Update format

.github/CODE_OF_CONDUCT.md

@@ -0,0 +1,25 @@
+# Contributor Code of Conduct
+
+As contributors and maintainers of this project, we pledge to respect all people who 
+contribute through reporting issues, posting feature requests, updating documentation,
+submitting pull requests or patches, and other activities.
+
+We are committed to making participation in this project a harassment-free experience for
+everyone, regardless of level of experience, gender, gender identity and expression,
+sexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion.
+
+Examples of unacceptable behavior by participants include the use of sexual language or
+imagery, derogatory comments or personal attacks, trolling, public or private harassment,
+insults, or other unprofessional conduct.
+
+Project maintainers have the right and responsibility to remove, edit, or reject comments,
+commits, code, wiki edits, issues, and other contributions that are not aligned to this 
+Code of Conduct. Project maintainers who do not follow the Code of Conduct may be removed 
+from the project team.
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by 
+opening an issue or contacting one or more of the project maintainers.
+
+This Code of Conduct is adapted from the Contributor Covenant 
+(http://contributor-covenant.org), version 1.0.0, available at 
+http://contributor-covenant.org/version/1/0/0/

.github/workflows/check_source.yml

@@ -0,0 +1,15 @@
+on:
+  push:
+    branches-ignore:
+      - gh-pages
+
+name: check-source
+
+jobs:
+  check-src:
+    runs-on: ubuntu-latest
+    name: "Check coding style"
+    steps:
+      - uses: inbo/actions/check_src@master
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}

R/_metadata.R

@@ -14,7 +14,7 @@ library(EMLassemblyline)
 
 ### Start ###
 rm(list = ls())
-setwd(here("data/raw"))
+setwd(here("data", "raw"))
 
 
 

R/_prepare_data_spatial.R

@@ -42,7 +42,8 @@ sites <- read_csv(here("data", "raw", "data_raw_sites.csv"),
       exposition = "f"
     )
 ) %>%
-  select(id, location, longitude, latitude, constructionYear, sandPerc, phosphorus, phosphorusClass) %>%
+  select(id, location, longitude, latitude, constructionYear, sandPerc,
+         phosphorus, phosphorusClass) %>%
   mutate(
     plot = str_sub(id, start = 1, end = 2),
     locationAbb = str_sub(location, 1, 3),
@@ -62,9 +63,14 @@ rm(coord)
 #### Calculate center of locations ###
 locations <- sites_basic %>%
   group_by(locationYear) %>%
-  summarise(across(c(longitude, latitude, constructionYear), mean, na.rm = TRUE)) %>%
+  summarise(across(c(longitude, latitude, constructionYear),
+                   mean, na.rm = TRUE)) %>%
   rename(longitude_center = longitude, latitude_center = latitude)
-sites_basic <- left_join(sites_basic, locations %>% select(-constructionYear), by = "locationYear")
+sites_basic <- left_join(
+  sites_basic,
+  locations %>% select(-constructionYear),
+  by = "locationYear"
+  )
 
 
 ## 2 Transform shp files ################################################
@@ -103,7 +109,8 @@ st_intersection(bbox) # problem
 # plot(st_geometry(danube_isar))
 # rm(data)
 ### Here the digitized file ###
-danube_isar <- st_read(here("data", "raw", "spatial", "danube_isar_digitized_epsg4326.shp"))
+danube_isar <- st_read(here("data", "raw", "spatial",
+                            "danube_isar_digitized_epsg4326.shp"))
 
 
 ## 4 Background map #####################################################
@@ -208,7 +215,7 @@ st_write(dikes,
   dsn = here("data", "processed", "spatial")
 )
 st_write(conservation_area,
-  layer = "conservation_area_epsg4326.shp", driver = "ESRI Shapefile", delete_layer = T,
+  layer = "conservation_area_epsg4326.shp", driver = "ESRI Shapefile", delete_layer = TRUE,
   dsn = here("data", "processed", "spatial")
 )
 st_write(ffh_area,

R/_prepare_data_vegetation.R

@@ -20,9 +20,9 @@ remotes::install_github(file.path("inbo", "checklist"))
 ### Start ###
 # installr::updateR(browse_news = FALSE, install_R = TRUE, copy_packages = TRUE, copy_Rprofile.site = TRUE, keep_old_packages = TRUE, update_packages = TRUE, start_new_R = FALSE, quit_R = TRUE, print_R_versions = TRUE, GUI = TRUE)
 # sessionInfo()
-checklist::setup_source()
-checklist::check_source()
-devtools::check()
+#checklist::setup_source()
+#checklist::check_source()
+#devtools::check()
 
 rm(list = ls())
 setwd(here("data", "raw"))
@@ -232,12 +232,12 @@ rm(list = ls(pattern = "[^species|traits|sites]"))
 
 
 
-#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-# B Create variables ############################################################
-#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+# B Create variables ###########################################################
+#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
 
-## 1 Create simple variables ####################################################
+## 1 Create simple variables ###################################################
 
 traits <- traits %>%
   mutate(
@@ -271,7 +271,7 @@ sites <- sites %>%
   )
 
 
-## 2 Coverages ######################################################################
+## 2 Coverages #################################################################
 
 cover <- left_join(species, traits, by = "name") %>%
   select(
@@ -947,7 +947,9 @@ eigenvals <- pca %>%
   summary() %>%
   as_tibble() %>%
   select(PC1:PC3) %>%
-  bind_cols(c("Eigenvalues", "Proportion Explained", "Cumulative Proportion")) %>%
+  bind_cols(c("Eigenvalues",
+              "Proportion Explained",
+              "Cumulative Proportion")) %>%
   rename("variables" = "...4") %>%
   mutate(across(where(is.numeric), as.double))
 values <- pca %>%
@@ -989,7 +991,8 @@ rm(list = setdiff(ls(), c("sites", "species", "traits", "pcaSoil")))
 ### b Climate PCA  -----------------------------------------------------
 
 ### * Temperature ####
-data <- read_csv(here("data", "raw", "temperature", "data", "data_OBS_DEU_P1M_T2M.csv"),
+data <- read_csv(here("data", "raw", "temperature", "data",
+                      "data_OBS_DEU_P1M_T2M.csv"),
   col_names = TRUE, na = c("", "NA", "na"), col_types =
     cols(
       .default = "?"
@@ -1032,7 +1035,9 @@ data <- read_csv(here("data", "raw", "temperature", "data", "data_OBS_DEU_P1M_T2
     names_from = season, values_from = seasonMean
   ) %>%
   group_by(year) %>%
-  summarise(across(where(is.numeric), ~ max(., na.rm = TRUE)), .groups = "keep") %>% # warnings because of lates year (summer, autumn, winter), can be ignored
+  summarise(across(where(is.numeric), ~ max(., na.rm = TRUE)),
+            .groups = "keep") %>%
+  # warnings because of lates year (summer, autumn, winter), can be ignored
   mutate(year = factor(year))
 
 sites <- sites %>%
@@ -1073,7 +1078,8 @@ sites <- sites %>%
   )
 
 ### * Precipitation ####
-data <- read_csv(here("data", "raw", "precipitation", "data", "data_OBS_DEU_P1M_RR.csv"),
+data <- read_csv(here("data", "raw", "precipitation", "data",
+                      "data_OBS_DEU_P1M_RR.csv"),
   col_names = TRUE, na = c("", "NA", "na"), col_types =
     cols(
       .default = "?"
@@ -1120,7 +1126,8 @@ data <- read_csv(here("data", "raw", "precipitation", "data", "data_OBS_DEU_P1M_
     .groups = "keep"
   ) %>%
   mutate(currentMean = if_else(season == "spring", currentMean, NA_real_)) %>%
-  pivot_wider(id_cols = c(year, yearMean, currentMean), names_from = season, values_from = seasonMean) %>%
+  pivot_wider(id_cols = c(year, yearMean, currentMean),
+              names_from = season, values_from = seasonMean) %>%
   group_by(year) %>%
   summarise(across(where(is.numeric), ~ max(., na.rm = TRUE)),
     .groups = "keep"
@@ -1184,7 +1191,9 @@ eigenvals <- pca %>%
   summary() %>%
   as_tibble() %>%
   select(PC1:PC3) %>%
-  bind_cols(c("Eigenvalues", "Proportion Explained", "Cumulative Proportion")) %>%
+  bind_cols(c("Eigenvalues",
+              "Proportion Explained",
+              "Cumulative Proportion")) %>%
   rename("variables" = "...4") %>%
   mutate(across(where(is.numeric), as.double))
 values <- pca %>%
@@ -1248,7 +1257,9 @@ eigenvals <- pca %>%
   summary() %>%
   as_tibble() %>%
   select(PC1:PC3) %>%
-  bind_cols(c("Eigenvalues", "Proportion Explained", "Cumulative Proportion")) %>%
+  bind_cols(c("Eigenvalues",
+              "Proportion Explained",
+              "Cumulative Proportion")) %>%
   rename("variables" = "...4") %>%
   mutate(across(where(is.numeric), as.double))
 values <- pca %>%
@@ -1266,7 +1277,9 @@ pcaConstuctionYear <- values$species[, 1:3] %>%
     "precMean_constructionYear", "precSpring_constructionYear",
     "precSummer_constructionYear", "precAutumn_constructionYear",
     "precWinter_constructionYear",
-    "precMean_constructionYearPlus", "precSpring_constructionYearPlus", "precSummer_constructionYearPlus", "precAutumn_constructionYearPlus", "precWinter_constructionYearPlus"
+    "precMean_constructionYearPlus",
+    "precSpring_constructionYearPlus", "precSummer_constructionYearPlus",
+    "precAutumn_constructionYearPlus", "precWinter_constructionYearPlus"
   )) %>%
   rename(variables = "...4") %>%
   bind_rows(eigenvals)

R/show_figure_1_map.R

@@ -27,9 +27,11 @@ setwd(here("data", "processed", "spatial"))
 
 ### Load data ###
 germany <- st_read("germany_epsg4326.shp")
-danube <- st_read(here("data", "raw", "spatial", "danube_isar_digitized_epsg4326.shp"))
+danube <- st_read(here("data", "raw", "spatial",
+                       "danube_isar_digitized_epsg4326.shp"))
 danube$river[2] <- "Isar"
-filter <- read_csv(here("data", "processed", "data_processed_sites_spatial.csv"),
+filter <- read_csv(here("data", "processed",
+                        "data_processed_sites_spatial.csv"),
   col_names = TRUE, na = c("na", "NA"), col_types =
     cols(
       .default = "?"
@@ -46,7 +48,7 @@ conservation_area <- st_read("conservation_area_epsg4326.shp")
 ffh_area <- st_read("ffh_area_epsg4326.shp")
 dikes <- st_read("dikes_epsg4326.shp")
 locations <- read_csv("locations.csv",
-  col_names = T, col_types =
+  col_names = TRUE, col_types =
     cols(
       locationYear = "f"
     )
@@ -138,7 +140,7 @@ graphSites + inset_element(graphGermany,
   bottom = .65,
   right = .99,
   top = .99,
-  on_top = T
+  on_top = TRUE
 )
 
 
@@ -198,7 +200,7 @@ graphSites + inset_element(graphGermany,
   bottom = .65,
   right = .99,
   top = .99,
-  on_top = T
+  on_top = TRUE
 )
 
 ### c Save --------------------------------------------------------------
@@ -227,10 +229,10 @@ tmap <- tm_shape(danube) +
   tm_dots(col = "red", size = .2, shape = 16) +
   tm_compass(position = c("left", "bottom"), size = 2) +
   tm_scale_bar(position = c("left", "bottom", with = 0.4)) +
-  tm_layout(frame = F)
+  tm_layout(frame = FALSE)
 tmap_ger <- tm_shape(germany) +
   tm_borders(col = "black") +
-  tm_layout(frame = F)
+  tm_layout(frame = FALSE)
 
 ### b Save --------------------------------------------------------------
 
@@ -242,6 +244,7 @@ tmap_save(tmap,
     width = unit(3, "cm"),
     height = unit(4, "cm")
   ),
-  filename = paste0(here("outputs", "figures"), "/", "figure_1_map_tmap_300dpi_8x11cm.tiff"),
+  filename = paste0(here("outputs", "figures"), "/",
+                    "figure_1_map_tmap_300dpi_8x11cm.tiff"),
   dpi = 300
 )

R/show_figure_2a_comparison.R

@@ -58,8 +58,10 @@ theme_mb <- function() {
     panel.background = element_rect(fill = "white"),
     text = element_text(size = 9, color = "black"),
     strip.text = element_text(size = 10),
-    axis.text = element_text(angle = 0, hjust = 0.5, size = 9, color = "black"),
-    axis.title = element_text(angle = 0, hjust = 0.5, size = 9, color = "black"),
+    axis.text = element_text(angle = 0, hjust = 0.5, size = 9,
+                             color = "black"),
+    axis.title = element_text(angle = 0, hjust = 0.5, size = 9,
+                              color = "black"),
     axis.line = element_line(),
     legend.key = element_rect(fill = "white"),
     legend.position = "none",
@@ -75,18 +77,25 @@ theme_mb <- function() {
 #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
 
-data_model <- ggeffect(m2, type = "emm", c("comparison"), back.transform = TRUE) %>%
+data_model <- ggeffect(m2, type = "emm", c("comparison"),
+                       back.transform = TRUE) %>%
   mutate(
     predicted = exp(predicted),
     conf.low = exp(conf.low),
     conf.high = exp(conf.high),
     cross = if_else(x %in% c("1819"), "filled", "open"),
-    x = fct_recode(x, "2017 vs 2018" = "1718", "2018 vs 2019" = "1819", "2019 vs 2021" = "1921")
+    x = fct_recode(x,
+                   "2017 vs 2018" = "1718",
+                   "2018 vs 2019" = "1819",
+                   "2019 vs 2021" = "1921")
   )
 
 data <- sites %>%
   rename(predicted = y, x = comparison) %>%
-  mutate(x = fct_recode(x, "2017 vs 2018" = "1718", "2018 vs 2019" = "1819", "2019 vs 2021" = "1921"))
+  mutate(x = fct_recode(x,
+                        "2017 vs 2018" = "1718",
+                        "2018 vs 2019" = "1819",
+                        "2019 vs 2021" = "1921"))
 
 (graph_a <- ggplot() +
   geom_quasirandom(

R/show_figure_2b_location.R

@@ -51,7 +51,7 @@ m2 <- blmer(log(y) ~ comparison + exposition * PC1soil + PC2soil + PC3soil +
             data = sites)
 
 ### * Functions ####
-theme_mb <- function(){
+theme_mb <- function() {
   theme(
     panel.background = element_rect(fill = "white"),
     text  = element_text(size = 9, color = "black"),
@@ -83,26 +83,28 @@ data_model <- ggeffect(m2, type = "emm", c("locationYear"), back.transform = TRU
          cross = if_else(x %in% c("PFE-2008", "IRL-2003"), "filled", "open"))
 
 data <- sites %>%
-  rename(predicted = y, x = locationYear) 
+  rename(predicted = y, x = locationYear)
 
 (graph_b <- ggplot() +
-    geom_quasirandom(data = data, 
+    geom_quasirandom(data = data,
                      aes(x = x, predicted),
-                     dodge.width = .6, size = 1, shape = 16, color = "grey70") + 
+                     dodge.width = .6, size = 1, shape = 16,
+                     color = "grey70") +
     geom_hline(yintercept = c(mean(sites$y),
                               mean(sites$y) + 0.5 * sd(sites$y),
                               mean(sites$y) - 0.5 * sd(sites$y)),
                linetype = c(1, 2, 2),
                color = "grey70") +
-    geom_errorbar(data = data_model, 
-                  aes(x, predicted, ymin = conf.low, ymax = conf.high), 
+    geom_errorbar(data = data_model,
+                  aes(x, predicted, ymin = conf.low, ymax = conf.high),
                   width = 0.0, size = 0.4) +
     geom_point(data = data_model,
                aes(x, predicted, shape = cross),
                size = 2) +
     scale_y_continuous(limits = c(0, .92), breaks = seq(0, 400, .1)) +
     scale_shape_manual(values = c("circle", "circle open")) +
-    labs(x = "", y = expression(Temporal~"beta"~diversity~"["*italic('D')[sor]*"]")) +
+    labs(x = "",
+         y = expression(Temporal ~ "beta" ~ diversity ~ "[" * italic("D")[sor] * "]")) +
     theme_mb())
 
 ### Save ###