versus: Compare Data Frames

Description

Compare two tables

Author(s)

Maintainer: Ryan Dickerson fresh.tent5866@fastmail.com [copyright holder]

See Also

Useful links:

• https://eutwt.github.io/versus/

• https://github.com/eutwt/versus

• Report bugs at https://github.com/eutwt/versus/issues

Compare two data frames

Description

compare() creates a representation of the differences between two tables, along with a shallow copy of the tables. This output is used as the comparison argument when exploring the differences further with other versus functions e.g. ⁠slice_*()⁠ and ⁠weave_*()⁠.

Usage

compare(table_a, table_b, by, allow_both_NA = TRUE, coerce = TRUE)

Arguments

Value

compare()

A list of data frames having the following elements:

tables

A data frame with one row per input table showing the number of rows and columns in each.

by

A data frame with one row per by column showing the class of the column in each of the input tables.

intersection

A data frame with one row per column common to table_a and table_b and columns "n_diffs" showing the number of values which are different between the two tables, "class_a"/"class_b" the class of the column in each table, and "value_diffs" a (nested) data frame showing the the values in each table which are unequal and the by columns

unmatched_cols

A data frame with one row per column which is in one input table but not the other and columns "table": which table the column appears in, "column": the name of the column, and "class": the class of the column.

unmatched_rows

A data frame which, for each row present in one input table but not the other, contains the column "table" showing which table the row appears in and the by columns for that row.

data.table inputs

If the input is a data.table, you may want compare() to make a deep copy instead of a shallow copy so that future changes to the table don't affect the comparison. To achieve this, you can set options(versus.copy_data_table = TRUE).

Examples

compare(example_df_a, example_df_b, by = car)

Modified version of datasets::mtcars - version a

Description

A version of mtcars with some values altered and some rows/columns removed. Not for informational purposes, used only to demonstrate the comparison of two slightly different data frames. Since some values were altered at random, the values do not necessarily reflect the true original values. The variables are as follows:

Usage

example_df_a

Format

A data frame with 9 rows and 9 variables:

car

The rowname in the corresponding datasets::mtcars row

mpg

Miles/(US) gallon

cyl

Number of cylinders

disp

Displacement (cu.in.)

hp

Gross horsepower

drat

Rear axle ratio

wt

Weight (1000 lbs)

vs

Engine (0 = V-shaped, 1 = straight)

am

Transmission (0 = automatic, 1 = manual)

Source

Sourced from the CRAN datasets package, with modified values. Originally from Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411.

Modified version of datasets::mtcars - version b

Description

A version of mtcars with some values altered and some rows/columns removed. Not for informational purposes, used only to demonstrate the comparison of two slightly different data frames. Since some values were altered at random, the values do not necessarily reflect the true original values. The variables are as follows:

Usage

example_df_b

Format

A data frame with 9 rows and 9 variables:

car

The rowname in the corresponding datasets::mtcars row

wt

Weight (1000 lbs)

mpg

Miles/(US) gallon

hp

Gross horsepower

cyl

Number of cylinders

disp

Displacement (cu.in.)

carb

Number of carburetors

drat

Rear axle ratio

vs

Engine (0 = V-shaped, 1 = straight)

Source

Sourced from the CRAN datasets package, with modified values. Originally from Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411.

Get rows with differing values

Description

Get rows with differing values

Usage

slice_diffs(comparison, table, column = everything())

Arguments

Value

The input table is filtered to the rows for which comparison shows differing values for one of the columns selected by column

Examples

comp <- compare(example_df_a, example_df_b, by = car)
comp |> slice_diffs("a", mpg)
comp |> slice_diffs("b", mpg)
comp |> slice_diffs("a", c(mpg, disp))

Get rows in only one table

Description

Get rows in only one table

Usage

slice_unmatched(comparison, table)

slice_unmatched_both(comparison)

Arguments

Value

Examples

comp <- compare(example_df_a, example_df_b, by = car)
comp |> slice_unmatched("a")
comp |> slice_unmatched("b")

# slice_unmatched(comp, "a") output is the same as
example_df_a |> dplyr::anti_join(example_df_b, by = comp$by$column)

comp |> slice_unmatched_both()

Get the differing values from a comparison

Description

Get the differing values from a comparison

Usage

value_diffs(comparison, column)

value_diffs_stacked(comparison, column = everything())

Arguments

Value

Examples

comp <- compare(example_df_a, example_df_b, by = car)
value_diffs(comp, disp)
value_diffs_stacked(comp, c(disp, mpg))

Argument type: tidy-select

Description

This page describes the ⁠<tidy-select>⁠ argument modifier which indicates that the argument uses tidy selection, a sub-type of tidy evaluation. If you've never heard of tidy evaluation before, start with the practical introduction in https://r4ds.hadley.nz/functions.html#data-frame-functions then then read more about the underlying theory in https://rlang.r-lib.org/reference/topic-data-mask.html.

Overview of selection features

tidyselect implements a DSL for selecting variables. It provides helpers for selecting variables:

• var1:var10: variables lying between var1 on the left and var10 on the right.

• starts_with("a"): names that start with "a".

• ends_with("z"): names that end with "z".

• contains("b"): names that contain "b".

• matches("x.y"): names that match regular expression x.y.

• num_range(x, 1:4): names following the pattern, x1, x2, ..., x4.

• all_of(vars)/any_of(vars): matches names stored in the character vector vars. all_of(vars) will error if the variables aren't present; any_of(var) will match just the variables that exist.

• everything(): all variables.

• last_col(): furthest column on the right.

• where(is.numeric): all variables where is.numeric() returns TRUE.

As well as operators for combining those selections:

• !selection: only variables that don't match selection.

• selection1 & selection2: only variables included in both selection1 and selection2.

• selection1 | selection2: all variables that match either selection1 or selection2.

Key techniques

• If you want the user to supply a tidyselect specification in a function argument, you need to tunnel the selection through the function argument. This is done by embracing the function argument {{ }}, e.g unnest(df, {{ vars }}).

• If you have a character vector of column names, use all_of() or any_of(), depending on whether or not you want unknown variable names to cause an error, e.g unnest(df, all_of(vars)), unnest(df, !any_of(vars)).

• To suppress ⁠R CMD check⁠ NOTEs about unknown variables use "var" instead of var:

# has NOTE
df %>% select(x, y, z)

# no NOTE
df %>% select("x", "y", "z")

Get differences in context

Description

Get differences in context

Usage

weave_diffs_long(comparison, column = everything())

weave_diffs_wide(comparison, column = everything())

Arguments

Value

Examples

comp <- compare(example_df_a, example_df_b, by = car)
comp |> weave_diffs_wide(disp)
comp |> weave_diffs_wide(c(mpg, disp))
comp |> weave_diffs_long(disp)
comp |> weave_diffs_long(c(mpg, disp))