CRAN Package Check Results for Package GGally

Last updated on 2017-05-23 10:48:59.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.3.0 2.46 191.17 193.63 ERROR
r-devel-linux-x86_64-debian-gcc 1.3.0 2.43 179.69 182.12 ERROR
r-devel-linux-x86_64-fedora-clang 1.3.0 355.14 ERROR --no-stop-on-test-error
r-devel-linux-x86_64-fedora-gcc 1.3.0 354.99 ERROR --no-stop-on-test-error
r-devel-windows-ix86+x86_64 1.3.0 12.00 312.00 324.00 ERROR
r-patched-linux-x86_64 1.3.0 2.58 181.91 184.49 ERROR
r-patched-solaris-sparc 1.3.0 1848.20 ERROR
r-patched-solaris-x86 1.3.0 405.90 ERROR
r-release-linux-x86_64 1.3.0 2.45 190.64 193.09 ERROR
r-release-windows-ix86+x86_64 1.3.0 8.00 316.00 324.00 ERROR
r-release-osx-x86_64 1.3.0 ERROR
r-oldrel-windows-ix86+x86_64 1.3.0 10.00 367.00 377.00 ERROR
r-oldrel-osx-x86_64 1.3.0 OK

Check Details

Version: 1.3.0
Check: examples
Result: ERROR
    Running examples in ‘GGally-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: ggduo
    > ### Title: ggduo - A ggplot2 generalized pairs plot for two columns sets of
    > ### a data.frame
    > ### Aliases: ggduo
    >
    > ### ** Examples
    >
    > # small function to display plots only if it's interactive
    > p_ <- GGally::print_if_interactive
    >
    > data(baseball, package = "plyr")
    >
    > # Keep players from 1990-1995 with at least one at bat
    > # Add how many singles a player hit
    > # (must do in two steps as X1b is used in calculations)
    > dt <- transform(
    + subset(baseball, year >= 1990 & year <= 1995 & ab > 0),
    + X1b = h - X2b - X3b - hr
    + )
    > # Add
    > # the player's batting average,
    > # the player's slugging percentage,
    > # and the player's on base percentage
    > # Make factor a year, as each season is discrete
    > dt <- transform(
    + dt,
    + batting_avg = h / ab,
    + slug = (X1b + 2*X2b + 3*X3b + 4*hr) / ab,
    + on_base = (h + bb + hbp) / (ab + bb + hbp),
    + year = as.factor(year)
    + )
    >
    >
    > pm <- ggduo(
    + dt,
    + c("year", "g", "ab", "lg"),
    + c("batting_avg", "slug", "on_base"),
    + mapping = ggplot2::aes(color = lg)
    + )
    > # Prints, but
    > # there is severe over plotting in the continuous plots
    > # the labels could be better
    > # want to add more hitting information
    > p_(pm)
    >
    >
    > # Make a fake column that will be calculated when printing
    > dt$hit_type <- paste("hit_type:", seq_len(nrow(dt)))
    >
    > display_hit_type <- function(plot_fn, is_ratio) {
    + function(data, mapping, ...) {
    + # change the color aesthetic to fill aesthetic
    + mapping <- mapping_color_to_fill(mapping)
    +
    + # If the y varaible is not 'hit_type', continue like normal
    + if (deparse(mapping$y) != "hit_type") {
    + p <- plot_fn(data, mapping, ...)
    + return(p)
    + }
    +
    + # Capture any extra column names needed
    + extra_columns <- unname(unlist(lapply(
    + mapping[! names(mapping) %in% c("x", "y")],
    + deparse
    + )))
    + extra_columns <- extra_columns[extra_columns %in% colnames(data)]
    +
    + x_name <- deparse(mapping$x)
    +
    + # get the types of hits
    + hit_types <- c("X1b", "X2b", "X3b", "hr")
    + hit_names <- c("single", "double", "tripple", "home\nrun")
    + if (is_ratio) {
    + hit_types <- rev(hit_types)
    + hit_names <- rev(hit_names)
    + }
    +
    + # retrieve the columns and rename them
    + data <- data[, c(x_name, hit_types, extra_columns)]
    + colnames(data) <- c(x_name, hit_names, extra_columns)
    +
    + # melt the data to get the counts of the unique hit occurances
    + dt_melt <- reshape::melt.data.frame(data, id = c(x_name, extra_columns))
    + dt_value <- dt_melt$value
    +
    + # Make a new data.frame with all the necessary variables repeated
    + dt_ratio <- data.frame(variable = logical(sum(dt_value)))
    + for (col in c(x_name, "variable", extra_columns)) {
    + dt_ratio[[col]] <- rep(dt_melt[[col]], dt_value)
    + }
    +
    + # copy the old mapping and overwrite the x and y values
    + mapping_ratio <- mapping
    + mapping_ratio[c("x", "y")] <- ggplot2::aes_string(x = x_name, y = "variable")
    +
    + # make ggplot2 object!
    + plot_fn(dt_ratio, mapping_ratio, ...)
    + }
    + }
    >
    >
    > display_hit_type_combo <- display_hit_type(ggally_facethist, FALSE)
    > display_hit_type_discrete <- display_hit_type(ggally_ratio, TRUE)
    >
    > # remove the strips, as the same information is displayed in the bottom axis area
    > pm <- ggduo(
    + dt,
    + c("year", "g", "ab", "lg"),
    + c("batting_avg", "slug", "on_base", "hit_type"),
    + columnLabelsX = c("year", "player game count", "player at bat count", "league"),
    + columnLabelsY = c("batting avg", "slug %", "on base %", "hit type"),
    + title = "Baseball Hitting Stats from 1990-1995",
    + mapping = ggplot2::aes(color = lg),
    + types = list(
    + # change the shape and add some transparency to the points
    + continuous = wrap("smooth_loess", alpha = 0.50, shape = "+"),
    + # all combinations that are continuous horizontally should have a binwidth of 15
    + comboHorizontal = wrap(display_hit_type_combo, binwidth = 15),
    + # the ratio plot should have a black border around the rects of size 0.15
    + discrete = wrap(display_hit_type_discrete, color = "black", size = 0.15)
    + ),
    + showStrips = FALSE, cardinality_threshold = NULL
    + );
    >
    > p_(pm)
    >
    >
    >
    > # Example derived from:
    > ## R Data Analysis Examples: Canonical Correlation Analysis. UCLA: Statistical
    > ## Consulting Group. from http://www.ats.ucla.edu/stat/r/dae/canonical.htm
    > ## (accessed June 23, 2016).
    > # "Example 1. A researcher has collected data on three psychological variables, four
    > # academic variables (standardized test scores) and gender for 600 college freshman.
    > # She is interested in how the set of psychological variables relates to the academic
    > # variables and gender. In particular, the researcher is interested in how many
    > # dimensions (canonical variables) are necessary to understand the association between
    > # the two sets of variables."
    > mm <- read.csv("http://www.ats.ucla.edu/stat/data/mmreg.csv")
    > colnames(mm) <- c("Control", "Concept", "Motivation", "Read", "Write", "Math",
    + "Science", "Sex")
    Error in names(x) <- value :
     'names' attribute [8] must be the same length as the vector [1]
    Calls: colnames<-
    Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.3.0
Flags: --no-stop-on-test-error
Check: examples
Result: ERROR
    Running examples in ‘GGally-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: ggduo
    > ### Title: ggduo - A ggplot2 generalized pairs plot for two columns sets of
    > ### a data.frame
    > ### Aliases: ggduo
    >
    > ### ** Examples
    >
    > # small function to display plots only if it's interactive
    > p_ <- GGally::print_if_interactive
    >
    > data(baseball, package = "plyr")
    >
    > # Keep players from 1990-1995 with at least one at bat
    > # Add how many singles a player hit
    > # (must do in two steps as X1b is used in calculations)
    > dt <- transform(
    + subset(baseball, year >= 1990 & year <= 1995 & ab > 0),
    + X1b = h - X2b - X3b - hr
    + )
    > # Add
    > # the player's batting average,
    > # the player's slugging percentage,
    > # and the player's on base percentage
    > # Make factor a year, as each season is discrete
    > dt <- transform(
    + dt,
    + batting_avg = h / ab,
    + slug = (X1b + 2*X2b + 3*X3b + 4*hr) / ab,
    + on_base = (h + bb + hbp) / (ab + bb + hbp),
    + year = as.factor(year)
    + )
    >
    >
    > pm <- ggduo(
    + dt,
    + c("year", "g", "ab", "lg"),
    + c("batting_avg", "slug", "on_base"),
    + mapping = ggplot2::aes(color = lg)
    + )
    > # Prints, but
    > # there is severe over plotting in the continuous plots
    > # the labels could be better
    > # want to add more hitting information
    > p_(pm)
    >
    >
    > # Make a fake column that will be calculated when printing
    > dt$hit_type <- paste("hit_type:", seq_len(nrow(dt)))
    >
    > display_hit_type <- function(plot_fn, is_ratio) {
    + function(data, mapping, ...) {
    + # change the color aesthetic to fill aesthetic
    + mapping <- mapping_color_to_fill(mapping)
    +
    + # If the y varaible is not 'hit_type', continue like normal
    + if (deparse(mapping$y) != "hit_type") {
    + p <- plot_fn(data, mapping, ...)
    + return(p)
    + }
    +
    + # Capture any extra column names needed
    + extra_columns <- unname(unlist(lapply(
    + mapping[! names(mapping) %in% c("x", "y")],
    + deparse
    + )))
    + extra_columns <- extra_columns[extra_columns %in% colnames(data)]
    +
    + x_name <- deparse(mapping$x)
    +
    + # get the types of hits
    + hit_types <- c("X1b", "X2b", "X3b", "hr")
    + hit_names <- c("single", "double", "tripple", "home\nrun")
    + if (is_ratio) {
    + hit_types <- rev(hit_types)
    + hit_names <- rev(hit_names)
    + }
    +
    + # retrieve the columns and rename them
    + data <- data[, c(x_name, hit_types, extra_columns)]
    + colnames(data) <- c(x_name, hit_names, extra_columns)
    +
    + # melt the data to get the counts of the unique hit occurances
    + dt_melt <- reshape::melt.data.frame(data, id = c(x_name, extra_columns))
    + dt_value <- dt_melt$value
    +
    + # Make a new data.frame with all the necessary variables repeated
    + dt_ratio <- data.frame(variable = logical(sum(dt_value)))
    + for (col in c(x_name, "variable", extra_columns)) {
    + dt_ratio[[col]] <- rep(dt_melt[[col]], dt_value)
    + }
    +
    + # copy the old mapping and overwrite the x and y values
    + mapping_ratio <- mapping
    + mapping_ratio[c("x", "y")] <- ggplot2::aes_string(x = x_name, y = "variable")
    +
    + # make ggplot2 object!
    + plot_fn(dt_ratio, mapping_ratio, ...)
    + }
    + }
    >
    >
    > display_hit_type_combo <- display_hit_type(ggally_facethist, FALSE)
    > display_hit_type_discrete <- display_hit_type(ggally_ratio, TRUE)
    >
    > # remove the strips, as the same information is displayed in the bottom axis area
    > pm <- ggduo(
    + dt,
    + c("year", "g", "ab", "lg"),
    + c("batting_avg", "slug", "on_base", "hit_type"),
    + columnLabelsX = c("year", "player game count", "player at bat count", "league"),
    + columnLabelsY = c("batting avg", "slug %", "on base %", "hit type"),
    + title = "Baseball Hitting Stats from 1990-1995",
    + mapping = ggplot2::aes(color = lg),
    + types = list(
    + # change the shape and add some transparency to the points
    + continuous = wrap("smooth_loess", alpha = 0.50, shape = "+"),
    + # all combinations that are continuous horizontally should have a binwidth of 15
    + comboHorizontal = wrap(display_hit_type_combo, binwidth = 15),
    + # the ratio plot should have a black border around the rects of size 0.15
    + discrete = wrap(display_hit_type_discrete, color = "black", size = 0.15)
    + ),
    + showStrips = FALSE, cardinality_threshold = NULL
    + );
    >
    > p_(pm)
    >
    >
    >
    > # Example derived from:
    > ## R Data Analysis Examples: Canonical Correlation Analysis. UCLA: Statistical
    > ## Consulting Group. from http://www.ats.ucla.edu/stat/r/dae/canonical.htm
    > ## (accessed June 23, 2016).
    > # "Example 1. A researcher has collected data on three psychological variables, four
    > # academic variables (standardized test scores) and gender for 600 college freshman.
    > # She is interested in how the set of psychological variables relates to the academic
    > # variables and gender. In particular, the researcher is interested in how many
    > # dimensions (canonical variables) are necessary to understand the association between
    > # the two sets of variables."
    > mm <- read.csv("http://www.ats.ucla.edu/stat/data/mmreg.csv")
    > colnames(mm) <- c("Control", "Concept", "Motivation", "Read", "Write", "Math",
    + "Science", "Sex")
    Error in names(x) <- value :
     'names' attribute [8] must be the same length as the vector [1]
    Calls: colnames<-
    Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Version: 1.3.0
Check: examples
Result: ERROR
    Running examples in 'GGally-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: ggduo
    > ### Title: ggduo - A ggplot2 generalized pairs plot for two columns sets of
    > ### a data.frame
    > ### Aliases: ggduo
    >
    > ### ** Examples
    >
    > # small function to display plots only if it's interactive
    > p_ <- GGally::print_if_interactive
    >
    > data(baseball, package = "plyr")
    >
    > # Keep players from 1990-1995 with at least one at bat
    > # Add how many singles a player hit
    > # (must do in two steps as X1b is used in calculations)
    > dt <- transform(
    + subset(baseball, year >= 1990 & year <= 1995 & ab > 0),
    + X1b = h - X2b - X3b - hr
    + )
    > # Add
    > # the player's batting average,
    > # the player's slugging percentage,
    > # and the player's on base percentage
    > # Make factor a year, as each season is discrete
    > dt <- transform(
    + dt,
    + batting_avg = h / ab,
    + slug = (X1b + 2*X2b + 3*X3b + 4*hr) / ab,
    + on_base = (h + bb + hbp) / (ab + bb + hbp),
    + year = as.factor(year)
    + )
    >
    >
    > pm <- ggduo(
    + dt,
    + c("year", "g", "ab", "lg"),
    + c("batting_avg", "slug", "on_base"),
    + mapping = ggplot2::aes(color = lg)
    + )
    > # Prints, but
    > # there is severe over plotting in the continuous plots
    > # the labels could be better
    > # want to add more hitting information
    > p_(pm)
    >
    >
    > # Make a fake column that will be calculated when printing
    > dt$hit_type <- paste("hit_type:", seq_len(nrow(dt)))
    >
    > display_hit_type <- function(plot_fn, is_ratio) {
    + function(data, mapping, ...) {
    + # change the color aesthetic to fill aesthetic
    + mapping <- mapping_color_to_fill(mapping)
    +
    + # If the y varaible is not 'hit_type', continue like normal
    + if (deparse(mapping$y) != "hit_type") {
    + p <- plot_fn(data, mapping, ...)
    + return(p)
    + }
    +
    + # Capture any extra column names needed
    + extra_columns <- unname(unlist(lapply(
    + mapping[! names(mapping) %in% c("x", "y")],
    + deparse
    + )))
    + extra_columns <- extra_columns[extra_columns %in% colnames(data)]
    +
    + x_name <- deparse(mapping$x)
    +
    + # get the types of hits
    + hit_types <- c("X1b", "X2b", "X3b", "hr")
    + hit_names <- c("single", "double", "tripple", "home\nrun")
    + if (is_ratio) {
    + hit_types <- rev(hit_types)
    + hit_names <- rev(hit_names)
    + }
    +
    + # retrieve the columns and rename them
    + data <- data[, c(x_name, hit_types, extra_columns)]
    + colnames(data) <- c(x_name, hit_names, extra_columns)
    +
    + # melt the data to get the counts of the unique hit occurances
    + dt_melt <- reshape::melt.data.frame(data, id = c(x_name, extra_columns))
    + dt_value <- dt_melt$value
    +
    + # Make a new data.frame with all the necessary variables repeated
    + dt_ratio <- data.frame(variable = logical(sum(dt_value)))
    + for (col in c(x_name, "variable", extra_columns)) {
    + dt_ratio[[col]] <- rep(dt_melt[[col]], dt_value)
    + }
    +
    + # copy the old mapping and overwrite the x and y values
    + mapping_ratio <- mapping
    + mapping_ratio[c("x", "y")] <- ggplot2::aes_string(x = x_name, y = "variable")
    +
    + # make ggplot2 object!
    + plot_fn(dt_ratio, mapping_ratio, ...)
    + }
    + }
    >
    >
    > display_hit_type_combo <- display_hit_type(ggally_facethist, FALSE)
    > display_hit_type_discrete <- display_hit_type(ggally_ratio, TRUE)
    >
    > # remove the strips, as the same information is displayed in the bottom axis area
    > pm <- ggduo(
    + dt,
    + c("year", "g", "ab", "lg"),
    + c("batting_avg", "slug", "on_base", "hit_type"),
    + columnLabelsX = c("year", "player game count", "player at bat count", "league"),
    + columnLabelsY = c("batting avg", "slug %", "on base %", "hit type"),
    + title = "Baseball Hitting Stats from 1990-1995",
    + mapping = ggplot2::aes(color = lg),
    + types = list(
    + # change the shape and add some transparency to the points
    + continuous = wrap("smooth_loess", alpha = 0.50, shape = "+"),
    + # all combinations that are continuous horizontally should have a binwidth of 15
    + comboHorizontal = wrap(display_hit_type_combo, binwidth = 15),
    + # the ratio plot should have a black border around the rects of size 0.15
    + discrete = wrap(display_hit_type_discrete, color = "black", size = 0.15)
    + ),
    + showStrips = FALSE, cardinality_threshold = NULL
    + );
    >
    > p_(pm)
    >
    >
    >
    > # Example derived from:
    > ## R Data Analysis Examples: Canonical Correlation Analysis. UCLA: Statistical
    > ## Consulting Group. from http://www.ats.ucla.edu/stat/r/dae/canonical.htm
    > ## (accessed June 23, 2016).
    > # "Example 1. A researcher has collected data on three psychological variables, four
    > # academic variables (standardized test scores) and gender for 600 college freshman.
    > # She is interested in how the set of psychological variables relates to the academic
    > # variables and gender. In particular, the researcher is interested in how many
    > # dimensions (canonical variables) are necessary to understand the association between
    > # the two sets of variables."
    > mm <- read.csv("http://www.ats.ucla.edu/stat/data/mmreg.csv")
    Warning in file(file, "rt") :
     InternetOpenUrl failed: 'Eine Umleitungsanforderung ändert eine nicht sichere in eine sichere Verbindung.'
    Error in file(file, "rt") : cannot open the connection
    Calls: read.csv -> read.table -> file
    Execution halted
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.3.0
Check: examples
Result: ERROR
    Running examples in ‘GGally-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: ggduo
    > ### Title: ggduo - A ggplot2 generalized pairs plot for two columns sets of
    > ### a data.frame
    > ### Aliases: ggduo
    >
    > ### ** Examples
    >
    > # small function to display plots only if it's interactive
    > p_ <- GGally::print_if_interactive
    >
    > data(baseball, package = "plyr")
    >
    > # Keep players from 1990-1995 with at least one at bat
    > # Add how many singles a player hit
    > # (must do in two steps as X1b is used in calculations)
    > dt <- transform(
    + subset(baseball, year >= 1990 & year <= 1995 & ab > 0),
    + X1b = h - X2b - X3b - hr
    + )
    > # Add
    > # the player's batting average,
    > # the player's slugging percentage,
    > # and the player's on base percentage
    > # Make factor a year, as each season is discrete
    > dt <- transform(
    + dt,
    + batting_avg = h / ab,
    + slug = (X1b + 2*X2b + 3*X3b + 4*hr) / ab,
    + on_base = (h + bb + hbp) / (ab + bb + hbp),
    + year = as.factor(year)
    + )
    >
    >
    > pm <- ggduo(
    + dt,
    + c("year", "g", "ab", "lg"),
    + c("batting_avg", "slug", "on_base"),
    + mapping = ggplot2::aes(color = lg)
    + )
    > # Prints, but
    > # there is severe over plotting in the continuous plots
    > # the labels could be better
    > # want to add more hitting information
    > p_(pm)
    >
    >
    > # Make a fake column that will be calculated when printing
    > dt$hit_type <- paste("hit_type:", seq_len(nrow(dt)))
    >
    > display_hit_type <- function(plot_fn, is_ratio) {
    + function(data, mapping, ...) {
    + # change the color aesthetic to fill aesthetic
    + mapping <- mapping_color_to_fill(mapping)
    +
    + # If the y varaible is not 'hit_type', continue like normal
    + if (deparse(mapping$y) != "hit_type") {
    + p <- plot_fn(data, mapping, ...)
    + return(p)
    + }
    +
    + # Capture any extra column names needed
    + extra_columns <- unname(unlist(lapply(
    + mapping[! names(mapping) %in% c("x", "y")],
    + deparse
    + )))
    + extra_columns <- extra_columns[extra_columns %in% colnames(data)]
    +
    + x_name <- deparse(mapping$x)
    +
    + # get the types of hits
    + hit_types <- c("X1b", "X2b", "X3b", "hr")
    + hit_names <- c("single", "double", "tripple", "home\nrun")
    + if (is_ratio) {
    + hit_types <- rev(hit_types)
    + hit_names <- rev(hit_names)
    + }
    +
    + # retrieve the columns and rename them
    + data <- data[, c(x_name, hit_types, extra_columns)]
    + colnames(data) <- c(x_name, hit_names, extra_columns)
    +
    + # melt the data to get the counts of the unique hit occurances
    + dt_melt <- reshape::melt.data.frame(data, id = c(x_name, extra_columns))
    + dt_value <- dt_melt$value
    +
    + # Make a new data.frame with all the necessary variables repeated
    + dt_ratio <- data.frame(variable = logical(sum(dt_value)))
    + for (col in c(x_name, "variable", extra_columns)) {
    + dt_ratio[[col]] <- rep(dt_melt[[col]], dt_value)
    + }
    +
    + # copy the old mapping and overwrite the x and y values
    + mapping_ratio <- mapping
    + mapping_ratio[c("x", "y")] <- ggplot2::aes_string(x = x_name, y = "variable")
    +
    + # make ggplot2 object!
    + plot_fn(dt_ratio, mapping_ratio, ...)
    + }
    + }
    >
    >
    > display_hit_type_combo <- display_hit_type(ggally_facethist, FALSE)
    > display_hit_type_discrete <- display_hit_type(ggally_ratio, TRUE)
    >
    > # remove the strips, as the same information is displayed in the bottom axis area
    > pm <- ggduo(
    + dt,
    + c("year", "g", "ab", "lg"),
    + c("batting_avg", "slug", "on_base", "hit_type"),
    + columnLabelsX = c("year", "player game count", "player at bat count", "league"),
    + columnLabelsY = c("batting avg", "slug %", "on base %", "hit type"),
    + title = "Baseball Hitting Stats from 1990-1995",
    + mapping = ggplot2::aes(color = lg),
    + types = list(
    + # change the shape and add some transparency to the points
    + continuous = wrap("smooth_loess", alpha = 0.50, shape = "+"),
    + # all combinations that are continuous horizontally should have a binwidth of 15
    + comboHorizontal = wrap(display_hit_type_combo, binwidth = 15),
    + # the ratio plot should have a black border around the rects of size 0.15
    + discrete = wrap(display_hit_type_discrete, color = "black", size = 0.15)
    + ),
    + showStrips = FALSE, cardinality_threshold = NULL
    + );
    >
    > p_(pm)
    >
    >
    >
    > # Example derived from:
    > ## R Data Analysis Examples: Canonical Correlation Analysis. UCLA: Statistical
    > ## Consulting Group. from http://www.ats.ucla.edu/stat/r/dae/canonical.htm
    > ## (accessed June 23, 2016).
    > # "Example 1. A researcher has collected data on three psychological variables, four
    > # academic variables (standardized test scores) and gender for 600 college freshman.
    > # She is interested in how the set of psychological variables relates to the academic
    > # variables and gender. In particular, the researcher is interested in how many
    > # dimensions (canonical variables) are necessary to understand the association between
    > # the two sets of variables."
    > mm <- read.csv("http://www.ats.ucla.edu/stat/data/mmreg.csv")
    > colnames(mm) <- c("Control", "Concept", "Motivation", "Read", "Write", "Math",
    + "Science", "Sex")
    Error in names(x) <- value :
     'names' attribute [8] must be the same length as the vector [1]
    Calls: colnames<-
    Execution halted
Flavors: r-patched-solaris-sparc, r-patched-solaris-x86

Version: 1.3.0
Check: package dependencies
Result: ERROR
    Package suggested but not available for checking: ‘packagedocs’
    
    VignetteBuilder package required for checking but not installed: ‘packagedocs’
    
    See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’
    manual.
Flavor: r-release-osx-x86_64