# Data-Frame to (3D+) Array or Matrix `d2a` converts a data.frame to a (3D+) array or matrix. This function assumes the data.frame contains 2+ variable dimensions, which will correspond to the returned arrays/matrix dimensions. One or multiple variables can contain the elements of the returned array (only one variable can contain the elements for returning a matrix). In the case of multiple variables, they will be binded as the last dimension in the returned array with dimnames equal to the variable names. ```r d2a(d, dim.nm = names(d)[-ncol(d)], rtn.dim.lab = "el_nm", check = TRUE) ``` ## Arguments - `d`: data.frame with at least 3 columns, where 2+ columns are variable dimensions and 1+ columns contain the to-be returned array/matrix elements. - `dim.nm`: character vector of 2+ length specifying the colnames in `d` that contain the variable dimensions. These do not need to be factors or character vectors. Note, all columns in `d` other than `dim.nm` are assumed to be element columns. - `rtn.dim.lab`: character vector of length 1 specifying the dimlabel to use for the last dimension in the returned array when there are multiple element columns in `d`. Note, that `NA` will be converted to "NA" and `NULL` will return an error. If you don't want any dimlabel to show, `""` is probably the best option. If there is only one element column in `d`, this argument is ignored by `d2a`. - `check`: logical vector of length 1 specifying whether to check the structure of the input arguments. For example, check whether `d` is a data.frame. This argument is available to allow flexibility in whether the user values informative error messages (TRUE) vs. computational efficiency (FALSE). ## Returns (3D+) array or matrix formed from the dimensions `d[dim.nm]` with dimlabels = `dim.nm` (and `rtn.dim.lab` if there are multiple element columns). The dimnames are the unique elements `d[dim.nm]` and are ordered by position (rather than alphabetically), which allow for conversions back to the original array after a call to `a2d()` or matrix after a call to `m2d()`. ## Details `d2a` is a wrapper for `reshape::cast` with the addition of reordering the dimnames by position, which sorts the dimnames by the position they first appear in the variable dimensions of the data.frame (`reshape::cast` sorts all the dimnames alphabetically). ## Examples ```r # 3D array print(HairEyeColor) d <- reshape::melt.array(HairEyeColor) a <- reshape::cast(d, Hair ~ Eye ~ Sex) identical(a, unclass(HairEyeColor)) # not the same as HairEyeColor d <- a2d(HairEyeColor) a <- d2a(d, dim.nm = c("Hair","Eye","Sex")) identical(a, unclass(HairEyeColor)) # yes the same as HairEyeColor # matrix attitude_mat <- d2m(attitude) d <- m2d(attitude_mat, col = 0) m <- d2a(d) identical(m, attitude_mat) # yes the same as attitude_mat # correlation data.frame example for p-values using psych::corr.test(attitude[1:3]) # corr_test <- psych::corr.test(attitude) # a <- lm2a(corr_test[c("r","se","t","p")]) r <- matrix(c(1.0000000, 0.8254176, 0.4261169, 0.8254176, 1.0000000, 0.5582882, 0.4261169, 0.5582882, 1.0000000), nrow = 3, ncol = 3, byrow = FALSE) se <- matrix(c(0.0000000, 0.1066848, 0.1709662, 0.1066848, 0.0000000, 0.1567886, 0.1709662, 0.1567886, 0.0000000), nrow = 3, ncol = 3, byrow = FALSE) t <- matrix(c(Inf, 7.736978, 2.492404, 7.736978, Inf, 3.560771, 2.492404, 3.560771, Inf), nrow = 3, ncol = 3, byrow = FALSE) p <- matrix(c(0.000000e+00, 1.987682e-08, 1.887702e-02, 5.963047e-08, 0.000000e+00, 1.345519e-03, 0.018877022, 0.002691039, 0.000000000), nrow = 3, ncol = 3, byrow = FALSE) a <- abind::abind(r, se, t, p, along = 3L) dimnames(a) <- list(names(attitude[1:3]), names(attitude[1:3]), c("r","se","t","p")) d <- a2d(a = a, col = 3) a2 <- d2a(d = d, dim.nm = c("X1","X2")) all.equal(a, a2) # dimlabels differ dimnames(a2) <- unname(dimnames(a2)) all.equal(a, a2) # now it is true # correlation data.frame example for confidence intervals using psych::corr.test(attitude[1:3]) # corr_test <- psych::corr.test(attitude[1:3]) # d <- corr_test[["ci"]][c("r","p","lower","upper")] # cbind(d, after = 0L) <- reshape::colsplit(row.names(d), split = "-", names = c("X1","X2")) # tmp <- d[c("X2","X1","r","p","lower","upper")] # d2 <- plyr::rename(tmp, c("X1" = "X2", "X2" = "X1")) # short_nm <- unique(c(fct2v(d[["X1"]]), fct2v(d[["X2"]]))) # d3 <- data.frame("X1" = short_nm, "X2" = short_nm, # "r" = NA_real_, "p" = NA_real_, "lower" = NA_real_, "upper" = NA_real_) # d_all <- ld2d(ld = list(d, d2, d3), rtn.listnames.nm = NULL, rtn.rownames.nm = NULL) d_all <- data.frame( "X1" = c("ratng","ratng","cmpln","cmpln","prvlg","prvlg","ratng","cmpln","prvlg"), "X2" = c("cmpln","prvlg","prvlg","ratng","ratng","cmpln","ratng","cmpln","prvlg"), "r" = c(0.8254176, 0.4261169, 0.5582882, 0.8254176, 0.4261169, 0.5582882, NA, NA, NA), "p" = c(1.987682e-08, 1.887702e-02, 1.345519e-03, 1.987682e-08, 1.887702e-02, 1.345519e-03, NA, NA, NA), "lower" = c(0.66201277, 0.07778967, 0.24787510, 0.66201277, 0.07778967, 0.24787510, NA, NA, NA), "upper" = c(0.9139139, 0.6817292, 0.7647418, 0.9139139, 0.6817292, 0.7647418, NA, NA, NA) ) tmp <- d2a(d = d_all, dim.nm = c("X1","X2"), rtn.dim.lab = "stat") short_nm <- c("ratng","cmpln","prvlg") dim_names <- list(short_nm, short_nm, c("r","p","lower","upper")) a <- do.call(what = `[`, args = c(list(tmp), dim_names)) print(a) ```

d2a function