Awesome Open Source
Awesome Open Source

nanny: A tidyverse machine-learning suite

It tidies up your playground!

Lifecycle:maturing

Please have a look also to

  • tidygate for adding custom gate information to your tibble
  • tidyHeatmap for heatmaps produced with tidy principles
  • tidybulk brings transcriptomics to the tidyverse

Functions/utilities available

It does a lot! cluster, PCA, permute, impute, rotate, redundancy-removal, triangular, smart-subset, identify abundant and variable features.

Function Description
reduce_dimensions Perform dimensionality reduction (PCA, MDS, tSNE)
rotate_dimensions Rotate two dimensions of a degree
cluster_elements Labels elements with cluster identity
remove_redundancy Filter out elements with highly correlated features
fill_missing Fill values of missing element/feature pairs
impute_missing Impute values of missing element/feature pairs
permute_nest From one column build a two permuted columns with nested information
combine_nest From one column build a two combination columns with nested information
keep_variable Keep top variable features
lower_triangular keep rows corresponding to a lower triangular matrix
Utilities Description
as_matrix Robustly convert a tibble to matrix
subset Select columns with information relative to a column of interest

Minimal input data frame

element feature value
chr or fctr chr or fctr numeric

Output data frame

element feature value new information
chr or fctr chr or fctr numeric

Installation

devtools::install_github("stemangiola/nanny")

Introduction

nanny is a collection of wrapper functions for high level data analysis and manipulation following the tidy paradigm.

Tidy data

mtcars_tidy = 
    mtcars %>% 
    as_tibble(rownames="car_model") %>% 
    mutate_at(vars(-car_model,- hp, -vs), scale) %>%
    gather(feature, value, -car_model, -hp, -vs)

mtcars_tidy
## # A tibble: 288 x 5
##    car_model            hp    vs feature  value
##    <chr>             <dbl> <dbl> <chr>    <dbl>
##  1 Mazda RX4           110     0 mpg      0.151
##  2 Mazda RX4 Wag       110     0 mpg      0.151
##  3 Datsun 710           93     1 mpg      0.450
##  4 Hornet 4 Drive      110     1 mpg      0.217
##  5 Hornet Sportabout   175     0 mpg     -0.231
##  6 Valiant             105     1 mpg     -0.330
##  7 Duster 360          245     0 mpg     -0.961
##  8 Merc 240D            62     1 mpg      0.715
##  9 Merc 230             95     1 mpg      0.450
## 10 Merc 280            123     1 mpg     -0.148
## # … with 278 more rows

reduce_dimensions

We may want to reduce the dimensions of our data, for example using PCA, MDS of tSNE algorithms. reduce_dimensions takes a tibble, column names (as symbols; for element, feature and value) and a method (e.g., MDS, PCA or tSNE) as arguments and returns a tibble with additional columns for the reduced dimensions.

MDS

mtcars_tidy_MDS =
  mtcars_tidy %>%
  reduce_dimensions(car_model, feature, value, method="MDS", .dims = 3)

On the x and y axes axis we have the reduced dimensions 1 to 3, data is coloured by cell type.

mtcars_tidy_MDS %>% subset(car_model)  %>% select(contains("Dim"), everything())
## # A tibble: 32 x 6
##       Dim1    Dim2     Dim3 car_model            hp    vs
##      <dbl>   <dbl>    <dbl> <chr>             <dbl> <dbl>
##  1 -0.342   0.499  -0.0853  Mazda RX4           110     0
##  2 -0.323   0.439   0.00461 Mazda RX4 Wag       110     0
##  3 -0.787  -0.140  -0.130   Datsun 710           93     1
##  4  0.164  -0.670  -0.133   Hornet 4 Drive      110     1
##  5  0.602  -0.205  -0.339   Hornet Sportabout   175     0
##  6  0.284  -0.777  -0.0186  Valiant             105     1
##  7  0.810   0.172  -0.179   Duster 360          245     0
##  8 -0.417  -0.452   0.335   Merc 240D            62     1
##  9 -0.526  -0.715   0.679   Merc 230             95     1
## 10 -0.0277  0.0696  0.387   Merc 280            123     1
## # … with 22 more rows
mtcars_tidy_MDS %>%
    subset(car_model) %>%
  GGally::ggpairs(columns = 4:6, ggplot2::aes(colour=factor(vs)))

PCA

mtcars_tidy_PCA =
  mtcars_tidy %>%
  reduce_dimensions(car_model, feature, value, method="PCA", .dims = 3)

On the x and y axes axis we have the reduced dimensions 1 to 3, data is coloured by cell type.

mtcars_tidy_PCA %>% subset(car_model) %>% select(contains("PC"), everything())
## # A tibble: 32 x 6
##        PC1     PC2       PC3 car_model            hp    vs
##      <dbl>   <dbl>     <dbl> <chr>             <dbl> <dbl>
##  1 -0.136   0.274  -0.0602   Mazda RX4           110     0
##  2 -0.145   0.253  -0.0261   Mazda RX4 Wag       110     0
##  3 -0.212  -0.0619 -0.146    Datsun 710           93     1
##  4  0.0767 -0.331  -0.130    Hornet 4 Drive      110     1
##  5  0.205  -0.0498 -0.196    Hornet Sportabout   175     0
##  6  0.0920 -0.306  -0.0449   Valiant             105     1
##  7  0.210   0.130   0.000724 Duster 360          245     0
##  8 -0.124  -0.252   0.219    Merc 240D            62     1
##  9 -0.0997 -0.283   0.243    Merc 230             95     1
## 10 -0.0242  0.0185  0.547    Merc 280            123     1
## # … with 22 more rows
mtcars_tidy_PCA %>%
     subset(car_model) %>%
  GGally::ggpairs(columns = 4:6, ggplot2::aes(colour=factor(vs)))

tSNE

mtcars_tidy_tSNE =
    mtcars_tidy %>% 
    reduce_dimensions(car_model, feature, value, method = "tSNE")

Plot

mtcars_tidy_tSNE %>%
    subset(car_model) %>%
    select(contains("tSNE"), everything()) 
## # A tibble: 32 x 5
##     tSNE1   tSNE2 car_model            hp    vs
##     <dbl>   <dbl> <chr>             <dbl> <dbl>
##  1 -63.2    0.149 Mazda RX4           110     0
##  2 -65.6   -7.21  Mazda RX4 Wag       110     0
##  3 -60.8  -50.2   Datsun 710           93     1
##  4  28.4    0.525 Hornet 4 Drive      110     1
##  5  98.9   23.6   Hornet Sportabout   175     0
##  6  36.0    2.21  Valiant             105     1
##  7  74.7   48.5   Duster 360          245     0
##  8  -5.49  -6.48  Merc 240D            62     1
##  9  -7.30 -15.1   Merc 230             95     1
## 10 -33.1    1.88  Merc 280            123     1
## # … with 22 more rows
mtcars_tidy_tSNE %>%
    subset(car_model) %>%
    ggplot(aes(x = `tSNE1`, y = `tSNE2`, color=factor(vs))) + geom_point() + my_theme

rotate_dimensions

We may want to rotate the reduced dimensions (or any two numeric columns really) of our data, of a set angle. rotate_dimensions takes a tibble, column names (as symbols; for element, feature and value) and an angle as arguments and returns a tibble with additional columns for the rotated dimensions. The rotated dimensions will be added to the original data set as <NAME OF DIMENSION> rotated <ANGLE> by default, or as specified in the input arguments.

mtcars_tidy_MDS.rotated =
  mtcars_tidy_MDS %>%
    rotate_dimensions(`Dim1`, `Dim2`, .element = car_model, rotation_degrees = 45, action="get")

Original On the x and y axes axis we have the first two reduced dimensions, data is coloured by cell type.

mtcars_tidy_MDS.rotated %>%
    ggplot(aes(x=`Dim1`, y=`Dim2`, color=factor(vs) )) +
  geom_point() +
  my_theme

Rotated On the x and y axes axis we have the first two reduced dimensions rotated of 45 degrees, data is coloured by cell type.

mtcars_tidy_MDS.rotated %>%
    ggplot(aes(x=`Dim1 rotated 45`, y=`Dim2 rotated 45`, color=factor(vs) )) +
  geom_point() +
  my_theme

cluster_elements

We may want to cluster our data (e.g., using k-means element-wise). cluster_elements takes as arguments a tibble, column names (as symbols; for element, feature and value) and returns a tibble with additional columns for the cluster annotation. At the moment only k-means clustering is supported, the plan is to introduce more clustering methods.

k-means

mtcars_tidy_cluster = mtcars_tidy_MDS %>%
  cluster_elements(car_model, feature, value, method="kmeans",  centers = 2, action="get" )

We can add cluster annotation to the MDS dimension reduced data set and plot.

 mtcars_tidy_cluster %>%
    ggplot(aes(x=`Dim1`, y=`Dim2`, color=cluster_kmeans)) +
  geom_point() +
  my_theme

SNN

mtcars_tidy_SNN =
    mtcars_tidy_tSNE %>%
    cluster_elements(car_model, feature, value, method = "SNN")

We can add cluster annotation to the tSNE dimension reduced data set and plot.

mtcars_tidy_SNN %>%
    subset(car_model) %>%
    select(contains("tSNE"), everything()) 
## # A tibble: 32 x 6
##     tSNE1   tSNE2 car_model            hp    vs cluster_SNN
##     <dbl>   <dbl> <chr>             <dbl> <dbl> <fct>      
##  1 -63.2    0.149 Mazda RX4           110     0 0          
##  2 -65.6   -7.21  Mazda RX4 Wag       110     0 0          
##  3 -60.8  -50.2   Datsun 710           93     1 0          
##  4  28.4    0.525 Hornet 4 Drive      110     1 0          
##  5  98.9   23.6   Hornet Sportabout   175     0 0          
##  6  36.0    2.21  Valiant             105     1 0          
##  7  74.7   48.5   Duster 360          245     0 0          
##  8  -5.49  -6.48  Merc 240D            62     1 0          
##  9  -7.30 -15.1   Merc 230             95     1 0          
## 10 -33.1    1.88  Merc 280            123     1 0          
## # … with 22 more rows
mtcars_tidy_SNN %>%
    subset(car_model) %>%
    ggplot(aes(x = `tSNE1`, y = `tSNE2`, color=cluster_SNN)) + geom_point() + my_theme

gating

mtcars_tidy_MDS %>%
    cluster_elements(car_model, c(Dim1, Dim2), method="gate", .color=group)

## # A tibble: 288 x 9
##    car_model    hp    vs feature  value    Dim1   Dim2  Dim3 gate 
##    <chr>     <dbl> <dbl> <chr>    <dbl>   <dbl>  <dbl> <dbl> <chr>
##  1 Merc 280    123     1 mpg     -0.148 -0.0277 0.0696 0.387 1    
##  2 Merc 280C   123     1 mpg     -0.380 -0.0137 0.0166 0.464 1    
##  3 Merc 280    123     1 cyl     -0.105 -0.0277 0.0696 0.387 1    
##  4 Merc 280C   123     1 cyl     -0.105 -0.0137 0.0166 0.464 1    
##  5 Merc 280    123     1 disp    -0.509 -0.0277 0.0696 0.387 1    
##  6 Merc 280C   123     1 disp    -0.509 -0.0137 0.0166 0.464 1    
##  7 Merc 280    123     1 drat     0.605 -0.0277 0.0696 0.387 1    
##  8 Merc 280C   123     1 drat     0.605 -0.0137 0.0166 0.464 1    
##  9 Merc 280    123     1 wt       0.228 -0.0277 0.0696 0.387 1    
## 10 Merc 280C   123     1 wt       0.228 -0.0137 0.0166 0.464 1    
## # … with 278 more rows

drop_redundant

We may want to remove redundant elements from the original data set (e.g., elements or features), for example if we want to define cell-type specific signatures with low element redundancy. remove_redundancy takes as arguments a tibble, column names (as symbols; for element, feature and value) and returns a tibble dropped recundant elements (e.g., elements). Two redundancy estimation approaches are supported:

removal of highly correlated clusters of elements (keeping a representative) with method=“correlation”

mtcars_tidy_non_redundant =
    mtcars_tidy_MDS %>%
  remove_redundancy(car_model, feature, value)

We can visualise how the reduced redundancy with the reduced dimentions look like

mtcars_tidy_non_redundant %>%
    subset(car_model) %>%
    ggplot(aes(x=`Dim1`, y=`Dim2`, color=factor(vs))) +
  geom_point() +
  my_theme

fill_missing

This function allows to obtain a rectangular underlying data structure, where every element has one feature, filling missing element/feature pairs with a value of choice (e.g., 0)

We create a non-rectangular data frame

mtcars_tidy_non_rectangular = mtcars_tidy %>% slice(-1)

We fill the missing value with the value of 0

mtcars_tidy_non_rectangular %>% fill_missing(car_model, feature, value, fill_with = 0)
## # A tibble: 288 x 5
##    car_model            hp    vs feature  value
##    <chr>             <dbl> <dbl> <chr>    <dbl>
##  1 Mazda RX4           110     0 mpg      0    
##  2 Mazda RX4 Wag       110     0 mpg      0.151
##  3 Datsun 710           93     1 mpg      0.450
##  4 Hornet 4 Drive      110     1 mpg      0.217
##  5 Hornet Sportabout   175     0 mpg     -0.231
##  6 Valiant             105     1 mpg     -0.330
##  7 Duster 360          245     0 mpg     -0.961
##  8 Merc 240D            62     1 mpg      0.715
##  9 Merc 230             95     1 mpg      0.450
## 10 Merc 280            123     1 mpg     -0.148
## # … with 278 more rows

impute_missing

This function allows to obtain a rectangular underlying data structure, where every element has one feature, imputig missing element/feature pairs with a function of choice (e.g., median)

We impute the missing value with the a summary value (median by default) according to a grouping

mtcars_tidy_non_rectangular %>% mutate(vs = factor(vs)) %>% 
    impute_missing( car_model, feature, value,  ~ vs) %>%
    
    # Print imputed first
    arrange(car_model != "Mazda RX4" | feature != "mpg")
## # A tibble: 288 x 5
##    car_model              hp vs    feature  value
##    <chr>               <dbl> <fct> <chr>    <dbl>
##  1 Mazda RX4             110 0     mpg     -0.762
##  2 Mazda RX4 Wag         110 0     mpg      0.151
##  3 Hornet Sportabout     175 0     mpg     -0.231
##  4 Duster 360            245 0     mpg     -0.961
##  5 Merc 450SE            180 0     mpg     -0.612
##  6 Merc 450SL            180 0     mpg     -0.463
##  7 Merc 450SLC           180 0     mpg     -0.811
##  8 Cadillac Fleetwood    205 0     mpg     -1.61 
##  9 Lincoln Continental   215 0     mpg     -1.61 
## 10 Chrysler Imperial     230 0     mpg     -0.894
## # … with 278 more rows

permute_nest

From one column build a two permuted columns with nested information

mtcars_tidy_permuted = 
    mtcars_tidy %>%
    permute_nest(car_model, c(feature,value))

mtcars_tidy_permuted
## # A tibble: 992 x 3
##    car_model_1 car_model_2        data             
##    <fct>       <fct>              <list>           
##  1 AMC Javelin Cadillac Fleetwood <tibble [18 × 3]>
##  2 AMC Javelin Camaro Z28         <tibble [18 × 3]>
##  3 AMC Javelin Chrysler Imperial  <tibble [18 × 3]>
##  4 AMC Javelin Datsun 710         <tibble [18 × 3]>
##  5 AMC Javelin Dodge Challenger   <tibble [18 × 3]>
##  6 AMC Javelin Duster 360         <tibble [18 × 3]>
##  7 AMC Javelin Ferrari Dino       <tibble [18 × 3]>
##  8 AMC Javelin Fiat 128           <tibble [18 × 3]>
##  9 AMC Javelin Fiat X1-9          <tibble [18 × 3]>
## 10 AMC Javelin Ford Pantera L     <tibble [18 × 3]>
## # … with 982 more rows

combine_nest

From one column build a two combination columns with nested information

mtcars_tidy %>%
    combine_nest(car_model, value)
## # A tibble: 496 x 3
##    car_model_1 car_model_2        data             
##    <fct>       <fct>              <list>           
##  1 AMC Javelin Cadillac Fleetwood <tibble [18 × 2]>
##  2 AMC Javelin Camaro Z28         <tibble [18 × 2]>
##  3 AMC Javelin Chrysler Imperial  <tibble [18 × 2]>
##  4 AMC Javelin Datsun 710         <tibble [18 × 2]>
##  5 AMC Javelin Dodge Challenger   <tibble [18 × 2]>
##  6 AMC Javelin Duster 360         <tibble [18 × 2]>
##  7 AMC Javelin Ferrari Dino       <tibble [18 × 2]>
##  8 AMC Javelin Fiat 128           <tibble [18 × 2]>
##  9 AMC Javelin Fiat X1-9          <tibble [18 × 2]>
## 10 AMC Javelin Ford Pantera L     <tibble [18 × 2]>
## # … with 486 more rows

lower_triangular

keep rows corresponding to a lower triangular matrix

mtcars_tidy_permuted %>%
    
    # Summarise mpg
    mutate(data = map(data, ~ .x %>% filter(feature == "mpg") %>% summarise(mean(value)))) %>%
    unnest(data) %>%
    
    # Lower triangular
    lower_triangular(car_model_1, car_model_2,  `mean(value)`)
## # A tibble: 496 x 3
##    car_model_1    car_model_2       `mean(value)`
##    <fct>          <fct>                     <dbl>
##  1 Mazda RX4      Mazda RX4 Wag           0.151  
##  2 Mazda RX4      Datsun 710              0.300  
##  3 Mazda RX4 Wag  Datsun 710              0.300  
##  4 Mazda RX4      Hornet 4 Drive          0.184  
##  5 Mazda RX4 Wag  Hornet 4 Drive          0.184  
##  6 Datsun 710     Hornet 4 Drive          0.333  
##  7 Mazda RX4      Hornet Sportabout      -0.0399 
##  8 Mazda RX4 Wag  Hornet Sportabout      -0.0399 
##  9 Datsun 710     Hornet Sportabout       0.109  
## 10 Hornet 4 Drive Hornet Sportabout      -0.00674
## # … with 486 more rows

keep_variable

Keep top variable features

mtcars_tidy %>%
    keep_variable(car_model, feature, value, top=10)
## # A tibble: 288 x 5
##    car_model            hp    vs feature  value
##    <chr>             <dbl> <dbl> <chr>    <dbl>
##  1 Mazda RX4           110     0 mpg      0.151
##  2 Mazda RX4 Wag       110     0 mpg      0.151
##  3 Datsun 710           93     1 mpg      0.450
##  4 Hornet 4 Drive      110     1 mpg      0.217
##  5 Hornet Sportabout   175     0 mpg     -0.231
##  6 Valiant             105     1 mpg     -0.330
##  7 Duster 360          245     0 mpg     -0.961
##  8 Merc 240D            62     1 mpg      0.715
##  9 Merc 230             95     1 mpg      0.450
## 10 Merc 280            123     1 mpg     -0.148
## # … with 278 more rows

as_matrix

Robustly convert a tibble to matrix

mtcars_tidy %>%
    select(car_model, feature, value) %>%
    spread(feature, value) %>%
    as_matrix(rownames = car_model) %>%
    head()
##                            am       carb       cyl       disp       drat
## AMC Javelin        -0.8141431 -0.5030337  1.014882  0.5912449 -0.8351978
## Cadillac Fleetwood -0.8141431  0.7352031  1.014882  1.9467538 -1.2466598
## Camaro Z28         -0.8141431  0.7352031  1.014882  0.9623962  0.2495658
## Chrysler Imperial  -0.8141431  0.7352031  1.014882  1.6885616 -0.6855752
## Datsun 710          1.1899014 -1.1221521 -1.224858 -0.9901821  0.4739996
## Dodge Challenger   -0.8141431 -0.5030337  1.014882  0.7042040 -1.5646078
##                          gear        mpg        qsec         wt
## AMC Javelin        -0.9318192 -0.8114596 -0.30708866  0.2225442
## Cadillac Fleetwood -0.9318192 -1.6078826  0.07344945  2.0775048
## Camaro Z28         -0.9318192 -1.1267104 -1.36476075  0.6364610
## Chrysler Imperial  -0.9318192 -0.8944204 -0.23993487  2.1745964
## Datsun 710          0.4235542  0.4495434  0.42600682 -0.9170046
## Dodge Challenger   -0.9318192 -0.7616832 -0.54772305  0.3094156

subset

Select columns with information relative to a column of interest

mtcars_tidy %>%
    subset(car_model)
## # A tibble: 32 x 3
##    car_model            hp    vs
##    <chr>             <dbl> <dbl>
##  1 Mazda RX4           110     0
##  2 Mazda RX4 Wag       110     0
##  3 Datsun 710           93     1
##  4 Hornet 4 Drive      110     1
##  5 Hornet Sportabout   175     0
##  6 Valiant             105     1
##  7 Duster 360          245     0
##  8 Merc 240D            62     1
##  9 Merc 230             95     1
## 10 Merc 280            123     1
## # … with 22 more rows

nest_subset

Nest a data frame based on the columns with information relative to the column provided to nest

mtcars_tidy %>% nest_subset(data = -car_model)
## # A tibble: 32 x 4
##    car_model            hp    vs data            
##    <chr>             <dbl> <dbl> <list>          
##  1 Mazda RX4           110     0 <tibble [9 × 2]>
##  2 Mazda RX4 Wag       110     0 <tibble [9 × 2]>
##  3 Datsun 710           93     1 <tibble [9 × 2]>
##  4 Hornet 4 Drive      110     1 <tibble [9 × 2]>
##  5 Hornet Sportabout   175     0 <tibble [9 × 2]>
##  6 Valiant             105     1 <tibble [9 × 2]>
##  7 Duster 360          245     0 <tibble [9 × 2]>
##  8 Merc 240D            62     1 <tibble [9 × 2]>
##  9 Merc 230             95     1 <tibble [9 × 2]>
## 10 Merc 280            123     1 <tibble [9 × 2]>
## # … with 22 more rows

ADD versus GET versus ONLY modes

Every function takes a tidyfeatureomics structured data as input, and (i) with action=“add” outputs the new information joint to the original input data frame (default), (ii) with action=“get” the new information with the element or feature relative informatin depending on what the analysis is about, or (iii) with action=“only” just the new information. For example, from this data set

  mtcars_tidy
## # A tibble: 288 x 5
##    car_model            hp    vs feature  value
##    <chr>             <dbl> <dbl> <chr>    <dbl>
##  1 Mazda RX4           110     0 mpg      0.151
##  2 Mazda RX4 Wag       110     0 mpg      0.151
##  3 Datsun 710           93     1 mpg      0.450
##  4 Hornet 4 Drive      110     1 mpg      0.217
##  5 Hornet Sportabout   175     0 mpg     -0.231
##  6 Valiant             105     1 mpg     -0.330
##  7 Duster 360          245     0 mpg     -0.961
##  8 Merc 240D            62     1 mpg      0.715
##  9 Merc 230             95     1 mpg      0.450
## 10 Merc 280            123     1 mpg     -0.148
## # … with 278 more rows

action=“add” (Default) We can add the MDS dimensions to the original data set

  mtcars_tidy %>%
    reduce_dimensions(
        car_model, feature, value, 
        method="MDS" ,
        .dims = 3,
        action="add"
    )
## # A tibble: 288 x 8
##    car_model            hp    vs feature  value    Dim1    Dim2     Dim3
##    <chr>             <dbl> <dbl> <chr>    <dbl>   <dbl>   <dbl>    <dbl>
##  1 Mazda RX4           110     0 mpg      0.151 -0.342   0.499  -0.0853 
##  2 Mazda RX4 Wag       110     0 mpg      0.151 -0.323   0.439   0.00461
##  3 Datsun 710           93     1 mpg      0.450 -0.787  -0.140  -0.130  
##  4 Hornet 4 Drive      110     1 mpg      0.217  0.164  -0.670  -0.133  
##  5 Hornet Sportabout   175     0 mpg     -0.231  0.602  -0.205  -0.339  
##  6 Valiant             105     1 mpg     -0.330  0.284  -0.777  -0.0186 
##  7 Duster 360          245     0 mpg     -0.961  0.810   0.172  -0.179  
##  8 Merc 240D            62     1 mpg      0.715 -0.417  -0.452   0.335  
##  9 Merc 230             95     1 mpg      0.450 -0.526  -0.715   0.679  
## 10 Merc 280            123     1 mpg     -0.148 -0.0277  0.0696  0.387  
## # … with 278 more rows

action=“get” We can add the MDS dimensions to the original data set selecting just the element-wise column

  mtcars_tidy %>%
    reduce_dimensions(
        car_model, feature, value, 
        method="MDS" ,
        .dims = 3,
        action="get"
    )
## # A tibble: 32 x 6
##    car_model            hp    vs    Dim1    Dim2     Dim3
##    <chr>             <dbl> <dbl>   <dbl>   <dbl>    <dbl>
##  1 Mazda RX4           110     0 -0.342   0.499  -0.0853 
##  2 Mazda RX4 Wag       110     0 -0.323   0.439   0.00461
##  3 Datsun 710           93     1 -0.787  -0.140  -0.130  
##  4 Hornet 4 Drive      110     1  0.164  -0.670  -0.133  
##  5 Hornet Sportabout   175     0  0.602  -0.205  -0.339  
##  6 Valiant             105     1  0.284  -0.777  -0.0186 
##  7 Duster 360          245     0  0.810   0.172  -0.179  
##  8 Merc 240D            62     1 -0.417  -0.452   0.335  
##  9 Merc 230             95     1 -0.526  -0.715   0.679  
## 10 Merc 280            123     1 -0.0277  0.0696  0.387  
## # … with 22 more rows

action=“only” We can get just the MDS dimensions relative to each element

  mtcars_tidy %>%
    reduce_dimensions(
        car_model, feature, value, 
        method="MDS" ,
        .dims = 3,
        action="only"
    )
## # A tibble: 32 x 4
##    car_model            Dim1    Dim2     Dim3
##    <chr>               <dbl>   <dbl>    <dbl>
##  1 Mazda RX4         -0.342   0.499  -0.0853 
##  2 Mazda RX4 Wag     -0.323   0.439   0.00461
##  3 Datsun 710        -0.787  -0.140  -0.130  
##  4 Hornet 4 Drive     0.164  -0.670  -0.133  
##  5 Hornet Sportabout  0.602  -0.205  -0.339  
##  6 Valiant            0.284  -0.777  -0.0186 
##  7 Duster 360         0.810   0.172  -0.179  
##  8 Merc 240D         -0.417  -0.452   0.335  
##  9 Merc 230          -0.526  -0.715   0.679  
## 10 Merc 280          -0.0277  0.0696  0.387  
## # … with 22 more rows

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