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
TokyoR#93
Search
soriente
July 03, 2021
Technology
270
0
Share
TokyoR#93
TokyoR#93の初心者セッション可視化パートです。
soriente
July 03, 2021
Other Decks in Technology
See All in Technology
Oracle AI Database@AWS:サービス概要のご紹介
oracle4engineer
PRO
4
2.7k
AI Adaptable なテストを整える工夫 / Ways to Make Your Tests AI-Adaptable
bitkey
PRO
2
170
AI時代の私の技術インプットとアウトプット術
tonkotsuboy_com
15
7.8k
イベントストーミングとKiroの仕様駆動開発で実現する要件の認識合わせプロセス
syobochim
7
950
layerx-fde-practices
cipepser
6
2.9k
Gradle×GitHub_ActionsでCI時間を約50%短縮 ジョブ分割の設計と落とし穴 / Cutting CI Time by ~50% with Gradle and GitHub Actions: Job-Splitting Design and Pitfalls
takatty
0
530
シンデレラなんかになりたくない!ガラスの靴が割れた時代にどう歩く?
nomizone
0
220
APIテストとは?
nagix
0
160
Agentic AI時代における メルカリのAIガバナンスとガードレール実装
naoichihara
16
17k
Amazon CloudFrontにおけるAIボットアクセス制御のポイント
kizawa2020
5
310
はじめてのDatadog
kairim0
0
220
JJUG CCC 2026 Spring AI時代の開発こそ標準化を武器に! ― 方式・プロセス・プラットフォームの標準化
s27watanabe
2
620
Featured
See All Featured
DevOps and Value Stream Thinking: Enabling flow, efficiency and business value
helenjbeal
1
210
Chrome DevTools: State of the Union 2024 - Debugging React & Beyond
addyosmani
10
1.2k
The Cult of Friendly URLs
andyhume
79
6.9k
Introduction to Domain-Driven Design and Collaborative software design
baasie
1
800
Agile that works and the tools we love
rasmusluckow
331
21k
Design and Strategy: How to Deal with People Who Don’t "Get" Design
morganepeng
133
19k
Why Your Marketing Sucks and What You Can Do About It - Sophie Logan
marketingsoph
0
160
The AI Search Optimization Roadmap by Aleyda Solis
aleyda
1
5.8k
Hiding What from Whom? A Critical Review of the History of Programming languages for Music
tomoyanonymous
2
820
Primal Persuasion: How to Engage the Brain for Learning That Lasts
tmiket
0
350
State of Search Keynote: SEO is Dead Long Live SEO
ryanjones
0
200
brightonSEO & MeasureFest 2025 - Christian Goodrich - Winning strategies for Black Friday CRO & PPC
cargoodrich
3
710
Transcript
5PLZP3σʔλՄࢹԽ ॳ৺ऀηογϣϯ
ࣗݾհ w TPSJFOUF w *5اۀۈ w 3ྺ ࡉͬͯ͘͘·͢ɻ w
ͱ͍ͬͯ࠷ۙ1ZUIPO͕ϝΠϯ w 1)1ॻ͍ͯͨ࣌ظ͋Γ·ͨ͠ɻ
ՄࢹԽͱ w จࣈͷ௨Γɺݟ͑ΔԽ͢Δɻ σʔλੳͷจ຺ͰɺσʔλͷؔੑΛݟ͑ ΔԽ͢Δɻ w ՄࢹԽΛ͚ͨͩ͠ͰΘ͔Δ͜ͱଟ͍ɻ w ՄࢹԽΛ͢ΔͱɺΘ͔Γ͍͢ɻ
w ՄࢹԽΛͨ͋͠ͱʹԿΒ͔ͷҙࢥܾఆΛߦ͏͜ͱ͕ଟ͍ɻ ੳऀ͕ࣗҙࢥ ܾఆ͢Δ͜ͱɺ୭͔ʹҙࢥܾఆͯ͠Β͏͜ͱ͋Δɻ
None
HHQMPUͷجຊ
HHQMPUͱ w ՄࢹԽͷͨΊͷϥΠϒϥϦ w UJEZWFSTFͷϥΠϒϥϦ܈ͷҰͭ w ʰ5IF(SBNNBSPG(SBQIJDTʱΛϕʔεʹ࡞ΒΕ͍ͯΔ ˠҰ؏ੑͷ͋Δจ๏Ͱ߹ཧతʹॻ͚Δʂ
ࠓճ͏σʔλQFOHVJOT JOTUBMMQBDLBHFT QBMNFSQFOHVJOT MJCSBSZ QBMNFSQFOHVJOT IFBE QFOHVJOT
TQFDJFT JTMBOE CJMM@MFOHUI@NN CJMM@EFQUI@NN fl JQQFS@MFOHUI@NN CPEZ@NBTT@H TFY ZFBS "EFMJF 5PSHFSTFO NBMF "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO /" /" /" /" /" "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO NBMF
QFOHVJOT
HHQMPUΠϯετʔϧಡΈࠐΈ JOTUBMMQBDLBHFT HHQMPU JOTUBMMQBDLBHFT UJEZWFSTF ͰՄ MJCSBSZ HHQMPU MJCSBSZ UJEZWFSTF
ͰՄ
ࠓճॻ͘άϥϑͷछྨ w ࢄਤ w άϥϑ w ંΕઢάϥϑ
ࠓճ͏σʔλQFOHVJOT JOTUBMMQBDLBHFT QBMNFSQFOHVJOT MJCSBSZ QBMNFSQFOHVJOT IFBE QFOHVJOT
TQFDJFT JTMBOE CJMM@MFOHUI@NN CJMM@EFQUI@NN fl JQQFS@MFOHUI@NN CPEZ@NBTT@H TFY ZFBS "EFMJF 5PSHFSTFO NBMF "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO /" /" /" /" /" "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO NBMF
ࠓճ͏σʔλQFOHVJOT JOTUBMMQBDLBHFT QBMNFSQFOHVJOT MJCSBSZ QBMNFSQFOHVJOT IFBE QFOHVJOT
TQFDJFT JTMBOE CJMM@MFOHUI@NN CJMM@EFQUI@NN fl JQQFS@MFOHUI@NN CPEZ@NBTT@H TFY ZFBS "EFMJF 5PSHFSTFO NBMF "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO /" /" /" /" /" "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO NBMF
ࢄਤ ॻ͖ํओʹ3ύλʔϯ > ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm))
+ geom_point() > ggplot(penguins) + geom_point(aes(x = bill_length_mm, y = bill_depth_mm)) > ggplot() + geom_point( data = penguins, aes(x = bill_length_mm, y = bill_depth_mm) )
ࠓճॻ͘άϥϑͷछྨ w ࢄਤ w ંΕઢάϥϑ w άϥϑ
ࠓճ͏σʔλQFOHVJOT JOTUBMMQBDLBHFT QBMNFSQFOHVJOT MJCSBSZ QBMNFSQFOHVJOT IFBE QFOHVJOT
TQFDJFT JTMBOE CJMM@MFOHUI@NN CJMM@EFQUI@NN fl JQQFS@MFOHUI@NN CPEZ@NBTT@H TFY ZFBS "EFMJF 5PSHFSTFO NBMF "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO /" /" /" /" /" "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO NBMF
ࠓճ͏σʔλQFOHVJOT JOTUBMMQBDLBHFT QBMNFSQFOHVJOT MJCSBSZ QBMNFSQFOHVJOT IFBE QFOHVJOT
TQFDJFT JTMBOE CJMM@MFOHUI@NN CJMM@EFQUI@NN fl JQQFS@MFOHUI@NN CPEZ@NBTT@H TFY ZFBS "EFMJF 5PSHFSTFO NBMF "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO /" /" /" /" /" "EFMJF 5PSHFSTFO GFNBMF "EFMJF 5PSHFSTFO NBMF
σʔλूܭ MJCBSBSZ EQMZS QFOHVJOT@GPS@MJOFQFOHVJOT HSPVQ@CZ ZFBS TVNNBSJTF NFBO@NBTTNFBO
CPEZ@NBTT@H OBSN536& QFOHVJOT@GPS@MJOF ZFBS NFBO@NBTT
ંΕઢάϥϑ ॻ͖ํओʹ3ύλʔϯ > ggplot(penguins_for_line, aes(x = year, y = mean_mass))
+ geom_line() > penguins_for_line %>% ggplot() + geom_line(aes(x = year, y = mean_mass)) > ggplot(penguins_for_line) + geom_line(aes(x = year, y = mean_mass)) > ggplot() + geom_line( data = penguins_for_line, aes(x = year, y = mean_mass) )
άϥϑ ॻ͖ํ3ύλʔϯ > ggplot(penguins_for_line, aes(x = year, y = mean_mass))
+ geom_bar(stat = "identity") > ggplot(penguins_for_line) + geom_bar(aes(x = year, y = mean_mass), stat = "identity") > ggplot() + geom_bar( data = penguins_for_line, aes(x = year, y = mean_mass), stat = "identity") ҎԼͰՄ > ggplot() + geom_bar( data = penguins, aes(x = year, y = body_mass_g), stat = "summary", fun = "mean" )
ͦͷଞͷάϥϑɻɻɻ w άάΔ w ެࣜνʔτγʔτ IUUQTHJUIVCDPNSTUVEJPDIFBUTIFFUTCMPCNBTUFSEBUB WJTVBMJ[BUJPOQEG w 4MBDLͷSXBLBMBOH࣭
͍͔ͭ͘άϥϑॻ͍ͯΈͯ w λΠτϧ͚͍ͭͨɻ w ͕࣠ؾʹͳΔɻ
> ggplot() + geom_line( data = penguins_for_line, aes(x = year,
y = mean_mass) ) + ggtitle("ંΕઢάϥϑ") + theme_gray(base_family = "HiraKakuPro-W3") λΠτϧઃఆ
λΠτϧઃఆ > ggplot() + geom_line( data = penguins_for_line, aes(x =
year, y = mean_mass) ) + ggtitle("ંΕઢάϥϑ") + theme_gray(base_family = "HiraKakuPro-W3")
Y࣠ > ggplot() + geom_line( data = penguins_for_line, aes(x =
year, y = mean_mass)) + ggtitle("ંΕઢάϥϑ") + theme_gray(base_family = "HiraKakuPro-W3") + scale_x_continuous(breaks=seq(2007,2009,1))
Z࣠ > ggplot() + geom_line( data = penguins_for_line, aes(x =
year, y = mean_mass) ) + ggtitle("ࢄਤ") + theme_gray(base_family = "HiraKakuPro-W3") + scale_x_continuous( breaks = seq( min(penguins_for_line$year), max(penguins_for_line$year), 1 ) ) + ylim(0, 4300)
ࢄਤ छྨʹΑͬͯ৭͚͍ͨ > ggplot() + geom_point( data = penguins, aes(x
= bill_length_mm, y = bill_depth_mm, color = species) )
·ͱΊ w ՄࢹԽ͔ͳΓधཁͳύʔτ͕ͩɺ͍͠ɻ w άϥϑHHQMPU ͱHFPN@YYY Λ͏ͱॻ͘͜ͱ͕Ͱ͖Δɻ w ؔϓϥεͰͭͳ͙ɻ w
Γ͍ͨ͜ͱΛάάͬͯΈͯɺࢼͯ͠ΈͯɺΘ͔Βͳ͚Εɺ4MBDLͷSXBLBMBOHʹ࣭ͯͯ͠ Έ·͠ΐ͏ʂ
&/+0: