Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
RBC202003_Day1_Period3
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
sakaue
March 19, 2020
Education
110
0
Share
RBC202003_Day1_Period3
sakaue
March 19, 2020
More Decks by sakaue
See All by sakaue
SappoRo.R #11「R によるThe Multilingual Eye-tracking COrpus (MECO) の探索的データ分析」
sakaue
0
120
RBC202003_Day2_Period5
sakaue
0
49
RBC202003_Day2_Period6
sakaue
0
110
RBC202003_Day2_Period7
sakaue
0
110
Rbootcamp202003_Day2_p8.pdf
sakaue
0
97
RBC202003_Day1_Period1
sakaue
1
89
RBC202003_Day1_Period2
sakaue
0
80
RBC202003_Day1_Period4
sakaue
0
69
Other Decks in Education
See All in Education
Padlet opetuksessa
matleenalaakso
12
15k
事業紹介資料(トレーナー養成講座)
kentaro1981
0
210
We部コミュニティスライド2026-04-24
junhat6
0
130
2026年度春学期 統計学 講義の進め方と成績評価について (2026. 4. 9)
akiraasano
PRO
0
140
Implicit and Cross-Device Interaction - Lecture 10 - Next Generation User Interfaces (4018166FNR)
signer
PRO
2
2.2k
共感から、つくる: 変わり続ける自分と、誰かのための創造
micknerd
1
320
Protecting Patrons with Digital Vendors
dsalo
0
100
SSH_handshake_easy_explain
kenbo
0
970
AI時代において英語学習は本当に必要? ~未経験からのバイリンガルキャリアの始め方を教えます~
kekekenta
0
130
モブ社員がモブエンジニアを名乗って得られたこと_20260413
masakiokuda
4
450
Blueprint for Strengthening Community Colleges Training Grant Success
territorium
PRO
0
310
バージョン管理とは / 01-a-vcs
kaityo256
PRO
1
310
Featured
See All Featured
For a Future-Friendly Web
brad_frost
183
10k
Prompt Engineering for Job Search
mfonobong
0
270
Agile Actions for Facilitating Distributed Teams - ADO2019
mkilby
0
180
Pawsitive SEO: Lessons from My Dog (and Many Mistakes) on Thriving as a Consultant in the Age of AI
davidcarrasco
0
120
Bootstrapping a Software Product
garrettdimon
PRO
307
120k
Designing Powerful Visuals for Engaging Learning
tmiket
1
350
Unsuck your backbone
ammeep
672
58k
The Art of Delivering Value - GDevCon NA Keynote
reverentgeek
16
1.9k
My Coaching Mixtape
mlcsv
0
110
The Myth of the Modular Monolith - Day 2 Keynote - Rails World 2024
eileencodes
27
3.4k
What does AI have to do with Human Rights?
axbom
PRO
1
2.1k
Digital Projects Gone Horribly Wrong (And the UX Pros Who Still Save the Day) - Dean Schuster
uxyall
0
1.2k
Transcript
2020-03-19 ୈ3ݶ ϕΫτϧͱߦྻ bootcamp
Agenda 1. ϕΫτϧͱԿ͔ (20) 2. ߦྻͱԿ͔ (20) 3. વσʔλͱԿ͔ (15)
4. ԋशʹ͙࣍ԋश (35)
Agenda 1. ϕΫτϧͱԿ͔ (20) 2. ߦྻͱԿ͔ (20) 3. વσʔλͱԿ͔ (15)
4. ԋशʹ͙࣍ԋश (35)
ɹɹͱ͍͑ ม ໋ 1. ϕΫτϧͱԿ͔
มͱ ̍ͭҎ্ͷΛ ·ͱΊ͍ͯΕ͓ͯ͘ ʮശʯͷ͜ͱ 1. ϕΫτϧͱԿ͔
Ͱ ϕΫτϧͱݺΕ ෳͷΛ̍ͭʹ ·ͱΊͨͷΛࢦ͢ 1. ϕΫτϧͱԿ͔ ʢ̍࣍ݩྻͱݴΘΕΔ͜ͱʣ
•> hako <- c(1,2,3,4,5) •> hako • c() ؔɿcombine (
cf. https://twitter.com/#!/sakaue/status/193708048030760960 ) • Λ̍ͭʹ·ͱΊΔؔ • ٯʹॻ͍ͯʢҰԠʣOK 1. ϕΫτϧͱԿ͔
c()ؔͷ “<-” Կʁ hako <- c(1,2,3,4,5) ͷ “<-” ࠨ͖ͷҹʢˡʣ
Λදݱ ʢೖΕସ͑ͯಈ͖·͢ɻʮ=ʯ͑·͢ɻʣ 1. ϕΫτϧͱԿ͔
͍· “hako” ͱ͍͏໊લͷ ʮมʯͷதʹ 1͔Β5·Ͱͷ5ͭͷࣈ͕ ·ͱΊͯೖ͍ͬͯΔঢ়ଶ 1. ϕΫτϧͱԿ͔
1. ϕΫτϧͱԿ͔ • ·ͣϕΫτϧͷதʢཁૉʣΛ֬ೝ • ίϯιʔϧͰʮhakoʯͱͷΈೖྗ • ग़ྗ݁ՌΛ֬ೝ: 5ͭͷ͕͋Δ͔ •
ϕΫτϧΛ࡞ͬͨΒ͙֬͢ೝ (p. 55)
1. ϕΫτϧͱԿ͔ • ࣍ʹϕΫτϧͷ͞ʢཁૉʣΛ֬ೝ • ίϯιʔϧͰʮlength(hako)ʯͱೖྗ • ग़ྗ݁ՌΛ֬ೝ: 5 ͱग़Δ͔
• ϕΫτϧΛ࡞ͬͨΒ͙֬͢ೝ (p. 55)
1. ϕΫτϧͱԿ͔ • ϕΫτϧͷಛఆͷཁૉΛऔΓग़͢ • 3൪ͷཁૉ͚ͩΛऔΓग़͢ • hako[3] • 3
͚͕ͩදࣔ͞ΕΔ • 2൪͔Β4൪ͷཁૉΛऔΓग़͢ • hako[2 : 4] • 2, 3, 4 ͷ3ཁૉ͕දࣔ͞ΕΔ (p. 56)
1. ϕΫτϧͱԿ͔ • ϕΫτϧΛͬͨܭࢉ • ͯ͢ͷཁૉΛ2ഒ͢Δ • hako * 2
• ผͷϕΫτϧΛ࡞ͦ͠ΕͧΕΛ͢ • hako2 <- c(6, 7, 8, 9, 10) • hako + hako2 • ͦΕͧΕͷཁૉಉ͕࢜͞ΕΔ • ཁૉ͕͚ܽΔͱΤϥʔ͕ग़Δ (p. 56)
1. ϕΫτϧͱԿ͔ • ϕΫτϧෳͷΛ·ͱΊͨͷ • σʔλΛ݁߹͢Δ • vector.1 <- append(hako,
hako2) • vector.1 ͱೖྗ͠தΛ֬ೝ • vector.2 <- append(hako2, hako) • vector.2 ͱೖྗ͠தΛ֬ೝ • ࢦఆͨ͠ॱং௨Γʹ݁߹͞ΕΔ (p. 56)
Ͱ ෳͷΛ̍ͭʹ ·ͱΊͨͷΛ ϕΫτϧͱݺͿ 1. ϕΫτϧͱԿ͔ ʢ̍࣍ݩྻͱݴΘΕΔ͜ͱʣ
Agenda 1. ϕΫτϧͱԿ͔ (20) 2. ߦྻͱԿ͔ (20) 3. વσʔλͱԿ͔ (15)
4. ԋशʹ͙࣍ԋश (35)
Agenda 1. ϕΫτϧͱԿ͔ (20) 2. ߦྻͱԿ͔ (20) 3. વσʔλͱԿ͔ (15)
4. ԋशʹ͙࣍ԋश (35)
2. ߦྻͱԿ͔ ͖͞΄Ͳ ҰߦͰΛ·ͱΊͨ ϕΫτϧΛհ͠·͕ͨ͠
࣮ࡍͷσʔλ ෳߦ(ྻ)͋Δͣ 2. ߦྻͱԿ͔
ྫ͑... •ͱମॏ •ྸͱऩ •֮͑ͨ୯ޠͱTOEIC είΞ 2. ߦྻͱԿ͔
දʹ͢Ε... ਓ ମॏ A 180 75 B 170 65
C 165 60 D 175 70 E 190 80 2. ߦྻͱԿ͔
ෳͷߦྻͰද͞ΕΔ σʔλΛѻ͏ͨΊʹ ɹɹͰʮߦྻʯΛ͏ 2. ߦྻͱԿ͔
ߦྻͱ ͕ॎԣʹฒΒΕͨͷ 2. ߦྻͱԿ͔
1 2 3 4 5 6 7 8 9
ߦ
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
ྻ
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9
ͦΜͳߦྻΛѻ͏ͨΊʹ matrix() ؔ Λ͏ 2. ߦྻͱԿ͔
•matrix() ؔ: ߦྻΛ࡞Δؔ •matrix(ཁૉ, ߦͷ, ྻͷ) •σϑΥϧτͰྻํʹஔ 2. ߦྻͱԿ͔
• ϕΫτϧΛ࡞͔ͯ͠Βߦྻʹม Part 1 • hako3 <- c(1, 2, 3,
4, 5, 6, 7, 8, 9) • matrix.1 <- matrix(hako3, nrow=3, ncol=3) • Ҿʢargumentʣͱͯ͠ߦྻΛࢦఆ • nrow: ߦΛࢦఆɼncol: ྻΛࢦఆ • matrix.1 ͚ͩΛೖྗͯ͠தΛ֬ೝ 2. ߦྻͱԿ͔
• ϕΫτϧΛ࡞͔ͯ͠Βߦྻʹม Part 2 • matrix.2 <- matrix(hako3, nrow=3, ncol=3,
byrow= TRUE) • byrow = TRUE ʹΑΓԣํཁૉΛஔ ɹ • nrow: ߦΛࢦఆɼncol: ྻΛࢦఆ • matrix.2 ͚ͩΛೖྗͯ͠தΛ֬ೝ 2. ߦྻͱԿ͔
1 4 7 2 5 8 3 6 9 matrix(1:9,nrow=3,ncol=3)
2. ߦྻͱԿ͔
1 2 3 4 5 6 7 8 9 matrix(1:9,nrow=3,ncol=3,byrow=TRUE)
2. ߦྻͱԿ͔
2. ߦྻͱԿ͔ • ߦྻͷߦྻΛΔʹ • nrow(matrix.2) #ߦͷΈ֬ೝ • ncol(matrix.2) #ྻͷΈ֬ೝ
• dim(matrix.2) #ߦͱྻΛಉ࣌ʹ֬ೝ
2. ߦྻͱԿ͔ • ߦྻΛͬͨܭࢉ • matrix.2 + 1 #֤ཁૉʹ1Λ͢ •
ผͷߦྻΛ࡞ͦ͠ΕͧΕΛ͢ • matrix.3 <- matrix(c(10:18), nrow=3, ncol=3, byrow=TRUE) • matrix.2 + matrix.3 • 9ͭͷཁૉ͕͞Ε͍ͯΔ͔֬ೝ
2. ߦྻͱԿ͔ • ߦྻͷ݁߹ • rbind() ؔ: ߦํʢԼʣʹߦྻΛ݁߹ • rbind(matrix.2,
matrix.3) • cbind() ؔ: ྻํʢӈʣʹߦྻΛ݁߹ • cbind(matrix.2, matrix.3)
2. ߦྻͱԿ͔ • ߦྻͷཁૉΛऔΓग़͢ • matrix.2[2, 3] #2ߦͷ3ྻʹ͋Δཁૉ • matrix.2[2,
] #2ߦͷཁૉͯ͢ • matrix.2[, 3] #3ྻͷཁૉͯ͢ • matrix.2[-2, ] #2ߦ<Ҏ֎>ͷཁૉͯ͢ • matrix.2[, -3] #3ྻ<Ҏ֎>ͷཁૉͯ͢
2. ߦྻͱԿ͔ • ߦྻΛసஔ͢ΔʢߦͱྻΛೖΕସ͑Δʣ • t(matrix.2) • matrix.2 ͷ࣮ߦ݁Ռͱൺֱ
2. ߦྻͱԿ͔ • ߦྻʹϥϕϧʢ໊લʣΛ͚ͭΔ • rownames(matrix.2) <- c("R1", "R2", "R3")
• ߦϥϕϧͷ༩ • colnames(matrix.2) <- c("C1", "C2", "C3") • ྻϥϕϧͷ༩ • matrix.2 Λೖྗ݁͠ՌΛ֬ೝ
ߦྻ·ͱΊ • ԣํ͕ߦɺॎํ͕ྻ • σϑΥϧτͰͷͷฒͼʹҙ • ඞཁͳཁૉΛదٓऔΓग़ͯ͠Λ֬ೝ
Agenda 1. ϕΫτϧͱԿ͔ (20) 2. ߦྻͱԿ͔ (20) 3. વσʔλͱԿ͔ (15)
4. ԋशʹ͙࣍ԋश (35)
Agenda 1. ϕΫτϧͱԿ͔ (20) 2. ߦྻͱԿ͔ (20) 3. વσʔλͱԿ͔ (15)
4. ԋशʹ͙࣍ԋश (35)
3. વσʔλͱԿ͔ • R քͷਆɼHadley Wickham ࢯఏএͷ "Tidy Data" •
จ: http://vita.had.co.nz/papers/tidy-data.html • ࢀߟ: http://id.fnshr.info/2017/01/09/tidy-data-intro/ • ʮ1ྻʹʢॎํʣ1มʯͷܗࣜʹ͢Δ͜ͱ • ੳ༻ͷσʔλܗࣜ͜Ε͕େݪଇ • มΛԣʢߦʣํʹฒͨΓ͠ͳ͍ • Excel Ͱηϧͷ݁߹ͳΜͧ͠ΑͬͨΒ...ʢౖʣ
ʘ݄ 4݄ 5݄ 6݄ H30 124 183 241 H31 205
367 307 R01 582 759 998 3. વσʔλͱԿ͔ • Α͘ݟ͔͚ΔλΠϓͷද • ਓʹݟͤʢͯղऍ͢ʣΔදͱͯ͠ OK • σʔλੳ༻ͷදͱͯ͠ NG • ॎͱԣʹม͕ަࠩͨ͠ঢ়ଶ͔ͩΒ
݄ ΞΫηε H30 4 124 H30 5 183 H30
6 241 H31 4 205 H31 5 367 H31 6 307 R01 4 582 R01 5 759 R01 6 998 3. વσʔλͱԿ͔ • ੳ༻ʹʮ1ྻʹʢॎํʣ1มʯ • 1ߦʢԣํʣʹ1έʔεɾ1Ϩίʔυ • ݄ΛԣʢߦʣํʹฒͨΓ͠ͳ͍
• ࢝Ί͔Βવσʔλʹͳ͍ͬͯΔ͜ͱগͳ͍(?) • ͦ͏ͨ͠σʔλΛมܗɾཧ͢ΔͨΊʹɼR Ͱ "tidyverse" ͱ͍͏ύοέʔδ͕ར༻Մೳ • tidyverse ʹؚ·ΕΔύοέʔδΛ·ͱΊͯΠϯ
ετʔϧ͢ΔͨΊͷύοέʔδ • ggplot2: άϥϑඳը • dplyr: σʔλૢ࡞ʢ݅நग़ɼྻՃͳͲʣ • tidyr: વσʔλ࡞ • ͦͷଞଟͷύοέʔδ͋Γ 3. વσʔλͱԿ͔
• ຊߨशձͰ "Tidy Data" ͷઆ໌ͱɼ"tidyverse" ύοέʔδͷհͷΈʢૢ࡞͕Ұ෦ಛघͳͨΊʣ • େྔͷσʔλΛܗ͢Δࡍɼ΄΅ඞਢͷύο έʔδͱͳΓͭͭ͋Δ •
ࢀߟ1: https://r4ds.had.co.nz/ (R for Data Science) • ࢀߟ2: https://moderndive.com/index.html ɹɹɹɹɹɹ (A moderndive into R and the tidyverse) • େࣄͳ͜ͱɼʮݟͯղऍ͢ΔදʯͱʮσʔλΛ อଘ͢ΔදʯʢʹTidy DataʣΛ۠ผͯ͠อଘͯ͠ ͓͘͜ͱ 3. વσʔλͱԿ͔
Agenda 1. ϕΫτϧͱԿ͔ (20) 2. ߦྻͱԿ͔ (20) 3. વσʔλͱԿ͔ (15)
4. ԋशʹ͙࣍ԋश (35)
Agenda 1. ϕΫτϧͱԿ͔ (20) 2. ߦྻͱԿ͔ (20) 3. વσʔλͱԿ͔ (15)
4. ԋशʹ͙࣍ԋश (35)
4. ԋशʹ͙࣍ԋश 1. ͱମॏͷߦྻΛ࡞ΔʢਓΛআ͘ʣ ਓ ମॏ A 180 75
B 170 65 C 165 60 D 175 70 E 190 80
ώϯτ 1. c() ؔͰɺΛ࿈݁ 2. matrix() ؔͰɺߦྻʹม • ʮ5ߦͰ2ྻʯʹ͢Δͱ͍͏ࢦఆΛ͢Δ 3.
มʹೖ͢Δ͜ͱΛ͓Εͳ͘ 4. ԋशʹ͙࣍ԋश
> karada ͱೖྗͯ͠ มͷதΛ֬ೝ
2. 1͔Β50·ͰͷΛɼ10ߦ5ྻͷߦྻʹม 3. 2 Ͱ࡞ͨ͠ߦྻͷ7ߦͷཁૉΛऔΓग़͢ 4. 3 ͰऔΓग़ͨ͠7ߦͷཁૉͷ߹ܭΛࢉग़͢Δʢ1ߦͰʣ 5. 2
Ͱ࡞ͨ͠ߦྻͷ3ྻͷཁૉΛऔΓग़͢ 6. 2 Ͱ࡞ͨ͠ߦྻͷ5ߦʻҎ֎ʼͷཁૉΛऔΓग़͢ 7. 2 Ͱ࡞ͨ͠ߦྻͷ2ߦͱ7ߦͷཁૉΛಉ࣌ʹऔΓग़͢ 8. 2 Ͱ࡞ͨ͠ߦྻͷ2ྻͱ4ྻͷཁૉΛಉ࣌ʹऔΓग़͢ 9. 2 Ͱ࡞ͨ͠ߦྻͷ2ྻͱ4ྻͷཁૉͷฏۉΛࢉग़͢Δ 10. 2 Ͱ࡞ͨ͠ߦྻʹϥϕϧΛ͚ͭΔʢR1 … R10, C1 … C5ʣ 4. ԋशʹ͙࣍ԋश
2. matrix() ؔɼҾͷ nrow / ncol, byrow ʹ༻৺ 3. ΧοίͷछྨͱΧϯϚͷҐஔʹҙ
4. ߹ܭΛٻΊΔʹɼS** ؔ 5. ΧοίͷछྨͱΧϯϚͷҐஔʹҙ 6. ʮҎ֎ʯɼϋΠϑϯͰࢦఆ 7. ಉ࣌ʹࢦఆ͢Δͱ͖ɼc() ؔΛΈ߹Θ࣮ͤͯߦ 8. ಉ࣌ʹࢦఆ͢Δͱ͖ɼc() ؔΛΈ߹Θ࣮ͤͯߦ 9. ฏۉΛٻΊΔʹɼm*** ؔ 10. rownames/colnames ͰɼจࣈྻʹೋॏҾ༻ූΛه 4. ԋशʹ͙࣍ԋशʢώϯτʣ
Enjoy ! twitter: @sakaue e-mail: tsakaue<AT>hiroshima-u.ac.jp