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.
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
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