散布図と相関(と Git) / Scatter plots and correlations (and Git)

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Boxes and whiskers — generated by Stable Diffusion XL v1.0 2024 5 ( Git) (WBS) 2024 5 ( Git) — 2024-11 – p.1/16

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https://speakerdeck.com/ks91/collections/corporate-data-analysis-2024-winter 2024 5 ( Git) — 2024-11 – p.2/16

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( 20 ) 1 • 2 R • 3 • 4 • 5 • 6 ( ) 7 (1) 8 (2) 9 R ( ) (1) 10 R ( ) (2) 11 R ( ) (1) 12 R ( ) (2) 13 GPT-4 14 GPT-4 15 ( ) LaTeX Overleaf 8 (12/16 ) / (2 ) OK / 2024 5 ( Git) — 2024-11 – p.3/16

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RStudio Git 2 2024 5 ( Git) — 2024-11 – p.4/16

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RStudio Git RStudio Git Git ( GPL) GitHub Git ( ) RStudio pull 2024 5 ( Git) — 2024-11 – p.5/16

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Git RStudio Git (OS ) Linux : ( OK) macOS : Xcode (Apple ) Xcode AppStore https://apps.apple.com/jp/app/xcode/id497799835 Windows : https://gitforwindows.org OK https://github.com/ks91/cda-demo Git ( ) 2024 5 ( Git) — 2024-11 – p.6/16

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(scatter plot) 2 x y ( ) (◦ ) plot (verb): mark out or allocate (points) on a graph cda-demo “ .R” 1 2024 5 ( Git) — 2024-11 – p.7/16

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“ .txt” 1 1 <- read.table(" .txt", header=T) plot( 1, xlim=c(0, 100), ylim=c(0, 100), xlab=" ", ylab=" ", main=" ") : 2024 5 ( Git) — 2024-11 – p.8/16

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0 20 40 60 80 100 0 20 40 60 80 100 ṇࡢ┦㛵ࡢ౛ ⱥㄒࡢヨ㦂⤖ᯝ ᩘᏛࡢヨ㦂⤖ᯝ 2024 5 ( Git) — 2024-11 – p.9/16

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“ .txt” 2 2 <- read.table(" .txt", header=T) plot( 2, xlim=c(0, 20.0), ylim=c(13.0, 18.0), xlab=" ", ylab="100m ( )", main=" ") : 2024 5 ( Git) — 2024-11 – p.10/16

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0 5 10 15 20 13 14 15 16 17 18 ㈇ࡢ┦㛵ࡢ౛ 㐌ᙜࡓࡾࡢㄢእ㐠ື᫬㛫 100m㉮ࡢࢱ࢖࣒ (⛊) 2024 5 ( Git) — 2024-11 – p.11/16

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1 2 plot( 1$ , 2$ , xlim=c(0, 100), ylim=c(13.0, 18.0), xlab=" ", ylab="100m ( )", main=" ") ( ) : 2024 5 ( Git) — 2024-11 – p.12/16

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0 20 40 60 80 100 13 14 15 16 17 18 ↓┦㛵ࡢ౛ ⱥㄒࡢヨ㦂⤖ᯝ 100m㉮ࡢࢱ࢖࣒ (⛊) 2024 5 ( Git) — 2024-11 – p.13/16

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3 1 2 3 3 <- data.frame( = 1$ , = 1$ , = 2$ , = 2$ ) plot( 3) 2 12 : plot 2024 5 ( Git) — 2024-11 – p.14/16

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ⱥㄒ 20 40 60 80 20 40 60 80 100 13 14 15 16 17 20 40 60 80 ᩘᏛ 㐠ື᫬㛫 0 5 10 15 13 14 15 16 17 20 40 60 80 100 0 5 10 15 ▷㊥㞳 2024 5 ( Git) — 2024-11 – p.15/16

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2024 5 ( Git) — 2024-11 – p.16/16