Package 'AnalysisLin' reference manual

Title:	Exploratory Data Analysis
Description:	A quick and effective data exploration toolkit. It provides essential features, including a descriptive statistics table for a quick overview of your dataset, interactive distribution plots to visualize variable patterns, Principal Component Analysis for dimensionality reduction and feature analysis, missing value imputation methods, and correlation analysis.
Authors:	Zhiwei Lin [aut, cre]
Maintainer:	Zhiwei Lin <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.0
Built:	2025-02-23 03:17:29 UTC
Source:	https://github.com/zhiweilin27/analysislin

Principle Component Analysis

Description

Principle Component Analysis

Usage

automate_pca(data, scale = TRUE, variance_threshold = 0.9, scree_plot = TRUE)
automate_pca(data, scale = TRUE, variance_threshold = 0.9, scree_plot = TRUE)

Arguments

`data`	input dataset
`scale`	a logical argument(default TRUE) that determines if appling standardized scaling to the dataset
`variance_threshold`	an argument(default is 0.9) for set a variance_threshold
`scree_plot`	a logical argument(default TRUE) that determines if scree plot is generated

Value

rotation and score of PCA and scree plot(optional)

Examples

automate_pca(data(mtcars))
automate_pca(data(mtcars))

Bar Plots for Categorical Variables

Description

This function generates bar plots for all categorical variables in the input data frame. Bar plots offer a visual representation of the distribution of categorical variables, making it easy to understand the frequency of each category. They are particularly useful for exploring patterns, identifying dominant categories, and comparing the relative frequencies of different levels within each variable.

Usage

bar_plot(
  data,
  fill = "skyblue",
  color = "black",
  width = 0.7,
  subplot = FALSE,
  nrow = 2,
  margin = 0.1
)
bar_plot(
  data,
  fill = "skyblue",
  color = "black",
  width = 0.7,
  subplot = FALSE,
  nrow = 2,
  margin = 0.1
)

Arguments

`data`	The input data frame containing categorical variables.
`fill`	Fill color for the bars (default: "skyblue").
`color`	Border color of the bars (default: "black").
`width`	Width of the bars (default: 0.7).
`subplot`	A logical argument (default: FALSE) indicating whether to create subplots.
`nrow`	Number of rows for subplots (if subplot is TRUE, default: 2).
`margin`	Margin for subplots (if subplot is TRUE, default: 0.1).

Value

A list of bar plots.

Examples

data(iris)
bar_plot(iris)

data(iris)
bar_plot(iris)

Categorical Variables Plots

Description

Categorical Variables Plots

Usage

categoric_plot(
  data,
  pie = TRUE,
  pie_legend = TRUE,
  pie_legend_size = 0.5,
  pie_legend_position = "bottom",
  pie_inset = c(0, -0.15),
  bar = TRUE,
  bar_legend = TRUE,
  bar_legend_size = 0.5,
  bar_legend_position = "bottom",
  bar_inset = c(0, -0.4),
  n_col = 1,
  bar_width = 0.8,
  bar_height = NULL
)
categoric_plot(
  data,
  pie = TRUE,
  pie_legend = TRUE,
  pie_legend_size = 0.5,
  pie_legend_position = "bottom",
  pie_inset = c(0, -0.15),
  bar = TRUE,
  bar_legend = TRUE,
  bar_legend_size = 0.5,
  bar_legend_position = "bottom",
  bar_inset = c(0, -0.4),
  n_col = 1,
  bar_width = 0.8,
  bar_height = NULL
)

Arguments

`data`	input data
`pie`	a logical argument(default TRUE) that determines if pie plot is generated
`pie_legend`	a logical argument(default TRUE) that determines if a legend of pie is generated
`pie_legend_size`	an argument(default is 0.5) that determines size of the legend
`pie_legend_position`	an argument(dafult is bottom) that determines the position of the legend
`bar`	a logical argument(default TRUE) that determines if bar plot is generated
`bar_legend`	a logical argument(default TRUE) that determines if a legend of bar is generated
`bar_legend_size`	an argument(default is 0.5) that determines size of the legend
`bar_legend_position`	an argument(dafult is bottom) that determines the position of the legend
`n_col`	an argument that determine how many plot being put on the same rows
`bar_width`	width of bar plot
`bar_height`	height of bar plot

Value

a list of pie charts

Examples

categoric_plot(data(mtcars))
categoric_plot(data(mtcars))

Correlation Clustering

Description

This function performs hierarchical clustering on a correlation matrix, providing insights into the relationships between variables. It generates a dendrogram visualizing the hierarchical clustering of variables based on their correlation patterns.

Usage

corr_cluster(data, type = "pearson", method = "complete", hclust_method = NULL)
corr_cluster(data, type = "pearson", method = "complete", hclust_method = NULL)

Arguments

`data`	Input data frame.
`type`	The type of correlation to be computed. It can be "pearson", "kendall", or "spearman".
`method`	The method for hierarchical clustering. It can be "complete", "single", "average", "ward.D", "ward.D2", "mcquitty", "median", or "centroid".
`hclust_method`	The hierarchical clustering method. It can be "complete", "single", "average", "ward.D", "ward.D2", "mcquitty", "median", or "centroid".

Value

A dendrogram visualizing the hierarchical clustering of variables based on the correlation matrix.

Examples

data(mtcars)
corr_cluster(data = mtcars, type = 'pearson', method = 'complete')

data(mtcars)
corr_cluster(data = mtcars, type = 'pearson', method = 'complete')

Correlation Matrix

Description

Column 1: Row names representing Variable 1 in the correlation test.

Column 2: Column names representing Variable 2 in the correlation test.

Column 3: The correlation coefficients quantifying the strength and direction of the relationship.

Column 4: The p-values associated with the correlations, indicating the statistical significance of the observed relationships. Lower p-values suggest stronger evidence against the null hypothesis.

The table provides valuable insights into the relationships between variables, helping to identify statistically significant correlations.

Usage

corr_matrix(
  data,
  type = "pearson",
  corr_plot = FALSE,
  sig.level = 0.01,
  highlight = FALSE
)
corr_matrix(
  data,
  type = "pearson",
  corr_plot = FALSE,
  sig.level = 0.01,
  highlight = FALSE
)

Arguments

`data`	Input dataset.
`type`	Pearson or Spearman correlation, default is Pearson.
`corr_plot`	Generate a correlation matrix plot, default is false.
`sig.level`	Significant level. Default is 0.01.
`highlight`	Highlight p-value(s) that is less than sig.level, default is FALSE

Value

A data frame which contains row names, column names, correlation coefficients, and p-values.

A plot of the correlation if corrplot is set to be true.

Examples

data(mtcars)
corr_matrix(mtcars, type = 'pearson')
data(mtcars)
corr_matrix(mtcars, type = 'pearson')

Numerical Variables Density Plots

Description

This function generates density plots for all numerical variables in the input data frame. It offers a vivid and effective visual summary of the distribution of each numerical variable, helping in a quick understanding of their central tendency, spread, and shape.

Usage

dens_plot(
  data,
  fill = "skyblue",
  color = "black",
  alpha = 0.7,
  subplot = FALSE,
  nrow = 2,
  margin = 0.1
)
dens_plot(
  data,
  fill = "skyblue",
  color = "black",
  alpha = 0.7,
  subplot = FALSE,
  nrow = 2,
  margin = 0.1
)

Arguments

`data`	The input data frame containing numerical variables.
`fill`	The fill color of the density plot (default: "skyblue").
`color`	The line color of the density plot (default: "black").
`alpha`	The transparency of the density plot (default: 0.7).
`subplot`	A logical argument (default: FALSE) indicating whether to create subplots.
`nrow`	Number of rows for subplots (if subplot is TRUE, default: 2).
`margin`	Margin for subplots (if subplot is TRUE, default: 0.1).

Value

A list of density plots.

Examples

data(mtcars)
dens_plot(mtcars)

data(mtcars)
dens_plot(mtcars)

Descriptive Statistics

Description

desc_stat() function calculates various key descriptive statistics for each variables in the provided data set. The function computes the count, number of unique values, duplicate count, number of missing values, null rate, data type, minimum value, 25th percentile, mean, median, 75th percentile, maximum value, standard deviation, kurtosis, skewness, and jarque_pvalue for each variable.

Usage

desc_stat(
  data,
  count = TRUE,
  unique = TRUE,
  duplicate = TRUE,
  null = TRUE,
  null_rate = TRUE,
  type = TRUE,
  min = TRUE,
  p25 = TRUE,
  mean = TRUE,
  median = TRUE,
  p75 = TRUE,
  max = TRUE,
  sd = TRUE,
  skewness = FALSE,
  kurtosis = FALSE,
  shapiro = FALSE,
  kolmogorov = FALSE,
  anderson = FALSE,
  lilliefors = FALSE,
  jarque = FALSE
)
desc_stat(
  data,
  count = TRUE,
  unique = TRUE,
  duplicate = TRUE,
  null = TRUE,
  null_rate = TRUE,
  type = TRUE,
  min = TRUE,
  p25 = TRUE,
  mean = TRUE,
  median = TRUE,
  p75 = TRUE,
  max = TRUE,
  sd = TRUE,
  skewness = FALSE,
  kurtosis = FALSE,
  shapiro = FALSE,
  kolmogorov = FALSE,
  anderson = FALSE,
  lilliefors = FALSE,
  jarque = FALSE
)

Arguments

`data`	input dataset
`count`	An logical argument(default TRUE) that determines if count is included in the output
`unique`	An logical argument(default TRUE) that determines if unique is included in the output
`duplicate`	An logical argument(default TRUE) that determines if duplicate is included in the output
`null`	An logical argument(default TRUE) that determines if null is included in the output
`null_rate`	An logical argument(default TRUE) that determines if null_rate is included in the output
`type`	An logical argument(default TRUE) that determines if type is included in the output
`min`	An logical argument(default TRUE) that determines if min is included in the output
`p25`	An logical argument(default TRUE) that determines if p25 is included in the output
`mean`	An logical argument(default TRUE) that determines if mean is included in the output
`median`	An logical argument(default TRUE) that determines if median is included in the output
`p75`	An logical argument(default TRUE) that determines if p75 is included in the output
`max`	An logical argument(default TRUE) that determines if max is included in the output
`sd`	An logical argument(default TRUE) that determines if sd is included in the output
`skewness`	An logical argument(default FALSE) that determines if skewness is included in the output
`kurtosis`	An logical argument(default FALSE) that determines if kurtosis is included in the output
`shapiro`	An logical argument(default FALSE) that determines if shapiro p-value is included in the output
`kolmogorov`	An logical argument(default FALSE) that determines if kolmogorov p-value is included in the output
`anderson`	An logical argument(default FALSE) that determines if anderson p-value is included in the output
`lilliefors`	An logical argument(default FALSE) that determines if lilliefors p-value is included in the output
`jarque`	An logical argument(default FALSE) that determines if jarque p-value is included in the output

Value

A data frame which summarizes the characteristics of a data set

Examples

data(mtcars)
desc_stat(mtcars)
data(mtcars)
desc_stat(mtcars)

Histogram Plot for Numerical Variables

Description

This function generates histogram plots for all numerical variables in the input data frame. It offers a vivid and effective visual summary of the distribution of each numerical variable, helping in a quick understanding of their central tendency, spread, and shape.

Usage

hist_plot(
  data,
  fill = "skyblue",
  color = "black",
  alpha = 0.7,
  subplot = FALSE,
  nrow = 2,
  margin = 0.1
)
hist_plot(
  data,
  fill = "skyblue",
  color = "black",
  alpha = 0.7,
  subplot = FALSE,
  nrow = 2,
  margin = 0.1
)

Arguments

`data`	The input data frame containing numerical variables.
`fill`	The fill color for the histogram bars (default: "skyblue").
`color`	The border color for the histogram bars (default: "black").
`alpha`	The alpha (transparency) value for the histogram bars (default: 0.7).
`subplot`	A logical argument (default: FALSE) indicating whether to create subplots for each variable.
`nrow`	Number of rows for subplots (used when subplot is TRUE, default: 2).
`margin`	Margin for subplots (used when subplot is TRUE, default: 0.1).

Value

A list of histogram plot.

Examples

hist_plot(data = mtcars, fill = "skyblue", color = "black", alpha = 0.7, subplot = FALSE)
hist_plot(data = mtcars, fill = "skyblue", color = "black", alpha = 0.7, subplot = FALSE)

Missing Value Imputation

Description

This function performs missing value imputation in the input data using various methods. The available imputation methods are:

- "mean": Imputes missing values with the mean of the variable. - "median": Imputes missing values with the median of the variable. - "mode": Imputes missing values with the mode of the variable (for categorical data). - "locf": Imputes missing values using the Last Observation Carried Forward method. - "knn": Imputes missing values using the k-Nearest Neighbors algorithm (specify k).

Usage

impute_missing(data, method = "mean", k = NULL)
impute_missing(data, method = "mean", k = NULL)

Arguments

`data`	Input data.
`method`	Method of handling missing values: "mean," "median," "mode," "locf," or "knn."
`k`	Value of the number of neighbors to be checked (only for knn method). Default is NULL.

Value

a data frame with imputed missing values

Examples

data(airquality)
impute_missing(airquality, method='mean')

data(airquality)
impute_missing(airquality, method='mean')

Missing Values Plot

Description

This function generates plots to visualize missing values in a data frame. It includes two types of plots: - A percentage plot: Displays the percentage of missing values for each variable, allowing quick identification of variables with high missingness. - A row plot: Illustrates the distribution of missing values across rows, providing insights into patterns of missingness.

Usage

missing_values_plot(df, percentage = TRUE, row = TRUE)
missing_values_plot(df, percentage = TRUE, row = TRUE)

Arguments

`df`	The input data frame.
`percentage`	A logical argument (default: TRUE) to generate a percentage plot.
`row`	A logical argument (default: TRUE) to generate a row plot.

Value

A list of plots, including a percentage plot and/or a row plot.

Examples


data("airquality")
missing_values_plot(df = airquality, percentage = TRUE, row = TRUE)

data("airquality")
missing_values_plot(df = airquality, percentage = TRUE, row = TRUE)

Numerical Variables Distribution

Description

Numerical Variables Distribution

Usage

numeric_plot(data, hist = TRUE, prob = FALSE, dens = FALSE)
numeric_plot(data, hist = TRUE, prob = FALSE, dens = FALSE)

Arguments

`data`	input data
`hist`	a logical argument(default TRUE) that determines if histogram is generated
`prob`	a logical argument(default FALSE) that determines it is a probability histogram or relative frequency histogram.
`dens`	a logical argument(default FALSE) that determine if density line is generated

Value

a list of plots

Examples

numeric_plot(data(mtcars),prob=T,dens=T)
numeric_plot(data(mtcars),prob=T,dens=T)

Principal Component Analysis (PCA)

Description

This function performs Principal Component Analysis (PCA) on the input data, providing a detailed analysis of variance, eigenvalues, and eigenvectors. It offers options to generate a scree plot for visualizing variance explained by each principal component and a biplot to understand the relationship between variables and observations in reduced dimensions.

Usage

pca(
  data,
  variance_threshold = 0.9,
  center = TRUE,
  scale = FALSE,
  scree_plot = FALSE,
  biplot = FALSE,
  choices = 1:2,
  groups = NULL,
  length_scale = 1,
  scree_legend = TRUE,
  scree_legend_pos = c(0.7, 0.5)
)
pca(
  data,
  variance_threshold = 0.9,
  center = TRUE,
  scale = FALSE,
  scree_plot = FALSE,
  biplot = FALSE,
  choices = 1:2,
  groups = NULL,
  length_scale = 1,
  scree_legend = TRUE,
  scree_legend_pos = c(0.7, 0.5)
)

Arguments

`data`	Numeric matrix or data frame containing the variables for PCA.
`variance_threshold`	Proportion of total variance to retain (default: 0.90).
`center`	Logical, indicating whether to center the data (default: TRUE).
`scale`	Logical, indicating whether to scale the data (default: FALSE).
`scree_plot`	Logical, whether to generate a scree plot (default: FALSE).
`biplot`	Logical, whether to generate a biplot (default: FALSE).
`choices`	Numeric vector of length 2, indicating the principal components to plot in the biplot.
`groups`	Optional grouping variable for coloring points in the biplot.
`length_scale`	Scaling factor for adjusting the length of vectors in the biplot (default: 1).
`scree_legend`	Logical, indicating whether to show legend in scree plot (default: True).
`scree_legend_pos`	A vector c(x, y) to adjust the position of the legend.

Value

A list containing: - summary_table: A matrix summarizing eigenvalues and cumulative variance explained. - scree_plot: A scree plot if scree_plot is TRUE. - biplot: A biplot if biplot is TRUE.

Examples

data(mtcars)
pca_result <- pca(mtcars, scree_plot = TRUE, biplot = TRUE)
pca_result$summary_table
pca_result$scree_plot
pca_result$biplot

data(mtcars)
pca_result <- pca(mtcars, scree_plot = TRUE, biplot = TRUE)
pca_result$summary_table
pca_result$scree_plot
pca_result$biplot

Pie Plots for Categorical Variables

Description

This function generates pie charts for categorical variables in the input data frame using plotly. Pie plots offer a visual representation of the distribution of categorical variables, making it easy to understand the frequency of each category. They are particularly useful for exploring patterns, identifying dominant categories, and comparing the relative frequencies of different levels within each variable.

Usage

pie_plot(data)
pie_plot(data)

Arguments

data

The input data frame containing categorical variables.

Value

A list of pie charts.

Examples

data(iris)
pie_plot(iris)
data(iris)
pie_plot(iris)

QQ Plots for Numerical Variables

Description

This function generates QQ plots for all numerical variables in the input data frame. QQ plots are valuable for assessing the distributional similarity between observed data and a theoretical normal distribution. It acts as a guide, revealing deviations from the expected norm, outliers, and the contours of distribution tails.

Usage

qq_plot(data, color = "skyblue", subplot = FALSE, nrow = 2, margin = 0.1)
qq_plot(data, color = "skyblue", subplot = FALSE, nrow = 2, margin = 0.1)

Arguments

`data`	The input data frame containing numerical variables.
`color`	The color of the QQ plot line (default: "skyblue").
`subplot`	A logical argument (default: FALSE) indicating whether to create subplots.
`nrow`	Number of rows for subplots (if subplot is TRUE, default: 2).
`margin`	Margin for subplots (if subplot is TRUE, default: 0.1).

Value

A list of QQ plots.

Examples

data(mtcars)
qq_plot(mtcars)

data(mtcars)
qq_plot(mtcars)

Package 'AnalysisLin'

Help Index

Principle Component Analysis

Description

Usage

Arguments

Value

Examples

Bar Plots for Categorical Variables

Description

Usage

Arguments

Value

Examples

Categorical Variables Plots

Description

Usage

Arguments

Value

Examples

Correlation Clustering

Description

Usage

Arguments

Value

Examples

Correlation Matrix

Description

Usage

Arguments

Value

Examples

Numerical Variables Density Plots

Description

Usage

Arguments

Value

Examples

Descriptive Statistics

Description

Usage

Arguments

Value

Examples

Histogram Plot for Numerical Variables

Description

Usage

Arguments

Value

Examples

Missing Value Imputation

Description

Usage

Arguments

Value

Examples

Missing Values Plot

Description

Usage

Arguments

Value

Examples

Numerical Variables Distribution

Description

Usage

Arguments

Value

Examples

Principal Component Analysis (PCA)

Description

Usage

Arguments

Value

Examples

Pie Plots for Categorical Variables

Description

Usage

Arguments

Value

Examples