Fixed #90 #94 #98 #100

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: DataExplorer
 Title: Automate data exploration and treatment
-Version: 0.7.1
+Version: 0.7.1.9000
 Authors@R: person("Boxuan", "Cui", email = "[email protected]", role = c("aut", "cre"))
 Description: Automated data exploration process for analytical tasks and predictive modeling, so
     that users could focus on understanding data and extracting insights. The package scans and
@@ -31,6 +31,6 @@ License: MIT + file LICENSE
 LazyData: true
 URL: http://boxuancui.github.io/DataExplorer/
 BugReports: https://github.com/boxuancui/DataExplorer/issues
-RoxygenNote: 6.1.0
+RoxygenNote: 6.1.1
 Encoding: UTF-8
 VignetteBuilder: knitr

NEWS.md

@@ -1,3 +1,12 @@
+# DataExplorer 0.7.1.9000
+## Bug Fixes
+* [#90](https://github.com/boxuancui/DataExplorer/issues/90): Added first plot in `plot_prcomp` to output and `page_0`.
+* [#94](https://github.com/boxuancui/DataExplorer/issues/94): Fixed typo.
+
+## Enhancements
+* [#98](https://github.com/boxuancui/DataExplorer/issues/98): Added band customization to `plot_missing`.
+* [#100](https://github.com/boxuancui/DataExplorer/issues/100): Switched `geom_text` to `geom_label`.
+
 # DataExplorer 0.7.1
 ## Enhancements
 * [#86](https://github.com/boxuancui/DataExplorer/issues/86): Replaced `gridExtra::grid.arrange` with facets.
@@ -38,7 +47,7 @@
 
 # DataExplorer 0.6.0
 ## New Features
-* [#15](https://github.com/boxuancui/DataExplorer/issues/15): Added `plot_prcomp` to visualize principle component analysis.
+* [#15](https://github.com/boxuancui/DataExplorer/issues/15): Added `plot_prcomp` to visualize principal component analysis.
 * [#54](https://github.com/boxuancui/DataExplorer/issues/54): Extracted `dummify` from `plot_correlation` as a new function.
 * [#59](https://github.com/boxuancui/DataExplorer/issues/59): Added `introduce` for basic metadata.
 

R/plot_intro.r

@@ -39,7 +39,7 @@ plot_intro <- function(data, title = NULL, ggtheme = theme_gray(), theme_config
 	## Create ggplot object
 	output <- ggplot(intro2, aes(x = reorder(variable, -id), y = value, fill = dimension)) +
 		geom_bar(stat = "identity") +
-		geom_text(aes(label = percent(value))) +
+		geom_label(aes(label = percent(value))) +
 		scale_y_continuous(labels = percent) +
 		scale_fill_discrete("Dimension") +
 		coord_flip() +

R/plot_missing.r

@@ -2,6 +2,7 @@
 #'
 #' This function returns and plots frequency of missing values for each feature.
 #' @param data input data
+#' @param group missing profile band taking a list of group name and group upper bounds. Default is \code{list("Good" = 0.05, "OK" = 0.4, "Bad" = 0.8, "Remove" = 1)}.
 #' @param title plot title
 #' @param ggtheme complete ggplot2 themes. The default is \link{theme_gray}.
 #' @param theme_config a list of configurations to be passed to \link{theme}.
@@ -12,25 +13,35 @@
 #' @seealso \link{profile_missing}
 #' @examples
 #' plot_missing(airquality)
+#' plot_missing(airquality, group = list("B1" = 0, "B2" = 0.06, "B3" = 1))
 
-plot_missing <- function(data, title = NULL, ggtheme = theme_gray(), theme_config = list("legend.position" = c("bottom"))) {
-	## Declare variable first to pass R CMD check
-	pct_missing <- NULL
-	## Profile missing values
-	missing_value <- profile_missing(data)
-	## Create ggplot object
-	output <- ggplot(missing_value, aes_string(x = "feature", y = "num_missing", fill = "group")) +
-		geom_bar(stat = "identity") +
-		geom_text(aes(label = paste0(round(100 * pct_missing, 2), "%"))) +
-		scale_fill_manual("Group", values = c("Good" = "#1a9641", "OK" = "#a6d96a", "Bad" = "#fdae61", "Remove" = "#d7191c"), breaks = c("Good", "OK", "Bad", "Remove")) +
-		coord_flip() +
-		xlab("Features") + ylab("Missing Rows")
-	## Plot object
-	class(output) <- c("single", class(output))
-	plotDataExplorer(
-		plot_obj = output,
-		title = title,
-		ggtheme = ggtheme,
-		theme_config = theme_config
-	)
+plot_missing <- function(data, group = list("Good" = 0.05, "OK" = 0.4, "Bad" = 0.8, "Remove" = 1), title = NULL, ggtheme = theme_gray(), theme_config = list("legend.position" = c("bottom"))) {
+  ## Declare variable first to pass R CMD check
+  pct_missing <- Band <- NULL
+  ## Profile missing values
+  missing_value <- data.table(profile_missing(data))
+  ## Sort group based on value
+  group <- group[sort.list(unlist(group))]
+  invisible(lapply(seq_along(group), function(i) {
+    if (i == 1) {
+      missing_value[pct_missing <= group[[i]], Band := names(group)[i]]
+    } else {
+      missing_value[pct_missing > group[[i-1]] & pct_missing <= group[[i]], Band := names(group)[i]]
+    }
+  }))
+  ## Create ggplot object
+  output <- ggplot(missing_value, aes_string(x = "feature", y = "num_missing", fill = "Band")) +
+    geom_bar(stat = "identity") +
+    geom_label(aes(label = paste0(round(100 * pct_missing, 2), "%"))) +
+    scale_fill_discrete("Band") +
+    coord_flip() +
+    xlab("Features") + ylab("Missing Rows")
+  ## Plot object
+  class(output) <- c("single", class(output))
+  plotDataExplorer(
+    plot_obj = output,
+    title = title,
+    ggtheme = ggtheme,
+    theme_config = theme_config
+  )
 }

R/plot_prcomp.r

@@ -1,8 +1,8 @@
-#' Visualize principle component analysis
+#' Visualize principal component analysis
 #'
 #' Visualize output of \link{prcomp}.
 #' @param data input data
-#' @param variance_cap maximum cumulative explained variance allowed for all principle components. Default is 80\%.
+#' @param variance_cap maximum cumulative explained variance allowed for all principal components. Default is 80\%.
 #' @param maxcat maximum categories allowed for each discrete feature. The default is 50.
 #' @param prcomp_args a list of other arguments to \link{prcomp}
 #' @param title plot title starting from page 2.
@@ -13,7 +13,7 @@
 #' @param parallel enable parallel? Default is \code{FALSE}.
 #' @return invisibly return the named list of ggplot objects
 #' @keywords plot_prcomp
-#' @details When cumulative explained variance exceeds \code{variance_cap}, remaining principle components will be ignored. Set \code{variance_cap} to 1 for all principle components.
+#' @details When cumulative explained variance exceeds \code{variance_cap}, remaining principal components will be ignored. Set \code{variance_cap} to 1 for all principal components.
 #' @details Discrete features containing more categories than \code{maxcat} specifies will be ignored.
 #' @note Discrete features will be \link{dummify}-ed first before passing to \link{prcomp}.
 #' @note Missing values may create issues in \link{prcomp}. Consider \link{na.omit} your input data first.
@@ -29,77 +29,88 @@
 #' plot_prcomp(diamonds, maxcat = 7L)
 
 plot_prcomp <- function(data, variance_cap = 0.8, maxcat = 50L, prcomp_args = list("scale." = TRUE), title = NULL, ggtheme = theme_gray(), theme_config = list(), nrow = 3L, ncol = 3L, parallel = FALSE) {
-	## Declare variable first to pass R CMD check
-	pc <- pct <- cum_pct <- Feature <- variable <- value <- NULL
-	## Check if input is data.table
-	if (!is.data.table(data)) data <- data.table(data)
-	## Dummify data
-	dt <- suppressWarnings(split_columns(dummify(data, maxcat = maxcat))$continuous)
-	prcomp_args_list <- list("x" = dt, "retx" = FALSE)
-	## Analyze principle components
-	pca <- tryCatch(
-		do.call("prcomp", c(prcomp_args_list, prcomp_args)),
-		error = function(e) {
-			message(e$message)
-			if (grepl("missing", e$message)) stop("\nConsider passing `na.omit(data)` as input.")
-		}
-	)
-
-	## Calcualte principle components standard deviation
-	var_exp <- pca$sdev ^ 2
-	pc_var <- data.table(
-		"pc" = paste0("PC", seq_along(pca$sdev)),
-		"var" = var_exp,
-		"pct" = var_exp / sum(var_exp),
-		"cum_pct" = cumsum(var_exp) / sum(var_exp)
-	)
-	min_cum_pct <- min(pc_var$cum_pct)
-	pc_var2 <- pc_var[cum_pct <= max(variance_cap, min_cum_pct)]
-	## Create explained variance plot
-	varexp_plot <- ggplot(pc_var2, aes(x = reorder(pc, pct), y = pct)) +
-		geom_bar(stat = "identity") +
-		geom_text(aes(label = percent(cum_pct)), color = "white", hjust = 1.1) +
-		scale_y_continuous(labels = percent) +
-		coord_flip() +
-		ggtitle(
-			label = "% Variance Explained By Principle Components",
-			subtitle = "Note: White texts indicate cumulative % explained variance"
-		) +
-		labs(x = "Principle Components", y = "% Variance Explained")
-	print(varexp_plot)
-	## Format rotation data
-	rotation_dt <- data.table(
-		"Feature" = rownames(pca$rotation),
-		data.table(pca$rotation)[, seq.int(nrow(pc_var2)), with = FALSE]
-	)
-	melt_rotation_dt <- melt.data.table(rotation_dt, id.vars = "Feature")
-	feature_names <- rotation_dt[["Feature"]]
-	## Calculate number of pages
-	layout <- .getPageLayout(nrow, ncol, ncol(rotation_dt) - 1L)
-	## Create list of ggplot objects
-	plot_list <- .lapply(
-		parallel = parallel,
-		X = layout,
-		FUN = function(x) {
-			ggplot(melt_rotation_dt[variable %in% paste0("PC", x)], aes(x = Feature, y = value)) +
-				geom_bar(stat = "identity") +
-				coord_flip() +
-				ylab("Relative Importance")
-		}
-	)
-	## Plot objects
-	class(plot_list) <- c("multiple", class(plot_list))
-	plotDataExplorer(
-		plot_obj = plot_list,
-		page_layout = layout,
-		title = title,
-		ggtheme = ggtheme,
-		theme_config = theme_config,
-		facet_wrap_args = list(
-			"facet" = ~ variable,
-			"nrow" = nrow,
-			"ncol" = ncol,
-			"scales" = "free_x"
-		)
-	)
+  ## Declare variable first to pass R CMD check
+  pc <- pct <- cum_pct <- Feature <- variable <- value <- NULL
+  ## Check if input is data.table
+  if (!is.data.table(data)) data <- data.table(data)
+  ## Dummify data
+  dt <- suppressWarnings(split_columns(dummify(data, maxcat = maxcat))$continuous)
+  prcomp_args_list <- list("x" = dt, "retx" = FALSE)
+  ## Analyze principal components
+  pca <- tryCatch(
+    do.call("prcomp", c(prcomp_args_list, prcomp_args)),
+    error = function(e) {
+      message(e$message)
+      if (grepl("missing", e$message)) stop("\nConsider passing `na.omit(data)` as input.")
+    }
+  )
+
+  ## Calcualte principal components standard deviation
+  var_exp <- pca$sdev ^ 2
+  pc_var <- data.table(
+    "pc" = paste0("PC", seq_along(pca$sdev)),
+    "var" = var_exp,
+    "pct" = var_exp / sum(var_exp),
+    "cum_pct" = cumsum(var_exp) / sum(var_exp)
+  )
+  min_cum_pct <- min(pc_var$cum_pct)
+  pc_var2 <- pc_var[cum_pct <= max(variance_cap, min_cum_pct)]
+  ## Create explained variance plot
+  varexp_plot <- ggplot(pc_var2, aes(x = reorder(pc, pct), y = pct)) +
+    geom_bar(stat = "identity") +
+    geom_label(aes(label = percent(cum_pct))) +
+    scale_y_continuous(labels = percent) +
+    coord_flip() +
+    labs(x = "Principal Components", y = "% Variance Explained")
+  # ggtitle(
+  #   label = "% Variance Explained By Principal Components",
+  #   subtitle = "Note: Labels indicate cumulative % explained variance"
+  # ) +
+  #   
+  # print(varexp_plot)
+  ## Format rotation data
+  rotation_dt <- data.table(
+    "Feature" = rownames(pca$rotation),
+    data.table(pca$rotation)[, seq.int(nrow(pc_var2)), with = FALSE]
+  )
+  melt_rotation_dt <- melt.data.table(rotation_dt, id.vars = "Feature")
+  feature_names <- rotation_dt[["Feature"]]
+  ## Calculate number of pages
+  layout <- .getPageLayout(nrow, ncol, ncol(rotation_dt) - 1L)
+  ## Create list of ggplot objects
+  plot_list <- .lapply(
+    parallel = parallel,
+    X = layout,
+    FUN = function(x) {
+      ggplot(melt_rotation_dt[variable %in% paste0("PC", x)], aes(x = Feature, y = value)) +
+        geom_bar(stat = "identity") +
+        coord_flip() +
+        ylab("Relative Importance")
+    }
+  )
+  ## Plot objects
+  class(varexp_plot) <- c("single", class(varexp_plot))
+  class(plot_list) <- c("multiple", class(plot_list))
+  invisible(c(
+    list(
+      "page_0" = plotDataExplorer(
+        plot_obj = varexp_plot,
+        title = "% Variance Explained By Principal Components\n(Note: Labels indicate cumulative % explained variance)",
+        ggtheme = ggtheme,
+        theme_config = theme_config
+      )),
+    plotDataExplorer(
+      plot_obj = plot_list,
+      page_layout = layout,
+      title = title,
+      ggtheme = ggtheme,
+      theme_config = theme_config,
+      facet_wrap_args = list(
+        "facet" = ~ variable,
+        "nrow" = nrow,
+        "ncol" = ncol,
+        "scales" = "free_x"
+      )
+    )
+  ))
 }

R/profile_missing.r

@@ -25,11 +25,7 @@ profile_missing <- function(data) {
 		"num_missing" = sapply(data, function(x) {sum(is.na(x))})
 	)
 	missing_value[, feature := factor(feature, levels = feature[order(-rank(num_missing))])]
-	missing_value[, pct_missing := num_missing / nrow(data)]
-	missing_value[pct_missing < 0.05, group := "Good"]
-	missing_value[pct_missing >= 0.05 & pct_missing < 0.4, group := "OK"]
-	missing_value[pct_missing >= 0.4 & pct_missing < 0.8, group := "Bad"]
-	missing_value[pct_missing >= 0.8, group := "Remove"][]
+	missing_value[, pct_missing := num_missing / nrow(data)][]
 	## Set data class back to original
 	if (!is_data_table) class(missing_value) <- data_class
 

README.Rmd

@@ -181,7 +181,7 @@ plot_scatterplot(split_columns(diamonds)$continuous, by = "price", sampled_rows
 ```
 
 ```{r plot_prcomp-template}
-## Visualize principle component analysis
+## Visualize principal component analysis
 plot_prcomp(diamonds, maxcat = 5L)
 ```
 

README.md

@@ -93,7 +93,7 @@ plot_boxplot(diamonds, by = "cut")
 ## Scatterplot `price` with all other continuous features
 plot_scatterplot(split_columns(diamonds)$continuous, by = "price", sampled_rows = 1000L)
 
-## Visualize principle component analysis
+## Visualize principal component analysis
 plot_prcomp(diamonds, maxcat = 5L)
 ```
 

inst/rmd_template/report.rmd

@@ -204,9 +204,9 @@ if ("plot_correlation" %in% names(report_config)) {
 }
 ```
 
-```{r principle_component_analysis}
+```{r principal_component_analysis}
 if ("plot_prcomp" %in% names(report_config)) {
-	cat("### Principle Component Analysis", fill = TRUE)
+	cat("### Principal Component Analysis", fill = TRUE)
 	if (intro[["complete_rows"]] > 0) {
 		if (!is.null(response)) {
 			dt <- na.omit(data[, -response, with = FALSE])
@@ -215,7 +215,7 @@ if ("plot_prcomp" %in% names(report_config)) {
 			do_call("plot_prcomp", na_omit = TRUE)
 		}
 	} else {
-		message("Insufficient complete rows! Skipping principle component analysis.")
+		message("Insufficient complete rows! Skipping principal component analysis.")
 	}
 }
 ```