Helper functions used in our online R courses
# Install development version from GitHub
# install.packages("devtools")
devtools::install_github("rsquaredacademy/mbar")
mbar_prep_data() will modify the data from one row per item to one row
per transaction. It takes 3 inputs:
- data set
- invoice number column
- product/item column
# original data
head(mba_sample)
#> InvoiceNo StockCode Description Quantity
#> 1 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6
#> 2 536365 71053 WHITE METAL LANTERN 6
#> 3 536365 84406B CREAM CUPID HEARTS COAT HANGER 8
#> 4 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6
#> 5 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6
#> 6 536365 22752 SET 7 BABUSHKA NESTING BOXES 2
#> InvoiceDate UnitPrice CustomerID Country
#> 1 2010-12-01 08:26:00 2.55 17850 United Kingdom
#> 2 2010-12-01 08:26:00 3.39 17850 United Kingdom
#> 3 2010-12-01 08:26:00 2.75 17850 United Kingdom
#> 4 2010-12-01 08:26:00 3.39 17850 United Kingdom
#> 5 2010-12-01 08:26:00 3.39 17850 United Kingdom
#> 6 2010-12-01 08:26:00 7.65 17850 United Kingdom
# modified data
mbar_prep_data(mba_sample, InvoiceNo, Description)
#> # A tibble: 66 x 85
#> item_1 item_2 item_3 item_4 item_5 item_6 item_7 item_8 item_9 item_10
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 WHITE~ WHITE~ CREAM~ KNITT~ RED W~ SET 7~ GLASS~ "" "" ""
#> 2 HAND ~ HAND ~ "" "" "" "" "" "" "" ""
#> 3 ASSOR~ POPPY~ POPPY~ FELTC~ IVORY~ BOX O~ BOX O~ BOX O~ HOME ~ LOVE B~
#> 4 JAM M~ RED C~ YELLO~ BLUE ~ "" "" "" "" "" ""
#> 5 BATH ~ "" "" "" "" "" "" "" "" ""
#> 6 ALARM~ ALARM~ ALARM~ PANDA~ STARS~ INFLA~ VINTA~ SET/2~ ROUND~ SPACEB~
#> 7 PAPER~ "" "" "" "" "" "" "" "" ""
#> 8 HAND ~ HAND ~ "" "" "" "" "" "" "" ""
#> 9 WHITE~ WHITE~ CREAM~ EDWAR~ RETRO~ SAVE ~ VINTA~ VINTA~ WOOD ~ WOOD S~
#> 10 VICTO~ "" "" "" "" "" "" "" "" ""
#> # ... with 56 more rows, and 75 more variables: item_11 <chr>,
#> # item_12 <chr>, item_13 <chr>, item_14 <chr>, item_15 <chr>,
#> # item_16 <chr>, item_17 <chr>, item_18 <chr>, item_19 <chr>,
#> # item_20 <chr>, item_21 <chr>, item_22 <chr>, item_23 <chr>,
#> # item_24 <chr>, item_25 <chr>, item_26 <chr>, item_27 <chr>,
#> # item_28 <chr>, item_29 <chr>, item_30 <chr>, item_31 <chr>,
#> # item_32 <chr>, item_33 <chr>, item_34 <chr>, item_35 <chr>,
#> # item_36 <chr>, item_37 <chr>, item_38 <chr>, item_39 <chr>,
#> # item_40 <chr>, item_41 <chr>, item_42 <chr>, item_43 <chr>,
#> # item_44 <chr>, item_45 <chr>, item_46 <chr>, item_47 <chr>,
#> # item_48 <chr>, item_49 <chr>, item_50 <chr>, item_51 <chr>,
#> # item_52 <chr>, item_53 <chr>, item_54 <chr>, item_55 <chr>,
#> # item_56 <chr>, item_57 <chr>, item_58 <chr>, item_59 <chr>,
#> # item_60 <chr>, item_61 <chr>, item_62 <chr>, item_63 <chr>,
#> # item_64 <chr>, item_65 <chr>, item_66 <chr>, item_67 <chr>,
#> # item_68 <chr>, item_69 <chr>, item_70 <chr>, item_71 <chr>,
#> # item_72 <chr>, item_73 <chr>, item_74 <chr>, item_75 <chr>,
#> # item_76 <chr>, item_77 <chr>, item_78 <chr>, item_79 <chr>,
#> # item_80 <chr>, item_81 <chr>, item_82 <chr>, item_83 <chr>,
#> # item_84 <chr>, item_85 <chr>optimal_cp() will extract the optimal complexity parameter from an
object of class rpart for pruning a tree.
# grow tree
model <- rpart::rpart(Species ~ ., data = iris)
best_cp <- optimal_cp(model)
# prune tree
rpart::prune(model, cp = best_cp)
#> n= 150
#>
#> node), split, n, loss, yval, (yprob)
#> * denotes terminal node
#>
#> 1) root 150 100 setosa (0.33333333 0.33333333 0.33333333)
#> 2) Petal.Length< 2.45 50 0 setosa (1.00000000 0.00000000 0.00000000) *
#> 3) Petal.Length>=2.45 100 50 versicolor (0.00000000 0.50000000 0.50000000)
#> 6) Petal.Width< 1.75 54 5 versicolor (0.00000000 0.90740741 0.09259259) *
#> 7) Petal.Width>=1.75 46 1 virginica (0.00000000 0.02173913 0.97826087) *Please note that the ‘mbar’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.