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This repository is no longer maintained and superseeded bytidybulk
It tidies up your playground!
Please have a look also to
- tidygate for addingcustom gate information to your tibble
- tidyHeatmap forheatmaps produced with tidy principles
- tidybulk bringstranscriptomics to the tidyverse
It does a lot! cluster, PCA, permute, impute, rotate,redundancy-removal, triangular, smart-subset, identify abundant andvariable 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 |
| element | feature | value |
|---|---|---|
chr orfctr | chr orfctr | numeric |
| element | feature | value | new information |
|---|---|---|---|
chr orfctr | chr orfctr | numeric | … |
devtools::install_github("stemangiola/nanny")
nanny is a collection of wrapper functions for high level data analysisand manipulation following the tidy paradigm.
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 rowsWe 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; forelement,feature andvalue) and a method (e.g.,MDS, PCA or tSNE) as arguments and returns a tibble with additionalcolumns 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 iscoloured 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 rowsmtcars_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 iscoloured 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 rowsmtcars_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 rowsmtcars_tidy_tSNE %>% subset(car_model) %>% ggplot(aes(x=`tSNE1`,y=`tSNE2`,color=factor(vs)))+ geom_point()+my_theme
We may want to rotate the reduced dimensions (or any two numeric columnsreally) of our data, of a set angle.rotate_dimensions takes a tibble,column names (as symbols; forelement,feature andvalue) and anangle as arguments and returns a tibble with additional columns for therotated dimensions. The rotated dimensions will be added to the originaldata set as<NAME OF DIMENSION> rotated <ANGLE> by default, or asspecified 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 reduceddimensions, 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 reduceddimensions 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
We may want to cluster our data (e.g., using k-means element-wise).cluster_elements takes as arguments a tibble, column names (assymbols; forelement,feature andvalue) and returns a tibble withadditional columns for the cluster annotation. At the moment onlyk-means clustering is supported, the plan is to introduce moreclustering 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 andplot.
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 andplot.
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 rowsmtcars_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 rowsWe may want to remove redundant elements from the original data set(e.g., elements or features), for example if we want to define cell-typespecific signatures with low element redundancy.remove_redundancytakes as arguments a tibble, column names (as symbols; forelement,feature andvalue) and returns a tibble dropped recundant elements(e.g., elements). Two redundancy estimation approaches are supported:
removal of highly correlated clusters of elements (keeping arepresentative) 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 dimentionslook like
mtcars_tidy_non_redundant %>% subset(car_model) %>% ggplot(aes(x=`Dim1`,y=`Dim2`,color=factor(vs)))+ geom_point()+my_theme
This function allows to obtain a rectangular underlying data structure,where every element has one feature, filling missing element/featurepairs 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 rowsThis function allows to obtain a rectangular underlying data structure,where every element has one feature, imputig missing element/featurepairs 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 rowsFrom 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 rowsFrom 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 rowskeep 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 rowsKeep 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 rowsRobustly 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 0.3094156Select 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 rowsNest a data frame based on the columns with information relative to thecolumn 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 rowsEvery function takes a tidyfeatureomics structured data as input, and(i) with action=“add” outputs the new information joint to the originalinput data frame (default), (ii) with action=“get” the new informationwith the element or feature relative informatin depending on what theanalysis 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 rowsaction=“add” (Default) We can add the MDS dimensions to the originaldata 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 rowsaction=“get” We can add the MDS dimensions to the original data setselecting 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 rowsaction=“only” We can get just the MDS dimensions relative to eachelement
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 rowsAbout
A tidyverse suite for (pre-) machine-learning: cluster, PCA, permute, impute, rotate, redundancy, triangular, smart-subset, abundant and variable features.
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