year  id  y.b   sex   income  married   pens   weight
2002  1   1950   F    100000     1       0      1.12
2002  2   1943   M    55000      1       1      0.55
2004  1   1950   F    88000      1       1      1.1
2004  2   1943   M    66000      1       1      0.6
2006  3   1966   M    12000      0       1      0.23
2008  3   1966   M    24000      0       1      0.23
2008  4   1972   F    33000      1       0      0.66
2010  4   1972   F    35000      1       0      0.67

d.s <- svydesign(ids=~1, data=df, weights=~weight)

# same setup
library(survey)

df <- data.frame(sex = c('F', 'M', 'F', 'M', 'M', 'M', 'F', 'F'),
                married = c(1,1,1,1,0,0,1,1),
                pens = c(0, 1, 1, 1, 1, 1, 0, 0),
                weight = c(1.12, 0.55, 1.1, 0.6, 0.23, 0.23, 0.66, 0.67))

d.s <- svydesign(ids=~1, data=df, weights=~weight)

# subset to women only then calculate the share with a pension
svymean( ~ pens , subset( d.s , sex == 'F' ) )

df <- data.frame(sex = c('F', 'M', 'F', 'M', 'M', 'M', 'F', 'F'),
                married = c(1,1,1,1,0,0,1,1),
                pens = c(0, 1, 1, 1, 1, 1, 0, 0),
                weight = c(1.12, 0.55, 1.1, 0.6, 0.23, 0.23, 0.66, 0.67))

d.s <- svydesign(ids=~1, data=df, weights=~weight)

# data frame of women with a pension
women_with_pension <- d.s$variables %>%
  filter(sex == 'F' & pens == 1)

# number of rows (i.e. number of women with a pension) in that df
n_women_with_pension <- nrow(women_with_pension)

# data frame of all women
all_women <- d.s$variables %>%
  filter(sex == 'F')

# number of rows (i.e. number of women) in that df
n_women <- nrow(all_women)

# divide the number of women with a pension by the total number of women
proportion_women_with_pension <- n_women_with_pension/n_women

# data frame of women with a pension
women_with_pension <- d.s$variables %>%
  filter(sex == 'F' & pens == 1) %>%
  summarise(total_weight = sum(weight))

# number of rows (i.e. number of women with a pension) in that df
women_with_pension_weight = women_with_pension[[1]]

# data frame of all women
all_women <- d.s$variables %>%
  filter(sex == 'F') %>%
  summarise(total_weight = sum(weight))

# number of rows (i.e. number of women) in that df
all_women_weight <- all_women[[1]]

# divide the number of women with a pension by the total number of women
# 0.3098592 for this sample data
prop_weight_women_with_pension <- women_with_pension_weight/all_women_weight