R/tests_t.R
In oislen/BuenaVista: Functions for Everyday Data Science Tasks
#' @title Performs One Sample and Two Sample T-tests on a given dataset
#'
#' @description Performs One Sample and Two Sample T-tests on a given dataset.
#' The data can be a mixture of numric and factor variables.
#' The results are outputed as a data frame.
#' Furthermore the results an be saved as .csv file to a specified directory.
#'
#' @param dataset A dataset on which T-tests are performed.
#'
#' @param y_index An integer value, the column index of the response variable, the default is NULL.
#'
#' @param y_name A character value, the column name of the response variable, the default is NULL.
#'
#' @param mu An numeric value specifying the mean.
#'
#' @param alternative The type of hypothesis being tested; two.sided, greater, less.
#' The default is "two.sided"
#'
#' @param conf.level The level of confidence used in the t-test, default is 0.95
#'
#' @param paired Logical value indicating a paired t-test
#'
#' @param var.equal Logical value indicating the two tested variables have equal variance
#'
#' @param file_name A character object indicating the file name when saving the data frame.
#' The default is NULL.
#' The name must include the .csv suffixs.
#'
#' @param directory A character object specifying the directory where the data frame is to be saved as a .csv file.
#' The default is NULL.
#'
#' @return Outputs the T-test information as a data frame.
#'
#' @export
#'
#' @seealso \code{\link{tests_chisq}}, \code{\link{tests_cors}}, \code{\link{tests_ks}}, \code{\link{tests_norm}}, \code{\link{tests_proptest}}, \code{\link{tests_var}}, \code{\link{tests_wilcoxon}}
#'
#' @keywords T-tests
#'
#' @examples
#' #-- Example Lung Capacity Data --#
#'
#' # Perform T-tests on the entire dataset
#' tests_t(dataset = lungcap)
#'
#' # Perform T-tests in relation to the second column
#' tests_t(dataset = lungcap, y_index = 2)
#'
#' # Perform T-tests in relation to the 'Age' column
#' tests_t(dataset = lungcap, y_name = "Age")
#'
tests_t <- function(dataset,
y_index = NULL,
y_name = NULL,
mu = NULL,
alternative = c("two.sided", "greater", "less"),
conf.level = 0.95,
paired = FALSE,
var.equal = FALSE,
file_name = NULL,
directory = NULL)
{
#------------------------------------------------------------------------------#
# When y_index = NULL and y_name = NULL #
#------------------------------------------------------------------------------#
if(is.null(y_index) & is.null(y_name)){
# Confirm correct choice for alternative
alternative <- match.arg(alternative)
# Convert the dataset set to a data frame
dataset <- as.data.frame(dataset)
# the number of numeric columns in the dataset
n_cols <- sum(sapply(X = dataset, FUN = function(x) is.factor(x))) - 1
# First create a dataframe to store the relevent t-test data
ttestdf <- as.data.frame(matrix(nrow = ((n_cols)^2 - (n_cols)) / 2,
ncol = 7))
# rename the columns of the data frame
colnames(ttestdf) <- c("Xi", "Xj", "Xim", "Xjm",
"TT Stat", "TT P.V.", "Ha")
# r represents the row index and will be used to input the relevent data
r = 1
#--------------------------------------------------------------#
# When mu = NULL #
#--------------------------------------------------------------#
if(is.null(mu)){
for (i in 1:(ncol(dataset))) {
j = i + 1
while (j <= ncol(dataset)) {
if((is.numeric(dataset[,i]) & is.numeric(dataset[,j]))) {
# Save the variables name being tested
ttestdf[r,1] <- colnames(dataset)[i]
ttestdf[r,2] <- colnames(dataset)[j]
# Save the means of the variables
ttestdf[r,3] <- mean(x = dataset[,i],
na.rm = TRUE)
ttestdf[r,4] <- mean(x = dataset[,j],
na.rm = TRUE)
# Perform the T-Test
TT <- t.test(x = dataset[,i],
y = dataset[,j],
alternative = alternative,
conf.level = conf.level,
paired = paired,
var.equal = var.equal)
# Extract the TT Stat
ttestdf[r,5] <- round(TT$statistic,
digits = 4)
# Extract the TT P-Vlaue
ttestdf[r,6] <- round(TT$p.value,
digits = 4)
# Enter "Ha"
ttestdf[r,7] <- alternative
# update the r index
r = r + 1
}
# update j
j = j + 1
}
}
# Remove the incomplete cases
ttestdf <- ttestdf[complete.cases(ttestdf[,]), ]
#---------------------------------------------------------------#
# When mu != NULL #
#---------------------------------------------------------------#
} else if(!is.null(mu)){
for (i in 1:ncol(dataset)) {
if(is.numeric(dataset[,i])) {
# Save the variables name being tested
ttestdf[r,1] <- colnames(dataset)[i]
ttestdf[r,2] <- "Mu"
# Save the means of the variables
ttestdf[r,3] <- mean(x = dataset[,i],
na.rm = TRUE)
ttestdf[r,4] <- mu
# Perform the T-Test
TT <- t.test(x = dataset[,i],
mu = mu ,
alternative = alternative,
conf.level = conf.level)
# Extract the TT Stat
ttestdf[r,5] <- round(TT$statistic,
digits = 4)
# Extract the TT P-Vlaue
ttestdf[r,6] <- round(TT$p.value,
digits = 4)
# Enter "Ha"
ttestdf[r,7] <- alternative
# update the r index
r = r + 1
}
}
# Remove the incomplete cases
ttestdf <- ttestdf[complete.cases(ttestdf[,]), ]
}
#-------------------------------------------------------------------------------#
# When y_index != NULL or y_name != NULL #
#-------------------------------------------------------------------------------#
} else if(!is.null(y_index) | !is.null(y_name)){
if(!is.null(y_name)){
# find the column index of the given response name
y_index = which(colnames(dataset) == y_name)
}
# Confirm correct choice for alternative
alternative <- match.arg(alternative)
# Convert the dataset set to a data frame
dataset <- as.data.frame(dataset)
# extract the test data
test_data <- dataset[,-y_index]
# the number of numeric columns in the dataset
n_cols <- sum(sapply(X = dataset, FUN = function(x) is.factor(x))) - 1
# First create a dataframe to store the relevent t-test data
ttestdf <- as.data.frame(matrix(nrow = ((n_cols)^2 - (n_cols)) / 2,
ncol = 7))
# rename the columns of the data frame
colnames(ttestdf) <- c("Xi", "Y", "Xim", "Xjm",
"TT Stat", "TT P.V.", "Ha")
# r represents the row index and will be used to input the relevent data
r = 1
#--------------------------------------------------------------#
# When mu = NULL #
#--------------------------------------------------------------#
if(is.null(mu)){
for (i in 1:(ncol(test_data))) {
if((is.numeric(test_data[,i]) & is.numeric(dataset[,y_index]))) {
# Save the variables name being tested
ttestdf[r,1] <- colnames(test_data)[i]
ttestdf[r,2] <- colnames(dataset)[y_index]
# Save the means of the variables
ttestdf[r,3] <- mean(x = test_data[,i],
na.rm = TRUE)
ttestdf[r,4] <- mean(x = dataset[,y_index],
na.rm = TRUE)
# Perform the T-Test
TT <- t.test(x = test_data[,i],
y = dataset[,y_index],
alternative = alternative,
conf.level = conf.level,
paired = paired,
var.equal = var.equal)
# Extract the TT Stat
ttestdf[r,5] <- round(TT$statistic,
digits = 4)
# Extract the TT P-Vlaue
ttestdf[r,6] <- round(TT$p.value,
digits = 4)
# Enter "Ha"
ttestdf[r,7] <- alternative
# update the r index
r = r + 1
}
}
# Remove the incomplete cases
ttestdf <- ttestdf[complete.cases(ttestdf[,]), ]
#---------------------------------------------------------------#
# When mu != NULL #
#---------------------------------------------------------------#
} else if(!is.null(mu)){
if(is.numeric(dataset[,y_index])) {
# Save the variables name being tested
ttestdf[1,1] <- colnames(dataset)[y_index]
ttestdf[1,2] <- "Mu"
# Save the means of the variables
ttestdf[1,3] <- mean(x = dataset[,y_index],
na.rm = TRUE)
ttestdf[1,4] <- mu
# Perform the T-Test
TT <- t.test(x = dataset[,y_index],
mu = mu ,
alternative = alternative,
conf.level = conf.level)
# Extract the TT Stat
ttestdf[1,5] <- round(TT$statistic,
digits = 4)
# Extract the TT P-Vlaue
ttestdf[1,6] <- round(TT$p.value,
digits = 4)
# Enter "Ha"
ttestdf[1,7] <- alternative
}
# Remove the incomplete cases
ttestdf <- ttestdf[complete.cases(ttestdf[,]), ]
}
}
# Write the data frame to the specified directory
if(!is.null(directory)) {
write.csv(x = ttestdf,
file = paste(directory, "/", file_name, sep = ""),
row.names = F)
}
# return the ttestdf
return(ttestdf)
}
oislen/BuenaVista documentation built on May 16, 2019, 8:12 p.m.
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#' @title Performs One Sample and Two Sample T-tests on a given dataset
#'
#' @description Performs One Sample and Two Sample T-tests on a given dataset.
#' The data can be a mixture of numric and factor variables.
#' The results are outputed as a data frame.
#' Furthermore the results an be saved as .csv file to a specified directory.
#'
#' @param dataset A dataset on which T-tests are performed.
#'
#' @param y_index An integer value, the column index of the response variable, the default is NULL.
#'
#' @param y_name A character value, the column name of the response variable, the default is NULL.
#'
#' @param mu An numeric value specifying the mean.
#'
#' @param alternative The type of hypothesis being tested; two.sided, greater, less.
#' The default is "two.sided"
#'
#' @param conf.level The level of confidence used in the t-test, default is 0.95
#'
#' @param paired Logical value indicating a paired t-test
#'
#' @param var.equal Logical value indicating the two tested variables have equal variance
#'
#' @param file_name A character object indicating the file name when saving the data frame.
#' The default is NULL.
#' The name must include the .csv suffixs.
#'
#' @param directory A character object specifying the directory where the data frame is to be saved as a .csv file.
#' The default is NULL.
#'
#' @return Outputs the T-test information as a data frame.
#'
#' @export
#'
#' @seealso \code{\link{tests_chisq}}, \code{\link{tests_cors}}, \code{\link{tests_ks}}, \code{\link{tests_norm}}, \code{\link{tests_proptest}}, \code{\link{tests_var}}, \code{\link{tests_wilcoxon}}
#'
#' @keywords T-tests
#'
#' @examples
#' #-- Example Lung Capacity Data --#
#'
#' # Perform T-tests on the entire dataset
#' tests_t(dataset = lungcap)
#'
#' # Perform T-tests in relation to the second column
#' tests_t(dataset = lungcap, y_index = 2)
#'
#' # Perform T-tests in relation to the 'Age' column
#' tests_t(dataset = lungcap, y_name = "Age")
#'
tests_t <- function(dataset,
y_index = NULL,
y_name = NULL,
mu = NULL,
alternative = c("two.sided", "greater", "less"),
conf.level = 0.95,
paired = FALSE,
var.equal = FALSE,
file_name = NULL,
directory = NULL)
{
#------------------------------------------------------------------------------#
# When y_index = NULL and y_name = NULL #
#------------------------------------------------------------------------------#
if(is.null(y_index) & is.null(y_name)){
# Confirm correct choice for alternative
alternative <- match.arg(alternative)
# Convert the dataset set to a data frame
dataset <- as.data.frame(dataset)
# the number of numeric columns in the dataset
n_cols <- sum(sapply(X = dataset, FUN = function(x) is.factor(x))) - 1
# First create a dataframe to store the relevent t-test data
ttestdf <- as.data.frame(matrix(nrow = ((n_cols)^2 - (n_cols)) / 2,
ncol = 7))
# rename the columns of the data frame
colnames(ttestdf) <- c("Xi", "Xj", "Xim", "Xjm",
"TT Stat", "TT P.V.", "Ha")
# r represents the row index and will be used to input the relevent data
r = 1
#--------------------------------------------------------------#
# When mu = NULL #
#--------------------------------------------------------------#
if(is.null(mu)){
for (i in 1:(ncol(dataset))) {
j = i + 1
while (j <= ncol(dataset)) {
if((is.numeric(dataset[,i]) & is.numeric(dataset[,j]))) {
# Save the variables name being tested
ttestdf[r,1] <- colnames(dataset)[i]
ttestdf[r,2] <- colnames(dataset)[j]
# Save the means of the variables
ttestdf[r,3] <- mean(x = dataset[,i],
na.rm = TRUE)
ttestdf[r,4] <- mean(x = dataset[,j],
na.rm = TRUE)
# Perform the T-Test
TT <- t.test(x = dataset[,i],
y = dataset[,j],
alternative = alternative,
conf.level = conf.level,
paired = paired,
var.equal = var.equal)
# Extract the TT Stat
ttestdf[r,5] <- round(TT$statistic,
digits = 4)
# Extract the TT P-Vlaue
ttestdf[r,6] <- round(TT$p.value,
digits = 4)
# Enter "Ha"
ttestdf[r,7] <- alternative
# update the r index
r = r + 1
}
# update j
j = j + 1
}
}
# Remove the incomplete cases
ttestdf <- ttestdf[complete.cases(ttestdf[,]), ]
#---------------------------------------------------------------#
# When mu != NULL #
#---------------------------------------------------------------#
} else if(!is.null(mu)){
for (i in 1:ncol(dataset)) {
if(is.numeric(dataset[,i])) {
# Save the variables name being tested
ttestdf[r,1] <- colnames(dataset)[i]
ttestdf[r,2] <- "Mu"
# Save the means of the variables
ttestdf[r,3] <- mean(x = dataset[,i],
na.rm = TRUE)
ttestdf[r,4] <- mu
# Perform the T-Test
TT <- t.test(x = dataset[,i],
mu = mu ,
alternative = alternative,
conf.level = conf.level)
# Extract the TT Stat
ttestdf[r,5] <- round(TT$statistic,
digits = 4)
# Extract the TT P-Vlaue
ttestdf[r,6] <- round(TT$p.value,
digits = 4)
# Enter "Ha"
ttestdf[r,7] <- alternative
# update the r index
r = r + 1
}
}
# Remove the incomplete cases
ttestdf <- ttestdf[complete.cases(ttestdf[,]), ]
}
#-------------------------------------------------------------------------------#
# When y_index != NULL or y_name != NULL #
#-------------------------------------------------------------------------------#
} else if(!is.null(y_index) | !is.null(y_name)){
if(!is.null(y_name)){
# find the column index of the given response name
y_index = which(colnames(dataset) == y_name)
}
# Confirm correct choice for alternative
alternative <- match.arg(alternative)
# Convert the dataset set to a data frame
dataset <- as.data.frame(dataset)
# extract the test data
test_data <- dataset[,-y_index]
# the number of numeric columns in the dataset
n_cols <- sum(sapply(X = dataset, FUN = function(x) is.factor(x))) - 1
# First create a dataframe to store the relevent t-test data
ttestdf <- as.data.frame(matrix(nrow = ((n_cols)^2 - (n_cols)) / 2,
ncol = 7))
# rename the columns of the data frame
colnames(ttestdf) <- c("Xi", "Y", "Xim", "Xjm",
"TT Stat", "TT P.V.", "Ha")
# r represents the row index and will be used to input the relevent data
r = 1
#--------------------------------------------------------------#
# When mu = NULL #
#--------------------------------------------------------------#
if(is.null(mu)){
for (i in 1:(ncol(test_data))) {
if((is.numeric(test_data[,i]) & is.numeric(dataset[,y_index]))) {
# Save the variables name being tested
ttestdf[r,1] <- colnames(test_data)[i]
ttestdf[r,2] <- colnames(dataset)[y_index]
# Save the means of the variables
ttestdf[r,3] <- mean(x = test_data[,i],
na.rm = TRUE)
ttestdf[r,4] <- mean(x = dataset[,y_index],
na.rm = TRUE)
# Perform the T-Test
TT <- t.test(x = test_data[,i],
y = dataset[,y_index],
alternative = alternative,
conf.level = conf.level,
paired = paired,
var.equal = var.equal)
# Extract the TT Stat
ttestdf[r,5] <- round(TT$statistic,
digits = 4)
# Extract the TT P-Vlaue
ttestdf[r,6] <- round(TT$p.value,
digits = 4)
# Enter "Ha"
ttestdf[r,7] <- alternative
# update the r index
r = r + 1
}
}
# Remove the incomplete cases
ttestdf <- ttestdf[complete.cases(ttestdf[,]), ]
#---------------------------------------------------------------#
# When mu != NULL #
#---------------------------------------------------------------#
} else if(!is.null(mu)){
if(is.numeric(dataset[,y_index])) {
# Save the variables name being tested
ttestdf[1,1] <- colnames(dataset)[y_index]
ttestdf[1,2] <- "Mu"
# Save the means of the variables
ttestdf[1,3] <- mean(x = dataset[,y_index],
na.rm = TRUE)
ttestdf[1,4] <- mu
# Perform the T-Test
TT <- t.test(x = dataset[,y_index],
mu = mu ,
alternative = alternative,
conf.level = conf.level)
# Extract the TT Stat
ttestdf[1,5] <- round(TT$statistic,
digits = 4)
# Extract the TT P-Vlaue
ttestdf[1,6] <- round(TT$p.value,
digits = 4)
# Enter "Ha"
ttestdf[1,7] <- alternative
}
# Remove the incomplete cases
ttestdf <- ttestdf[complete.cases(ttestdf[,]), ]
}
}
# Write the data frame to the specified directory
if(!is.null(directory)) {
write.csv(x = ttestdf,
file = paste(directory, "/", file_name, sep = ""),
row.names = F)
}
# return the ttestdf
return(ttestdf)
}
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