R Looping Through in Survey Package

R Looping through in survey package

library(survey)
data(api)
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)

# run a simple example svychisq() function
svychisq( ~sch.wide+stype , dclus1 )

# create a function that requires a character string (containing the variables)
# and the design object and runs the svychisq() 
# on the contents of that character string's columns
scsloop <- function( vars , design ){ svychisq( as.formula( paste0( "~" , vars ) ) , design ) }

# test it out
scsloop( "sch.wide+stype" , dclus1 )
scsloop( "sch.wide+comp.imp" , dclus1 )

# either create a character vector to run it multiple times
cols.to.chisq <- c( "sch.wide" , "comp.imp" , "stype" )

# or identify them based on column number, if you prefer
cols.to.chisq <- names( apiclus1 )[ c( 2 , 16 , 17 ) ]

# find every combination of that vector, taken two at a time
combos <- combn( cols.to.chisq , 2 )

# separate them by +
col.combos <- paste( combos[ 1 , ] , combos[ 2 , ] , sep = "+" )

# run it on each of those character strings (print to screen and save to list)
( x <- lapply( col.combos , scsloop , dclus1 ) )

# just for kicks, print everything to the screen
col.combos[1] ; x[[1]]
col.combos[2] ; x[[2]]
col.combos[3] ; x[[3]]

Looping through columns and printing proportion or mean table based on condition with survey package

I am not very sure why the bquote is giving you so much problem, but if I use formula, it works ok:

for(i in seq_along(cols)){
FORMULA= as.formula(paste("~",cols[i]))
ifelse(type[i]=="prop", 
print(prop.table(svytable(FORMULA, design))), 
            print(svymean(FORMULA, design))) }

var1
         2          3          4 
0.00000000 0.95238095 0.04761905 
       mean     SE
var2 6.0476 0.0786

Loop though columns and print tables using survey weights from survey package

Acknowledging the use of bquote as a partial substitution in expression from Ben's comment above, you could modify your for loop as follows:

cols <- c("var1", "var2") # columns to loop through
for(i in  seq_along(cols)){
  print(prop.table(svytable(bquote(~.(as.name(cols[i]))), design)))
} 
# var1
#          2          3          5 
# 0.00000000 0.95238095 0.04761905 
# var2
#          2          6          7 
# 0.00000000 0.95238095 0.04761905 

Data

library(survey)
dat <- data.frame(id=c(1,2,3), weight=c(0,2,0.1), var1=c(2,3,5), var2=c(2,6,7))
design <- svydesign(id=~1, weights=~weight, data=dat)

Looping through dataset variables in svy package

You need to use as.formula to paste the appropriate columns for evaluation. I created a custom function for your case:

mysvy <- function(data, columns, ...) {
    model <- lapply(as.list(columns), function(x) {
          summary(svyglm(as.formula(paste0(names(data)[x], "~ell+meals+mobility")), 
            data = data, ...))
        })
    return(model)
}

Then you can run your your desired columns through the function.

# To run columns 13 - 15 and get the results into a list
results <- mysvy(apistrat, 13:15, design = dstrat)
# should return a list of 3. results[[1]] to see the first

Scoring a survey in R (loop)

By using dplyr(I let Id as 1, 2, 3)

library(dplyr)

df %>%
  group_by(EmployeeId) %>%
  summarize(n = mean(Response))

  EmployeeId     n
       <dbl> <dbl>
1          1  3.25
2          2  3.75
3          3  2.75

Iterate through groups of variables in a survey - R

There are multiple ways. If we pass as string, one option is to make use of group_by_at which takes strings as argument

library(purrr)
library(dplyr)
library(survey)
library(srvyr)
map(c('sch.wide', 'cname'), ~
        df %>%
           group_by_at(vars("stype", .x)) %>%
           summarise(prop = srvyr::survey_mean()))
#[[1]]
# A tibble: 6 x 4
#  stype sch.wide   prop prop_se
#  <fct> <fct>     <dbl>   <dbl>
#1 E     No       0.0833  0.0231
#2 E     Yes      0.917   0.0231
#3 H     No       0.214   0.110 
#4 H     Yes      0.786   0.110 
#5 M     No       0.32    0.0936
#6 M     Yes      0.68    0.0936

#[[2]]
# A tibble: 30 x 4
#   stype cname          prop prop_se
#   <fct> <fct>         <dbl>   <dbl>
# 1 E     Alameda     0.0556  0.0191 
# 2 E     Fresno      0.0139  0.00978
# 3 E     Kern        0.00694 0.00694
# 4 E     Los Angeles 0.0833  0.0231 
# 5 E     Mendocino   0.0139  0.00978
# 6 E     Merced      0.0139  0.00978
# 7 E     Orange      0.0903  0.0239 
# 8 E     Plumas      0.0278  0.0137 
# 9 E     San Diego   0.347   0.0398 
#10 E     San Joaquin 0.208   0.0339 
# … with 20 more rows

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Or another option is to wrap with quos to create a quosure list and evaluate (!!) it in group_by

map(quos(sch.wide, cname), ~  
        df %>%
          group_by(stype, !!.x) %>% 
          summarise(prop = srvyr::survey_mean()))

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