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
0
110
RBC202003_Day1_Period3
sakaue
March 19, 2020
Tweet
Share
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
110
RBC202003_Day2_Period5
sakaue
0
44
RBC202003_Day2_Period6
sakaue
0
110
RBC202003_Day2_Period7
sakaue
0
110
Rbootcamp202003_Day2_p8.pdf
sakaue
0
96
RBC202003_Day1_Period1
sakaue
1
85
RBC202003_Day1_Period2
sakaue
0
79
RBC202003_Day1_Period4
sakaue
0
67
Other Decks in Education
See All in Education
核軍備撤廃に向けた次の大きな一歩─核兵器を先には使わないと核保有国が約束すること
hide2kano
0
300
IHLヘルスケアリーダーシップ研究会17期説明資料
ihlhealthcareleadership
0
2k
演習:GitHubの基本操作 / 06-github-basic
kaityo256
PRO
0
200
Data Representation - Lecture 3 - Information Visualisation (4019538FNR)
signer
PRO
1
2.9k
Padlet opetuksessa
matleenalaakso
12
15k
Flinga
matleenalaakso
4
15k
Use Cases and Course Review - Lecture 8 - Human-Computer Interaction (1023841ANR)
signer
PRO
0
1.4k
資格支援制度-株式会社HIT
kabushikigaisya_hit
0
440
L'artisanat logiciel à l'heure du numérique responsable
thirion
0
120
演習:Gitの応用操作 / 05-git-advanced
kaityo256
PRO
0
210
次期バージョン 14.5.1 Early Access Program が始まりました
harunakano
1
120
2026 Medicare 101 Presentation
robinlee
PRO
0
180
Featured
See All Featured
Utilizing Notion as your number one productivity tool
mfonobong
4
250
Exploring the relationship between traditional SERPs and Gen AI search
raygrieselhuber
PRO
2
3.7k
Agile Leadership in an Agile Organization
kimpetersen
PRO
0
110
[RailsConf 2023] Rails as a piece of cake
palkan
59
6.4k
Visual Storytelling: How to be a Superhuman Communicator
reverentgeek
2
470
Navigating Weather and Climate Data
rabernat
0
130
GraphQLとの向き合い方2022年版
quramy
50
14k
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
38
2.8k
The Impact of AI in SEO - AI Overviews June 2024 Edition
aleyda
5
760
Speed Design
sergeychernyshev
33
1.6k
Test your architecture with Archunit
thirion
1
2.2k
技術選定の審美眼(2025年版) / Understanding the Spiral of Technologies 2025 edition
twada
PRO
118
110k
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