在 R 中使用热图选择观察类别

Question

The simple example

library(heatmaply)
heatmaply(mtcars, k_col = 2, k_row = 3) %>% layout(margin = list(l = 130, b = 40))

and we get this plot We see that the observations were divided on 3 clusters

1 from honda civic to ferrari dino
2 from valiant to dodge challendger
3 from chrysler imperial to maserati bora

Also we see that technical parameters(variables) were divided by two classes

The first class **hp** and **disp**
The second class another parameters

how can I output in dataframe the name(or number) of those observations that, on the one hand, they in the first cluster (cars of the first cluster), and they are located at the area of the first cluster of technical variables. In other words, I identified this area on screen below

Accordingly, the output must be made for observations of all clusters of cars in different clusters of technical variables. It is easy to calculate that it will be 6 areas.

Answer 1

Here is an example of getting the same things using heatmapr (the function behind heatmaply):

example_data <- mtcars # replace this with your data.
library(dendextend) # how to cite it, see citation("heatmaply")
x <- heatmapr(example_data ) 
library(dendextend) # how to cite it, see citation("dendextend")
data_groups <- cutree(x$rows, k = 3) # choose the k/number of clusters you see/want.
data_groups 

# This is how to see the groups of your data 
split(example_data, data_groups )

The output will be:

$`1`
                mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280       19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C      17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

$`2`
                  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Hornet 4 Drive   21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Valiant          18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Merc 450SE       16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL       17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC      15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Dodge Challenger 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin      15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2

$`3`
                     mpg cyl disp  hp drat    wt  qsec vs am gear carb
Hornet Sportabout   18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Duster 360          14.3   8  360 245 3.21 3.570 15.84  0  0    3    4
Cadillac Fleetwood  10.4   8  472 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8  460 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8  440 230 3.23 5.345 17.42  0  0    3    4
Camaro Z28          13.3   8  350 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8  400 175 3.08 3.845 17.05  0  0    3    2
Ford Pantera L      15.8   8  351 264 4.22 3.170 14.50  0  1    5    4
Maserati Bora       15.0   8  301 335 3.54 3.570 14.60  0  1    5    8