朝食欠食率の年次推移
Hiroaki Yutani
2015/3/19
前処理後のデータ
library(dplyr)
library(tidyr)
library(ggplot2)
library(printr)(raw_data <- read.csv("/vagrant/60-1.csv", stringsAsFactors = FALSE))| 性別 | 年齢 | X19 | 75 X19 | 80 X19 | 85 X19 | 90 X19 | 95 X20 | 00 X20 | 01 X20 | 02 X20 | 03 X20 | 04 X20 | 05 X20 | 06 X20 | 07 X20 | 08 X20 | 09 X20 | 10 X20 | 11 X2012 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 男性 | all | 6.7 | 7.6 | 8.7 | 9.8 | 10.1 | 10.7 | 11.1 | 11.6 | 11.8 | 12.6 | 13.0 | 13.1 | 13.3 | 14.6 | 14.1 | 13.7 | 14.4 | 12.8 |
| 男性 | 15~19 | 10.7 | 12.7 | 10.3 | 14.1 | 13.4 | 13.3 | 12.3 | 14.4 | 14.0 | 14.2 | 18.8 | 14.6 | 13.4 | 18.4 | 15.5 | 14.5 | 8.7 | 12.3 |
| 男性 | 20~29 | 15.5 | 19.5 | 23.5 | 25.5 | 30.8 | 30.5 | 27.9 | 26.5 | 29.5 | 34.3 | 33.1 | 30.5 | 28.6 | 30.0 | 33.0 | 29.7 | 34.1 | 29.5 |
| 男性 | 30~39 | 8.5 | 11.6 | 13.8 | 17.8 | 16.6 | 20.1 | 23.6 | 24.7 | 23.0 | 25.9 | 27.0 | 22.8 | 30.2 | 27.7 | 29.2 | 27.0 | 31.5 | 25.8 |
| 男性 | 40~49 | 4.9 | 6.6 | 8.7 | 11.1 | 11.6 | 10.5 | 14.4 | 14.3 | 15.9 | 19.0 | 16.2 | 20.8 | 17.9 | 25.7 | 19.3 | 20.5 | 23.5 | 19.6 |
| 男性 | 50~59 | 4.6 | 4.6 | 6.1 | 6.5 | 5.1 | 9.2 | 9.1 | 10.8 | 10.0 | 10.6 | 11.7 | 13.1 | 11.8 | 15.1 | 12.4 | 13.7 | 15.0 | 13.1 |
| 男性 | 60~ | 3.4 | 2.8 | 3.5 | 2.0 | 1.7 | 2.9 | 2.5 | 3.4 | 3.7 | 3.6 | 4.2 | 3.9 | 5.4 | 6.3 | 7.0 | 6.7 | 4.9 | 5.8 |
| 女性 | all | 5.9 | 5.9 | 5.9 | 6.1 | 6.1 | 5.8 | 6.9 | 7.9 | 8.5 | 8.7 | 8.6 | 8.5 | 10.1 | 11.9 | 10.1 | 10.3 | 11.1 | 9.0 |
| 女性 | 15~19 | 14.1 | 8.3 | 13.1 | 10.1 | 8.9 | 9.2 | 8.2 | 11.4 | 17.2 | 10.2 | 10.4 | 13.2 | 11.5 | 10.0 | 10.2 | 14.0 | 13.3 | 10.7 |
| 女性 | 20~29 | 11.7 | 12.9 | 14.7 | 14.3 | 18.2 | 16.3 | 16.9 | 20.6 | 23.6 | 22.0 | 23.5 | 22.5 | 24.9 | 26.2 | 23.2 | 28.6 | 28.8 | 22.1 |
| 女性 | 30~39 | 5.3 | 8.1 | 6.4 | 6.5 | 5.6 | 7.5 | 11.8 | 12.1 | 12.7 | 15.0 | 15.0 | 13.9 | 16.3 | 21.7 | 18.1 | 15.1 | 18.1 | 14.8 |
| 女性 | 40~49 | 5.8 | 5.7 | 6.7 | 7.5 | 6.1 | 6.6 | 7.9 | 9.0 | 7.6 | 7.8 | 10.3 | 11.0 | 12.8 | 14.8 | 12.1 | 15.2 | 16.0 | 12.1 |
| 女性 | 50~59 | 4.5 | 4.3 | 5.3 | 5.1 | 4.0 | 5.3 | 6.8 | 6.3 | 6.7 | 9.1 | 8.3 | 7.7 | 9.7 | 13.4 | 10.6 | 10.4 | 11.2 | 9.2 |
| 女性 | 60~ | 4.1 | 3.1 | 3.4 | 2.8 | 2.5 | 2.1 | 2.3 | 3.7 | 4.1 | 4.0 | 4.1 | 3.2 | 4.4 | 6.8 | 5.8 | 5.0 | 5.5 | 5.3 |
Tidyにする
tidy_data <- gather(raw_data, "year", "rate", -性別, -年齢)
tidy_data$year %<>%
as.character %>%
stringr::str_replace("X", "") %>%
as.numericグラフを描く
ggplot(tidy_data, aes(x = year, y = rate, colour = 年齢)) + geom_line() + facet_grid(. ~ 性別)
ちょっとみんな朝食抜きすぎでは…？