## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
## ----Install from github, eval = FALSE-----------------------------------
# library(devtools)
# install_github("dinilu/EPDr", force = TRUE)
## ----Install from CRAN, eval = FALSE-------------------------------------
# install.packages("EPDr")
## ----Setting up EPD server, eval = FALSE---------------------------------
# vignette("EPD-PostgreSQL", package = "EPDr")
## ----Loading EPDr--------------------------------------------------------
library(EPDr)
## ----Connect to local EPD, eval = FALSE----------------------------------
# epd.connection <- connect_to_epd(database = "epd",
# user = "epdr",
# password = "epdrpw")
## ----Connect to real remote EPD, include = FALSE-------------------------
epd.connection <- connect_to_epd(database = "epd",
user = "epdr",
password = "epdrpw",
host = "rabbot19.uco.es")
## ----Connect to remote EPD, eval = FALSE---------------------------------
# epd.connection <- connect_to_epd(database = "epd",
# user = "epdr",
# password = "epdrpw",
# host = "http://remote.epd.server")
## ----Test the connection-------------------------------------------------
library(DBI)
dbListTables(epd.connection)
## ----list_countries and list_taxagroups, R.options = list(max.print = 15)----
list_countries(epd.connection)
list_taxagroups(epd.connection)
## ----list_regions, R.options = list(max.print = 40)----------------------
list_regions(epd.connection)
list_regions(epd.connection, country = "Spain")
## ----list_taxa, R.options = list(max.print = 20)-------------------------
list_taxa(epd.connection)
list_taxa(epd.connection, group_id = "HERB")
## ----list_sites, R.options = list(max.print = 30)------------------------
list_sites(epd.connection)
list_sites(epd.connection, country = "Spain", region = "Andalucia")
list_sites(epd.connection, coords = c(-4, 10, 36, 40))
## ----list_e, R.options = list(max.print = 50)----------------------------
list_e(epd.connection)
list_e(epd.connection, site = "Adange")
list_e(epd.connection, lastname = "Tzedakis")
## ----list_e multiple countries, R.options = list(max.print = 60)---------
list_e(epd.connection, country = c("Spain", "Portugal", "France",
"Switzerland", "Austria", "Italy",
"Malta", "Algeria", "Tunisia",
"Morocco", "Atlantic ocean",
"Mediterranean Sea"))
## ----list_publ, R.options = list(max.print = 10)-------------------------
list_publ(epd.connection)
list_publ(epd.connection, e_ = 1)
## ----get_ent, R.options = list(max.print = 10)---------------------------
ent.1 <- get_ent(1, epd.connection)
class(ent.1)
slotNames(ent.1)
## ----get_site, R.options = list(max.print = 10)--------------------------
site.1 <- get_site(1, epd.connection)
class(site.1)
slotNames(site.1)
## ----get_geochron, R.options = list(max.print = 10)----------------------
geochron.1 <- get_geochron(1, epd.connection)
class(geochron.1)
slotNames(geochron.1)
## ----get_chron, R.options = list(max.print = 10)-------------------------
chron.1 <- get_chron(1, epd.connection)
class(chron.1)
slotNames(chron.1)
## ----get_sample, R.options = list(max.print = 10)------------------------
samples.1 <- get_samples(1, epd.connection)
class(samples.1)
slotNames(samples.1)
## ----get_entity, R.options = list(max.print = 10)------------------------
epd.1 <- get_entity(1, epd.connection)
class(epd.1)
slotNames(epd.1)
## ----epd.entity structure, R.options = list(max.print = 10)--------------
epd.1@e_
slot(epd.1, "e_")
epd.1@postbombzone
epd.1@numberofchron
epd.1@isingiesecke
epd.1@defaultchron
slotNames(epd.1@entity)
slotNames(epd.1@site)
slotNames(epd.1@geochron)
slotNames(epd.1@chron)
slotNames(epd.1@samples)
## ----check_--------------------------------------------------------------
check_restriction(epd.1)
check_defaultchron(epd.1)
## ----export_c14, R.options = list(max.print = 25)------------------------
export_c14("clam", epd.1@geochron@c14, epd.1@geochron@geochron)
export_c14("bacon", epd.1@geochron@c14, epd.1@geochron@geochron)
export_c14("clam", epd.1)
## ----export_agebasis, R.options = list(max.print = 10)-------------------
export_agebasis("clam", epd.1@chron@agebasis)
export_agebasis("bacon", epd.1)
## ----export_events, R.options = list(max.print = 10)---------------------
export_events("clam", epd.1@chron@synevent, epd.1@chron@event)
export_events("bacon", epd.1)
## ----export_depths, R.options = list(max.print = 10)---------------------
export_depths(epd.1@samples@psamples)
export_depths(epd.1)
## ----export_entity function, eval = FALSE, R.options = list(max.print = 10)----
# export_entity("clam", epd.1)
#
# ## Chronology has coincident data with C14 data and, hence, the later will
# ## be used
# ## C14 data:
# ## lab_ID C14_age cal_age error reserv. depth thickn.
# ## KIGI-350 910 NA 20 NA 83 5
# ## KIGI-349 2420 NA 200 NA 118 5
# ## KIGI-348 2900 NA 190 NA 140 10
# ##
# ## Chronology data:
# ## lab_ID C14_age cal_age error reserv. depth thickn.
# ## E1_CH1_S2 910 NA 1 NA 83 NA
# ## E1_CH1_S3 2420 NA 1 NA 118 NA
# ## E1_CH1_S4 2900 NA 1 NA 140 NA
# ##
# ## Chronology has additional no-C14 data.
# ## Chronology data:
# ## lab_ID C14_age cal_age error reserv. depth thickn.
# ## E1_CH1_S1 0 NA 1 NA 2 NA
# ## E1_CH1_S5 4000 NA 1 NA 200 NA
# ##
# ## Incorporate these data to the chronology? (Yes: TRUE then Intro,
# ## No: FALSE then Intro)TRUE
# ##
# ## lab_ID C14_age cal_age error reservoir depth thickness
# ## 11 E1_CH1_S1 0 NA 1 NA 2 NA
# ## 1 KIGI-350 910 NA 20 NA 83 5
# ## 2 KIGI-349 2420 NA 200 NA 118 5
# ## 3 KIGI-348 2900 NA 190 NA 140 10
# ## 5 E1_CH1_S5 4000 NA 1 NA 200 NA
## ----entity_to_matrices, R.options = list(max.print = 20)----------------
epd.1 <- entity_to_matrices(epd.1)
slotNames(epd.1)
## ----epd.entity.df structure, R.options = list(max.print = 20)-----------
epd.1@countstype
epd.1@countsprocessing
epd.1@taxatype
epd.1@taxaprocessing
## ----samplesdf structure, R.options = list(max.print = 20)---------------
slotNames(epd.1@samplesdf)
## ----agesdf structure, R.options = list(max.print = 20)------------------
slotNames(epd.1@agesdf)
## ----commdf structure, R.options = list(max.print = 20)------------------
slotNames(epd.1@commdf)
## ----nopodf structure, R.options = list(max.print = 20)------------------
slotNames(epd.1@nopodf)
## ----get_taxonomy_epd, R.options = list(max.print = 60)------------------
epd.taxonomy <- get_taxonomy_epd(epd.connection)
epd.taxonomy
## ----taxa_to_acceptedtaxa, R.options = list(max.print = 50)--------------
epd.1@commdf@taxanames
epd.1 <- taxa_to_acceptedtaxa(epd.1, epd.taxonomy)
epd.1@commdf@taxanames
## ----taxa_to_highertaxa, R.options = list(max.print = 50)----------------
epd.1@commdf@taxanames
epd.1.ht <- taxa_to_highertaxa(epd.1, epd.taxonomy)
epd.1.ht@commdf@taxanames
## ----taxa_to_highertaxa @counts, R.options = list(max.print = 300)-------
rowSums(epd.1@commdf@counts)
rowSums(epd.1.ht@commdf@counts)
## ----filter_taxagroups, R.options = list(max.print = 40)-----------------
epd.1 <- filter_taxagroups(epd.1, c("DWAR", "HERB", "LIAN",
"TRSH", "UPHE", "INUN"))
epd.1@commdf@taxanames
rowSums(epd.1@commdf@counts)
## ----filter_taxa, R.options = list(max.print = 40)-----------------------
epd.1.ft <- filter_taxa(epd.1, c("Alnus", "Artemisia", "Betula",
"Carpinus betulus", "Corylus"),
epd.taxonomy)
head(epd.1.ft@commdf@counts)
## ----filter_taxa misspelling, R.options = list(max.print = 40)-----------
epd.1.ft <- filter_taxa(epd.1, c("Aluns", "Artemisia", "Betula",
"Carpinus betulus", "Carylus"),
epd.taxonomy)
head(epd.1.ft@commdf@counts)
## ----counts_to_percentages, R.options = list(max.print = 40)-------------
epd.1@countstype
epd.1 <- counts_to_percentages(epd.1)
epd.1@countstype
head(epd.1@commdf@counts)
## ----counts_to_percentages check, R.options = list(max.print = 40)-------
rowSums(epd.1@commdf@counts)
## ----giesecke_default_chron----------------------------------------------
epd.1@defaultchron
epd.1@numberofchron
epd.1@isingiesecke
epd.1 <- giesecke_default_chron(epd.1)
epd.1@defaultchron
## ----interpolate_counts--------------------------------------------------
epd.1.int <- interpolate_counts(epd.1,
c(0, 1000, 2000, 3000, 4000, 5000),
method = "linear")
## ----interpolate_counts check @commdf@counts, R.options = list(max.print = 50)----
epd.1@commdf@counts[, 1:5]
epd.1.int@commdf@counts[, 1:5]
## ----interpolate_counts check @countsprocessing--------------------------
epd.1@countsprocessing
epd.1.int@countsprocessing
## ----interpolate_counts check @agesdf, R.options = list(max.print = 18)----
epd.1@agesdf
epd.1.int@agesdf
## ----interpolate_counts check @samplesdf, R.options = list(max.print = 20)----
epd.1@samplesdf
epd.1.int@samplesdf
## ----intervals_counts----------------------------------------------------
epd.1.ran <- intervals_counts(epd.1,
c(0, 1000, 2000, 3000, 4000, 5000),
c(999, 1999, 2999, 3999, 4999, 5999))
epd.1.ran@commdf@counts[, 1:5]
## ----intervals_counts check @countsprocessing----------------------------
epd.1.ran@countsprocessing
## ----intervals_counts check @agesdf, R.options = list(max.print = 18)----
epd.1.ran@agesdf
## ----intervals_counts check @samplesdf, R.options = list(max.print = 20)----
epd.1.ran@samplesdf
## ----blois_quality, R.options = list(max.print = 20)---------------------
epd.1 <- blois_quality(epd.1)
epd.1.int <- blois_quality(epd.1.int)
epd.1@agesdf@dataquality
epd.1.int@agesdf@dataquality
## ----taxa_by_taxa_age, R.options = list(max.print = 20)------------------
table_by_taxa_age(epd.1, c("Pinus", "Quercus"), as.character(c(1:10)))
table_by_taxa_age(epd.1.int, "Quercus", c("1000", "2000", "3000"))
## ----plot_diagram, fig.width = 7, fig.height = 9, fig.align = "center"----
plot_diagram(epd.1)
plot_diagram(epd.1.int)
## ----disconnect_from_epd-------------------------------------------------
disconnect_from_epd(epd.connection)
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