第七回課題

ライブラリ

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.3     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(magrittr)

## 
## Attaching package: 'magrittr'
## 
## The following object is masked from 'package:purrr':
## 
##     set_names
## 
## The following object is masked from 'package:tidyr':
## 
##     extract

データ読み込み

PY <- read_csv("/Users/myang/Desktop/23A/社会調査/社会調査/data/PY110.csv")

## Rows: 3367 Columns: 5377
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (5377): PanelID, BLOCK, SIZE, sex, ybirth, mbirth, ZQ02A, zq02ay48, zq02...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

PM  <- read_csv("/Users/myang/Desktop/23A/社会調査/社会調査/data/PM110.csv")

## Rows: 1433 Columns: 5377
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (5377): PanelID, BLOCK, SIZE, sex, ybirth, mbirth, ZQ02A, zq02ay48, zq02...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

JLPS <- rbind(PY,PM)

「男性の仕事は収入を得ること、女性の仕事や家庭と家族の面倒をみることだ」という意識について、性別ごとの時系列を可視化してください。

JLPS %>% 
  select(c("PanelID",
           "sex",
           "ZQ39A",
           "BQ32A",
           "DQ28A",
           "FQ30A",
           "HQ31A",
           "JQ30A",
           
  )) -> gender_role_awareness

gender_role_awareness %>% 
  pivot_longer(col = -c("PanelID","sex"),names_to = "year", values_to = "gender_role_awareness") -> gender_role_awareness_long

gender_role_awareness_long %<>% 
  mutate(year = case_when(str_detect(year,"ZQ") ~ 2006,
                          str_detect(year,"AQ") ~ 2007,
                          str_detect(year,"BQ") ~ 2008,
                          str_detect(year,"CQ") ~ 2009,
                          str_detect(year,"DQ") ~ 2010,
                          str_detect(year,"EQ") ~ 2011,
                          str_detect(year,"FQ") ~ 2012,
                          str_detect(year,"HQ") ~ 2013,
                          str_detect(year,"IQ") ~ 2014,
                          str_detect(year,"JQ") ~ 2015
  )
  )

gender_role_awareness_long %>% 
  mutate(gender = factor(sex,
                         levels = 1:2,
                         labels = c("male","female")
  )
  ) %>% 
  group_by(gender,year) %>% 
  summarize(gender_role_awareness_mean = mean(gender_role_awareness, na.rm = TRUE)) %>% 
  ggplot(aes(x=year, y= gender_role_awareness_mean, color = gender)) +
  geom_line() +
  geom_point() +
  scale_x_continuous(breaks = 2005:2016) +
  theme_bw()

## `summarise()` has grouped output by 'gender'. You can override using the
## `.groups` argument.

その他質問や感想などあれば記入してください。質問ですが、今自分のgender_role_awarenessの項目は高ければ高いほど「男性の仕事は収入を得ること、女性の仕事や家庭と家族の面倒をみることだ」に賛同していないようになっているのですが、それを逆にしたいです。pivot_longer()を用いて図において１ー５の項目を反転したい時はどうすればいいですか？それとも事前にデータをmutateした方が良いでしょうか？

第七回課題

YANG Mengyu

2023-12-17