R help - Mar 2018 - Calculate weighted proportions for several factors at once

Hi,

I have a grouped data set and would like to calculate weighted proportions for a
large number of factor variables within each group member. Rather than using
dplyr::count() on each of these factors individually, the idea would be to do it
for all factors at once. Does anyone know how this would work? Here is a
reproducible example:

############################################################
# reproducible example
df1 <- data.frame(wt=rnorm(90),
                  group=paste0('reg', 1:5),
                  var1=rep(c('male','female'), times=45),
                  var2=rep(c('low','med','high'),
each=30)) %>% tbl_df()

# instead of doing this separately for each factor ...
df2 <- df1 %>%
  group_by(group) %>%
  dplyr::count(var1, wt=wt) %>%
  mutate(prop1=n/sum(n))

df3 <- df1 %>%
  group_by(group) %>%
  dplyr::count(var2, wt=wt) %>%
  mutate(prop2=n/sum(n)) %>%
  left_join(df2, by='group')

# I would like to do something like the following (which does of course not
work):
my_fun <- function(x,wt){
  freq1 <- dplyr::count(x, wt=wt)
  prop1 <- freq1 / sum(freq1)
  return(prop)
}

df1 %>%
  group_by(group) %>%
  summarise_all(.funs=my_fun(.), .vars=c('var1', 'var2'))
############################################################

Best regards,
Erich

	[[alternative HTML version deleted]]

> On Mar 22, 2018, at 3:34 PM, Striessnig, Erich <Erich.Striessnig at
oeaw.ac.at> wrote:
> 
> Hi,
> 
> I have a grouped data set and would like to calculate weighted proportions
for a large number of factor variables within each group member. Rather than
using dplyr::count() on each of these factors individually, the idea would be to
do it for all factors at once. Does anyone know how this would work? Here is a
reproducible example:
> 
> ############################################################
> # reproducible example
> df1 <- data.frame(wt=rnorm(90),
>                  group=paste0('reg', 1:5),
>                  var1=rep(c('male','female'), times=45),
>                  var2=rep(c('low','med','high'),
each=30)) %>% tbl_df()
> 
> # instead of doing this separately for each factor ...
> df2 <- df1 %>%
>  group_by(group) %>%
>  dplyr::count(var1, wt=wt) %>%
>  mutate(prop1=n/sum(n))
> 
> df3 <- df1 %>%
>  group_by(group) %>%
>  dplyr::count(var2, wt=wt) %>%
>  mutate(prop2=n/sum(n)) %>%
>  left_join(df2, by='group')
> 
> # I would like to do something like the following (which does of course not
work):
> my_fun <- function(x,wt){
>  freq1 <- dplyr::count(x, wt=wt)
>  prop1 <- freq1 / sum(freq1)
>  return(prop)
> }
> 
> df1 %>%
>  group_by(group) %>%
>  summarise_all(.funs=my_fun(.), .vars=c('var1', 'var2'))
> ############################################################
You might find useful functions in the ?freqweights? package. It appears from
its description that it was design to fit into the tidyverse paradigm. I think
the survey package might also be useful, but it is not particularly designed for
use with tibbles and `%>%`. Might work. Might not.

HTH;
Dadid.
 
> 
> Best regards,
> Erich
> 
> 	[[alternative HTML version deleted]]
> 
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David Winsemius
Alameda, CA, USA

'Any technology distinguishable from magic is insufficiently advanced.' 
-Gehm's Corollary to Clarke's Third Law