# Constraints -------------------------------------------------------------
constraint_levels <- function(x) {
filter(constraints, constraint == x) %>%
pull(levels_clean) %>%
.[[1]]
}
constraints <- tribble(
~constraint, ~constraint_clean, ~levels_clean,
"create_network", "Network creation", list("Network creation" = "T", "No network creation" = "F"),
"select", "Selection", list("Selection" = "T", "No selection" = "F"),
"disperse", "Dispersal", list("Dispersal" = "T", "No dispersal" = "F"),
"compete", "Competition", list("Competition" = "T", "No competition" = "F"),
"selectfor_d", "Selection for D", list("Selection" = "T", "No selection" = "F"),
"catastrophe", "Catastrophe", list("Catastrophe" = "T", "No catastrophe" = "F")
)
saveRDS(constraints, here("data", "derived_data", "constraints.rds"))
# High/low ----------------------------------------------------------------
BHL <- read_rds(here("data", "raw_data", "BHL.rds")) %>%
clean_names(case = "snake") %>% # Get rid of invalid characters in column names
rename_all(list(~str_remove_all(., "yn$"))) %>% # Get rid of "yn" in column names
# Clean up constraint values
mutate(create_network = fct_recode(create_network, !!!constraint_levels("create_network")),
select = fct_recode(select, !!!constraint_levels("select")),
disperse = fct_recode(disperse, !!!constraint_levels("disperse")),
compete = fct_recode(compete, !!!constraint_levels("compete")),
selectfor_d = fct_recode(selectfor_d, !!!constraint_levels("selectfor_d")),
catastrophe = fct_recode(catastrophe, !!!constraint_levels("catastrophe"))) %>%
mutate_at(vars(one_of(constraints$constraint)), list(~ fct_inorder(.))) %>%
mutate(repp = factor(repp, levels = c(1, 2), labels = c("High", "Low"), ordered = TRUE))
BHL_small <- BHL %>%
group_by(repp) %>%
sample_frac(size = 0.1) %>%
ungroup()
sim_hl <- BHL_small %>%
# Make new columns with "_constraint" suffix that show if constraint is T/F
# instead of using the label. e.g. "Selection" becomes TRUE, "No selection"
# becomes FALSE
mutate_at(vars(one_of(constraints$constraint)),
list(constraint = ~as.logical(-as.integer(.) + 2L))) %>%
# Count how many of the constraints are TRUE in each row
mutate(n_constraints = reduce(select(., ends_with("_constraint")), `+`)) %>%
# Make a factor version of the constraint count for plotting
mutate(n_constraints_f = as.factor(n_constraints)) %>%
mutate(turnover_diff = landscape_linked_species_mean_turnover - landscape_unlinked_species_mean_turnover)
saveRDS(BHL, here("data", "derived_data", "BHL.rds"))
saveRDS(BHL_small, here("data", "derived_data", "BHL_small.rds"))
saveRDS(sim_hl, here("data", "derived_data", "sim_hl.rds"))
# Latin squares -----------------------------------------------------------
BLS <- read_rds(here("data", "raw_data", "BLS.rds")) %>%
clean_names(case = "snake") %>% # Get rid of invalid characters in column names
rename_all(list(~str_remove_all(., "yn$"))) %>% # Get rid of "yn" in column names
# Clean up constraint values
mutate(create_network = fct_recode(create_network, !!!constraint_levels("create_network")),
select = fct_recode(select, !!!constraint_levels("select")),
disperse = fct_recode(disperse, !!!constraint_levels("disperse")),
compete = fct_recode(compete, !!!constraint_levels("compete")),
selectfor_d = fct_recode(selectfor_d, !!!constraint_levels("selectfor_d")),
catastrophe = fct_recode(catastrophe, !!!constraint_levels("catastrophe"))) %>%
mutate_at(vars(one_of(constraints$constraint)), list(~fct_inorder(.)))
BLS_small <- BLS %>%
sample_frac(size = 0.1)
sim_ls <- BLS_small %>%
# Make new columns with "_constraint" suffix that show if constraint is T/F
# instead of using the label. e.g. "Selection" becomes TRUE, "No selection"
# becomes FALSE
mutate_at(vars(one_of(constraints$constraint)),
list(constraint = ~as.logical(-as.integer(.) + 2L))) %>%
# Count how many of the constraints are TRUE in each row
mutate(n_constraints = reduce(select(., ends_with("_constraint")), `+`)) %>%
# Make a factor version of the constraint count for plotting
mutate(n_constraints_f = as.factor(n_constraints)) %>%
mutate(turnover_diff = landscape_linked_species_mean_turnover - landscape_unlinked_species_mean_turnover)
saveRDS(BLS, here("data", "derived_data", "BLS.rds"))
saveRDS(BLS_small, here("data", "derived_data", "BLS_small.rds"))
saveRDS(sim_ls, here("data", "derived_data", "sim_ls.rds"))Create columns for every combination of constraint in the data (e.g. select, select & disperse, select & disperse & create_network, etc.)
(By the inimitable Vincent Arel-Bundock)
make_combinations <- function(df, m = 5) {
com <- colnames(df)[2:ncol(df)] %>%
combn(m) %>%
as_tibble()
out <- com %>%
map(~ df[.]) %>%
map(~ rowSums(.) == ncol(.)) %>%
setNames(map(com, paste, collapse = " + ")) %>%
as_tibble()
return(out)
}
outcomes <- c("landscape_fitness_linked", "avg_evenness_t", "var_fitness",
"landscape_richness_mean", "landscape_linked_species_mean_turnover",
"turnover_diff")
# High/low ----------------------------------------------------------------
# Select just the run number and *_constraint TRUE/FALSE columns
constraint_combinations_hl <- sim_hl %>%
select(runnum, ends_with("_constraint")) %>%
# Shrink names by removing "_constraint"
rename_at(vars(ends_with("constraint")),
list(~str_replace_all(., "_constraint", "")))
# Find all combinations of variables (m = number of items in combination; m = 2
# means pairs, m = 3 means triplets, etc.)
all_constraint_combos_hl <- map(2:6, ~make_combinations(constraint_combinations_hl, m = .)) %>%
bind_cols(constraint_combinations_hl, .)
# Select the outcome variables we care about and join the constraint combinations
constraint_combo_outcomes_hl <- sim_hl %>%
select(runnum, repp, n_constraints, one_of(outcomes)) %>%
right_join(all_constraint_combos_hl, by = "runnum")
# Don't double count rows. If a row has two constraints like select and
# disperse, it'll also have select + disperse set to TRUE. If that's the case,
# we don't want to include it in just select or just disperse
constraint_combo_outcomes_hl_nested <- constraint_combo_outcomes_hl %>%
select(-n_constraints) %>%
# Make long
gather(constraint_combo, value, -runnum, -repp, -one_of(outcomes)) %>%
# Count how many constraints there are within each row based on + signs
mutate(n = str_count(constraint_combo, "\\+") + 1) %>%
# Only keep rows where the constraint is turned on
filter(value == TRUE) %>%
# Nest all the constraint combinations within each row
group_by(runnum) %>%
nest()
# Only keep the values where n == max(n) for that row
# This takes ≈2 minutes
constraint_combo_outcomes_hl_filtered <- constraint_combo_outcomes_hl_nested %>%
mutate(filtered = data %>% map(~filter(., n == max(.$n)))) %>%
select(-data) %>%
unnest(filtered)
# This omitted all the rows where n_constraints == 0, so add those back in
no_constraints_hl <- constraint_combo_outcomes_hl %>%
filter(n_constraints == 0) %>%
mutate(constraint_combo = "No constraints", n = 0) %>%
select(runnum, repp, one_of(outcomes), constraint_combo, n)
constraint_combo_outcomes_hl_done <- bind_rows(constraint_combo_outcomes_hl_filtered,
no_constraints_hl) %>%
select(-value)
saveRDS(constraint_combo_outcomes_hl_done,
here("data", "derived_data", "constraint_combo_outcomes_hl.rds"))
# Latin squares -----------------------------------------------------------
# Select just the run number and *_constraint TRUE/FALSE columns
constraint_combinations_ls <- sim_ls %>%
select(runnum, ends_with("_constraint")) %>%
# Shrink names by removing "_constraint"
rename_at(vars(ends_with("constraint")),
list(~str_replace_all(., "_constraint", "")))
# Find all combinations of variables (m = number of items in combination; m = 2
# means pairs, m = 3 means triplets, etc.)
all_constraint_combos_ls <- map(2:6, ~make_combinations(constraint_combinations_ls, m = .)) %>%
bind_cols(constraint_combinations_ls, .)
# Select the outcome variables we care about and join the constraint combinations
constraint_combo_outcomes_ls <- sim_ls %>%
select(runnum, n_constraints, one_of(outcomes)) %>%
right_join(all_constraint_combos_ls, by = "runnum")
# Don't double count rows. If a row has two constraints like select and
# disperse, it'll also have select + disperse set to TRUE. If that's the case,
# we don't want to include it in just select or just disperse
constraint_combo_outcomes_ls_nested <- constraint_combo_outcomes_ls %>%
select(-n_constraints) %>%
# Make long
gather(constraint_combo, value, -runnum, -one_of(outcomes)) %>%
# Count how many constraints there are within each row based on + signs
mutate(n = str_count(constraint_combo, "\\+") + 1) %>%
# Only keep rows where the constraint is turned on
filter(value == TRUE) %>%
# Nest all the constraint combinations within each row
group_by(runnum) %>%
nest()
# Only keep the values where n == max(n) for that row
# This takes ≈1 minute
constraint_combo_outcomes_ls_filtered <- constraint_combo_outcomes_ls_nested %>%
mutate(filtered = data %>% map(~filter(., n == max(.$n)))) %>%
select(-data) %>%
unnest(filtered)
# This omitted all the rows where n_constraints == 0, so add those back in
no_constraints_ls <- constraint_combo_outcomes_ls %>%
filter(n_constraints == 0) %>%
mutate(constraint_combo = "No constraints", n = 0) %>%
select(runnum, one_of(outcomes), constraint_combo, n)
constraint_combo_outcomes_ls_done <- bind_rows(constraint_combo_outcomes_ls_filtered,
no_constraints_ls) %>%
select(-value)
saveRDS(constraint_combo_outcomes_ls_done,
here("data", "derived_data", "constraint_combo_outcomes_ls.rds"))Data details:
## # http://dirk.eddelbuettel.com/blog/2017/11/27/#011_faster_package_installation_one
## # VER=
## # CCACHE=ccache
## # CC=$(CCACHE) gcc$(VER)
## # CXX=$(CCACHE) g++$(VER)
## # CXXFLAGS=-O3 #-Wno-unused-variable -Wno-unused-function -Wno-unused-local-typedefs
## # CXX11=$(CCACHE) g++$(VER)
## # CXX14=$(CCACHE) g++$(VER)
## # FC=$(CCACHE) gfortran$(VER)
## # F77=$(CCACHE) gfortran$(VER)
##
## # clang: start
## CFLAGS=-isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk
## CCFLAGS=-isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk
## CXXFLAGS=-isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk
## CPPFLAGS=-isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk -I/usr/local/include
##
## SHLIB_CXXLDFLAGS+=-Wl,-rpath,${R_HOME}/lib ${R_HOME}/lib/libc++abi.1.dylib
## SHLIB_CXX14LDFLAGS+=-Wl,-rpath,${R_HOME}/lib ${R_HOME}/lib/libc++abi.1.dylib
## # clang: end
##
## CXX14FLAGS=-O3 -march=native -mtune=native
## CXX14FLAGS += -arch x86_64 -ftemplate-depth-256## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.3 (2020-02-29)
## os macOS Catalina 10.15.4
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2020-04-23
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib source
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.6.0)
## backports 1.1.6 2020-04-05 [1] CRAN (R 3.6.2)
## base64enc 0.1-3 2015-07-28 [1] CRAN (R 3.6.0)
## broom 0.5.3.9000 2020-04-01 [1] Github (tidymodels/broom@3c922d5)
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## cli 2.0.2 2020-02-28 [1] CRAN (R 3.6.0)
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## crayon 1.3.4 2017-09-16 [1] CRAN (R 3.6.0)
## DBI 1.1.0 2019-12-15 [1] CRAN (R 3.6.0)
## dbplyr 1.4.2 2019-06-17 [1] CRAN (R 3.6.0)
## desc 1.2.0 2018-05-01 [1] CRAN (R 3.6.0)
## devtools 2.2.2 2020-02-17 [1] CRAN (R 3.6.0)
## digest 0.6.25 2020-02-23 [1] CRAN (R 3.6.0)
## dplyr * 0.8.5 2020-03-07 [1] CRAN (R 3.6.0)
## ellipsis 0.3.0 2019-09-20 [1] CRAN (R 3.6.0)
## evaluate 0.14 2019-05-28 [1] CRAN (R 3.6.0)
## fansi 0.4.1 2020-01-08 [1] CRAN (R 3.6.0)
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## ggplot2 * 3.3.0.9000 2020-04-23 [1] Github (tidyverse/ggplot2@d3d47be)
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## haven 2.2.0 2019-11-08 [1] CRAN (R 3.6.0)
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## htmltools 0.4.0 2019-10-04 [1] CRAN (R 3.6.0)
## httr 1.4.1 2019-08-05 [1] CRAN (R 3.6.0)
## janitor * 2.0.1 2020-04-12 [1] CRAN (R 3.6.2)
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## pkgbuild 1.0.6 2019-10-09 [1] CRAN (R 3.6.0)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 3.6.0)
## pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.6.0)
## prettyunits 1.1.1 2020-01-24 [1] CRAN (R 3.6.0)
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## R6 2.4.1 2019-11-12 [1] CRAN (R 3.6.0)
## Rcpp 1.0.4.6 2020-04-09 [1] CRAN (R 3.6.3)
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## rmarkdown 2.1 2020-01-20 [1] CRAN (R 3.6.0)
## rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.6.0)
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## sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.0)
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## stringi 1.4.6 2020-02-17 [1] CRAN (R 3.6.0)
## stringr * 1.4.0 2019-02-10 [1] CRAN (R 3.6.0)
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## tidyverse * 1.3.0 2019-11-21 [1] CRAN (R 3.6.0)
## usethis 1.5.1 2019-07-04 [1] CRAN (R 3.6.0)
## vctrs 0.2.4 2020-03-10 [1] CRAN (R 3.6.0)
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## xfun 0.13 2020-04-13 [1] CRAN (R 3.6.2)
## xml2 1.3.1 2020-04-09 [1] CRAN (R 3.6.2)
## yaml 2.2.1 2020-02-01 [1] CRAN (R 3.6.0)
##
## [1] /Library/Frameworks/R.framework/Versions/3.6/Resources/library