Untitled

Data

library(revealjs)
library(readr)
data <- read_csv("/Users/admin/Downloads/INF.csv")

## Rows: 16925 Columns: 827
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (25): name, proddate, cntry, cntbrth, cntbrtha, cntbrthb, cntbrthc, cnt...
## dbl (293): essround, edition, idno, dweight, pspwght, pweight, anweight, pro...
## lgl (509): vteurmmb, vteumbgb, rlgdnal, rlgdnat, rlgdnbat, rlgdnbe, rlgdncy,...
## 
## 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.

ESS_1 <- read_csv("/Users/admin/Downloads/INF.csv")

## Rows: 16925 Columns: 827
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (25): name, proddate, cntry, cntbrth, cntbrtha, cntbrthb, cntbrthc, cnt...
## dbl (293): essround, edition, idno, dweight, pspwght, pweight, anweight, pro...
## lgl (509): vteurmmb, vteumbgb, rlgdnal, rlgdnat, rlgdnbat, rlgdnbe, rlgdncy,...
## 
## 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.

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

data <- data %>% 
  filter (sclmeet <= 6) %>% 
  filter (gndr != 9) %>% 
  select(essround, sclmeet, gndr)

df <- data %>%
  group_by(essround, gndr) %>%
  summarise(mean_sclmeet = mean(sclmeet))

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

class(df$essround)

## [1] "numeric"

class(df$gndr)

## [1] "numeric"

class(df$mean_sclmeet)

## [1] "numeric"

df$gndr[df$gndr == 1] <- "Male"
df$gndr[df$gndr == 2] <- "Female"

df$essround[df$essround == 1] <- 2002
df$essround[df$essround == 2] <- 2004
df$essround[df$essround == 3] <- 2006
df$essround[df$essround == 4] <- 2008
df$essround[df$essround == 5] <- 2010
df$essround[df$essround == 6] <- 2012
df$essround[df$essround == 7] <- 2014
df$essround[df$essround == 8] <- 2016
df$essround[df$essround == 9] <- 2018
df$essround[df$essround == 10] <- 2020

class(df$essround)

## [1] "numeric"

class(df$gndr)

## [1] "character"

class(df$mean_sclmeet)

## [1] "numeric"

Plot1

library(ggstream)
library(ggplot2)

plot1 <- ggplot(df, aes(x = essround, y = mean_sclmeet, fill = gndr)) +
  geom_area()+
  scale_fill_manual(values = c("#DE77AE", "#7FBC41"))+   
  theme_bw()+ 
  labs(title = "Gender differences in 'Loyalty to others' value", x = "Year of ESS round", y = "Mean value")+
    theme(plot.title = element_text(size = 12,hjust = 0, color = "deeppink"))

ESS_sclmeet <- ESS_1 %>%
  filter(sclmeet != 77) %>% 
  filter(sclmeet != 88) %>% 
  filter (sclmeet != 99)%>% 
  filter (gndr != 9)
class(ESS_sclmeet$sclmeet)

## [1] "numeric"

ESS_sclmeet$gndr <- factor(ESS_sclmeet$gndr, labels = c("Male", "Female"), ordered= F)
ESS_sclmeet$essround <- factor(ESS_sclmeet$essround, labels = c("2002", "2004", "2006", "2008", "2010", "2012", "2014", "2016", "2018", "2020"), ordered= F)

library (dplyr)
df1<- ESS_sclmeet %>%
  group_by(essround, gndr) %>%
  summarise(mean_sclmeet = mean(sclmeet)) %>% 
  arrange(gndr)

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

Plotly

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

grid.arrange(plot1, plot2, ncol = 2)