nanny

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

(★ 32)

A tidyverse suite for (pre-) machine-learning: cluster, PCA, permute, impute, rotate, redundancy, triangular, smart-subset, abundant and variable features.

nanny 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

This repository is no longer maintained and superseeded by [tidybulk](https://github.com/stemangiola/tidybulk)
================

**It tidies up your playground\!**

# <img src="inst/logo-02.png" height="139px" width="120px" />

<!---

[![Build Status](https://travis-ci.org/stemangiola/nanny.svg?branch=master)](https://travis-ci.org/stemangiola/nanny) [![Coverage Status](https://coveralls.io/repos/github/stemangiola/nanny/badge.svg?branch=master)](https://coveralls.io/github/stemangiola/nanny?branch=master)

-->

[![Lifecycle:maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing)

Please have a look also to

- [tidygate](https://github.com/stemangiola/tidygate) for adding
    custom gate information to your tibble
  - [tidyHeatmap](https://github.com/stemangiola/tidyHeatmap) for
    heatmaps produced with tidy principles
  - [tidybulk](https://github.com/stemangiola/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

``` r
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

``` r
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**

``` r
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.

``` r
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

``` r
mtcars_tidy_MDS %>%
    subset(car_model) %>%
  GGally::ggpairs(columns = 4:6, ggplot2::aes(colour=factor(vs)))
```

![](https://raw.githubusercontent.com/stemangiola/nanny/master/README_files/figure-gfm/plot_mds-1.png)

**PCA**

``` r
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.

``` r
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

``` r
mtcars_tidy_PCA %>%
     subset(car_model) %>%
  GGally::ggpairs(columns = 4:6, ggplot2::aes(colour=factor(vs)))
```

![](https://raw.githubusercontent.com/stemangiola/nanny/master/README_files/figure-gfm/plot_pca-1.png)

**tSNE**

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

Plot

``` r
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

``` r
mtcars_tidy_tSNE %>%
    subset(car_model) %>%
    ggplot(aes(x = `tSNE1`, y = `tSNE2`, color=factor(vs))) + geom_point() + my_theme
```

![](https://raw.githubusercontent.com/stemangiola/nanny/master/README_files/figure-gfm/unnamed-chunk-4-1.png)

## `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.

``` r
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.

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

![](https://raw.githubusercontent.com/stemangiola/nanny/master/README_files/figure-gfm/plot_rotate_1-1.png)

**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.

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

![](https://raw.githubusercontent.com/stemangiola/nanny/master/README_files/figure-gfm/plot_rotate_2-1.png)

## `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**

``` r
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.

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

![](https://raw.githubusercontent.com/stemangiola/nanny/master/README_files/figure-gfm/plot_cluster-1.png)

**SNN**

``` r
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.

``` r
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

``` r
mtcars_tidy_SNN %>%
    subset(car_model) %>%
    ggplot(aes(x = `tSNE1`, y = `tSNE2`, color=cluster_SNN)) + geom_point() + my_theme
```

![](https://raw.githubusercontent.com/stemangiola/nanny/master/README_files/figure-gfm/SNN_plot-1.png)

**gating**

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

![](https://raw.githubusercontent.com/stemangiola/nanny/master/inst/tidygate.gif)

## # 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”

``` r
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

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

![](https://raw.githubusercontent.com/stemangiola/nanny/master/README_files/figure-gfm/plot_drop-1.png)

## `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

``` r
mtcars_tidy_non_rectangular = mtcars_tidy %>% slice(-1)
```

We fill the missing value with the value of 0

``` r
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

``` r
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

``` r
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

``` r
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

``` r
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

``` r
mtcars_tidy %>%
    keep_variable(car_model, feature, value, top=10)
```

## `as_matrix`

Robustly convert a tibble to matrix

``` r
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

``` r
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

``` r
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

``` r
  mtcars_tidy
```

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

``` r
  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

``` r
  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

``` r
  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

nanny

nanny 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

nanny

nanny 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README