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Data Carpentry with Tidyverse
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Muhammad Aswan Syahputra
May 11, 2019
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Data Carpentry with Tidyverse
Meetup Algoritma X Machine Learning ID X Komunitas R Indonesia
Muhammad Aswan Syahputra
May 11, 2019
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Transcript
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• Sensory Scientist @ Sensolution.ID • Trainer @ R-Academy Telkom
University and The Datanomics Institute (TDI) • Initiator of Komunitas R Indonesia • Pkgs: sensehubr, nusandata, bandungjuara, prakiraan, etc • Shinyapps: sensehub, thermostats, aquastats, bcrp, bandungjuara, etc aswansyahputra @aswansyahputra_
R Indonesia R Indonesia www.r-indonesia.id Komunitas t.me/GNURIndonesia @r_indonesia_ indo-r www.r-indonesia.id
R Indonesia www.r-indonesia.id
Know your neighbour! • Who are you? • What you
do with data? • How would you describe your experience with R?
Artwork by @allison_horst
Artwork by @allison_horst
Data Carpentry?
It’s so relatable, is it not?
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“ Do not underestimate DATA PREPROCESSING
is not a single process but a thousand of little
skills and techniques “ - David Minmo
Artwork by @allison_horst The tidyverse is an opinionated collection of
R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.
Program Import Tidy Transform Visualise Model Communicate Understand
Program Import Tidy Transform Visualise Model Communicate Understand
Artwork by @allison_horst tidyr
Artwork by @allison_horst dplyr
dplyr basic functions: • filter() selects rows based on their
values • mutate() creates new variables • select() picks columns by name • summarise() calculates summary statistics • arrange() sorts the rows dplyr basic functions: • filter() selects rows based on their values • mutate() creates new variables • select() picks columns by name • summarise() calculates summary statistics • arrange() sorts the rows Credits to Michael Toth tidyr basic functions: • gather() wide-format >> long-format • spread() long-format >> wide-format • fill() fills value based on previous entry • complete() turns implicit missing values into explicit tidyr basic functions: • gather() wide-format >> long-format • spread() long-format >> wide-format • fill() fills value based on previous entry • complete() turns implicit missing values into explicit Operators: • ! (not) • I (or) • & (and) • ==, != • <, <=, >, >= • %in% • is.na() Operators: • ! (not) • I (or) • & (and) • ==, != • <, <=, >, >= • %in% • is.na()
How can I chain?
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1. diputar 2. dijilat 3. dicelupin 4. dimakan :D
1. putar(apa) 2. jilat(apa, berapa_kali) 3. celup(apa, ke) 4. makan(apa,
output)
a > oreo_putar ← putar(apa = “oreo”) > oreo_jilat ←
jilat(apa = oreo_putar, berapa_kali = 2) > oreo_celup ← celup(apa = oreo_jilat, ke = “susu”) > makan(apa = oreo_celup, output = “kenyang.perut”)
> oreo_putar ← putar(apa = “oreo”) > oreo_jilat ← jilat(apa
= oreo_putar, berapa_kali = 2) > oreo_celup ← celup(apa = oreo_jilat, ke = “susu”) > makan(apa = oreo_celup, output = “kenyang.perut”) a
> makan( celup( jilat( putar(apa = “oreo”), berapa_kali = 2
), ke = “susu” ), output = “kenyang.perut” ) b
function(arg1, arg2, arg3,...) arg1 %>% function(arg2, arg3,...) function(arg1, arg2, arg3,...)
arg2 %>% function(arg1, arg2=.,arg3,...) magrittr
> putar(apa = “oreo”) %>% jilat(berapa_kali = 2) %>% celup(ke
= “susu”) %>% makan(output = “kenyang.perut”) c
What to do today?
None
www.onepiece.fandom.com www.onepiece.fandom.com
Let’s get started! • Let’s write R scripts together! •
I will demonstrate and explain the use of each code • Access this presesentation at: s.id/data-carpentry- with-tidyverse
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s.id/yt_aswansyahputra s.id/yt_aswansyahputra
Thanks!
[email protected]
www.aswansyahputra.com speakerdeck.com/ aswansyahputra R Indonesia www.r-indonesia.id