Project 3-d compositional data onto x-y plane and vice versa
Simplex_projection.RdPoints in the 3-d simplex space with coordinates (x, y ,z) such that
x + y + z = 1 are projected into the 2-d plane they reside in. This function
can be used to convert the 3-d compositional data into 2-d and then be overlayed
on the plots output by ternary_plot,
conditional_ternary_plot and grouped_ternary_plot.
Usage
prop_to_tern_proj(data, prop, x = ".x", y = ".y")
tern_to_prop_proj(data, x, y, prop = c("p1", "p2", "p3"))Arguments
- data
A data-frame containing the x-y coordinates of the points.
- prop
A character vector specifying the columns names of variable containing the projected compositions. Default is "p1", "p2", and "p3".
- x
A character string specifying the name for the column containing the x component of the x-y projection of the simplex.
- y
A character string specifying the name for the column containing the y component of the x-y projection of the simplex.
Value
A data-frame with the following two columns appended (when transforming to x-y projection)
- .x (or value specified in "x")
The x component of the x-y projection of the simplex point.
- .y (or value specified in "y")
The y component of the x-y projection of the simplex point.
A data-frame with the following three columns appended (when transforming to compositional projection)
- p1 (or first value specified in "prop")
The first component of the 3-d simplex point.
- p2 (or second value specified in "prop")
The second component of the 3-d simplex point.
- p3 (or third value specified in "prop")
The third component of the 3-d simplex point.
Examples
## Convert proportions to x-y co-ordinates
library(DImodels)
data(sim0)
sim0 <- sim0[1:16, ]
prop_to_tern_proj(data = sim0, prop = c("p1", "p2", "p3"))
#> p1 p2 p3 .x .y community richness response
#> 1 1.0000000 0.0000000 0.0000000 0.50 0.8660254 1 1 24.855
#> 2 0.0000000 1.0000000 0.0000000 0.00 0.0000000 2 1 19.049
#> 3 0.0000000 0.0000000 1.0000000 1.00 0.0000000 3 1 16.292
#> 4 0.8000000 0.2000000 0.0000000 0.40 0.6928203 4 2 31.529
#> 5 0.2000000 0.8000000 0.0000000 0.10 0.1732051 5 2 25.102
#> 6 0.8000000 0.0000000 0.2000000 0.60 0.6928203 6 2 24.615
#> 7 0.2000000 0.0000000 0.8000000 0.90 0.1732051 7 2 18.654
#> 8 0.0000000 0.8000000 0.2000000 0.20 0.0000000 8 2 24.697
#> 9 0.0000000 0.2000000 0.8000000 0.80 0.0000000 9 2 25.017
#> 10 0.5000000 0.5000000 0.0000000 0.25 0.4330127 10 2 32.743
#> 11 0.5000000 0.0000000 0.5000000 0.75 0.4330127 11 2 25.320
#> 12 0.0000000 0.5000000 0.5000000 0.50 0.0000000 12 2 30.214
#> 13 0.6000000 0.2000000 0.2000000 0.50 0.5196152 13 3 30.511
#> 14 0.2000000 0.6000000 0.2000000 0.30 0.1732051 14 3 31.005
#> 15 0.2000000 0.2000000 0.6000000 0.70 0.1732051 15 3 28.964
#> 16 0.3333333 0.3333333 0.3333333 0.50 0.2886751 16 3 32.091
# Change names of the x and y projections
prop_to_tern_proj(data = sim0, prop = c("p1", "p2", "p3"),
x = "x-proj", y = "y-proj")
#> p1 p2 p3 x-proj y-proj community richness response
#> 1 1.0000000 0.0000000 0.0000000 0.50 0.8660254 1 1 24.855
#> 2 0.0000000 1.0000000 0.0000000 0.00 0.0000000 2 1 19.049
#> 3 0.0000000 0.0000000 1.0000000 1.00 0.0000000 3 1 16.292
#> 4 0.8000000 0.2000000 0.0000000 0.40 0.6928203 4 2 31.529
#> 5 0.2000000 0.8000000 0.0000000 0.10 0.1732051 5 2 25.102
#> 6 0.8000000 0.0000000 0.2000000 0.60 0.6928203 6 2 24.615
#> 7 0.2000000 0.0000000 0.8000000 0.90 0.1732051 7 2 18.654
#> 8 0.0000000 0.8000000 0.2000000 0.20 0.0000000 8 2 24.697
#> 9 0.0000000 0.2000000 0.8000000 0.80 0.0000000 9 2 25.017
#> 10 0.5000000 0.5000000 0.0000000 0.25 0.4330127 10 2 32.743
#> 11 0.5000000 0.0000000 0.5000000 0.75 0.4330127 11 2 25.320
#> 12 0.0000000 0.5000000 0.5000000 0.50 0.0000000 12 2 30.214
#> 13 0.6000000 0.2000000 0.2000000 0.50 0.5196152 13 3 30.511
#> 14 0.2000000 0.6000000 0.2000000 0.30 0.1732051 14 3 31.005
#> 15 0.2000000 0.2000000 0.6000000 0.70 0.1732051 15 3 28.964
#> 16 0.3333333 0.3333333 0.3333333 0.50 0.2886751 16 3 32.091
## Convert x-y co-ordinates to proportions
library(DImodels)
data(sim0)
sim0 <- sim0[1:16, ]
proj_data <- prop_to_tern_proj(data = sim0, prop = c("p1", "p2", "p3"))
tern_to_prop_proj(data = proj_data, x = ".x", y = ".y")
#> .x .y p1 p2 p3 community richness response
#> 1 0.50 0.8660254 1.0000000 0.0000000 0.0000000 1 1 24.855
#> 2 0.00 0.0000000 0.0000000 1.0000000 0.0000000 2 1 19.049
#> 3 1.00 0.0000000 0.0000000 0.0000000 1.0000000 3 1 16.292
#> 4 0.40 0.6928203 0.8000000 0.2000000 0.0000000 4 2 31.529
#> 5 0.10 0.1732051 0.2000000 0.8000000 0.0000000 5 2 25.102
#> 6 0.60 0.6928203 0.8000000 0.0000000 0.2000000 6 2 24.615
#> 7 0.90 0.1732051 0.2000000 0.0000000 0.8000000 7 2 18.654
#> 8 0.20 0.0000000 0.0000000 0.8000000 0.2000000 8 2 24.697
#> 9 0.80 0.0000000 0.0000000 0.2000000 0.8000000 9 2 25.017
#> 10 0.25 0.4330127 0.5000000 0.5000000 0.0000000 10 2 32.743
#> 11 0.75 0.4330127 0.5000000 0.0000000 0.5000000 11 2 25.320
#> 12 0.50 0.0000000 0.0000000 0.5000000 0.5000000 12 2 30.214
#> 13 0.50 0.5196152 0.6000000 0.2000000 0.2000000 13 3 30.511
#> 14 0.30 0.1732051 0.2000000 0.6000000 0.2000000 14 3 31.005
#> 15 0.70 0.1732051 0.2000000 0.2000000 0.6000000 15 3 28.964
#> 16 0.50 0.2886751 0.3333333 0.3333333 0.3333333 16 3 32.091
# Change prop names
tern_to_prop_proj(data = proj_data, x = ".x", y = ".y",
prop = c("prop1", "prop2", "prop3"))
#> .x .y prop1 prop2 prop3 p1 p2 p3
#> 1 0.50 0.8660254 1.0000000 0.0000000 0.0000000 1.0000000 0.0000000 0.0000000
#> 2 0.00 0.0000000 0.0000000 1.0000000 0.0000000 0.0000000 1.0000000 0.0000000
#> 3 1.00 0.0000000 0.0000000 0.0000000 1.0000000 0.0000000 0.0000000 1.0000000
#> 4 0.40 0.6928203 0.8000000 0.2000000 0.0000000 0.8000000 0.2000000 0.0000000
#> 5 0.10 0.1732051 0.2000000 0.8000000 0.0000000 0.2000000 0.8000000 0.0000000
#> 6 0.60 0.6928203 0.8000000 0.0000000 0.2000000 0.8000000 0.0000000 0.2000000
#> 7 0.90 0.1732051 0.2000000 0.0000000 0.8000000 0.2000000 0.0000000 0.8000000
#> 8 0.20 0.0000000 0.0000000 0.8000000 0.2000000 0.0000000 0.8000000 0.2000000
#> 9 0.80 0.0000000 0.0000000 0.2000000 0.8000000 0.0000000 0.2000000 0.8000000
#> 10 0.25 0.4330127 0.5000000 0.5000000 0.0000000 0.5000000 0.5000000 0.0000000
#> 11 0.75 0.4330127 0.5000000 0.0000000 0.5000000 0.5000000 0.0000000 0.5000000
#> 12 0.50 0.0000000 0.0000000 0.5000000 0.5000000 0.0000000 0.5000000 0.5000000
#> 13 0.50 0.5196152 0.6000000 0.2000000 0.2000000 0.6000000 0.2000000 0.2000000
#> 14 0.30 0.1732051 0.2000000 0.6000000 0.2000000 0.2000000 0.6000000 0.2000000
#> 15 0.70 0.1732051 0.2000000 0.2000000 0.6000000 0.2000000 0.2000000 0.6000000
#> 16 0.50 0.2886751 0.3333333 0.3333333 0.3333333 0.3333333 0.3333333 0.3333333
#> community richness response
#> 1 1 1 24.855
#> 2 2 1 19.049
#> 3 3 1 16.292
#> 4 4 2 31.529
#> 5 5 2 25.102
#> 6 6 2 24.615
#> 7 7 2 18.654
#> 8 8 2 24.697
#> 9 9 2 25.017
#> 10 10 2 32.743
#> 11 11 2 25.320
#> 12 12 2 30.214
#> 13 13 3 30.511
#> 14 14 3 31.005
#> 15 15 3 28.964
#> 16 16 3 32.091