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_Day2_Period5
Search
sakaue
March 20, 2020
Education
0
37
RBC202003_Day2_Period5
sakaue
March 20, 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
83
RBC202003_Day2_Period6
sakaue
0
86
RBC202003_Day2_Period7
sakaue
0
82
Rbootcamp202003_Day2_p8.pdf
sakaue
0
78
RBC202003_Day1_Period1
sakaue
1
64
RBC202003_Day1_Period2
sakaue
0
63
RBC202003_Day1_Period3
sakaue
0
84
RBC202003_Day1_Period4
sakaue
0
48
Other Decks in Education
See All in Education
サンキッズゾーン 春日井駅前 ご案内
sanyohomes
0
430
技術勉強会 〜 OAuth & OIDC 入門編 / 20250528 OAuth and OIDC
oidfj
5
1.3k
Pydantic(AI)とJSONの詳細解説
mickey_kubo
0
120
万博非公式マップとFOSS4G
barsaka2
0
400
GitHubとAzureを使って開発者になろう
ymd65536
1
130
自己紹介 / who-am-i
yasulab
PRO
3
5.2k
ふりかえり研修2025
pokotyamu
0
1.2k
(キラキラ)人事教育担当のつらみ~教育担当として知っておくポイント~
masakiokuda
0
110
SkimaTalk Introduction for Students
skimatalk
0
390
プレゼンテーション実践
takenawa
0
7.2k
America and the World
oripsolob
0
510
計算情報学研究室 (数理情報学第7研究室)紹介スライド (2025)
tomonatu8
0
550
Featured
See All Featured
The Language of Interfaces
destraynor
158
25k
実際に使うSQLの書き方 徹底解説 / pgcon21j-tutorial
soudai
PRO
181
54k
StorybookのUI Testing Handbookを読んだ
zakiyama
30
5.9k
Code Review Best Practice
trishagee
69
19k
The Web Performance Landscape in 2024 [PerfNow 2024]
tammyeverts
8
700
Distributed Sagas: A Protocol for Coordinating Microservices
caitiem20
331
22k
The Art of Programming - Codeland 2020
erikaheidi
54
13k
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
251
21k
The Cost Of JavaScript in 2023
addyosmani
51
8.5k
Practical Orchestrator
shlominoach
189
11k
Building Better People: How to give real-time feedback that sticks.
wjessup
367
19k
Why You Should Never Use an ORM
jnunemaker
PRO
58
9.4k
Transcript
2020-03-20 ୈ5ݶ σʔλͷཁ bootcamp
Agenda 1. σʔλͷཁͷඞཁੑ (5) 2. ϕΫτϧΛͬͨཁ (20) 3. ߦྻΛͬͨཁ (20)
4. ԋशʹ͙࣍ԋश (45)
Agenda 1. σʔλͷཁͷඞཁੑ (5) 2. ϕΫτϧΛͬͨཁ (20) 3. ߦྻΛͬͨཁ (20)
4. ԋशʹ͙࣍ԋश (45)
1. σʔλͷཁͷඞཁੑ • σʔλΛूΊ͚ͨͩͰԿஅͰ͖ͳ͍ • σʔλΛཁʢཁʣΛ͢Δ͜ͱͰɼશମ͕ Ͳ͏ͳ͍ͬͯͯɼΒ͖͕ͭͲ͏ͳ͍ͬͯ Δ͔͕ѲͰ͖Δ • தؒςετͰऔͬͨ55ྑ͍ʁɼѱ͍ʁ
• ظςετͷฏۉ͕80ͷ߹ɼશମతʹྑ͍Ͱ͖͙͍͋ʁ • ฏۉɾΒ͖ͭʢSDʣʹΑΔʢ͠ɼςετͷ༰ ʹΑΔʢ͠ɼԿΛ͔֬Ί͍͔ͨʹΑΔʣʣ
Agenda 1. σʔλͷཁͷඞཁੑ (5) 2. ϕΫτϧΛͬͨཁ (20) 3. ߦྻΛͬͨཁ (20)
4. ԋशʹ͙࣍ԋश (45)
Agenda 1. σʔλͷཁͷඞཁੑ (5) 2. ϕΫτϧΛͬͨཁ (20) 3. ߦྻΛͬͨཁ (20)
4. ԋशʹ͙࣍ԋश (45)
• sum() ؔ • x <- c(1, 2, 3, 4,
5) • sum(x) • sum(x[2 : 4]) • ϕΫτϧͷ2൪͔Β4൪ͷཁૉͷ૯ • y <- c(1:1000) • sum(y) • sum(y[27:89]) 2. ϕΫτϧΛͬͨཁ
• mean() ؔ: ฏۉΛٻΊΔ • ฏۉ: σʔλͷ૯ΛσʔλͷݸͰׂͬͨ • mean(x); mean(y)
• median() ؔ: தԝΛٻΊΔ • தԝ: খ͍͞ॱʹฒͨ࣌ʹਅΜதͷॱҐʹ͘Δ • median(x); median(y) • ฏۉͷ᠘ • ۃͳ͕ࠞ͟ΔͱӨڹΛड͚ͯ͠·͏ • a <- c(100, 200, 300, 400, 500) • mean(a); median(a) • b <- c(100, 200, 300, 400, 5000) • mean(b); median(b) 2. ϕΫτϧΛͬͨཁ
• max() ؔ: ࠷େΛٻΊΔ • min() ؔ: ࠷খΛٻΊΔ • var()
ؔ: ࢄΛٻΊΔ • sd() ؔ: ඪ४ภࠩΛٻΊΔ • summary() ؔ: ཁ౷ܭྔΛҰʹٻΊΔ • ࠷খ, தԝ, ฏۉ, ࠷େ, Լଆ25%, ্ଆ25% • max(x); max(y) • min(x); min(y) • var(x);var(y) • sd(x); sd(y) • summary(x); summary(y) 2. ϕΫτϧΛͬͨཁ
Agenda 1. σʔλͷཁͷඞཁੑ (5) 2. ϕΫτϧΛͬͨཁ (20) 3. ߦྻΛͬͨཁ (20)
4. ԋशʹ͙࣍ԋश (45)
Agenda 1. σʔλͷཁͷඞཁੑ (5) 2. ϕΫτϧΛͬͨཁ (20) 3. ߦྻΛͬͨཁ (20)
4. ԋशʹ͙࣍ԋश (45)
3. ߦྻΛͬͨཁ • ѻ͍ํϕΫτϧͷཁͱಉ͡ • matrix.4 <- matrix(c(1, 2, 3,
4, 5, 6, 7, 8, 9), nrow = 3, ncol = 3, byrow = TRUE) • matrix.4 ͰதΛ֬ೝ • sum(matrix.4) #ߦྻʹ͋Δͷ૯ • mean(matrix.4) #ߦྻશମͷฏۉ • sum(matrix.4[1,]) #ߦྻ1ߦͷ૯ • mean(matrix.4[,2:3]) #ߦྻ2-3ྻͷฏۉ
3. ߦྻΛͬͨཁ • ͪΐͬͱɾཁૉΛେ͖ͯ͘͠Έ·͠ΐ͏ • matrix.5 <- matrix(c(1:5000), nrow =
100, ncol = 50, byrow = TRUE) • matrix.5 ͰதΛ֬ೝ • sum(matrix.5) #ߦྻʹ͋Δͷ૯ • mean(matrix.5) #ߦྻશମͷฏۉ
3. ߦྻΛͬͨཁ • rowSums() ؔ; colSums() ؔ; • ߦ͝ͱྻ͝ͱʹ૯ΛٻΊΔؔ •
rowSums(matrix.4); rowSums(matrix.5) • colSums(matrix.4); colSums(matrix.5) • rowMeans () ؔ; colMeans() ؔ • ߦ͝ͱྻ͝ͱʹฏۉΛٻΊΔؔ • rowMeans(matrix.4); rowMeans(matrix.5) • colMeans(matrix.4); colMeans(matrix.5)
3. ߦྻΛͬͨཁ • apply() ؔ • ߦ͝ͱྻ͝ͱʹ༷ʑͳؔΛద༻ • apply(ߦྻ໊, Ϛʔδϯ,
ద༻͢Δؔʣ • Ϛʔδϯ͕1ͳΒߦ͝ͱɼ2ͳΒྻ͝ͱ • apply(matrix.4, 1, sum) #ߦ͝ͱͷ૯ • rowSums(matrix.4) ͱಉ͡ॲཧ • apply(matrix.4, 2, mean) #ྻ͝ͱͷฏۉ • colMeans(matrix.4) ͱಉ͡ॲཧ
3. ߦྻΛͬͨཁ • apply() ؔͷଓ͖ • apply(matrix.4, 1, max) •
#ߦ͝ͱͷ࠷େ • apply(matrix.4, 2, summary) • #ྻ͝ͱͷཁ౷ܭྔ
Agenda 1. σʔλͷཁͷඞཁੑ (5) 2. ϕΫτϧΛͬͨཁ (20) 3. ߦྻΛͬͨཁ (20)
4. ԋशʹ͙࣍ԋश (45)
Agenda 1. σʔλͷཁͷඞཁੑ (5) 2. ϕΫτϧΛͬͨཁ (20) 3. ߦྻΛͬͨཁ (20)
4. ԋशʹ͙࣍ԋश (45)
4. ԋशʹ͙࣍ԋश 1. 1͔Β50͔Β·Ͱͷ͕ೖͬͨϕΫτ ϧΛ࡞ͯ͠… 1. ૯, ฏۉ, தԝ, ࠷େ,
࠷খ , ࢄ, ඪ४ภࠩΛٻΊΔ 2. ཁ౷ܭྔΛٻΊΔ
4. ԋशʹ͙࣍ԋशʢώϯτʣ 1. Λൃੜͤ͞ΔʹίϩϯΛ͍·͠ΐ͏ 1. ֤ؔΛηϛίϩϯͰͭͳ͍ͰҰؾʹ࣮ߦ 2. summary() ؔΛ͍·͢
2. 1͔Β10000·Ͱͷ͕ೖͬͨߦྻʢ100ߦɾ100ྻʣΛ࡞ͯ͠… 1. 20ߦʮͱʯ70ߦͷ2ߦͷΈͷ֤૯ΛٻΊΔ 2. 31ྻʮ͔Βʯ80ྻ·Ͱͷ50ྻͷ֤ฏۉΛٻΊΔ 3. ߦ͝ͱɾྻ͝ͱͷ૯ΛٻΊΔʢapply ؔΛ༻ʣ 4.
ߦ͝ͱɾྻ͝ͱͷฏۉΛٻΊΔʢapply ؔΛ༻ʣ 5. ߦ͝ͱɾྻ͝ͱͷཁ౷ܭྔΛٻΊΔʢapply ؔΛ༻ʣ 6. psych ύοέʔδΛͬͯཁ౷ܭྔΛٻΊΔ • ҙͷߦɾྻʹରͯ͠ٻΊͯΈΔ 4. ԋशʹ͙࣍ԋश
2. matrix() ؔΛ͍ɼྻͷࢦఆͱ byrow ͷઃఆʹ༻৺ 1. ߦͷෳࢦఆ c() ؔΛ͍·͠ΐ͏ 2.
ྻͷൣғࢦఆίϩϯΛ͍·͠ΐ͏ 3. apply() ؔͱ sum() ؔΛΈ߹Θͤ·͠ΐ͏ 4. apply() ؔͱ mean() ؔΛΈ߹Θͤ·͠ΐ͏ 5. apply() ؔͷߦͱྻͷࢦఆɼ1 ͔ 2 Ͱ͚·͢ 6. ·ͣ install.packages() ͯ͠ library() Ͱ༗ޮԽ 4. ԋशʹ͙࣍ԋशʢώϯτʣ
Enjoy ! twitter: @sakaue e-mail: tsakaue<AT>hiroshima-u.ac.jp