve for specific parts of the data science process.
-These mini languages help you think about problems as a data scientist while supporting fluent interaction between your brain and the computer.
+But we strongly believe that it's best to master one tool at a time, and R is a great place to start.

 ## Prerequisites

 We've made a few assumptions about what you already know to get the most out of this book.
-You should be generally numerically literate, and it's helpful if you have some programming experience already.
+You should be generally numerically literate, and it's helpful if you have some basic programming experience already.
 If you've never programmed before, you might find [Hands on Programming with R](https://rstudio-education.github.io/hopr/) by Garrett to be a valuable adjunct to this book.

 You need four things to run the code in this book: R, RStudio, a collection of R packages called the **tidyverse**, and a handful of other packages.
@ -149,21 +128,16 @@ They include reusable functions, documentation that describes how to use them, a

 ### R

-To download R, go to CRAN, the **c**omprehensive **R** **a**rchive **n**etwork.
-CRAN is composed of a set of mirror servers distributed around the world and is used to distribute R and R packages.
-Don't pick a mirror close to you; instead, use the cloud mirror, <https://cloud.r-project.org>, which automatically figures it out for you.
-
+To download R, go to CRAN, the **c**omprehensive **R** **a**rchive **n**etwork, <https://cloud.r-project.org>.
 A new major version of R comes out once a year, and there are 2-3 minor releases each year.
 It's a good idea to update regularly.
 Upgrading can be a bit of a hassle, especially for major versions requiring you to re-install all your packages, but putting it off only makes it worse.
-You'll need at least R 4.1.0 for this book.
+We recommend R 4.2.0 or later for this book.

 ### RStudio

-RStudio is an integrated development environment, or IDE, for R programming.
-Download and install it from <https://posit.co/download/rstudio-desktop/>.
-RStudio is updated a couple of times a year.
-When a new version is available, RStudio will let you know.
+RStudio is an integrated development environment, or IDE, for R programming, which you can download from <https://posit.co/download/rstudio-desktop/>.
+RStudio is updated a couple of times a year, and it will automatically let you know when a new version is out so there's no need to check back.
 It's a good idea to upgrade regularly to take advantage of the latest and greatest features.
 For this book, make sure you have at least RStudio 2022.02.0.

@ -189,7 +163,7 @@ You'll also need to install some R packages.
 An R **package** is a collection of functions, data, and documentation that extends the capabilities of base R.
 Using packages is key to the successful use of R.
 The majority of the packages that you will learn in this book are part of the so-called tidyverse.
-All packages in the tidyverse share a common philosophy of data and R programming and are designed to work together naturally.
+All packages in the tidyverse share a common philosophy of data and R programming and are designed to work together.

 You can install the complete tidyverse with a single line of code:

@ -201,7 +175,6 @@ install.packages("tidyverse")

 On your computer, type that line of code in the console, and then press enter to run it.
 R will download the packages from CRAN and install them on your computer.
-If you have problems installing, make sure that you are connected to the internet and that <https://cloud.r-project.org/> isn't blocked by your firewall or proxy.

 You will not be able to use the functions, objects, or help files in a package until you load it with `library()`.
 Once you have installed a package, you can load it using the `library()` function:
@ -214,7 +187,7 @@ This tells you that tidyverse loads nine packages: dplyr, forcats, ggplot2, lubr
 These are considered the **core** of the tidyverse because you'll use them in almost every analysis.

 Packages in the tidyverse change fairly frequently.
-You can check whether updates are available and optionally install them by running `tidyverse_update()`.
+You can see if updates are available by running `tidyverse_update()`.

 ### Other packages