knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path='man/figures/README-' )
tableMatrix package provides two classes extending data.table class. Simple tableList
class wraps data.table and any additional structures together. More complex tableMatrix
class combines data.table and matrix.
From CRAN:
install.packages("tableMatrix")
From github:
# install.packages("devtools") devtools::install_github("InferenceTechnologies/tableMatrix")
Goal is to wrap data.table class and other structures together and preserve data.table behaviour.
Combine data and linear model into one object.
data(chickwts) # Bundle chickwts data.frame together with a linear model TL <- tableList(chickwts, lm(weight~feed, chickwts)) # tableList behaves like a data.table mean(TL[feed=="casein", weight]) # Aid part of the tableList object carries the linear model aid(TL)
Let's have a dataset with the following structure: first set of columns of varying types is intented as meta data, second set of columns of the same type is intended as main data. tableMatrix
stores meta data as a data.table and main data as a matrix. It also keeps track of dimensions of main data, thus allowing to combine rows of varying lengths into one object. As in tableList, tableMatrix can carry any additional aid data.
Working with bitmaps of different sizes. Datasets images8By8
and images10By10
contain 8x8 and 10x10 images in the form of vectors. For each row first three columns represent image meta data, remaining columns represent the image itself. For more information see ?images8By8
.
# Load datasets data(images8By8) data(images10By10) # Create a signle tableMatrix object from both datasets # First 3 columns used as meta data, the rest as main data with corresponding dimensions TM <- tableMatrix(list(images8By8, images10By10), list(1:3, 1:3), list(c(4:ncol(images8By8)),c(4:ncol(images10By10))), list(c(8,8), c(10,10))) # Default print displays the table (meta data) part TM # Number of matrices stored in the matrix (main data) part length(mat(TM)) # Dimensions of the matrix part matDim(TM) # Aid part is empty aid(TM) # Image data for first row img <- getRow(TM, 1) # Restoring dimensions of the image dim(img) <- getRowDim(TM, 1) # Visualising the image image(img, axes=F)
Let's create a heat map
# Subsetting via bracket passed to the table (meta data) part # We choose first matrix type, down direction TM1down <- TM[.(1)][direction=="down"] # One matrix in the matrix part of TM1down length(mat(TM1down)) # One dimension row matDim(TM1down) # Heatmap imgHeat <- colMeans(mat(TM1down, 1)) # Restoring dimensions of the heatmap dim(imgHeat) <- getRowDim(TM1down, 1) # Visualising heatmap image(imgHeat, axes=F)
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