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,
  html = FALSE
)

Arguments

Value

A list of bar plots.

Examples

data(iris)
bar_plot(iris)

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)

Arguments

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')

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,
  html = FALSE
)

Arguments

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')

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,
  html = FALSE
)

Arguments

Value

A list of density plots.

Examples

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
)

Arguments

Value

A data frame which summarizes the characteristics of a data set

Examples

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,
  html = FALSE
)

Arguments

Value

A list of histogram plot.

Examples

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)

Arguments

Value

a data frame with imputed missing values

Examples

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, html = FALSE)

Arguments

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)

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),
  html = FALSE
)

Arguments

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

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, html = FALSE)

Arguments

Value

A list of pie charts.

Examples

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,
  html = FALSE
)

Arguments

Value

A list of QQ plots.

Examples

data(mtcars)
qq_plot(mtcars)