Title:	Fixing Data Made Easy for Statistical Analysis
Version:	0.2.0
Description:	A set of functions that facilitate basic data manipulation and cleaning for statistical analysis including functions for finding and fixing duplicate rows and columns, missing values, outliers, and special characters in column and row names and functions for checking data consistency, distribution, quality, reliability, and structure.
License:	GPL-3
URL:	https://github.com/ambuvjyn/fixr
Encoding:	UTF-8
RoxygenNote:	7.3.2.9000
Imports:	RCurl, httr, xml2
NeedsCompilation:	no
Packaged:	2025-02-15 21:56:03 UTC; ambuvijayan
Author:	Ambu Vijayan [aut, cre], J. Sreekumar [aut] (Principal Scientist, ICAR - Central Tuber Crops Research Institute)
Maintainer:	Ambu Vijayan <ambuvjyn@gmail.com>
Repository:	CRAN
Date/Publication:	2025-02-15 22:10:02 UTC

Check Data Consistency Between Two Data Frames

Description

This function compares the column names and number of rows in two data frames and returns a message indicating whether the data is consistent or not.

Usage

check_data_consistency(df1, df2)

Arguments

Value

A message indicating whether the data is consistent or not.

Examples

df1 <- data.frame(x = c(1,2,3), y = c(4,5,6))
df2 <- data.frame(x = c(1,2,3), y = c(4,5,6))
check_data_consistency(df1, df2)
# Data is consistent across the two sources.
df3 <- data.frame(a = c(1,2,3), b = c(4,5,6))
check_data_consistency(df1, df3)
# Data is not consistent across the two sources.

Check the data distribution of a data frame

Description

This function checks if the data is normally distributed for each numeric column in a data frame.

Usage

check_data_distribution(df)

Arguments

Value

This function does not return anything, it only prints messages to the console.

Examples

df <- data.frame(x = c("a", "b", "c"), y = c(4, 5, 6), z = c(7, 8, 9))

check_data_distribution(df)

Check Data Quality

Description

This function performs a series of data quality checks on a given dataframe, including checking the data structure, missing values, data accuracy, negative values, outliers, sample size, duplicate rows, and duplicate columns.

Usage

check_data_quality(df)

Arguments

Value

A message indicating the results of each data quality check.

Examples

df <- data.frame(w = c(7, 8, 180, 7), x = c("a", "b", "c", "a"),
                 y = c(4, NA, -6, 4), z = c(7, 8, 180, 7))

# Check the data quality of the example dataframe
check_data_quality(df)

Check inter-rater or test-retest reliability between numeric columns

Description

This function checks for inter-rater or test-retest reliability between all pairs of numeric columns in a data frame by computing the correlation between each pair and reporting if it is less than 0.8.

Usage

check_data_reliability(df)

Arguments

Value

A message indicating whether the data is reliable or not between each pair of columns.

Examples

df <- data.frame(x = c("a", "b", "c"), y = c(4, 5, 6), z = c(7, 8, 180))
check_data_reliability(df)

Check the structure of the data

Description

This function checks the structure of the given data frame and prints the number of rows, number of columns, column names, column data types, and number of missing values.

Usage

check_data_structure(df)

Arguments

Value

None

Examples

df <- data.frame(id = 1:10,
gender = c("male", "female", "male", "male", "male", "male", "male", "male", "female", "female"),
age = c(25, 32, 45, 19, 27, 56, 38, 42, 33, NA),
salary = c(50000, 60000, 75000, 45000, 55000, 90000, NA, 80000, 65000, 70000))

# Check the data structure of the example dataframe
check_data_structure(df)

Check if a data frame contains negative values.

Description

This function checks if a data frame contains negative values and returns their indices if any are found.

Usage

check_for_negative_values(df)

Arguments

Value

If negative values are found, the function returns their indices as an array index object. If no negative values are found, NULL is returned.

Examples

df <- data.frame(a = c(1, 2, 3), b = c(-1, 0, 1))
check_for_negative_values(df)
# [1] "Data frame contains negative values."
#      row col
# [1,]   2   1"

Check for Missing Values in Data Frame

Description

This function checks for missing values in a data frame and prints out the names of the columns with missing values and their counts.

Usage

check_missing_values(df)

Arguments

Value

A message indicating if missing values were found or not.

Examples

df <- data.frame(w = c(7, 8, 180, 7), x = c("a", "b", "c", "a"),
                 y = c(4, 5, -6, 4), z = c(7, 8, NA, 7))
check_missing_values(df)

Check for Outliers or Extreme Values in Data

Description

This function checks for outliers or extreme values in a given dataframe.

Usage

check_outliers(df)

Arguments

Value

A message indicating whether or not extreme values were found.

Examples

df <- data.frame(w = c(7, 8, 180, 7), x = c("a", "b", "c", "a"),
                 y = c(4, 5, -6, 4), z = c(7, 8, NA, 7))

check_outliers(df)

Check if sample size is adequate

Description

This function checks if the sample size of a data frame is adequate for statistical analysis.

Usage

check_sample_size(df)

Arguments

Value

A message indicating if the sample size is adequate or too small

Examples

df <- data.frame(w = c(7, 8, 180, 7), x = c("a", "b", "c", "a"),
                 y = c(4, 5, -6, 4), z = c(7, 8, 18, 7))
check_sample_size(df)

Find R packages that can import a file format

Description

This function searches the CRAN repository for R packages that can be used to import a file format

Usage

find.packages(file_extension)

Arguments

Value

A character vector of package names that can be used to import the file format

Find the R Packages to Import a File Format

Description

This function takes a file path as input and searches the CRAN repository for R packages that can import the file format.

Usage

find.packages_path(file_path)

Arguments

Value

A character string that lists the R packages that can be used to import the file format of the input file.

Examples

# Search for packages that can import a CSV file
find.packages_path("sample.csv")

# Search for packages that can import a JSON file
find.packages_path("sample.json")

Find duplicate rows in a data frame

Description

This function identifies and reports duplicate rows in a given data frame. It first removes any rows with no values in all cells, and then compares each row to subsequent rows to check for duplicates. Duplicate rows are identified by having the same values in all columns. The function returns a message stating whether or not duplicate rows were found, and if so, the row numbers of the duplicate and original rows.

Usage

find_duplicate_rows(df)

Arguments

Value

A message stating whether or not duplicate rows were found, and if so, the row numbers of the duplicate and original rows.

Examples

# Create example data frame
df <- data.frame(w = c(7, 8, 180, 7), x = c("a", "b", "c", "a"),
                 y = c(4, 5, -6, 4), z = c(7, 8, NA, 7))
# Find duplicate rows
find_duplicate_rows(df)

Fix data frame column and row names and remove symbols and blanks

Description

This function applies several data cleaning functions from the fixr package to a given data frame. The fix_data_names, remove_spaces, remove_symbols_data, and replace_blanks_with_na functions are used to add "X_" before column and row names that start with a number, remove leading/trailing spaces, remove non-alphanumeric characters from the data, replace spaces with underscores in column and row names, and replace empty string values with NAs, respectively.

Usage

fix.data(df)

Arguments

Value

The cleaned data frame.

Examples

df <- data.frame(" 1st col " = c("", "foo", ""), "2nd col" = c(" ", " ", "bar"),
                 "3rd col" = c(1, 2, 3))
fix.data(df)

Replace blanks with NA in a data frame

Description

This function replaces all empty string values ("") in a given data frame with NA values.

Usage

fix_blanks_with_na(df)

Arguments

Value

The data frame with empty string values replaced with NAs.

Examples

df <- data.frame(x = c("", "foo", ""), y = c("", "", "bar"), z = c(1, 2, 3))
fix_blanks_with_na(df)

Replace spaces in column names with underscores

Description

This function takes a data frame as an argument and replaces all spaces in the column names with underscores.

Usage

fix_col_spaces(df)

Arguments

Value

A modified data frame with spaces in column names replaced by underscores.

Examples

my_data <- data.frame("Column Name 1" = c(1, 2, 3), "Column Name 2" = c(4, 5, 6))

fix_col_spaces(my_data)
# Returns a data frame with column names where spaces are replaced by underscores.

Fix Column Names

Description

This function removes "X." or "X" from the beginning of column names and replaces any "." with "_". It also removes leading/trailing symbols and spaces, and ensures that there is only one underscore between two words. If there are duplicate column names, it appends a number to each duplicate column name to make it unique.

Usage

fix_column_names(data)

Arguments

Value

The modified data frame with fixed column names.

Examples

my_data <- data.frame(" Col1" = c(1, 2, 3), "Col.2" = c(4, 5, 6), check.names = FALSE)
fix_column_names(my_data)

Fix row and column names of a data frame

Description

This function fixes the row and column names of a data frame by removing leading and trailing spaces, replacing spaces with underscores, and modifying duplicate names.

Usage

fix_data_names(df)

Arguments

Value

A fixed data frame with modified row and column names

Examples

my_data <- data.frame(" Col1" = c(1, 2, 3), "Col.2" = c(4, 5, 6), check.names = FALSE)
rownames(my_data) <- c(" Row1", " Row.2", "Row.3 ")
fix_column_names(fix_row_names(my_data))

Remove duplicate columns from a data frame

Description

This function removes duplicate columns from a data frame.

Usage

fix_duplicate_cols(df)

Arguments

Value

A data frame with duplicate columns removed

Examples

df <- data.frame(a = c(1, 1, 2), b = c(2, 2, 3))
fix_duplicate_cols(df)

Remove duplicate rows from a data frame

Description

This function removes duplicate rows from a data frame.

Usage

fix_duplicate_rows(df)

Arguments

Value

A data frame with duplicate rows removed

Examples

df <- data.frame(a = c(1, 1, 2), b = c(2, 2, 3))
fix_duplicate_rows(df)

Fill missing values in alphanumeric columns

Description

This function imputes missing values in alphanumeric columns of a data frame. If a column is numeric, missing values are imputed with the column mean. Otherwise, missing values are imputed with the column mode (most common value).

Usage

fix_missing_alphanumeric_values(df)

Arguments

Value

A data frame with imputed missing values.

Examples

df <- data.frame(w = c(7, 8, 180, 7), x = c("a", "b", "c", NA),
                 y = c(4, 5, -6, 4), z = c(7, 8, NA, 7))
fix_missing_alphanumeric_values(df)

fill_missing_numeric_values

Description

A function to fill missing values in numeric columns of a data frame with the mean of the column.

Usage

fix_missing_numeric_values(df)

Arguments

Value

A data frame with missing numeric values filled with the column mean.

Examples

df <- data.frame(w = c(7, 8, 180, 7), x = c("a", "b", "c", "d"),
                 y = c(4, 5, -6, 4), z = c(7, 8, NA, 7))
fix_missing_numeric_values(df)

Remove Outliers from a Data Frame

Description

This function removes outlier rows from a data frame by identifying rows with values that are more than 2 standard deviations away from the mean in any column.

Usage

fix_outliers(df)

Arguments

Value

A cleaned data frame with outlier rows removed

Examples

df <- data.frame(x = c(1,2,3,4,5,6,7,8,9,10),
                 y = c(1,1,1,1,1,1,1,100,1,1))
fix_outliers(df)

Fix row names of a data frame

Description

This function removes any leading "X." or "X" from the row names of a data frame, replaces any "." with "_", removes any leading or trailing symbols and spaces, and ensures that there is only one underscore between two words. Additionally, if there are duplicate row names, the function appends a number to each duplicate row name to make it unique.

Usage

fix_row_names(data)

Arguments

Value

a modified data frame with fixed row names

Examples

my_data <- data.frame(" Col1" = c(1, 2, 3), "Col.2" = c(4, 5, 6), check.names = FALSE)
rownames(my_data) <- c(" Row1", " Row.2", "Row.3 ")
fix_row_names(my_data)

Replace spaces in row names with underscores

Description

This function takes a data frame as an argument and replaces all spaces in the row names with underscores.

Usage

fix_row_spaces(df)

Arguments

Value

A modified data frame with spaces in row names replaced by underscores.

Examples

my_data <- data.frame("Column Name 1" = c(1, 2, 3), "Column Name 2" = c(4, 5, 6))
rownames(my_data) <- c("Row Name 1", "Row Name 2", "Row Name 3")

fix_row_spaces(my_data)
# Returns a data frame with row names where spaces are replaced by underscores.

Remove Non-Alphanumeric Characters from Data Frame

Description

This function removes non-alphanumeric characters from all non-numeric columns in a data frame. The columns are modified in-place.

Usage

fix_special_characters_in_data(df)

Arguments

Value

A modified data frame where all non-numeric columns have had non-alphanumeric characters removed.

Examples

df <- data.frame(a = c("A*B", "C&D"), b = c("1.2", "3.4"))
fix_special_characters_in_data(df)
# Output:
#   a    b
# 1 AB  1.2
# 2 CD  3.4

Remove Special Characters from Data Frame Column and Row Names

Description

This function removes any non-alphanumeric characters from both the row and column names of a given data frame.

Usage

fix_special_characters_in_names(df)

Arguments

Value

A data frame with all non-alphanumeric characters removed from the column and row names.

Examples

df <- data.frame("Col1!" = c(1, 2, 3), "Col2?" = c(4, 5, 6))
rownames(df) <- c("Row1@", "Row2#", "Row3$")
fix_special_characters_in_names(df)