Special custom filtering for compositional data
custom_filter.RdA handy wrapper around the dplyr filter() function
enabling the user to filter rows which satisfy specific conditions
for compositional data like all equi-proportional communities, or communities
with a given value of richness without having to make any changes to the data
or adding any additional columns. All other functionalities are same as the
dplyr filter() function.
Arguments
- data
A data frame containing the compositional variables which should be used to perform the filtering.
- prop
A character/numeric vector indicating the columns containing the compositional variables in `data`.
- special
A character string specifying the filtering condition. Four special keywords can be specified here for filtering 1. richness: A positive integer value to filter communities with a specific number of compositional variables (variables with non-zero values). 2. evenness: A numeric value between 0 and 1, to filter rows based on the relative abundances of the compositional variables where a higher value signifies a more even community with equal proportions of all variables. 3. equi: A boolean variable indicating whether to filter rows containing equi-proportional communities, i.e., communities where all variables have the same non-zero proportion. 4. monos: A boolean value indicating whether to filter communities containing a single compositional variable, i.e., richness == 1. These keywords can be combined using any logical operators and can even be combined with any other variables in the data. Please use the exact keywords (case-sensitive) in the query to get appropriate results. See examples for more details.
- ...
Any additional arguments specified to the dplyr
filter()function. Filtering conditions for any additional variables can also be specified here.
Examples
library(DImodels)
library(dplyr)
## Load data
data(sim3)
# The special filter keywords should be specified as a string
# Filter communities containing 3 species
head(custom_filter(data = sim3, prop = 4:12,
special = "richness == 3"))
#> community richness treatment p1 p2 p3 p4 p5 p6 p7 p8
#> 1 46 3 A 0 0.0000000 0 0.3333333 0 0 0 0.3333333
#> 2 46 3 B 0 0.0000000 0 0.3333333 0 0 0 0.3333333
#> 3 46 3 A 0 0.0000000 0 0.3333333 0 0 0 0.3333333
#> 4 46 3 B 0 0.0000000 0 0.3333333 0 0 0 0.3333333
#> 5 47 3 A 0 0.3333333 0 0.0000000 0 0 0 0.3333333
#> 6 47 3 B 0 0.3333333 0 0.0000000 0 0 0 0.3333333
#> p9 response
#> 1 0.3333333 16.812
#> 2 0.3333333 9.902
#> 3 0.3333333 14.462
#> 4 0.3333333 8.845
#> 5 0.3333333 14.575
#> 6 0.3333333 10.924
# Filter communities at richness 6 OR evenness 0
head(custom_filter(data = sim3, prop = 4:12,
special = "richness == 6 | evenness == 0"), 12)
#> community richness treatment p1 p2 p3 p4 p5 p6 p7 p8 p9 response
#> 1 1 1 A 0 0 0 0 0 0 0 0 1 10.265
#> 2 1 1 B 0 0 0 0 0 0 0 0 1 7.740
#> 3 1 1 A 0 0 0 0 0 0 0 0 1 12.173
#> 4 1 1 B 0 0 0 0 0 0 0 0 1 8.497
#> 5 2 1 A 0 0 0 0 0 0 0 1 0 10.763
#> 6 2 1 B 0 0 0 0 0 0 0 1 0 8.989
#> 7 2 1 A 0 0 0 0 0 0 0 1 0 10.161
#> 8 2 1 B 0 0 0 0 0 0 0 1 0 7.193
#> 9 3 1 A 0 0 0 0 0 0 1 0 0 10.171
#> 10 3 1 B 0 0 0 0 0 0 1 0 0 6.053
#> 11 3 1 A 0 0 0 0 0 0 1 0 0 8.383
#> 12 3 1 B 0 0 0 0 0 0 1 0 0 4.569
# Filter all monoculture AND treatment "A" (treatment is column present in data)
head(custom_filter(data = sim3, prop = 4:12,
special = "monos == TRUE & treatment == 'A'"), 10)
#> community richness treatment p1 p2 p3 p4 p5 p6 p7 p8 p9 response
#> 1 1 1 A 0 0 0 0 0 0 0 0 1 10.265
#> 2 1 1 A 0 0 0 0 0 0 0 0 1 12.173
#> 3 2 1 A 0 0 0 0 0 0 0 1 0 10.763
#> 4 2 1 A 0 0 0 0 0 0 0 1 0 10.161
#> 5 3 1 A 0 0 0 0 0 0 1 0 0 10.171
#> 6 3 1 A 0 0 0 0 0 0 1 0 0 8.383
#> 7 4 1 A 0 0 0 0 0 1 0 0 0 10.182
#> 8 4 1 A 0 0 0 0 0 1 0 0 0 8.240
#> 9 5 1 A 0 0 0 0 1 0 0 0 0 15.467
#> 10 5 1 A 0 0 0 0 1 0 0 0 0 14.790
# Filter all equi proportional communities but NOT monocultures
head(custom_filter(data = sim3, prop = 4:12,
special = "equi == TRUE & monos == FALSE"))
#> community richness treatment p1 p2 p3 p4 p5 p6 p7 p8 p9 response
#> 1 10 2 A 0 0 0 0 0 0 0.0 0.5 0.5 10.734
#> 2 10 2 B 0 0 0 0 0 0 0.0 0.5 0.5 7.465
#> 3 10 2 A 0 0 0 0 0 0 0.0 0.5 0.5 10.054
#> 4 10 2 B 0 0 0 0 0 0 0.0 0.5 0.5 8.615
#> 5 11 2 A 0 0 0 0 0 0 0.5 0.0 0.5 8.614
#> 6 11 2 B 0 0 0 0 0 0 0.5 0.0 0.5 7.851
# Can also use normal filter
sim3 %>% custom_filter(p1 == 1, special = NULL, prop = NULL)
#> community richness treatment p1 p2 p3 p4 p5 p6 p7 p8 p9 response
#> 1 9 1 A 1 0 0 0 0 0 0 0 0 13.539
#> 2 9 1 B 1 0 0 0 0 0 0 0 0 11.768
#> 3 9 1 A 1 0 0 0 0 0 0 0 0 11.869
#> 4 9 1 B 1 0 0 0 0 0 0 0 0 10.393
# Both special filtering and normal filtering can be combined as well
sim3 %>% custom_filter(prop = paste0("p", 1:9),
special = "richness == 1",
community %in% c(7, 9))
#> community richness treatment p1 p2 p3 p4 p5 p6 p7 p8 p9 response
#> 1 7 1 A 0 0 1 0 0 0 0 0 0 12.413
#> 2 7 1 B 0 0 1 0 0 0 0 0 0 8.376
#> 3 7 1 A 0 0 1 0 0 0 0 0 0 12.264
#> 4 7 1 B 0 0 1 0 0 0 0 0 0 8.371
#> 5 9 1 A 1 0 0 0 0 0 0 0 0 13.539
#> 6 9 1 B 1 0 0 0 0 0 0 0 0 11.768
#> 7 9 1 A 1 0 0 0 0 0 0 0 0 11.869
#> 8 9 1 B 1 0 0 0 0 0 0 0 0 10.393