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library(mtb)
Assume that for each month, items purchased in each grocery store visit are recorded in a table. At the end of a year, we may want to generate a summary table that shows how many times each item being purchased over the year and also list some summary statistics.
This is a basic example which shows you how to summarize item frequency from multiple tables.
library(mtb)
head(exdt[[1]])
#> id name category1 category2 store quantity unitprice
#> 1: 7 spinach vegetable fresh 1 1 0.9810773
#> 2: 5 pear fruit fresh 1 4 0.8543127
#> 3: 5 pear fruit fresh 1 2 1.1581097
#> 4: 9 pumpkin vegetable canned 2 4 1.2131444
#> 5: 1 egg protein fresh 2 2 1.4947858
#> 6: 6 broccoli vegetable fresh 2 1 0.9342286
This is a basic example which shows you how to create a cross-count table:
head(bill_cross_count(exdt[1:6], id='name', gp=c('category1'), type = 'count' ) )
#> category1 name tbl_id:1 tbl_id:2 tbl_id:3 tbl_id:4 tbl_id:5 tbl_id:6
#> 1: dairy milk 3 4 2 7 5 5
#> 2: fruit apple 2 1 5 3 4 8
#> 3: fruit orange 1 4 4 3 3 2
#> 4: fruit pear 4 1 2 5 6 3
#> 5: protein egg 1 2 1 2 3 3
#> 6: vegetable broccoli 1 1 6 NA 6 4
This is a basic example which shows you how to create a cross-count table with conditions:
head(bill_cross_count(exdt[1:6], id='name', gp=c('category1'), type = 'cond', condstr='store==2' ) )
#> category1 name tbl_id:1 tbl_id:2 tbl_id:3 tbl_id:4 tbl_id:5 tbl_id:6
#> 1: dairy milk 1 2 1 3 0 2
#> 2: fruit apple 0 1 4 1 3 4
#> 3: fruit orange 0 2 1 1 0 1
#> 4: fruit pear 1 1 0 1 4 2
#> 5: protein egg 1 1 1 1 1 2
#> 6: vegetable broccoli 1 1 3 NA 3 3
This is a basic example which shows you how to create a cross-count table with conditions and total:
head(bill_cross_count(exdt[1:6], id='name', gp=c('category1'), type = 'condwt', condstr='store==1' ) )
#> category1 name tbl_id:1 tbl_id:2 tbl_id:3 tbl_id:4 tbl_id:5 tbl_id:6
#> 1: dairy milk 2(3) 2(4) 1(2) 4(7) 5(5) 3(5)
#> 2: fruit apple 2(2) 0(1) 1(5) 2(3) 1(4) 4(8)
#> 3: fruit orange 1(1) 2(4) 3(4) 2(3) 3(3) 1(2)
#> 4: fruit pear 3(4) 0(1) 2(2) 4(5) 2(6) 1(3)
#> 5: protein egg 0(1) 1(2) 0(1) 1(2) 2(3) 1(3)
#> 6: vegetable broccoli 0(1) 0(1) 3(6) <NA> 3(6) 1(4)
This is a basic example which shows you how to cross-check differences in two table:
head(bill_cross_check(exdt[[1]], exdt[[2]], id=c('category1', 'name','store') ) )
#> category1 name store tbl_id:1 tbl_id:2 same
#> 1: dairy milk 1 2 2 TRUE
#> 2: dairy milk 2 1 2 FALSE
#> 3: fruit apple 1 2 NA NA
#> 4: fruit apple 2 NA 1 NA
#> 5: fruit orange 1 1 2 FALSE
#> 6: fruit orange 2 NA 2 NA
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.