Adjust the variable parsing for vrb argument

DESCRIPTION

@@ -9,7 +9,8 @@ Authors@R: c(
     person("Thom", "Volker", role = "ctb", comment = c(ORCID = "0000-0002-2408-7820")),
     person("Gerko", "Vink", role = "ctb", comment = c(ORCID = "0000-0001-9767-1924")),
     person("Pepijn", "Vink", role = "ctb", comment = c(ORCID = "0000-0001-6960-9904")),
-    person("Jamie", "Wallis", role = "ctb", comment = c(ORCID = "0000-0003-2765-3813"))
+    person("Jamie", "Wallis", role = "ctb", comment = c(ORCID = "0000-0003-2765-3813")),
+    person("Kyle", "Lang", role = "ctb", comment = c(ORCID = "0000-0001-5340-7849"))
     )
 Description: Enhance a 'mice' imputation workflow with visualizations for
     incomplete and/or imputed data. The plotting functions produce
@@ -46,4 +47,4 @@ Config/testthat/edition: 3
 Copyright: 'ggmice' authors
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2

NEWS.md

@@ -2,7 +2,8 @@
 
 ## Bug fixes
 
-* Correct labeling of 'exclusion-restriction' variables in `plot_pred()` (#128) 
+* Correct labeling of 'exclusion-restriction' variables in `plot_pred()` (#128)
+* Parsing of `vrb` argument in all `plot_*()` functions: variable name(s) from object in global environment now recognized using `!!` notation (#157)
 
 ## Minor changes
 

R/ggmice.R

@@ -60,8 +60,7 @@
   }
   if (length(vrbs) > length(unique(vrbs))) {
     cli::cli_abort(
-      c("The data must have unique column names.",
-        "x" = "Duplication found in {vrbs[duplicated(vrbs)]}")
+      c("The data must have unique column names.", "x" = "Duplication found in {vrbs[duplicated(vrbs)]}")
     )
   }
   # extract mapping variables
@@ -107,8 +106,8 @@
           .imp = 0,
           .id = rownames(data$data),
           data$data
-        )[!miss_xy,],
-        data.frame(.where = "imputed", mice::complete(data, action = "long"))[where_xy,]
+        )[!miss_xy, ],
+        data.frame(.where = "imputed", mice::complete(data, action = "long"))[where_xy, ]
       ),
       .where = factor(
         .where,
@@ -133,7 +132,7 @@
 
   # edit plot to display missing values on the axes
   if (is.data.frame(data) &&
       !is.null(mapping$x) && !is.null(mapping$y)) {
     gg <- gg +
       ggplot2::coord_cartesian(clip = "off")
     if (!is.null(mapping$x)) {
@@ -154,7 +153,6 @@
   return(gg)
 }
 
-
 #' Utils function to extract mapping variables
 #'
 #' @param data Incomplete dataset or mids object.
@@ -187,21 +185,19 @@
     mapping_out <- mapping_text
   }
   if ((mice::is.mids(data) &&
        mapping_text %in% c(".id", ".imp", ".where"))) {
     mapping_out <- NULL
   }
   if (!is.null(mapping_in) &&
       mapping_text %nin% c(vrbs, ".id", ".imp", ".where")) {
     mapping_out <- vrbs[stringr::str_detect(mapping_text, vrbs)]
     if (identical(mapping_out, character(0)) ||
         inherits(try(dplyr::mutate(mapping_data,
                                    !!rlang::parse_quo(mapping_text, env = rlang::current_env())),
                      silent = TRUE)
-                 ,
-                 "try-error")) {
+                 , "try-error")) {
       cli::cli_abort(
-        c("Must provide a valid mapping variable.",
-          "x" = "Mapping variable '{mapping_text}' not found in the data or imputations.")
+        c("Must provide a valid mapping variable.", "x" = "Mapping variable '{mapping_text}' not found in the data or imputations.")
       )
     } else {
       cli::cli_warn(

R/plot_corr.R

@@ -11,7 +11,22 @@
 #' @return An object of class [ggplot2::ggplot].
 #'
 #' @examples
-#' plot_corr(mice::nhanes, label = TRUE)
+#' # plot correlations for all columns
+#' plot_corr(mice::nhanes)
+#'
+#' # plot correlations for specific columns by supplying a character vector
+#' plot_corr(mice::nhanes, c("chl", "hyp"))
+#'
+#' # plot correlations for specific columns by supplying unquoted variable names
+#' plot_corr(mice::nhanes, c(chl, hyp))
+#'
+#' # plot correlations for specific columns by passing an object with variable names
+#' # from the environment, unquoted with `!!`
+#' my_variables <- c("chl", "hyp")
+#' plot_corr(mice::nhanes, !!my_variables)
+#' # object with variable names must be unquoted with `!!`
+#' try(plot_corr(mice::nhanes, my_variables))
+#'
 #' @export
 plot_corr <-
   function(data,
@@ -25,38 +40,33 @@ plot_corr <-
       data <- as.data.frame(data)
     }
     verify_data(data = data, df = TRUE)
-    vrb <- substitute(vrb)
-    if (vrb != "all" && length(vrb) < 2) {
+    vrb <- rlang::enexpr(vrb)
+    vrb_matched <- match_vrb(vrb, names(data))
+    if (length(vrb_matched) < 2) {
       cli::cli_abort("The number of variables should be two or more to compute correlations.")
     }
-    if (vrb[1] == "all") {
-      vrb <- names(data)
-    } else {
-      data <- dplyr::select(data, {{vrb}})
-      vrb <- names(data)
-    }
     # check if any column is constant
-    constants <- apply(data, MARGIN = 2, function(x) {
+    constants <- apply(data[, vrb_matched], MARGIN = 2, function(x) {
       all(is.na(x)) || max(x, na.rm = TRUE) == min(x, na.rm = TRUE)
     })
     if (any(constants)) {
-      vrb <- names(data[, !constants])
+      vrb_matched <- vrb_matched[!constants]
       cli::cli_inform(
         c(
           "No correlations computed for variable(s):",
           " " = paste(names(constants[which(constants)]), collapse = ", "),
-          "x" = "Correlation undefined for constants."
+          "i" = "Correlations are undefined for constants."
         )
       )
     }
-
-    p <- length(vrb)
+    # compute correlations
+    p <- length(vrb_matched)
     corrs <- data.frame(
-      vrb = rep(vrb, each = p),
-      prd = vrb,
+      vrb = rep(vrb_matched, each = p),
+      prd = vrb_matched,
       corr = matrix(
         round(stats::cov2cor(
-          stats::cov(data.matrix(data[, vrb]), use = "pairwise.complete.obs")
+          stats::cov(data.matrix(data[, vrb_matched]), use = "pairwise.complete.obs")
         ), 2),
         nrow = p * p,
         byrow = TRUE
@@ -65,6 +75,7 @@ plot_corr <-
     if (!diagonal) {
       corrs[corrs$vrb == corrs$prd, "corr"] <- NA
     }
+    # create plot
     gg <-
       ggplot2::ggplot(corrs,
                       ggplot2::aes(
@@ -74,8 +85,8 @@ plot_corr <-
                         fill = .data$corr
                       )) +
       ggplot2::geom_tile(color = "black", alpha = 0.6) +
-      ggplot2::scale_x_discrete(limits = vrb, position = "top") +
-      ggplot2::scale_y_discrete(limits = rev(vrb)) +
+      ggplot2::scale_x_discrete(limits = vrb_matched, position = "top") +
+      ggplot2::scale_y_discrete(limits = rev(vrb_matched)) +
       ggplot2::scale_fill_gradient2(
         low = ggplot2::alpha("deepskyblue", 0.6),
         mid = "lightyellow",
@@ -91,13 +102,11 @@ plot_corr <-
           y = "Variable to impute",
           fill = "Correlation*
       ",
-      caption = "*pairwise complete observations"
+          caption = "*pairwise complete observations"
         )
     } else {
       gg <- gg +
-        ggplot2::labs(x = "Imputation model predictor",
-                      y = "Variable to impute",
-                      fill = "Correlation")
+        ggplot2::labs(x = "Imputation model predictor", y = "Variable to impute", fill = "Correlation")
     }
     if (label) {
       gg <-

R/plot_flux.R

@@ -8,25 +8,37 @@
 #' @return An object of class [ggplot2::ggplot].
 #'
 #' @examples
+#' # plot flux for all columns
 #' plot_flux(mice::nhanes)
+#'
+#' # plot flux for specific columns by supplying a character vector
+#' plot_flux(mice::nhanes, c("chl", "hyp"))
+#'
+#' # plot flux for specific columns by supplying unquoted variable names
+#' plot_flux(mice::nhanes, c(chl, hyp))
+#'
+#' # plot flux for specific columns by passing an object with variable names
+#' # from the environment, unquoted with `!!`
+#' my_variables <- c("chl", "hyp")
+#' plot_flux(mice::nhanes, !!my_variables)
+#' # object with variable names must be unquoted with `!!`
+#' try(plot_flux(mice::nhanes, my_variables))
+#'
 #' @export
 plot_flux <-
   function(data,
            vrb = "all",
            label = TRUE,
            caption = TRUE) {
     verify_data(data, df = TRUE)
-    vrb <- substitute(vrb)
-    if (vrb != "all" && length(vrb) < 2) {
+    vrb <- rlang::enexpr(vrb)
+    vrb_matched <- match_vrb(vrb, names(data))
+    if (length(vrb_matched) < 2) {
       cli::cli_abort("The number of variables should be two or more to compute flux.")
     }
-    if (vrb[1] == "all") {
-      vrb <- names(data)
-    } else {
-      vrb <- names(dplyr::select(data, {{vrb}}))
-    }
-    # plot in and outflux
-    flx <- mice::flux(data[, vrb])[, c("influx", "outflux")]
+    # compute flux
+    flx <- mice::flux(data[, vrb_matched])[, c("influx", "outflux")]
+    # create plot
     gg <-
       data.frame(
         vrb = rownames(flx),

R/plot_pattern.R

@@ -11,7 +11,22 @@
 #' @return An object of class [ggplot2::ggplot].
 #'
 #' @examples
+#' # plot missing data pattern for all columns
 #' plot_pattern(mice::nhanes)
+#'
+#' # plot missing data pattern for specific columns by supplying a character vector
+#' plot_pattern(mice::nhanes, c("chl", "hyp"))
+#'
+#' # plot missing data pattern for specific columns by supplying unquoted variable names
+#' plot_pattern(mice::nhanes, c(chl, hyp))
+#'
+#' # plot missing data pattern for specific columns by passing an object with variable names
+#' # from the environment, unquoted with `!!`
+#' my_variables <- c("chl", "hyp")
+#' plot_pattern(mice::nhanes, !!my_variables)
+#' # object with variable names must be unquoted with `!!`
+#' try(plot_pattern(mice::nhanes, my_variables))
+#'
 #' @export
 plot_pattern <-
   function(data,
@@ -21,21 +36,16 @@ plot_pattern <-
            cluster = NULL,
            npat = NULL,
            caption = TRUE) {
-    # input processing
     if (is.matrix(data) && ncol(data) > 1) {
       data <- as.data.frame(data)
     }
     verify_data(data, df = TRUE)
-    vrb <- substitute(vrb)
-    if (vrb != "all" && length(vrb) < 2) {
+    vrb <- rlang::enexpr(vrb)
+    vrb_matched <- match_vrb(vrb, names(data))
+    if (length(vrb_matched) < 2) {
       cli::cli_abort("The number of variables should be two or more to compute missing data patterns.")
     }
-    if (vrb[1] == "all") {
-      vrb <- names(data)
-    } else {
-      vrb <- names(dplyr::select(as.data.frame(data), {{vrb}}))
-    }
-    if (".x" %in% vrb || ".y" %in% vrb) {
+    if (".x" %in% vrb_matched || ".y" %in% vrb_matched) {
       cli::cli_abort(
         c(
           "The variable names '.x' and '.y' are used internally to produce the missing data pattern plot.",
@@ -44,7 +54,7 @@ plot_pattern <-
       )
     }
     if (!is.null(cluster)) {
-      if (cluster %nin% names(data[, vrb])) {
+      if (cluster %nin% names(data[, vrb_matched])) {
         cli::cli_abort(
           c("Cluster variable not recognized.",
             "i" = "Please provide the variable name as a character string.")
@@ -61,7 +71,7 @@ plot_pattern <-
     }
 
     # get missing data pattern
-    pat <- mice::md.pattern(data[, vrb], plot = FALSE)
+    pat <- mice::md.pattern(data[, vrb_matched], plot = FALSE)
     rows_pat_full <-
       (nrow(pat) - 1) # full number of missing data patterns
 

R/plot_pred.R

@@ -10,8 +10,25 @@
 #' @return An object of class `ggplot2::ggplot`.
 #'
 #' @examples
+#' # generate a predictor matrix
 #' pred <- mice::quickpred(mice::nhanes)
+#'
+#' # plot predictor matrix for all columns
 #' plot_pred(pred)
+#'
+#' # plot predictor matrix for specific columns by supplying a character vector
+#' plot_pred(pred, c("chl", "hyp"))
+#'
+#' # plot predictor matrix for specific columns by supplying unquoted variable names
+#' plot_pred(pred, c(chl, hyp))
+#'
+#' # plot predictor matrix for specific columns by passing an object with variable names
+#' # from the environment, unquoted with `!!`
+#' my_variables <- c("chl", "hyp")
+#' plot_pred(pred, !!my_variables)
+#' # object with variable names must be unquoted with `!!`
+#' try(plot_pred(pred, my_variables))
+#'
 #' @export
 plot_pred <-
   function(data,
@@ -21,7 +38,9 @@ plot_pred <-
            square = TRUE,
            rotate = FALSE) {
     verify_data(data, pred = TRUE)
-    p <- nrow(data)
+    vrb <- rlang::enexpr(vrb)
+    vrb_matched <- match_vrb(vrb, row.names(data))
+    p <- length(vrb_matched)
     if (!is.null(method) && is.character(method)) {
       if (length(method) == 1) {
         method <- rep(method, p)
@@ -37,17 +56,10 @@ plot_pred <-
     if (!is.character(method) || length(method) != p) {
       cli::cli_abort("Method should be NULL or a character string or vector (of length 1 or `ncol(data)`).")
     }
-    vrb <- substitute(vrb)
-    if (vrb[1] == "all") {
-      vrb <- names(data)
-    } else {
-      vrb <- names(dplyr::select(as.data.frame(data), {{vrb}}))
-    }
-    vrbs <- row.names(data)
     long <- data.frame(
       vrb = 1:p,
-      prd = rep(vrbs, each = p),
-      ind = matrix(data, nrow = p * p, byrow = TRUE)
+      prd = rep(vrb_matched, each = p),
+      ind = matrix(data[vrb_matched, vrb_matched], nrow = p * p, byrow = TRUE)
     ) %>% dplyr::mutate(clr = factor(
       .data$ind,
       levels = c(-3, -2, 0, 1, 2),
@@ -70,10 +82,10 @@ plot_pred <-
                         fill = .data$clr
                       )) +
       ggplot2::geom_tile(color = "black", alpha = 0.6) +
-      ggplot2::scale_x_discrete(limits = vrbs, position = "top") +
+      ggplot2::scale_x_discrete(limits = vrb_matched, position = "top") +
       ggplot2::scale_y_reverse(
         breaks = 1:p,
-        labels = vrbs,
+        labels = vrb_matched,
         sec.axis = ggplot2::dup_axis(labels = method, name = ylabel)
       ) +
       ggplot2::scale_fill_manual(