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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9.6
ケーススタディ
143
欠損値を処理するために知っておくべきツールがもう1つあります。データソースが基本的にデー
タエントリとして使われた場合、欠損値はその前の値を続けることを意味します。
treatment <- tribble(
~ person, ~ treatment, ~ response,
"Derrick Whitmore", 1, 7,
NA, 2, 10,
NA, 3, 9,
"Katherine Burke", 1, 4
)
この欠損値に対して
fill()
で値を補えます。列の集合を与え、欠損値を最近の非欠損値で置き換
えます（最近では観測値補完と呼ばれることもある）。
treatment %>%
fill(
person)
#> # A tibble: 4
×
3
#> person treatment response
#> <chr> <dbl> <dbl>
#> 1 Derrick Whitmore 1 7
#> 2 Derrick Whitmore 2 10
#> 3 Derrick Whitmore 3 9
#> 4 Katherine Burke 1 4
1.
fill
引数を
spread()
や
complete()
と比較対照しなさい。
2.
fill()
の
.direction
引数は何をするか。
9.6
ケーススタディ
最後に、学んだことすべてをまとめて実際に起こりそうなデータを整理する課題に取り組みま
しょう。
tidyr::who
データセットには結核（TB）のデータが年、国、年齢、性別、診断方法につ ...
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