Package 'impactR.utils'

Abord bad argument

Description

Abord bad argument

Usage

abort_bad_argument(arg1, must, not = NULL, arg2 = NULL, same = NULL)

Arguments

arg1	An argument
must	What arg must be
not	Optional. What arg must not be.
arg2	Another argument
same	The same type as arg1

Value

A stop statement

---

Count missing values for vars (by group)

Description

Count missing values for vars (by group)

Usage

count_missing_values(df, vars, group = NULL, pivot = TRUE)

Arguments

df	A data frame.
vars	Variable names.
group	Grouping column names. Default to NULL.
pivot	Logical. If TRUE, return a pivoted data frame.

---

Date to day and month

Description

Date to day and month

Usage

date_to_dm(vec)

Arguments

vec	A vector of type POSIXct.

Value

The same vector with only the day and month in the env locale.

---

Remove columns from a data frame

Description

Remove columns from a data frame

Usage

deselect(df, ...)

Arguments

df	A data frame.
...	Column names.

Value

A tibble with columns removed

---

Get colnames that start, end, or both with a string

Description

Get colnames that start, end, or both with a string

Usage

df_col_start(df, pattern_start)

df_col_end(df, pattern_end)

df_col_start_end(df, pattern_start, pattern_end)

Arguments

df	A data frame.
pattern_start	A start string
pattern_end	An end string.

Value

A vector of column names

---

Remove columns from a data frame that exists in another tibble

Description

Remove columns from a data frame that exists in another tibble

Usage

df_diff(df_a, df_b, ...)

Arguments

df_a	A data frame to remove columns from.
df_b	A data frame to extract columns names from.
...	Columns from df_b and df_a to keep in data frame.

Value

A tibble with some columns removed.

---

Stop statement "If not in colnames" with colnames

Description

Stop statement "If not in colnames" with colnames

Usage

if_not_in_stop(df, cols, df_name, arg = NULL)

Arguments

df	A dataframe
cols	A vector of column names (quoted)
df_name	Provide the tibble name as a character string
arg	Default to NULL.

Value

A stop statement

---

Initial import with clean names (not an option!)

Description

Import a dataset from a CSV or a XLSX file. It ensures that the right type of column is guessed using the max value of guess_max by default.

Usage

import_csv(path, delim = ",", clean_names = TRUE, guess_max = 21474836)

import_xlsx(path, sheet = 1, clean_names = TRUE, guess_max = 21474836)

import_full_xlsx(path = NULL, clean_names = TRUE, guess_max = 21474836)

Arguments

path	The path to the file.
delim	Field separator, default is ",".
clean_names	Boolean. If TRUE, uses janitor::clean_names() to column clean names.
guess_max	Maximum guess of types. Default to 21474836.
sheet	A character string of the sheet name or the sheet position

Value

A dataframe.

Functions

• import_csv(): Import a .csv file.

• import_xlsx(): Import one sheet from a .xlsx.

• import_full_xlsx(): Import all sheets from a .xlsx.

---

Left join a list of dataframes

Description

Left join a list of dataframes

Usage

left_join_dup(df_a, df_b, ..., duplicated_columns = 3)

Arguments

df_a	A dataframe.
df_b	Another dataframe.
...	Columns to join by.
duplicated_columns	Strategy between 1 and 4. Default to 3. See section "Treating duplicated columns".

Value

A left-joined data frame.

Treating duplicated columns

There are 4 ways implemented on treating columns with the same names in both datasets:

• 1: an usual left-join with suffix being empty for df_a and ".dup_col" for df_b;

• 2: removing all duplicated columns from df_b, keeping only those of df_a;

• 3: remove the column from df_b if all values from df_b that are not NAs equal the values from df_a;

• 4: remove the column from df_b if all values from df_b are identical to values from df_a.

---

Left join a list of dataframes

Description

left_joints() is a simple reducing functions based on dplyr::left_join(). left_joints_dup() is a reduce wrapper around left_join_dup().

Usage

left_joints(list, ...)

left_joints_dup(list, ..., duplicated_columns = 3)

Arguments

list	A list of data frames.
...	Columns to join by.
duplicated_columns	Strategy between 1 and 4. Default to 3. See section "Treating duplicated columns".

Value

A left-joined data frame.

---

Mutate with replacement if NULL or NA values

Description

Mutate with replacement if NULL or NA values

Usage

mutate_if_nulla(df, col, replacement)

Arguments

df	A data frame.
col	A column of df.
replacement	A replacement of the same type as col.

Value

A mutated data frame.

---

Named group split

Description

Named group split

Usage

named_group_split(df, group)

Arguments

df	A data frame.
group	Quoted column name to group by.

Value

A split and named list of data frames.

---

Replace values of a data frame's columns to one value

Description

recode_values() recodes a singular value. recode_na() recodes usual values of NAs, for instance 999, NULL, NaN, and NA.

Usage

recode_values(df, values, to_value, ...)

recode_na(df, ...)

Arguments

df	A data frame.
values	A vector of values.
to_value	A value.
...	Column names.

Details

If the column type is a character and the replacement a numeric, then the numeric is coerced to a character. If the column type is a numeric and the replacement is a character, then the column is coerced to character. NAs will remains NAs of the right type.

Value

A data frame with recoded values to one value.

---

Rename columns

Description

Rename columns

Usage

rename_cols(df, old_cols, new_cols)

Arguments

df	A data frame.
old_cols	A vector of old quoted variable names.
new_cols	A vector of new quoted variable names.

Value

Updated tibble with new names

---

Row-wise optimum (default to pmax) of several columns

Description

Row-wise optimum (default to pmax) of several columns

Usage

row_optimum(
  df,
  ...,
  optimum = "max",
  max_name = "pmax",
  min_name = "pmin",
  na_rm = TRUE,
  keep = "all"
)

Arguments

df	A data frame.
...	Numeric column names.
optimum	Should we calculate "max", "min" or "both"? Default to TRUE.
max_name	Column name for the mutated pmax.
min_name	Column name for the mutated pmin.
na_rm	Remove NAs. Default to TRUE.
keep	To be used within mutate. Default to "all".

Value

A data frame with pmax, pmin or both (and all columns or none, depending on "keep")

---

Count the number of row-wise occurrences of a pattern

Description

row_pattern_count() count row-wise occurences of a pattern over the data frame; row_na_count() count row-wise number of NAs over the data frame.

Usage

row_string_count(df, pattern = "", new_colname = "count")

row_na_count(df, new_colname = "count")

Arguments

df	A data frame.
pattern	A pattern to pass to stringr::str_count(). Default to "dnk".
new_colname	The newly-mutated column name. Default to "count".

Value

A mutated data frame.

---

Get a subset of a vector

Description

subvec_in() gets a subset of a vector if vector elements are in a given set. subvec_not_in() get a subset of a vector if vector elements are NOT in a fiven set.

Usage

subvec_in(vector, set)

subvec_not_in(vector, set)

Arguments

vector	A vector to subset.
set	A set-vector.

Value

A subset of a list or a vector.

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