utils_rows_cols {metan}

Utilities for handling with rows and columns

Description

• add_cols(): Add one or more columns to an existing data frame. If specified .before or .after columns does not exist, columns are appended at the end of the data. Return a data frame with all the original columns in .data plus the columns declared in .... In add_cols() columns in .data are available for the expressions. So, it is possible to add a column based on existing data.

• add_rows(): Add one or more rows to an existing data frame. If specified .before or .after rows does not exist, rows are appended at the end of the data. Return a data frame with all the original rows in .data plus the rows declared in ... argument.

• add_row_id(): Add a column with the row id as the first column in .data.

• add_prefix() and add_suffix() add prefixes and suffixes, respectively, in variable names selected in ... argument.

• all_pairs(): Get all the possible pairs between the levels of a factor.

• colnames_to_lower(): Translate all column names to lower case.

• colnames_to_upper(): Translate all column names to upper case.

• colnames_to_title(): Translate all column names to title case.

• column_exists(): Checks if a column exists in a data frame. Return a logical value.

• columns_to_first(): Move columns to first positions in .data.

• columns_to_last(): Move columns to last positions in .data.

• columns_to_rownames(): Move a column of .data to its row names.

• rownames_to_column(): Move the row names of .data to a new column.

• remove_rownames(): Remove the row names of .data.

• concatenate(): Concatenate columns of a data frame. If drop = TRUE then the existing variables are dropped. If pull = TRUE then the concatenated variable is pull out to a vector. This is specially useful when using concatenate to add columns to a data frame with add_cols().

• get_levels(): Get the levels of a factor variable.

• get_levels_comb(): Get the combination of the levels of a factor.

• get_level_size(): Get the size of each level of a factor variable.

• remove_cols(): Remove one or more columns from a data frame.

• remove_rows(): Remove one or more rows from a data frame.

• reorder_cols(): Reorder columns in a data frame.

• select_cols(): Select one or more columns from a data frame.

• select_first_col(): Select first variable, possibly with an offset.

• select_last_col(): Select last variable, possibly with an offset.

• select_numeric_cols(): Select all the numeric columns of a data frame.

• select_non_numeric_cols(): Select all the non-numeric columns of a data frame.

• select_rows(): Select one or more rows from a data frame.

• tidy_colnames(): Tidy up column names with tidy_strings().

Usage

add_cols(.data, ..., .before = NULL, .after = NULL)

add_rows(.data, ..., .before = NULL, .after = NULL)

add_row_id(.data, var = "row_id")

all_pairs(.data, levels)

add_prefix(.data, ..., prefix, sep = "_")

add_suffix(.data, ..., suffix, sep = "_")

colnames_to_lower(.data)

colnames_to_upper(.data)

colnames_to_title(.data)

column_to_first(.data, ...)

column_to_last(.data, ...)

column_to_rownames(.data, var = "rowname")

rownames_to_column(.data, var = "rowname")

remove_rownames(.data, ...)

column_exists(.data, cols)

concatenate(
  .data,
  ...,
  prefix = NULL,
  suffix = NULL,
  new_var = new_var,
  sep = "_",
  drop = FALSE,
  pull = FALSE,
  .before = NULL,
  .after = NULL
)

get_levels(.data, ...)

get_levels_comb(.data, ...)

get_level_size(.data, ...)

reorder_cols(.data, ..., .before = NULL, .after = NULL)

remove_cols(.data, ...)

remove_rows(.data, ...)

select_first_col(.data, offset = NULL)

select_last_col(.data, offset = NULL)

select_numeric_cols(.data)

select_non_numeric_cols(.data)

select_cols(.data, ...)

select_rows(.data, ...)

tidy_colnames(.data, sep = "_")

Arguments

.data

A data frame

...

The argument depends on the function used.

• For add_cols() and add_rows() is name-value pairs. All values must have one element for each row in .data when using add_cols() or one element for each column in .data when using add_rows(). Values of length 1 will be recycled when using add_cols().

• For remove_cols() and select_cols(), ... is the column name or column index of the variable(s) to be dropped.

• For add_prefix() and add_suffix(), ... is the column name to add the prefix or suffix, respectively. Select helpers are allowed.

• For columns_to_first() and columns_to_last(), ... is the column name or column index of the variable(s) to be moved to first or last in .data.

• For remove_rows() and select_rows(), ... is an integer row value.

• For concatenate(), ... is the unquoted variable names to be concatenated.

• For get_levels(), get_level_comb(), and get_level_size() ... is the unquoted variable names to get the levels, levels combinations and levels size, respectively.

.before, .after

For add_cols(), concatenate(), and reorder_cols(), one-based column index or column name where to add the new columns, default: .after last column. For add_rows(), one-based row index where to add the new rows, default: .after last row.

var

Name of column to use for rownames.

levels

The levels of a factor or a numeric vector.

prefix, suffix

The prefix and suffix used in add_prefix() and add_suffix(), respectively.

sep

The separator to appear when using concatenate(), add_prefix(), or add_suffix(). Defaults to to "_".

cols

A quoted variable name to check if it exists in .data.

new_var

The name of the new variable containing the concatenated values. Defaults to new_var.

drop

Logical argument. If TRUE keeps the new variable new_var and drops the existing ones. Defaults to FALSE.

pull

Logical argument. If TRUE, returns the last column (on the assumption that's the column you've created most recently), as a vector.

offset

Set it to n to select the nth variable from the end (for select_last_col()) of from the begin (for select_first_col())

Author(s)

Tiago Olivoto [e-mail]

Examples

library(metan)

################# Adding columns #################
# Variables x and y .after last column
data_ge %>%
  add_cols(x = 10,
           y = 30)
# Variables x and y .before the variable GEN
data_ge %>%
  add_cols(x = 10,
           y = 30,
           .before = GEN)

# Creating a new variable based on the existing ones.
data_ge %>%
  add_cols(GY2 = GY^2,
           GY2_HM = GY2 + HM,
           .after = GY)

############### Reordering columns ###############
reorder_cols(data_ge2, NKR, .before = ENV)
reorder_cols(data_ge2, where(is.factor), .after = last_col())

######## Selecting and removing columns ##########
select_cols(data_ge2, GEN, REP)
remove_cols(data_ge2, GEN, REP)

########## Selecting and removing rows ###########
select_rows(data_ge2, 2:3)
remove_rows(data_ge2, 2:3)

########### Concatenating columns ################
concatenate(data_ge, ENV, GEN, REP)
concatenate(data_ge, ENV, GEN, REP, drop = TRUE)

# Combine with add_cols() and replace_string()
data_ge2 %>%
 add_cols(ENV_GEN = concatenate(., ENV, GEN, pull = TRUE),
          .after = GEN) %>%
 replace_string(ENV_GEN,
                pattern = "H",
                replacement = "HYB_")

# Use prefixes and suffixes
concatenate(data_ge2, REP, prefix = "REP", new_var = REP)

# Use prefixes and suffixes (the ear traits EH, EP, EL, and ED)
add_prefix(data_ge2, PH, EH, EP, EL, prefix = "EAR")
add_suffix(data_ge2, PH, EH, EP, EL, suffix = "EAR", sep = ".")

# Use prefixes and suffixes (colnames)
concatenate(data_ge2, REP, prefix = "REP", new_var = REP)


########### formating column names ###############
# Creating data with messy column names
df <- head(data_ge, 3)
colnames(df) <- c("Env", "gen", "Rep", "GY", "hm")
df
colnames_to_lower(df)
colnames_to_upper(df)
colnames_to_title(df)


################### Adding rows ##################
data_ge %>%
  add_rows(GY = 10.3,
           HM = 100.11,
           .after = 1)

########## checking if a column exists ###########
column_exists(data_g, "GEN")

####### get the levels, level combinations and size of levels ########
get_levels(data_g, GEN)
get_levels_comb(data_ge, ENV, GEN)
get_level_size(data_g, GEN)

############## all possible pairs ################
all_pairs(data_g, GEN)

########## select numeric variables only #########
select_numeric_cols(data_g)
select_non_numeric_cols(data_g)