October 2017
Intermediate to advanced
480 pages
9h 49m
Japanese
Content preview from Rではじめるデータサイエンス
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342
19
章 モデル構築
19.3.1
曜日
このプロットには、非常に強い曜日効果があるので、長期的傾向が掴みづらくなっています。まず
曜日ごとの便数の分布を調べることから始めます。
daily <- daily %>%
mutate(wday = wday(date, label = TRUE))
ggplot(daily, aes(wday, n)) +
geom_boxplot()
ほとんどの旅行が商用なので、週末の便数は少なくなっています。この効果は土曜日効果と呼ば
れます。月曜の会議のために日曜に出発することがありますが、土曜日は家族と家で過ごすことが多
いので、土曜出発は稀です。
この強いパターンを除去する1 つの方法がモデルを使うことです。まず、モデルを適合させて、そ
の予測を元のデータに重ねて表示します。
mod <- lm(n ~ wday, data = daily)
grid <- daily %>%
data_grid(wday) %>%
add_predictions(mod, "n")
ggplot(daily, aes(wday, n)) +
geom_boxplot() +
geom_point(data = grid, color = "red", size = 4)
19.3
何が
1
日の便数に影響するか
343
次に残差を計算して可視化します。
daily <- daily %>%
add_residuals(mod)
daily %>%
ggplot(aes(date, ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.