Without weights

gss %>% filter(year == 2016) %>% count(reltrad) %>% mutate(pct = prop.table(n))

## Warning: package 'bindrcpp' was built under R version 3.4.4

## # A tibble: 8 x 3
##   reltrad     n    pct
##     <dbl> <int>  <dbl>
## 1       1   676 0.236 
## 2       2   303 0.106 
## 3       3   205 0.0715
## 4       4   649 0.226 
## 5       5    51 0.0178
## 6       6   169 0.0589
## 7       7   619 0.216 
## 8      NA   195 0.0680

Leave the NA Respondents in the Analysis

gss %>% filter(year == 2016) %>% count(reltrad, wt = wtss) %>% mutate(pct = prop.table(n))

## # A tibble: 8 x 3
##   reltrad     n    pct
##     <dbl> <dbl>  <dbl>
## 1       1 681.  0.238 
## 2       2 291.  0.102 
## 3       3 189.  0.0658
## 4       4 668.  0.233 
## 5       5  56.6 0.0198
## 6       6 171.  0.0596
## 7       7 618.  0.216 
## 8      NA 192.  0.0668

Removing the NAs

gss %>% filter(year == 2016) %>% filter(reltrad != "NA") %>% count(reltrad, wt = wtss) %>% mutate(pct = prop.table(n))

## # A tibble: 7 x 3
##   reltrad     n    pct
##     <dbl> <dbl>  <dbl>
## 1       1 681.  0.255 
## 2       2 291.  0.109 
## 3       3 189.  0.0705
## 4       4 668.  0.250 
## 5       5  56.6 0.0212
## 6       6 171.  0.0639
## 7       7 618.  0.231