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Title: Reshape Data Table
Version: 1.0.0
Description: A grammar of data manipulation with 'data.table', providing a consistent a series of utility functions that help you solve the most common data manipulation challenges.
Suggests: knitr, rmarkdown, testthat, dplyr
VignetteBuilder: knitr
License: MIT + file LICENSE
Imports: data.table
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.2
NeedsCompilation: no
Packaged: 2023-02-21 18:19:50 UTC; jienamclellan
Author: Jiena McLellan ORCID iD [aut, cre], Michael Condouris ORCID iD [ctb], Brittney Zykan [ctb], Sai Im [ctb]
Maintainer: Jiena McLellan <jienagu90@gmail.com>
Depends: R (≥ 3.5.0)
Repository: CRAN
Date/Publication: 2023-02-22 15:00:02 UTC

create a new column which is the percentage of other columns

Description

create a new column which is the percentage of other columns

Usage

col_cal_percent(df, new_col_name, numerator_cols, denominator_cols)

Arguments

df

input data frame

new_col_name

new column name

numerator_cols

numerator columns

denominator_cols

denominator columns

Value

data frame with a new percentage column

Examples

test_df <- data.frame(
  hc1 = c(2, 0, 1, 5, 6, 7, 10),
  hc2 = c(1, 0, 10, 12, 4, 1, 9 ),
  total = c(10, 2, 0, 39, 23, 27, 30)
)
dataMojo::col_cal_percent(test_df,
  new_col_name = "hc_percentage",
  numerator_cols = c("hc1", "hc2"),
  denominator_cols = "total"
)

Anonymized sample data

Description

Anonymized sample data

Usage

data(dt_dates)

Format

a data table with dates

Author(s)

Jiena Gu McLellan, 2020-05-26

Examples

data(dt_dates)

group by columns and return a summarized table

Description

group by columns and return a summarized table

Usage

dt_group_by(dt, group_by_cols, summarize_at, operation)

Arguments

dt

input data.table

group_by_cols

group by columns

summarize_at

column summarize at

operation

calculation operation, value should be one of following: sum, mean, median, max, min

Value

a summarized table

Examples

data("dt_groups")
dataMojo::dt_group_by(dt_groups,
                      group_by_cols = c("group1", "group2"),
                      summarize_at = "A1",
                      operation = "mean")

Anonymized sample data

Description

Anonymized sample data

Usage

data(dt_groups)

Format

a data table with groups

Author(s)

Jiena Gu McLellan, 2020-05-26

Examples

data(dt_groups)

Anonymized sample data

Description

Anonymized sample data

Usage

data(dt_long)

Format

a data table in long format

Author(s)

Jiena Gu McLellan, 2020-05-26

Examples

data(dt_long)

Anonymized sample data

Description

Anonymized sample data

Usage

data(dt_missing)

Format

a data table with missing values

Author(s)

Jiena Gu McLellan, 2020-05-26

Examples

data(dt_missing)

Anonymized sample data

Description

Anonymized sample data

Usage

data(dt_values)

Format

a data table with values

Author(s)

Jiena Gu McLellan, 2020-05-26

Examples

data(dt_values)

Fill missing values

Description

Fill missing values

Usage

fill_NA_with(dt, fill_cols, fill_value)

Arguments

dt

input data table

fill_cols

filter by this columns

fill_value

fill NA with this value

Value

data table which NAs are filled

Examples

data("dt_missing")
fill_NA_with(dt_missing, fill_cols = c("Full_name"), fill_value = "pending")

Filter all rows that meeting requirements

Description

Filter all rows that meeting requirements

Usage

filter_all(dt, operator, cutoff_value)

Arguments

dt

input data.table

operator

operator should be one of l, g. l means less than, g means greater than.

cutoff_value

threshold value

Value

filtered data table

Examples

data("dt_values")
dataMojo::filter_all(dt_values, operator = "l", .2)

Filter all rows that meet requirements with selected columns

Description

Filter all rows that meet requirements with selected columns

Usage

filter_all_at(dt, operator, cutoff_value, selected_cols)

Arguments

dt

input data table

operator

operator should be one of l, or g. l means less than, g means greater than

cutoff_value

cutoff value

selected_cols

selected columns from input data table

Value

filtered data table

Examples

data("dt_values")
dataMojo::filter_all_at(dt_values, operator = "l", .1, c("A1", "A2"))

Filter any rows that meeting requirements

Description

Filter any rows that meeting requirements

Usage

filter_any(dt, operator, cutoff_value)

Arguments

dt

input data.table

operator

operator should be one of l, g. l means less than, g means greater than.

cutoff_value

threshold value

Value

fitlered data table

Examples

data("dt_values")
dataMojo::filter_any(dt_values, operator = "l", .1)

Filter any rows that meet requirements with selected columns

Description

Filter any rows that meet requirements with selected columns

Usage

filter_any_at(dt, operator, cutoff_value, selected_cols)

Arguments

dt

input data table

operator

operator should be one of l, or g. l means less than, g means greater than

cutoff_value

cutoff value

selected_cols

selected columns from input data table

Value

filtered data table

Examples

data("dt_values")
dataMojo::filter_all_at(dt_values, operator = "l", .1, c("A1", "A2"))

Fetch one row from each grouped by group

Description

Fetch one row from each grouped by group

Usage

get_row_group_by(dt, group_by_cols, fetch_row)

Arguments

dt

input data table

group_by_cols

group by columns

fetch_row

first means to fetch first row and last means to fetch last row

Value

grouped by data table

Examples

data("dt_groups")
dataMojo::get_row_group_by(dt_groups,
                           group_by_cols = c("group1", "group2"),
                           fetch_row = "first")

Create an aggregated data table with all proportion of one selected column

Description

Create an aggregated data table with all proportion of one selected column

Usage

pivot_percent_at(dt, question_col, aggregated_by_cols)

Arguments

dt

data table

question_col

column selected as questions

aggregated_by_cols

grouped by columns

Value

aggregated data table

Examples

test_dt <- data.table::data.table(
Question = c(rep("Good", 3), rep("OK", 3), rep("Bad", 3)),
Gender = c(rep("F", 4), rep("M", 5))
)
dataMojo::pivot_percent_at(test_dt,
  question_col = "Question", aggregated_by_cols = "Gender")

Create an aggragated data table with all proportion of multiple selected column

Description

Create an aggragated data table with all proportion of multiple selected column

Usage

pivot_percent_at_multi(dt, question_col, aggregated_by_cols)

Arguments

dt

data table

question_col

columns selected as questions

aggregated_by_cols

grouped by columns

Value

an aggragated data table

Examples

test_dt <- data.table::data.table(
  Question1 = c(rep("Good", 3), rep("OK", 3), rep("Bad", 3)),
  Question2 = c(rep("Good", 2), rep("OK", 2), rep("Bad", 5)),
  Gender = c(rep("F", 4), rep("M", 5))
)
dataMojo::pivot_percent_at_multi(test_dt,
  question_col = c("Question1","Question2") , aggregated_by_cols = "Gender")

Reshape data frame to a longer format

Description

Reshape data frame to a longer format

Usage

reshape_longer(dt, keep_cols, label_cols, value_cols)

Arguments

dt

input data

keep_cols

columns to be kept

label_cols

column name that contains the melted columns

value_cols

column name that contains the value of melted columns

Value

data table in a longer format

Examples

data("dt_dates")
reshape_longer(dt_dates,
                        keep_cols = "Full_name",
                        label_cols = c("Date_Type"),
                        value_cols = "Exact_date")

Reshape data frame to a wider format

Description

Reshape data frame to a wider format

Usage

reshape_wider(dt, keep_cols, col_label, col_value)

Arguments

dt

input data table

keep_cols

columns to be kept

col_label

columns that each unique values will be reshaped as a column name

col_value

columns that fill the reshaped columns

Value

reshaped widen data table

Examples

data("dt_long")
dataMojo::reshape_wider(dt_long,
  keep_cols = c("Full_name"),
  col_label = c("Date_Type"),
  col_value = "Exact_date")

Expand row given start and end dates

Description

Expand row given start and end dates

Usage

row_expand_dates(dt, start_date_col, end_date_col, new_name)

Arguments

dt

input data table

start_date_col

start date column

end_date_col

end date column

new_name

new generated column name

Value

expanded data table

Examples

dt_dates_simple <- data.table::data.table(
  Start_Date = as.Date(c("2020-02-03", "2020-03-01") ),
  End_Date = as.Date(c("2020-02-05", "2020-03-02") ),
  group = c("A", "B")
)
row_expand_dates(dt_dates_simple, "Start_Date", "End_Date", "Date")[]

Expand row based on pattern

Description

Expand row based on pattern

Usage

row_expand_pattern(dt, col_name, split_by_pattern, new_name)

Arguments

dt

input data table

col_name

column to be expanded

split_by_pattern

split based on pattern

new_name

new generated column name

Value

expanded data table

Examples

data("starwars_simple")
row_expand_pattern(starwars_simple, "films", ", ", "film")[]

Convert count to percentage

Description

Convert count to percentage

Usage

row_percent_convert(data, cols_rowsum)

Arguments

data

data frame

cols_rowsum

columns need to be converted to percentage

Value

data frame with calculated row percentage

Examples

test_df <- data.frame(
  Group = c("A", "B", "C"),
  Female = c(2,3,5),
  Male = c(10,11, 13)
)
dataMojo::row_percent_convert(test_df, cols_rowsum = c("Female", "Male"))

Select columns

Description

Select columns

Usage

select_cols(dt, cols)

Arguments

dt

input data table

cols

select columns

Value

data table with selected columns

Examples

data("dt_dates")
select_cols(dt_dates, c("Start_Date", "Full_name"))

starwars data

Description

starwars data

Usage

data(starwars_simple)

Format

a data table as example

Author(s)

Jiena Gu McLellan, 2020-05-26

Examples

data(starwars_simple)

Split one column to multiple columns based on patterns

Description

Split one column to multiple columns based on patterns

Usage

str_split_col(dt, by_col, by_pattern, match_to_names = NULL)

Arguments

dt

input data table

by_col

by this column

by_pattern

split by this patter

match_to_names

created new columns names

Value

data table with new columns

Examples

data("dt_dates")
str_split_col(dt_dates,
              by_col = "Full_name",
              by_pattern = ", ",
              match_to_names = c("First Name", "Last Name"))

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.