## ---- eval = FALSE-------------------------------------------------------
# # load the libraries
# library(haven)
# library(dplyr)
# library(openxlsx)
# library(SimmonsResearchR)
## ---- eval = FALSE-------------------------------------------------------
#
# # read the data
# DatIn <- read_sav("W50123 AdultMasterDemoFile SB v2.sav") %>%
# filter(WAVE_ID %in% c('1516', '1616'))
#
# # read targ file which is a spreadsheet contains targ information
# targ <- read.xlsx("targ.xlsx")
#
# # subset the data
# work1.new <- DatIn %>%
# filter(group==1,
# dma %in% c(501, 504, 505, 506, 510, 511, 524, 528, 602, 618, 623, 641, 803, 807))
#
# work2.new <- DatIn %>%
# filter(group==3,
# dma %in% c(501, 504, 505, 506, 510, 511, 524, 528, 602, 618, 623, 641, 803, 807))
#
# work1.wgt <- work1.new[["DESIGN_WGT"]] /2
# work2.wgt <- work2.new[["DESIGN_WGT"]] /2
#
# # Initialize sample balance, save targ information to plan text file
# targs.list <- sample_balance_init(data=DatIn, targ=targ, out="targ.txt")
#
# # sample balancing using 6 times mean cap, and save diagnosis information to out1.xlsx
# sb1 <- sample_blance(data=work1.new,
# ID="BOOK_ID",
# targstr=targs.list[[1]],
# dweights=work1.wgt,
# cap=T,
# typeofcap=1,
# capval=6,
# floor=T,
# floorval=50,
# eps=.001,
# rounding=F,
# klimit=F,
# klimitval=Inf,
# out="out1.xlsx")
#
# # sample balancing using 2 times std cap, and save diagnosis information to out2.xlsx
# sb2 <- sample_blance(data=work2.new,
# ID="BOOK_ID",
# targstr=targs.list[[2]],
# dweights=work2.wgt,
# cap=T,
# typeofcap=2,
# capval=2,
# floor=T,
# floorval=50,
# eps=.001,
# rounding=F,
# klimit=F,
# klimitval=Inf,
# out="out2.xlsx")
#
# # Get new capped weights from the 2 sample balancing modules
# cap_wgt1<-sb1[[2]]
# cap_wgt2<-sb2[[2]]
#
## ----results='asis', echo = FALSE----------------------------------------
library(SimmonsResearchR)
data(targ)
knitr::kable(targ, caption = "Example of the Targ Spreadsheet")
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