I have a multiple svytable in list and from that list I want to make them separate dataframes by saving the same data structure.
For example:
library(survey)
data(api)
x <- apiclus1
dclus1 <- svydesign(id=~dnum, weights=~pw, data=x, fpc=~fpc)
n <- c("sch.wide", "cname")
for(k in seq_along(n)){
assign((paste0( n[[k]], "_1")),((svytable(as.formula(paste0("~", n[[k]], "+stype")), design = dclus1, na.action=na.pass))))
}
list<- list(sch.wide_1, cname_1)
result <- lapply(list, function(x) ((prop.table(x, margin =2)*100)))
How to make the separate data frames from result list tables?
Edit: simplified approach modifying your for loop and with the use of janitor package
for(k in seq_along(n)) {
assign((paste0(n[[k]], "_1")), ((
svytable(
as.formula(paste0("~", n[[k]], "+stype")),
design = dclus1,
na.action = na.pass
) %>% as.data.frame() %>%
pivot_wider(names_from = stype, values_from = Freq) %>%
adorn_percentages("col") %>% adorn_pct_formatting()
)))
}
now you got:
> sch.wide_1
sch.wide E H M
No 8.3% 21.4% 32.0%
Yes 91.7% 78.6% 68.0%
> cname_1
cname E H M
Alameda 5.6% 7.1% 8.0%
Fresno 1.4% 7.1% 4.0%
Kern 0.7% 0.0% 4.0%
Los Angeles 8.3% 0.0% 12.0%
Mendocino 1.4% 7.1% 4.0%
Merced 1.4% 7.1% 4.0%
Orange 9.0% 0.0% 12.0%
Plumas 2.8% 28.6% 4.0%
San Diego 34.7% 14.3% 12.0%
San Joaquin 20.8% 21.4% 16.0%
Santa Clara 13.9% 7.1% 20.0%
you can explore janitor package and modify pct formatting, total,... to get your desired output.
not sure if you're going to do it 1 by one or you need a loop for it: here's one way for getting them separately:
a <- data.frame(result[1]) %>%
pivot_wider(names_from = stype, values_from = Freq)
> a
# A tibble: 2 × 4
sch.wide E H M
<fct> <dbl> <dbl> <dbl>
1 No 8.33 21.4 32
2 Yes 91.7 78.6 68
b <- data.frame(result[2]) %>%
pivot_wider(names_from = stype, values_from = Freq)
b
# A tibble: 11 × 4
cname E H M
<fct> <dbl> <dbl> <dbl>
1 Alameda 5.56 7.14 8
2 Fresno 1.39 7.14 4
3 Kern 0.694 0 4
4 Los Angeles 8.33 0 12
5 Mendocino 1.39 7.14 4
6 Merced 1.39 7.14 4
7 Orange 9.03 0 12
8 Plumas 2.78 28.6 4
9 San Diego 34.7 14.3 12
10 San Joaquin 20.8 21.4 16
11 Santa Clara 13.9 7.14 20
want to make a loop for it?
for (ii in 1:length(result)) {
assign(
paste0("df_", ii),
as.data.frame(result[[ii]]) %>%
pivot_wider(names_from = stype, values_from = Freq)
)
}
now you have df_1 and df_2
> df_1
# A tibble: 2 × 4
sch.wide E H M
<fct> <dbl> <dbl> <dbl>
1 No 8.33 21.4 32
2 Yes 91.7 78.6 68
> df_2
# A tibble: 11 × 4
cname E H M
<fct> <dbl> <dbl> <dbl>
1 Alameda 5.56 7.14 8
2 Fresno 1.39 7.14 4
3 Kern 0.694 0 4
4 Los Angeles 8.33 0 12
5 Mendocino 1.39 7.14 4
6 Merced 1.39 7.14 4
7 Orange 9.03 0 12
8 Plumas 2.78 28.6 4
9 San Diego 34.7 14.3 12
10 San Joaquin 20.8 21.4 16
11 Santa Clara 13.9 7.14 20
there might be a shortcut for it but this is how I'm doing so far. good luck