group_filter: Group Filter

In delaneyj1786/REINFORCINATOR: xx

Description

A helper function which provides counts and proportions of all levels of a categorical DV according to two nesting variables.

This is to be used in conjunction with filter to remove possibly problematic groups (e.g., those with low base rates of target reinfroced behaviors)

Usage

group_filter(data, GROUP1, GROUP2, DV)

Arguments

data	A dataframe or tibble
GROUP1	A column / vector describing the highest order grouping factor (e.g., person )
GROUP2	A column / vector describing the second order grouping factor (e.g., segment)
DV	A column containing the behavioral dependent variable.

Details

This function is meant to be used to identify observation segments which meet a certain theshold as follows.

#1. Identify the segments meeting threshold group_filter(two_person_picture,VIDELT, TAR,BEH) VIDELT TAR BEH count n prop <dbl> <chr> <chr> <int> <int> <dbl> 1 1 ABE o 7 15 0.467 2 1 ABE x 6 15 0.4 3 2 JAN o 7 15 0.467 4 2 JAN x 6 15 0.4

#2. Filter dataset to said segments two_person_picture_filtered <- two_person_picture

#3. Apply recounter on filtered data recounter(two_person_picture_filtered, BEH, "o","A")$recounted_data_frame

Value

Returns a grouped data frame containing the 1) primary grouping variable, 2) the secondary grouping variable, 3) the DV, 4) count of DV (per secondary within primary grouping variable), 5) n (number of total observations within both groupings) 5) proportion of each level of DV per primary / secondary variable combination

Note

Future versions will produce a list output to be piped to list arguments for filter

Examples

## The function is currently defined as
function(dat, GROUP1, GROUP2, DV)
{
  #  https://stackoverflow.com/questions/58846126/using-rlang-quasiquotation-with-dplyr-join-functions
 require(tidyverse)
  data <- data.frame(dat)
  GROUP1 <- dplyr::enquo(GROUP1)
  GROUP2 <- dplyr::enquo(GROUP2)
  DV <- dplyr::enquo(DV)

  #  data <- data.frame(data)
  # dat
  # FRQ OF DV by group 2
  FRQ_G2_DV <- dat 
    dplyr::group_by(!!GROUP1, !!GROUP2, !!DV) 
    dplyr::summarize(count = n()) 
    ungroup()
  # FRQ_G2_DV
  # Number of cases of group2
  FRQ_F2 <- dat
    dplyr::group_by(!!GROUP1,!!GROUP2) 
    dplyr::summarize(n = n()) 
    ungroup()
  #FRQ_F2
  # Summary by video
  # vid_sum<-dplyr::left_join(FRQ_G2_DV,FRQ_F2, by = c(!!GROUP1,!!GROUP2) )
  vid_sum<-dplyr::left_join(FRQ_G2_DV,FRQ_F2)
  vid_sum<-vid_sum 
  vid_sum
  }

  group_filter(elevator,VIDELT, TAR,BEH)

# A tibble: 6 x 6
#  VIDELT TAR   BEH   count     n  prop
#   <dbl> <chr> <chr> <int> <int> <dbl>
#1      1 S     A         1     7 0.143
#2      1 S     o         2     7 0.286
#3      1 S     x         4     7 0.571
#4      2 S     A         1     7 0.143
#5      2 S     o         3     7 0.429
#6      2 S     x         3     7 0.429


#> two_person_picture
# A tibble: 30 x 3
#   VIDELT TAR   BEH
#    <dbl> <chr> <chr>
# 1      1 ABE   x
# 2      1 ABE   x
# 3      1 ABE   x
# 4      1 ABE   o

# > group_filter(two_person_picture,VIDELT, TAR,BEH)
#`summarise()` has grouped output by 'VIDELT', 'TAR'. You can override using the #`.groups` argument.
#`summarise()` has grouped output by 'VIDELT'. You can override using the `##
#.groups` argument.
#Joining, by = c("VIDELT", "TAR")

# A tibble: 6 x 6
#  VIDELT TAR   BEH   count     n  prop
#   <dbl> <chr> <chr> <int> <int> <dbl>
#1      1 ABE   A         2    15 0.133
#2      1 ABE   o         7    15 0.467
#3      1 ABE   x         6    15 0.4
#4      2 JAN   A         2    15 0.133
#5      2 JAN   o         7    15 0.467
#6      2 JAN   x         6    15 0.4

delaneyj1786/REINFORCINATOR documentation built on Jan. 14, 2022, 3:47 a.m.