rearrr

Rearrrange Data
Authors: Ludvig R. Olsen ( r-pkgs@ludvigolsen.dk )
License: MIT
Started: April 2020

CRAN_Status_Badge metacran downloads minimal R version Codecov test coverage Travis build status AppVeyor build status DOI

Overview

R package for rearranging data by a set of methods.

We distinguish between rearrangers and mutators, where the first reorders the data points and the second changes the values of the data points.

When performing an operation relative to a point in an n-dimensional vector space, we refer to the point as the origin. If we, for instance, wish to rotate our data points around the point at x = 3 and y = 7, those are the coordinates of our origin.


Install

CRAN (when available):

install.packages("rearrr")

Development version:

install.packages("devtools")

devtools::install_github("LudvigOlsen/rearrr")

Rearrangers

Function Description
center_max() Center the highest value with values decreasing around it.
center_min() Center the lowest value with values increasing around it.
position_max() Position the highest value with values decreasing around it.
position_min() Position the lowest value with values increasing around it.
pair_extremes() Arrange values as highest, lowest, second highest, second lowest, etc.
closest_to() Order values by shortest distance to an origin.
furthest_from() Order values by longest distance to an origin.
rev_windows() Reverse order window-wise.
roll_elements() Rolls/shifts positions of elements.
shuffle_hierarchy() Shuffle multi-column hierarchy of groups.

Mutators

Function Description Dimensions
rotate_2d(), rotate_3d() Rotate values around an origin in 2 or 3 dimensions. 2 or 3
swirl_2d(), swirl_3d() Swirl values around an origin in 2 or 3 dimensions. 2 or 3
shear_2d(), shear_3d() Shear values around an origin in 2 or 3 dimensions. 2 or 3
expand_distances() Expand distances to an origin. n
expand_distances_each() Expand distances to an origin separately for each dimension. n
cluster_groups() Move data points into clusters around group centroids. n
dim_values() Dim values of a dimension by the distance to an n-dimensional origin. n (alters 1)
flip_values() Flip the values around an origin. n
roll_values() Shifts values and wraps to a range. n
wrap_to_range() Wraps values to a range. n
transfer_centroids() Transfer centroids from one data.frame to another. n
apply_transformation_matrix() Apply transformation matrix to data.frame columns. n

Formers

Function Description
circularize() Create x-coordinates for y-coordinates so they form a circle.
hexagonalize() Create x-coordinates for y-coordinates so they form a hexagon.
square() Create x-coordinates for y-coordinates so they form a square.
triangularize() Create x-coordinates for y-coordinates so they form a triangle.

Generators

Function Description
generate_clusters() Generate n-dimensional clusters.

Additionally, some functions have *_vec() versions, that take and return a vector.

Note: The available utility functions (like scalers, converters and measuring functions) are listed at the bottom of the readme.

Table of Contents

Attach packages

Let’s see some examples. We start by attaching the necessary packages:

library(rearrr)
library(dplyr)

xpectr::set_test_seed(1)

While we can use the functions with data.frames, we showcase many of them with a vector for simplicity. At times, we use the *_vec() version of a function in order to get the output as a vector instead of a data.frame.

The functions work with grouped data.frames and in magrittr pipelines (%>%).

Rearranger examples

Rearrangers change the order of the data points.

Center min/max

center_max(data = 1:10)
#>  [1]  1  3  5  7  9 10  8  6  4  2
center_min(data = 1:10)
#>  [1] 10  8  6  4  2  1  3  5  7  9

Position min/max

position_max(data = 1:10, position = 3)
#>  [1]  6  8 10  9  7  5  4  3  2  1
position_min(data = 1:10, position = 3)
#>  [1]  5  3  1  2  4  6  7  8  9 10

Pair extremes

pair_extremes(data = 1:10)
#> # A tibble: 10 x 2
#>    Value .pair
#>    <int> <fct>
#>  1     1 1    
#>  2    10 1    
#>  3     2 2    
#>  4     9 2    
#>  5     3 3    
#>  6     8 3    
#>  7     4 4    
#>  8     7 4    
#>  9     5 5    
#> 10     6 5

Closest to / furthest from

We use the _vec() versions to get the reordered vectors. For data.frames, use closest_to()/furthest_from() instead.

The origin can be passed as either a specific coordinate (here, a value in data) or a function.

closest_to_vec(data = 1:10, origin_fn = create_origin_fn(median))
#>  [1]  5  6  4  7  3  8  2  9  1 10
furthest_from_vec(data = 1:10, origin = 5)
#>  [1] 10  1  9  2  8  3  7  4  6  5

Reverse windows

We use the _vec() version to get the reordered vector. For data.frames, use rev_windows() instead.

rev_windows_vec(data = 1:10, window_size = 3)
#>  [1]  3  2  1  6  5  4  9  8  7 10

Shuffle Hierarchy

When having a data.frame with multiple grouping columns, we can shuffle them one column (hierarchical level) at a time:

# Shuffle a given data frame 'df'
shuffle_hierarchy(df, group_cols = c("a", "b", "c"))

The columns are shuffled one at a time, as so:

Mutator examples

Mutators change the values of the data points.

Rotate values

2-dimensional rotation:

# Set seed for reproducibility
xpectr::set_test_seed(1)

# Draw random numbers 
random_sample <- round(runif(10), digits = 4)
random_sample
#>  [1] 0.2655 0.3721 0.5729 0.9082 0.2017 0.8984 0.9447 0.6608 0.6291 0.0618

rotate_2d(
  data = random_sample,
  degrees = 60,
  origin_fn = centroid
)
#> # A tibble: 10 x 6
#>    Index  Value Index_rotated Value_rotated .origin   .degrees
#>    <int>  <dbl>         <dbl>         <dbl> <list>       <dbl>
#>  1     1 0.266           3.50       -3.49   <dbl [2]>       60
#>  2     2 0.372           3.91       -2.57   <dbl [2]>       60
#>  3     3 0.573           4.23       -1.60   <dbl [2]>       60
#>  4     4 0.908           4.44       -0.569  <dbl [2]>       60
#>  5     5 0.202           5.55       -0.0564 <dbl [2]>       60
#>  6     6 0.898           5.45        1.16   <dbl [2]>       60
#>  7     7 0.945           5.91        2.05   <dbl [2]>       60
#>  8     8 0.661           6.66        2.77   <dbl [2]>       60
#>  9     9 0.629           7.18        3.62   <dbl [2]>       60
#> 10    10 0.0618          8.17        4.20   <dbl [2]>       60

3-dimensional rotation:

# Set seed
set.seed(3)

# Create a data frame
df <- data.frame(
  "x" = 1:12,
  "y" = c(1, 2, 3, 4, 9, 10, 11,
          12, 15, 16, 17, 18),
  "z" = runif(12)
)

# Perform rotation
rotate_3d(
  data = df,
  x_col = "x",
  y_col = "y",
  z_col = "z",
  x_deg = 45,
  y_deg = 90,
  z_deg = 135,
  origin_fn = centroid
)
#> # A tibble: 12 x 9
#>        x     y     z x_rotated y_rotated z_rotated .origin .degrees .degrees_str
#>    <int> <dbl> <dbl>     <dbl>     <dbl>     <dbl> <list>  <list>   <chr>       
#>  1     1     1 0.168    15.3        9.54    5.96   <dbl [… <dbl [3… x=45,y=90,z…
#>  2     2     2 0.808    14.3       10.2     4.96   <dbl [… <dbl [3… x=45,y=90,z…
#>  3     3     3 0.385    13.3        9.76    3.96   <dbl [… <dbl [3… x=45,y=90,z…
#>  4     4     4 0.328    12.3        9.70    2.96   <dbl [… <dbl [3… x=45,y=90,z…
#>  5     5     9 0.602     7.33       9.97    1.96   <dbl [… <dbl [3… x=45,y=90,z…
#>  6     6    10 0.604     6.33       9.98    0.962  <dbl [… <dbl [3… x=45,y=90,z…
#>  7     7    11 0.125     5.33       9.50   -0.0384 <dbl [… <dbl [3… x=45,y=90,z…
#>  8     8    12 0.295     4.33       9.67   -1.04   <dbl [… <dbl [3… x=45,y=90,z…
#>  9     9    15 0.578     1.33       9.95   -2.04   <dbl [… <dbl [3… x=45,y=90,z…
#> 10    10    16 0.631     0.333     10.0    -3.04   <dbl [… <dbl [3… x=45,y=90,z…
#> 11    11    17 0.512    -0.667      9.88   -4.04   <dbl [… <dbl [3… x=45,y=90,z…
#> 12    12    18 0.505    -1.67       9.88   -5.04   <dbl [… <dbl [3… x=45,y=90,z…

Swirl values

2-dimensional swirling:

# Rotate values
swirl_2d(data = rep(1, 50), radius = 95, origin = c(0, 0))
#> # A tibble: 50 x 7
#>    Index Value Index_swirled Value_swirled .origin   .degrees .radius
#>    <int> <dbl>         <dbl>         <dbl> <list>       <dbl>   <dbl>
#>  1     1     1         0.952          1.05 <dbl [2]>     2.68      95
#>  2     2     1         1.92           1.15 <dbl [2]>     4.24      95
#>  3     3     1         2.88           1.31 <dbl [2]>     5.99      95
#>  4     4     1         3.83           1.53 <dbl [2]>     7.81      95
#>  5     5     1         4.76           1.82 <dbl [2]>     9.66      95
#>  6     6     1         5.68           2.18 <dbl [2]>    11.5       95
#>  7     7     1         6.58           2.59 <dbl [2]>    13.4       95
#>  8     8     1         7.45           3.07 <dbl [2]>    15.3       95
#>  9     9     1         8.30           3.61 <dbl [2]>    17.2       95
#> 10    10     1         9.13           4.21 <dbl [2]>    19.0       95
#> # … with 40 more rows

3-dimensional swirling:

# Set seed
set.seed(4)

# Create a data frame
df <- data.frame(
  "x" = 1:50,
  "y" = 1:50,
  "z" = 1:50,
  "r1" = runif(50),
  "r2" = runif(50) * 35,
  "o" = 1,
  "g" = rep(1:5, each = 10)
)

# They see me swiiirling
swirl_3d(
  data = df,
  x_radius = 45,
  x_col = "x",
  y_col = "y",
  z_col = "z",
  origin = c(0, 0, 0),
  keep_original = FALSE
)
#> # A tibble: 50 x 7
#>    x_swirled y_swirled z_swirled .origin   .degrees  .radius   .radius_str 
#>        <dbl>     <dbl>     <dbl> <list>    <list>    <list>    <chr>       
#>  1         1     0.872      1.11 <dbl [3]> <dbl [3]> <dbl [3]> x=45,y=0,z=0
#>  2         2     1.46       2.42 <dbl [3]> <dbl [3]> <dbl [3]> x=45,y=0,z=0
#>  3         3     1.74       3.87 <dbl [3]> <dbl [3]> <dbl [3]> x=45,y=0,z=0
#>  4         4     1.68       5.40 <dbl [3]> <dbl [3]> <dbl [3]> x=45,y=0,z=0
#>  5         5     1.27       6.96 <dbl [3]> <dbl [3]> <dbl [3]> x=45,y=0,z=0
#>  6         6     0.508      8.47 <dbl [3]> <dbl [3]> <dbl [3]> x=45,y=0,z=0
#>  7         7    -0.604      9.88 <dbl [3]> <dbl [3]> <dbl [3]> x=45,y=0,z=0
#>  8         8    -2.05      11.1  <dbl [3]> <dbl [3]> <dbl [3]> x=45,y=0,z=0
#>  9         9    -3.80      12.1  <dbl [3]> <dbl [3]> <dbl [3]> x=45,y=0,z=0
#> 10        10    -5.82      12.9  <dbl [3]> <dbl [3]> <dbl [3]> x=45,y=0,z=0
#> # … with 40 more rows

Expand distances

# 1d expansion
expand_distances(
  data = random_sample,
  multiplier = 3,
  origin_fn = centroid,
  exponentiate = TRUE
)
#> # A tibble: 10 x 4
#>     Value Value_expanded .exponent .origin  
#>     <dbl>          <dbl>     <dbl> <list>   
#>  1 0.266         -0.575          3 <dbl [1]>
#>  2 0.372         -0.0891         3 <dbl [1]>
#>  3 0.573          0.617          3 <dbl [1]>
#>  4 0.908          2.05           3 <dbl [1]>
#>  5 0.202         -0.908          3 <dbl [1]>
#>  6 0.898          1.99           3 <dbl [1]>
#>  7 0.945          2.26           3 <dbl [1]>
#>  8 0.661          0.916          3 <dbl [1]>
#>  9 0.629          0.803          3 <dbl [1]>
#> 10 0.0618        -1.75           3 <dbl [1]>

2d expansion:

Expand differently in each axis:

# Expand x-axis and contract y-axis
expand_distances_each(
  data.frame("x" = runif(10),
             "y" = runif(10)),
  cols = c("x", "y"),
  multipliers = c(7, 0.5),
  origin_fn = centroid
)
#> # A tibble: 10 x 7
#>         x      y x_expanded y_expanded .multipliers .multipliers_str .origin  
#>     <dbl>  <dbl>      <dbl>      <dbl> <list>       <chr>            <list>   
#>  1 0.622  0.284      1.37        0.309 <dbl [2]>    x=7,y=0.5        <dbl [2]>
#>  2 0.675  0.610      1.74        0.472 <dbl [2]>    x=7,y=0.5        <dbl [2]>
#>  3 0.802  0.524      2.63        0.428 <dbl [2]>    x=7,y=0.5        <dbl [2]>
#>  4 0.260  0.0517    -1.17        0.192 <dbl [2]>    x=7,y=0.5        <dbl [2]>
#>  5 0.760  0.0757     2.33        0.204 <dbl [2]>    x=7,y=0.5        <dbl [2]>
#>  6 0.0199 0.414     -2.85        0.373 <dbl [2]>    x=7,y=0.5        <dbl [2]>
#>  7 0.955  0.578      3.70        0.455 <dbl [2]>    x=7,y=0.5        <dbl [2]>
#>  8 0.437  0.110      0.0675      0.222 <dbl [2]>    x=7,y=0.5        <dbl [2]>
#>  9 0.0892 0.511     -2.36        0.422 <dbl [2]>    x=7,y=0.5        <dbl [2]>
#> 10 0.361  0.169     -0.466       0.251 <dbl [2]>    x=7,y=0.5        <dbl [2]>

Cluster groups

# Set seed for reproducibility
xpectr::set_test_seed(3)

# Create data frame with random data and a grouping variable
df <- data.frame(
  "x" = runif(50),
  "y" = runif(50),
  "g" = rep(c(1, 2, 3, 4, 5), each = 10)
) 

cluster_groups(
  data = df, 
  cols = c("x", "y"), 
  group_col = "g"
)
#> # A tibble: 50 x 5
#>        x     y x_clustered y_clustered     g
#>    <dbl> <dbl>       <dbl>       <dbl> <dbl>
#>  1 0.168 0.229       0.335       0.420     1
#>  2 0.808 0.213       0.449       0.417     1
#>  3 0.385 0.877       0.374       0.540     1
#>  4 0.328 0.993       0.364       0.562     1
#>  5 0.602 0.844       0.413       0.534     1
#>  6 0.604 0.910       0.413       0.547     1
#>  7 0.125 0.471       0.328       0.465     1
#>  8 0.295 0.224       0.358       0.419     1
#>  9 0.578 0.128       0.408       0.401     1
#> 10 0.631 0.280       0.418       0.429     1
#> # … with 40 more rows

Dim values

# Add a column with 1s
df_clustered$o <- 1

# Dim the "o" column based on the data point's distance 
# to the most central point in the cluster
df_clustered %>% 
  dplyr::group_by(g) %>% 
  dim_values(
    cols = c("x_clustered", "y_clustered"), 
    dim_col = "o",
    origin_fn = most_centered
  )
#> # A tibble: 50 x 6
#>    x_clustered y_clustered     g     o o_dimmed .origin  
#>          <dbl>       <dbl> <dbl> <dbl>    <dbl> <list>   
#>  1       0.335       0.420     1     1    0.853 <dbl [2]>
#>  2       0.449       0.417     1     1    0.936 <dbl [2]>
#>  3       0.374       0.540     1     1    0.798 <dbl [2]>
#>  4       0.364       0.562     1     1    0.765 <dbl [2]>
#>  5       0.413       0.534     1     1    0.819 <dbl [2]>
#>  6       0.413       0.547     1     1    0.801 <dbl [2]>
#>  7       0.328       0.465     1     1    0.831 <dbl [2]>
#>  8       0.358       0.419     1     1    0.889 <dbl [2]>
#>  9       0.408       0.401     1     1    0.943 <dbl [2]>
#> 10       0.418       0.429     1     1    1     <dbl [2]>
#> # … with 40 more rows

Flip values

# The median value to flip around
median(random_sample)
#> [1] 0.601

# Flip the random numbers around the median
flip_values(
  data = random_sample, 
  origin_fn = create_origin_fn(median)
)
#> # A tibble: 10 x 3
#>     Value Value_flipped .origin  
#>     <dbl>         <dbl> <list>   
#>  1 0.266          0.936 <dbl [1]>
#>  2 0.372          0.830 <dbl [1]>
#>  3 0.573          0.629 <dbl [1]>
#>  4 0.908          0.294 <dbl [1]>
#>  5 0.202          1.00  <dbl [1]>
#>  6 0.898          0.304 <dbl [1]>
#>  7 0.945          0.257 <dbl [1]>
#>  8 0.661          0.541 <dbl [1]>
#>  9 0.629          0.573 <dbl [1]>
#> 10 0.0618         1.14  <dbl [1]>

Forming examples

Circularize points

circularize(runif(200))
#> # A tibble: 200 x 4
#>     Value .circle_x .degrees .origin  
#>     <dbl>     <dbl>    <dbl> <list>   
#>  1 0.766     -0.418    148.  <dbl [2]>
#>  2 0.682      0.461     21.3 <dbl [2]>
#>  3 0.209     -0.398    216.  <dbl [2]>
#>  4 0.712      0.448     25.1 <dbl [2]>
#>  5 0.605      0.484     12.0 <dbl [2]>
#>  6 0.341      0.467    341.  <dbl [2]>
#>  7 0.0412    -0.178    249.  <dbl [2]>
#>  8 0.402     -0.484    192.  <dbl [2]>
#>  9 0.0791     0.256    301.  <dbl [2]>
#> 10 0.313     -0.457    203.  <dbl [2]>
#> # … with 190 more rows

Hexagonalize points

hexagonalize(runif(200))
#> # A tibble: 200 x 3
#>     Value .hexagon_x .edge
#>     <dbl>      <dbl> <fct>
#>  1 0.0983   -0.0945  4    
#>  2 0.319    -0.413   5    
#>  3 0.996     0.00215 1    
#>  4 0.726    -0.413   5    
#>  5 0.687    -0.413   5    
#>  6 0.629    -0.413   5    
#>  7 0.803     0.335   1    
#>  8 0.543     0.413   2    
#>  9 0.862     0.234   1    
#> 10 0.984    -0.0222  6    
#> # … with 190 more rows

Square points

square(runif(200))
#> # A tibble: 200 x 3
#>    Value .square_x .edge
#>    <dbl>     <dbl> <fct>
#>  1 0.296    0.291  2    
#>  2 0.231    0.225  2    
#>  3 0.914    0.0854 1    
#>  4 0.332    0.327  2    
#>  5 0.556   -0.443  4    
#>  6 0.582   -0.418  4    
#>  7 0.217    0.212  2    
#>  8 0.205    0.200  2    
#>  9 0.970    0.0297 1    
#> 10 0.801   -0.199  4    
#> # … with 190 more rows

Triangularize points

triangularize(runif(200))
#> # A tibble: 200 x 3
#>      Value .triangle_x .edge
#>      <dbl>       <dbl> <fct>
#>  1 0.00823       0     3    
#>  2 0.986         0     3    
#>  3 0.519         0.473 1    
#>  4 0.662         0     3    
#>  5 0.632         0.360 1    
#>  6 0.734         0.258 1    
#>  7 0.668         0     3    
#>  8 0.642         0.350 1    
#>  9 0.584         0.409 1    
#> 10 0.741         0     3    
#> # … with 190 more rows

Generators

Generate clusters

generate_clusters(
  num_rows = 50,
  num_cols = 5,
  num_clusters = 5,
  compactness = 1.6
)
#> # A tibble: 50 x 6
#>       D1    D2    D3     D4    D5 .cluster
#>    <dbl> <dbl> <dbl>  <dbl> <dbl> <fct>   
#>  1 0.316 0.553 0.523 0.202  0.653 1       
#>  2 0.279 0.753 0.447 0.0774 0.788 1       
#>  3 0.301 0.516 0.530 0.0541 0.842 1       
#>  4 0.350 0.594 0.540 0.0701 0.922 1       
#>  5 0.239 0.497 0.677 0.102  0.621 1       
#>  6 0.264 0.632 0.670 0.0742 0.845 1       
#>  7 0.273 0.589 0.696 0.0681 0.885 1       
#>  8 0.273 0.592 0.559 0.0944 0.987 1       
#>  9 0.336 0.569 0.618 0.212  0.670 1       
#> 10 0.302 0.605 0.545 0.0601 0.938 1       
#> # … with 40 more rows

Utilities

Converters

Function Description
radians_to_degrees() Converts radians to degrees.
degrees_to_radians() Converts degrees to radians.

Scalers

Function Description
min_max_scale() Scale values to a range.
to_unit_length() Scale vectors to unit length row-wise or column-wise.

Measuring functions

Function Description
distance() Calculates distance to an origin.
angle() Calculates angle between points and an origin.
vector_length() Calculates vector length/magnitude row-wise or column-wise.

Helper functions

Function Description
create_origin_fn() Creates function for finding origin coordinates (like centroid()).
centroid() Calculates the mean of each supplied vector/column.
most_centered() Finds coordinates of data point closest to the centroid.
is_most_centered() Indicates whether a data point is the most centered.
midrange() Calculates the midrange of each supplied vector/column.
create_n_fn() Creates function for finding the number of positions to move.
median_index() Calculates median index of each supplied vector.
quantile_index() Calculates quantile of indices for each supplied vector.