The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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 0.9342286This 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 4This 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 3This 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 NAThese 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.
Health stats visible at Monitor.