Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setupDarwin, include=FALSE, eval = Sys.info()[["sysname"]] == "Darwin"----
# The following line seems to be required by pkgdown::build_site() on my machine,
# but causes build to break with R-CMD-CHECK on GH
knitr::opts_chunk$set(dev = "png", dev.args = list(type = "cairo-png"))
## ----message=FALSE------------------------------------------------------------
library(Rcompadre)
library(dplyr)
library(ggplot2)
library(popdemo)
## ----results=FALSE------------------------------------------------------------
y <- c(0.2, 4.1, 3.7)
mean(y) # 'normal' expression
y %>% mean() # piped expression
## ----results=FALSE------------------------------------------------------------
y %>% mean() # dot is implicit
y %>% mean(x = .) # dot is explicit
## ----results=FALSE------------------------------------------------------------
x <- 1:3
y %>% data.frame(col1 = x, col2 = .) # use dot to pass object to second argument
## ----eval=FALSE---------------------------------------------------------------
# # approach 1 (nested functions)
# compadre_use <- filter(cdb_flag(Compadre), check_NA_A == FALSE)
#
# # approach 2 (intermediate step)
# compadre_flag <- cdb_flag(Compadre)
# compadre_use <- filter(compadre_flag, check_NA_A == FALSE)
## -----------------------------------------------------------------------------
compadre_use <- Compadre %>%
cdb_flag() %>% # first argument is Compadre, from previous line
subset(check_NA_A == FALSE) # first argument is output of cdb_flag()
## ---- warning=FALSE-----------------------------------------------------------
compadre_euro <- Compadre %>%
filter(Continent == "Europe") %>%
mutate(Nordic = Country %in% c("NOR", "SWE", "DNK", "ISL", "FIN"))
## -----------------------------------------------------------------------------
compadre_use <- Compadre %>%
mutate(has_active = mpm_has_active(.)) %>%
filter(has_active == TRUE) %>%
mutate(StudyID = cdb_id_studies(.))
## -----------------------------------------------------------------------------
compadre_unnest <- Compadre %>%
mutate(
mat_U = matU(.),
m_class_organized = MatrixClassOrganized(.)
)
## -----------------------------------------------------------------------------
compadre_lambda <- Compadre %>%
cdb_flag() %>%
filter(check_NA_A == FALSE) %>% # remove matrices with missing values
mutate(mat_A = matA(.)) %>% # extract list-column of matA
mutate(lam = sapply(mat_A, popdemo::eigs, what = "lambda"))
## -----------------------------------------------------------------------------
compadre_stage_surv <- Compadre %>%
mutate(stage_survival = lapply(matU(.), colSums))
# print vector of stage-specific survival for 20th row
compadre_stage_surv$stage_survival[[20]]
## -----------------------------------------------------------------------------
SurvFirstActive <- function(matU, first_active) colSums(matU)[first_active]
## -----------------------------------------------------------------------------
compadre_surv_first_active <- Compadre %>%
mutate(
surv_1 = mapply(
FUN = SurvFirstActive, # function
matU = matU(.), # argument 1
first_active = mpm_first_active(.)
) # argument 2
)
## -----------------------------------------------------------------------------
# count number of unique populations by species
Compadre %>%
group_by(SpeciesAccepted) %>%
summarize(n_populations = length(unique(MatrixPopulation))) %>%
arrange(desc(n_populations)) # arrange in descending order of n_pops
## -----------------------------------------------------------------------------
# subset to species with 10+ unique populations
compadre_replicated_pops <- Compadre %>%
group_by(SpeciesAccepted) %>%
mutate(n_pops = length(unique(MatrixPopulation))) %>%
ungroup() %>%
subset(n_pops >= 10)
## -----------------------------------------------------------------------------
singleRepresentativeSpecies <- Compadre %>%
group_by(SpeciesAccepted) %>%
slice(sample(1))
## ----warning=FALSE, fig.width = 6, fig.height = 4-----------------------------
ggplot2::ggplot(Compadre, aes(Lon, Lat)) +
borders(database = "world", fill = "grey80", col = NA) +
geom_point(col = "steelblue", size = 1.8, alpha = 0.8)
## ----warning=FALSE, fig.width = 6, fig.height = 4-----------------------------
# function to calculate life expectancy
lifeExpectancy <- function(matU, startLife) {
N <- solve(diag(nrow(matU)) - matU)
return(colSums(N)[startLife])
}
compadre_life_expect <- Compadre %>%
filter(
MatrixComposite != "Seasonal", # filter is the dplyr version of subset
MatrixTreatment == "Unmanipulated",
MatrixCaptivity == "W",
ProjectionInterval == "1"
) %>%
mutate(StageID = cdb_id_stages(.)) %>%
cdb_collapse(columns = "StageID") %>%
cdb_flag() %>%
filter(
check_NA_U == FALSE,
check_zero_U == FALSE,
check_singular_U == FALSE
) %>%
mutate(matU = matU(.), start_life = mpm_first_active(.)) %>%
mutate(life_expectancy = mapply(lifeExpectancy, matU, start_life)) %>%
filter(life_expectancy >= 1) %>%
mutate(OrganismType = reorder(OrganismType, life_expectancy, median))
ggplot2::ggplot(compadre_life_expect, aes(OrganismType, life_expectancy)) +
geom_boxplot() +
scale_y_log10() +
coord_flip() +
labs(x = NULL, y = "Life expectancy (years)")
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