Package 'dataMojo'

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"))

---