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.
Installation
From CRAN:
install.packages("tableMatrix")From github:
# install.packages("devtools") devtools::install_github("InferenceTechnologies/tableMatrix")
tableList class
Motivation
Goal is to wrap data.table class and other structures together and preserve data.table behaviour.
Example
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]) #> [1] 323.5833 # Aid part of the tableList object carries the linear model aid(TL) #> #> Call: #> lm(formula = weight ~ feed, data = chickwts) #> #> Coefficients: #> (Intercept) feedhorsebean feedlinseed feedmeatmeal feedsoybean #> 323.583 -163.383 -104.833 -46.674 -77.155 #> feedsunflower #> 5.333
tableMatrix class
Motivation
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.
Example
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 #> direction dimX dimY #> 1: down 8 8 #> 2: down 8 8 #> 3: down 8 8 #> 4: down 8 8 #> 5: down 8 8 #> --- #> 176: both 10 10 #> 177: both 10 10 #> 178: both 10 10 #> 179: both 10 10 #> 180: both 10 10 # Number of matrices stored in the matrix (main data) part length(mat(TM)) #> [1] 2 # Dimensions of the matrix part matDim(TM) #> tm.matN tm.matDim1 tm.matDim2 #> 1: 1 8 8 #> 2: 2 10 10 # Aid part is empty aid(TM) #> list() # 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)) #> [1] 1 # One dimension row matDim(TM1down) #> tm.matN tm.matDim1 tm.matDim2 #> 1: 1 8 8 # Heatmap imgHeat <- colMeans(mat(TM1down, 1)) # Restoring dimensions of the heatmap dim(imgHeat) <- getRowDim(TM1down, 1) # Visualising heatmap image(imgHeat, axes=F)
