Laura R. - 1 year ago 66
R Question

# Calculate percentage/frequency of a value in a survey object in r

Maybe the answe to my question is trivial but I didn't find the right answer.

I have a national survey composed of many variables, like this one (for the sake of semplicity I omitted some variables):

``````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
``````

Where id is the person interviewed, y.b is year of birth, married is a dummy (1 married, 0 single), pens is a dummy that takes value one if the person invest in a complementary pension form; weight are the survey weights.

Consider that the original survey is made up to 40k observations from 2002 to 2014(I filtered it in order to have only individuals that appear more than one time). I use this command to create a survey object:

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

Now that the df is weighted I want to find for example the percentage of women or the percentage of married person that invest in complementary pension; I read on R help and on the web to find a command to get the percentage but I didn't find the right one.

I don't exactly know what you want to do with `weight`, but here is a very simple solution for the proportion of women with a pension in `dplyr`:

``````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
``````

That will give you a basic proportion of women with a pension. Apply this same logic to obtain the percentage of married people who have a pension.

As far as the `weight` variable goes, are you trying to do a weighted proportion of some sort? In that case, you would sum the `weight` values for women in each class (with pension and all women), like this:

``````# 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
``````
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