| Title: | Data Analysis and Automated R Notebook Generation |
|---|---|
| Description: | Easy data analysis and quality checks which are commonly used in data science. It combines the tabular and graphical visualization for easier usability. This package also creates an R Notebook with detailed data exploration with one function call. The notebook can be made interactive. |
| Authors: | Apurv Priyam [aut, cre] |
| Maintainer: | Apurv Priyam <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0.1 |
| Built: | 2025-01-31 03:39:16 UTC |
| Source: | https://github.com/cran/analyzer |
anderson.test performs Anderson-Darling test
anderson.test(x)anderson.test(x)
x |
a numeric vector. Length must be greater than 7. Missing values are allowed. |
Performs the Anderson-Darling test for the composite hypothesis of normality, see e.g. Thode (2002, Sec. 5.1.4).
A list with following elements:
the value of Anderson-Darling test statistic
p-value of the test
Test name
Vector name
anderson.test(mtcars$mpg)anderson.test(mtcars$mpg)
association finds association among all the variables in the data.
association( tb, categorical = NULL, method1 = c("auto", "pearson", "kendall", "spearman"), method3 = c("auto", "parametric", "non-parametric"), methodMats = NULL, use = "everything", normality_test_method = c("ks", "anderson", "shapiro"), normality_test_pval = 0.05, ... )association( tb, categorical = NULL, method1 = c("auto", "pearson", "kendall", "spearman"), method3 = c("auto", "parametric", "non-parametric"), methodMats = NULL, use = "everything", normality_test_method = c("ks", "anderson", "shapiro"), normality_test_pval = 0.05, ... )
tb |
tabular data |
categorical |
a vector specifying the names of categorical (character, factor) columns |
method1 |
method for association between continuous-continuous
variables. values can be |
method3 |
method for association between continuous-categorical
variables. Values can be |
methodMats |
This parameter can be used to define the methods for
calculating correlation and association at variables pair level. The input is
a square data.frame of dimension - number of columns in
Default is NULL. In that case the method used for calculating correlation and association will be the inputs from parameters. This parameter can also tale some other values. See example for more details. But its advisable to use like mentioned above. |
use |
an optional character string giving a method for computing association in the presence of missing values. This must be (complete or an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs". If use is "everything", NAs will propagate conceptually, i.e., a resulting value will be NA whenever one of its contributing observations is NA. If use is "all.obs", then the presence of missing observations will produce an error. If use is "complete.obs" then missing values are handled by case wise deletion (and if there are no complete cases, that gives an error). "na.or.complete" is the same unless there are no complete cases, that gives NA |
normality_test_method |
method for normality test for a variable.
Values can be |
normality_test_pval |
significance level for normality tests. Default is 0.05 |
... |
other parameters passed to |
This function calculates association value in three categories -
between continuous variables (using CCassociation function)
between categorical variables (using QQassociation function)
between continuous and categorical variables (using CQassociation
function)
For more details, look at the individual documentation of
CCassociation, QQassociation,
CQassociation
A list of three tables:
correlation among all the continuous variables
Table containing p-value for the correlation test
Cramer's V value among all the categorical variables
Chi Sq test p-value
association value among continuous and categorical variables
A data.frome showing the method used for all pairs of variables
CCassociation for Correlation between Continuous variables,
QQassociation for Association between Categorical variables,
CQassociation for Association between Continuous-Categorical
variables
tb <- mtcars tb$cyl <- as.factor(tb$cyl) tb$vs <- as.factor(tb$vs) out <- association(tb, categorical = c("cyl", "vs")) # To use the methodMats parameter, create a matrix like this methodMats <- out$method_used # the values can be changed as per requirement # NOTE: in addition to the values from parameters method1 and method3, # the values in methodMats can also be the values returned by # association function. But its advisable to use the options from # method1 and method3 arguements methodMats["mpg", "disp"] <- methodMats["disp", "mpg"] <- "spearman" out <- association(tb, categorical = c("cyl", "vs"), methodMats = methodMats) rm(tb)tb <- mtcars tb$cyl <- as.factor(tb$cyl) tb$vs <- as.factor(tb$vs) out <- association(tb, categorical = c("cyl", "vs")) # To use the methodMats parameter, create a matrix like this methodMats <- out$method_used # the values can be changed as per requirement # NOTE: in addition to the values from parameters method1 and method3, # the values in methodMats can also be the values returned by # association function. But its advisable to use the options from # method1 and method3 arguements methodMats["mpg", "disp"] <- methodMats["disp", "mpg"] <- "spearman" out <- association(tb, categorical = c("cyl", "vs"), methodMats = methodMats) rm(tb)
CCassociation finds correlation between all the variables in data
with only numeric columns
CCassociation( numtb, use = "everything", normality_test_method, normality_test_pval, method1 = c("auto", "pearson", "kendall", "spearman"), methodMat1 = NULL, methods_used )CCassociation( numtb, use = "everything", normality_test_method, normality_test_pval, method1 = c("auto", "pearson", "kendall", "spearman"), methodMat1 = NULL, methods_used )
numtb |
a data frame with all the numerical columns. This should have at least two columns |
use |
an optional character string giving a method for computing association in the presence of missing values. This must be (complete or an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs". If use is "everything", NAs will propagate conceptually, i.e., a resulting value will be NA whenever one of its contributing observations is NA. If use is "all.obs", then the presence of missing observations will produce an error. If use is "complete.obs" then missing values are handled by case wise deletion (and if there are no complete cases, that gives an error). "na.or.complete" is the same unless there are no complete cases, that gives NA |
normality_test_method |
method for normality test for a variable.
Values can be |
normality_test_pval |
significance level for normality tests. Default is 0.05 |
method1 |
method for association between continuous-continuous
variables. values can be |
methodMat1 |
method dataframe like methodMats from the function |
methods_used |
a square data.frame which will store the type of association used between the variables. Dimension will be number of variables * number of variables. |
This function calls cor function to calculate the correlation values.
The difference is that this doesn't take method as parameter, instead it
decides the methods itself using normality tests. If the variables satisfy
the assumption of Pearson correlation, then pearson correlation is calculated.
Otherwise Spearman is calculated. To learn more, check the
cor
a list of two tables with number of rows and column equal to number
of columns in numtb:
Table containing correlation values
Table containing p-value for the correlation test
association for association between any type of variables,
QQassociation for Association between Categorical variables,
CQassociation for Association between Continuous-Categorical
variables
consoleBoxplot prints the boxplot on console.
consoleBoxplot(x)consoleBoxplot(x)
x |
a numeric vector of length at least 3 |
This function is for the numeric vectors. It prints a boxplot in a single line on the console. It automatically adjusts for the width of the console. The input vector must have a length of three, otherwise the function will throw a warning and not print any plot.
In case of any potential outliers (based on 1.5*IQR criteria), this wil
give a warning.
This function is used in the explainer.
prints a boxplot on the console which has:
| at start and end means the minimum and maximum value respectively
<==*==> The IQR region
* shows the median
... everything else in between
Gives a warning of potential outliers (if present)
consoleBoxplot(mtcars$mpg)consoleBoxplot(mtcars$mpg)
CQassociation finds Association measure between one
categorical and one continuous variable.
CQassociation( numtb, factb, method3 = c("auto", "parametric", "non-parametric"), use = "everything", normality_test_method = c("ks", "anderson", "shapiro"), normality_test_pval, methodMat3 = NULL, methods_used )CQassociation( numtb, factb, method3 = c("auto", "parametric", "non-parametric"), use = "everything", normality_test_method = c("ks", "anderson", "shapiro"), normality_test_pval, methodMat3 = NULL, methods_used )
numtb |
a data frame with all the numerical columns. This should have at least two columns |
factb |
a data frame with all the categorical columns. This should have atleast two columns |
method3 |
method for association between continuous-categorical
variables. Values can be |
use |
an optional character string giving a method for computing association in the presence of missing values. This must be (complete or an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs". If use is "everything", NAs will propagate conceptually, i.e., a resulting value will be NA whenever one of its contributing observations is NA. If use is "all.obs", then the presence of missing observations will produce an error. If use is "complete.obs" then missing values are handled by case wise deletion (and if there are no complete cases, that gives an error). "na.or.complete" is the same unless there are no complete cases, that gives NA |
normality_test_method |
takes values as 'shapiro' or 'anderson'.
this parameter decides which test to perform for the normality test.
See details of |
normality_test_pval |
significance level for normality tests. Default is 0.05 |
methodMat3 |
method dataframe like methodMats from the function |
methods_used |
a square data.frame which will store the type of association used between the variables. Dimension will be number of variables * number of variables. |
This function measures the association between one categorical variable and one continuous variable present in different dataset. Two datasets are provided as input, one data has only numerical columns while other data has only categorical columns. This performs either t-test for the parametric case and 'Mann-Whitney’ test for the non-parametric case. If the method3 is passed as 'auto', the function defines the method itself based on different tests for equal variance and normality check which checks for assumptions for the t-test. If the assumptions are satisfied, then t-test (parametric) is performed, otherwise 'Mann-Whitney’ (non-parametric) test is performed.
a table with number of rows equal to number of columns in
numtb and number of columns equal to number of columns in
factb. Table containing p-values of performed test
norm_test_fun for normality test
association for association between any type of variables,
CCassociation for Association between Continuous (numeric)
variables,
QQassociation for Association between Categorical variables
This function is used by [plottr()] when independent variable is continuous. This function can be used as a template to define a custom function.
Cx(dat, xname, binwidth = NULL, inc.density = T, ...)Cx(dat, xname, binwidth = NULL, inc.density = T, ...)
dat |
a data.frame with only one column |
xname |
name of independent (x) variable in |
binwidth |
for the histograms (extra parameters can be added like this) |
inc.density |
Binary. True to include the density plot on histogram |
... |
required |
a grob of plot
This function is used by [plottr()] when both the dependent and independent variables are continuous. This function can be used as a template to define a custom function
CxCy(dat, xname, yname, ...)CxCy(dat, xname, yname, ...)
dat |
a data.frame with two columns (including the dependent) |
xname |
name of independent (x) variable in |
yname |
name of dependent (y) variable in |
... |
required |
a grob of plot
Generic function for printing the details of data. Based on the data type, this calls the appropriate method.
explainer(X, xname = NULL, ...)explainer(X, xname = NULL, ...)
X |
a data.frame or a vector |
xname |
name of the data to be printed. If missing then the
name of variable passed as |
... |
other parameters required for other methods of |
Current methods for explainer are for data.frame, numeric, integer,
character and factor vectors. To get the list of all available methods type
the command methods(explainer).
Prints the information on the console. For print information for the individual methods, see their documentation. Returns nothing.
# for numeric explainer(mtcars) explainer(mtcars$mpg) #same as explainer.numeric(mtcars$mpg) # for factor explainer(as.factor(mtcars$cyl)) #same as explainer.factor(as.factor(mtcars$cyl))# for numeric explainer(mtcars) explainer(mtcars$mpg) #same as explainer.numeric(mtcars$mpg) # for factor explainer(as.factor(mtcars$cyl)) #same as explainer.factor(as.factor(mtcars$cyl))
This is a explainer method for character vector.
## S3 method for class 'character' explainer(X, xname = NULL, ...)## S3 method for class 'character' explainer(X, xname = NULL, ...)
X |
a vector of character data type |
xname |
a placeholder for variable name |
... |
other parameters required |
This method removes all the missing values in x before computing the
summaries.
Prints the following information on console:
vector name
type
number of distinct values
number of missing values
a frequency table and histogram. If counts of all the factor levels are
less than half of length of X, then the histogram is scaled with
maximum of 50
alphabets <- sample(LETTERS[1:5], 50, replace = TRUE) explainer(alphabets) rm(alphabets)alphabets <- sample(LETTERS[1:5], 50, replace = TRUE) explainer(alphabets) rm(alphabets)
explainer shows detail of all the columns of the data
## S3 method for class 'data.frame' explainer(X, xname = NULL, ...)## S3 method for class 'data.frame' explainer(X, xname = NULL, ...)
X |
A data.frame |
xname |
variable name |
... |
parameters for explainer for other classes |
This function uses explainer on each column.
Prints details of the dataset which includes: dataset name,
type, number of columns, rows and unique rows. Also prints output of
explainer for all the columns. Returns nothing.
explainer(mtcars)explainer(mtcars)
This is a explainer method for factor vector.
## S3 method for class 'factor' explainer(X, xname = NULL, ...)## S3 method for class 'factor' explainer(X, xname = NULL, ...)
X |
a numeric (or integer) data type |
xname |
a placeholder for variable name |
... |
other parameters required |
This method removes all the missing values in x before computing the
summaries. This calls the method explainer.character
Prints the following information on console:
vector name
type
number of distinct values
number of missing values
a frequency table and histogram. If counts of all the factor levels are
less than half of length of X, then the histogram is scaled with
maximum of 50
alphabets <- as.factor(sample(LETTERS[1:5], 50, replace = TRUE)) explainer(alphabets) rm(alphabets)alphabets <- as.factor(sample(LETTERS[1:5], 50, replace = TRUE)) explainer(alphabets) rm(alphabets)
This is a explainer method for numeric vector.
## S3 method for class 'numeric' explainer( X, xname = NULL, include.numeric = NULL, round.digit = 2, quant.seq = seq(0, 1, 0.2), trim = 0.05, ... )## S3 method for class 'numeric' explainer( X, xname = NULL, include.numeric = NULL, round.digit = 2, quant.seq = seq(0, 1, 0.2), trim = 0.05, ... )
X |
a numeric (or integer) data type |
xname |
a placeholder for variable name |
include.numeric |
a vector having strings which is also required along with the default output. Can have values:
|
round.digit |
number of decimal places required in the output. |
quant.seq |
vector of fractions (0 to 1) for which the quantiles are
required |
trim |
the fraction (0 to 0.5) of observations to be trimmed from each
end of x before the mean is computed. Values of trim outside that range are
taken as the nearest endpoint. This only works if |
... |
other parameters required |
This method removes all the missing values in x before computing the
summaries.
Prints the following information on console:
vector name
type
number of distinct values
number of missing values
mean
sd (standard deviation)
median
quantiles based on quant.seq parameter
other information based on include.numeric
a box plot (only if number distinct numbers are > 2).
If counts of all the factor levels are less than half of length of
x, then the histogram is scaled with maximum of 50
?consoleBoxplot for how to read the table and histogram)
a frequency table and histogram (only if number of distinct
numbers are < 11)
(look at ?freqTable for how to read the table and histogram)
explainer(mtcars$mpg) explainer(mtcars$mpg, include.numeric = c('trimmed.means', 'skewness', 'kurtosis'), round.digit = 1, quant.seq = seq(0,1,0.1), trim = 0.05)explainer(mtcars$mpg) explainer(mtcars$mpg, include.numeric = c('trimmed.means', 'skewness', 'kurtosis'), round.digit = 1, quant.seq = seq(0,1,0.1), trim = 0.05)
freqTable prints a frequency table and histogram of a vector.
freqTable(Value, limit = NULL)freqTable(Value, limit = NULL)
Value |
a vector of any type |
limit |
Upper limit of the bars in histogram. Default is NULL, for which the function will automatically find the suitable limit. This value should be in fraction (between 0 to 1) |
This function works for all type of vector type. But calling freqTable
for vector with many unique values will print a very long table. If the
limit parameter is left blank, then the limit of
histogram is adjusted automatically and is shown at the end in brackets
(eg. 50
This function is used in the explainer.
Prints a table with columns
Value Value. Each row has a unique value in this table
Freq The frequency count of the Value
Proportion Proportion of the Value (= Freq / length(x))
This table is followed by a histogram with bars for each of the unique values present in the data.
freqTable(mtcars$cyl) freqTable(mtcars$mpg, limit = 0.08)freqTable(mtcars$cyl) freqTable(mtcars$mpg, limit = 0.08)
GenerateReport generates the markdown report in one command
GenerateReport( dtpath, catVars, yvar = NULL, model = "linReg", title = "Report", output_format = "html_document", output_dir = tempdir(), normality_test_method = "ks", interactive.plots = FALSE, include.vars = NULL )GenerateReport( dtpath, catVars, yvar = NULL, model = "linReg", title = "Report", output_format = "html_document", output_dir = tempdir(), normality_test_method = "ks", interactive.plots = FALSE, include.vars = NULL )
dtpath |
dataset path as a string |
catVars |
vector of categorical variables names |
yvar |
y variable name if present else |
model |
type of model - |
title |
Title of the generated report |
output_format |
output report format. |
output_dir |
Directory where the output files needs to be stored. |
normality_test_method |
method for normality test for a variable.
Values can be |
interactive.plots |
for interactive variable exploration |
include.vars |
include only these variables from the full data |
This function creates a rmarkdown report which can be converted to html or pdf format file.
creates a rmarkdown and html/pdf file. Returns the output directory
on successful run and FALSE in case of error
# Assigning the temporary folder using tempdir(). replace with required directory GenerateReport(dtpath = mtcars, catVars = c("cyl", "vs", "am", "gear"), yvar = "vs", model = "binClass", output_format = NULL, title = "Report", output_dir = tempdir(), # pass the output directory interactive.plots = FALSE) # set TRUE for interactive# Assigning the temporary folder using tempdir(). replace with required directory GenerateReport(dtpath = mtcars, catVars = c("cyl", "vs", "am", "gear"), yvar = "vs", model = "binClass", output_format = NULL, title = "Report", output_dir = tempdir(), # pass the output directory interactive.plots = FALSE) # set TRUE for interactive
kurtosis calculates the Kurtosis
kurtosis(x, na.rm = T)kurtosis(x, na.rm = T)
x |
a numeric vector, matrix or a data.frame |
na.rm |
(logical) Should missing values be removed? |
This function calculates the kurtosis of data which is a measure of the "tailedness" of the probability distribution of a real-valued random variable. Like skewness, kurtosis describes the shape of a probability distribution. The formula used is:
. This formula is the typical definition used in many older textbooks and wikipedia
returns a single value if x is a vector, otherwise a named
vector of size = ncol(x).
# for a single vector kurtosis(mtcars$mpg) # for a dataframe kurtosis(mtcars)# for a single vector kurtosis(mtcars$mpg) # for a dataframe kurtosis(mtcars)
Draws a horizontal line on console
linedivider(consolewidth = getOption("width"), st = "x")linedivider(consolewidth = getOption("width"), st = "x")
consolewidth |
a integer |
st |
a character or symbol of length to be used for creating the line |
Prints a horizontal line of width 'consolewidth'
linedivider(20)linedivider(20)
mergeAnalyzer analyzes the data drop after merge
mergeAnalyzer(x, y, round.digit = 2, ...)mergeAnalyzer(x, y, round.digit = 2, ...)
x |
left data to merge |
y |
right data to merge |
round.digit |
integer indicating the number of decimal places to be used |
... |
other parameters needs to be passed to merge function |
Prints the summary of data retained after merge and returns the merged data. This function uses data.table (if the package is installed) for faster data merge
Returns merged data with same class as that of input data. Prints summary of data retained after merging. The summary has 6 columns:
Column: Number of rows and union of column names of numeric columns in both the data
x, y: Sum of columns in both table
Merged: Sum of columns in merged data
remainingWRTx: ratio of remaining data in merged data after merging. example - 0.5 means that 50 of inner join). 1.5 means value became 150 duplicates present in data
remainingWRTy: same as above, but for y table
# Creating two tables to merge A <- data.frame(id = c("A", "A", "B", "D", "B"), valA = c(30, 83, 45, 2, 58)) B <- data.frame(id = c("A", "C", "A", "B", "C", "C"), valB = c(10, 20, 30, 40, 50, 60)) mergeAnalyzer(A, B, allow.cartesian = TRUE, all = FALSE)# Creating two tables to merge A <- data.frame(id = c("A", "A", "B", "D", "B"), valA = c(30, 83, 45, 2, 58)) B <- data.frame(id = c("A", "C", "A", "B", "C", "C"), valB = c(10, 20, 30, 40, 50, 60)) mergeAnalyzer(A, B, allow.cartesian = TRUE, all = FALSE)
norm_test_fun checks for the normality assumption
norm_test_fun(x, method = "anderson", pval = 0.05, xn = "x", bin = FALSE)norm_test_fun(x, method = "anderson", pval = 0.05, xn = "x", bin = FALSE)
x |
a numeric vector |
method |
|
pval |
significance level for normality tests. Default is 0.05 |
xn |
vector name |
bin |
TRUE if only TRUE/FALSE is required |
This function checks for normality assumption using
shapiro, Kolmogorov-Smirnov or Anderson Darling test.
If the parameter bin is TRUE, then TRUE is returned
if vector is normal, otherwise FALSE.
The significance level is passed through the parameter
pval
Logical TRUE/FALSE based on the performed test and pval.
If the vector follows the normality assumption, then TRUE is returned
anderson.test for Anderson Darling test
norm_test_fun(mtcars$mpg) norm_test_fun(mtcars$mpg, method = "shapiro", pval = 0.05, xn = "mpg", bin = TRUE)norm_test_fun(mtcars$mpg) norm_test_fun(mtcars$mpg, method = "shapiro", pval = 0.05, xn = "mpg", bin = TRUE)
This function plots the plot generated by the library analyzer
## S3 method for class 'analyzerPlot' plot(x, ...)## S3 method for class 'analyzerPlot' plot(x, ...)
x |
a plot of class |
... |
extra arguments if required |
Displays the plot
# creating the plot p <- plottr(mtcars) plot(p$mpg)# creating the plot p <- plottr(mtcars) plot(p$mpg)
plotNA returns a grob visualizing the missing values in data
plotNA(tb, order = T, limit = T, add_percent = T, row.level = F)plotNA(tb, order = T, limit = T, add_percent = T, row.level = F)
tb |
a data.frame |
order |
(logical) Whether to order the variables based on missing values in plot |
limit |
(logical) Whether to limit the plot to maximum missing value. FALSE means the limit of axis will be [0, nrow(tb)] |
add_percent |
(logical) Whether to add percent as data labels on bar plot |
row.level |
(logical) Whether to create plot at rows and variables level |
This is a function which helps in visualizing the missing values in data using plots. By default a bar plot is generated which shows the count of missing values in each variable.
If order is set as TRUE then the bars are arranged in order of
missing values. If limit is set as TRUE then limit of axis is
set to [0, nrow(tb)]. If add_percent is set as TRUE then
percent is added as text to the bars. If row.level is set to
TRUE then an additional plot is generated which shows which rows have
missing values and in which variable (reshape2
(https://CRAN.R-project.org/package=reshape2) library
is required for this).
This function returns a grob of class 'analyzePlot' which has a bar
plot showing the count of missing value for each variable. order,
limit, add_percent can be used to modify the bar plot. An additional plot
will be created and added to the grob if row.level is set as
TRUE
p <- plotNA(airquality) # function to show the 'analyzerPlot' class plot plot(p) p1 <- plotNA(airquality, order = FALSE) plot(p1)p <- plotNA(airquality) # function to show the 'analyzerPlot' class plot plot(p) p1 <- plotNA(airquality, order = FALSE) plot(p1)
plottr can be used to create plots for
all the variables in a dataframe or any one vector. The output is a list of
plots for each variable of class 'analyzerPlot'
plottr( tb, yvar = NULL, xclasses = NULL, yclass = NULL, printall = F, callasfactor = 1, FUN1 = Cx, FUN2 = Qx, FUN3 = CxCy, FUN4 = QxCy, FUN5 = CxQy, FUN6 = QxQy, ... )plottr( tb, yvar = NULL, xclasses = NULL, yclass = NULL, printall = F, callasfactor = 1, FUN1 = Cx, FUN2 = Qx, FUN3 = CxCy, FUN4 = QxCy, FUN5 = CxQy, FUN6 = QxQy, ... )
tb |
a data.frame or a vector. If |
yvar |
a string showing the response (dependent) variable name. Can be
|
xclasses |
a vector of length |
yclass |
class of response variable. Can be |
printall |
(logical) Whether user wants to show the plots. Setting this
as |
callasfactor |
minimum unique values needed for |
FUN1 |
an user-defined function for plotting 1 variables when the variable is Continuous. See details for more details on how to define these variables |
FUN2 |
same as FUN1 but for categorical variable |
FUN3 |
an user defined function for plotting 2 variables when both the independent variable (x) and dependent variable (y) are Continuous |
FUN4 |
same as FUN3, but when independent variable (x) is Categorical and dependent variable (y) is Continuous |
FUN5 |
same as FUN3, but when independent variable (x) is Continuous and dependent variable (y) is Categorical |
FUN6 |
same as FUN3, but when both the independent variable (x) and dependent variable (y) are Categorical |
... |
extra arguments passed to functions FUN1-FUN6 |
This is a function which helps in understanding the data through multiple
visualizations. This works either for a data.frame having multiple variables
or a single x variable or for a combination of predictor x and
response y variables. Based on
class of x and y different types of plots are automatically
generated.
Please note the following points:
Defining the class of variables: If yvar is not NULL, then
yclass has to be passed (which can be 'factor' for classification type
problem, or 'numeric' for regression). xclasses stores the class of
all the variables in the dataframe in same order of columns. Note -
if yvar is not NULL, then tb has to be a data.frame with
at least 2 columns (including the yvar). In such
case xclasses should also have the class of yvar although it is
also passed through yclass. This can also be set as NULL, in
such case the function assigns a class based on the contents. If variable is
factor/character type, then xclasses will have 'factor' as the
entry for that variable, else if x is
numeric with number of unique values less than callasfactor
parameter value, then xclasses will have 'factor', else 'numeric'.
DEFINING CUSTOM FUNCTIONS FOR THE PLOTS USING FUN1, FUN2, FUN3 ... FUN6:
Custom plots can be made using these functions passed as arguments. Following things must be followed while defining such functions:
the return plot must be of type 'grob' or 'gtables' or 'ggplot'.
Since these outputs will go to arrangeGrob, make
sure the output plots are acceptable by arrangeGrob function.
See code of CxCy for sample.
not all 6 functions are required to be passed. Only pass those functions for which plots need to be changed.
FUN1 and FUN2 must have 3 parameters: dat (of type data.frame
for the data. Even
if there is only one column, it should be passed as a data.frame of one
column), xname name of column in dat and ... In addition
to these three, any number of additional parameters can be added.
Look into source of code of Cx for sample.
FUN3, FUN4, FUN5 and FUN6
must have 4 parameters: dat (of type data.frame for the data.
Must have two columns for independent and dependent variables),
xname name of independent variable in dat,
yname name of dependent variable in dat and
... In addition to these four,
any number of additional parameters can be added.
Look into source of code of CxCy for sample.
... must be added as an argument in all the functions.
To get a better idea, see the code for function CxCy
and Cx
Default plots: If the y is NULL, then histogram with density
is generated for numeric x. Boxplot
is also shown in the same histogram using color and vertical lines. For
factor x, a pie chart showing the distribution. This are the
univariate plots which can be modified by using the FUN1 and FUN2 arguments.
If y is not
NULL, then additional plots are added which can be modified by
using the FUN3, FUN4, FUN5, FUN6 arguments:
factor x, factor y: Crosstab with
heatmap (modified by using FUN6)
factor x, numeric y: histogram and
boxplot of y for different values of x
(modified by using FUN4)
numeric x, factor y:
histogram and boxplot of x for different
values of y
(modified by using FUN5)
numeric x, numeric y: Scatter
plot of x and y with rug plot included
(modified by using FUN3)
A list of plots for all the variables. Each plot will have the class
analyzerPlot and can be displayed using plot(). If
printall = TRUE, then all plots will also be displayed.
# simple use for one variable p <- plottr(mtcars$mpg) # To display the plot plot(p$x) # With complete dataframe and assuming 'mpg' as a dependent variable p <- plottr(mtcars, yvar = "mpg", yclass = "numeric") plot(p$disp)# simple use for one variable p <- plottr(mtcars$mpg) # To display the plot plot(p$x) # With complete dataframe and assuming 'mpg' as a dependent variable p <- plottr(mtcars, yvar = "mpg", yclass = "numeric") plot(p$disp)
This function plots the plot generated by the library analyzer
## S3 method for class 'analyzerPlot' print(x, ...)## S3 method for class 'analyzerPlot' print(x, ...)
x |
a plot of class |
... |
other parameters |
Displays the plot
# creating the plot p <- plottr(mtcars$mpg) p$x# creating the plot p <- plottr(mtcars$mpg) p$x
QQassociation finds Association measure between all the variables in
data with only categorical columns.
QQassociation(factb, use = "everything", methods_used)QQassociation(factb, use = "everything", methods_used)
factb |
a data frame with all the categorical columns. This should have at least two columns |
use |
an optional character string giving a method for computing association in the presence of missing values. This must be (complete or an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs". If use is "everything", NAs will propagate conceptually, i.e., a resulting value will be NA whenever one of its contributing observations is NA. If use is "all.obs", then the presence of missing observations will produce an error. If use is "complete.obs" then missing values are handled by case wise deletion (and if there are no complete cases, that gives an error). "na.or.complete" is the same unless there are no complete cases, that gives NA |
methods_used |
a square data.frame which will store the type of association used between the variables. Dimension will be number of variables * number of variables. |
This function measures the association between categorical variables using Chi Square test. This also returns Cramers V value which is a measure of association between two nominal variables, giving a value between 0 and +1 (inclusive). Higher number indicates higher association. Note that, unlike Pearson correlation this doesn't give negative value.
The relation between Cramer's V and Chi Sq test is
where:
is derived from Pearson's chi-squared test
is the grand total of observations
being the number of columns
being the number of rows
The p-value for the significance of Cramer's V is the same one that is calculated using the Pearson's chi-squared test.
a list of two tables with number of rows and column equal to number
of columns in factb:
Table containing p-values of chi-square test
Table containing Cramer's V
association for association between any type of variables,
CCassociation for Association between Continuous (numeric)
variables,
CQassociation for Association between Continuous-Categorical
variables
skewness calculates the skewness
skewness(x, na.rm = T)skewness(x, na.rm = T)
x |
a numeric vector, matrix or a data.frame |
na.rm |
(logical) Should missing values be removed? |
This function calculates the skewness of data which is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The formula used is:
. This formula is the typical definition used in many older textbooks and wikipedia
returns a single value if x is a vector, otherwise a named
vector of size = ncol(x).
# for a single vector skewness(mtcars$mpg) # for a dataframe skewness(mtcars)# for a single vector skewness(mtcars$mpg) # for a dataframe skewness(mtcars)