#97 #104 tar_make()

README.md

@@ -44,8 +44,8 @@ analysis in *Gould et al.*, complete the following steps:
 2.  Run `renv::restore()` to load the packages used in the analysis
     pipeline locally on your machine (see
     [`renv::`](https://rstudio.github.io/renv/index.html) for details)
-3.  Run `tar_destroy()` to remove any record and caches of existing
-    targets
+3.  Run `targets::tar_destroy()` to remove any record and caches of
+    existing targets
 4.  Run `targets::tar_make()` in your console, depending on the power of
     your machine, the analysis pipeline will take between 2 and 7
     minutes to execute (plus or minus some!)
@@ -64,7 +64,8 @@ targets::tar_read("ManyEcoEvo")
     1 blue tit   <tibble [174 × 38]> <tibble [174 × 54]> Zr           
     2 eucalyptus <tibble [128 × 38]> <tibble [128 × 61]> Zr           
 
-The script that generates the ManyEcoEvo package datasets is located in
+The script that generates datasets used in the `ManyEcoEvo::` package is
+located in
 [`ManyEcoEvo/data-raw/tar_make.R`](https://github.com/egouldo/ManyEcoEvo/blob/main/data-raw/tar_make.R).
 
 ## License

data-raw/analysis_datasets/README.md

@@ -44,46 +44,50 @@ library(purrr)
 library(stringr)
 library(tidyr)
 library(tibble)
+data("ManyEcoEvo")
 
 # Constructed Variables Included in the ManyAnalysts meta-analysis
 ManyEcoEvo_constructed_vars <-
-  tribble(~response_variable_name,
-          "euc_sdlgs_all",
-          "euc_sdlgs>50cm",
-          "euc_sdlgs0_2m",
-          "small*0.25+medium*1.25+large*2.5",
-          "euc_sdlgs50cm_2m",
-          "average.proportion.of.plots.containing.at.least.one.euc.seedling.of.any.size",
-          "day_14_weight/(day_14_tarsus_length^2)",
-          "day_14_weight/day_14_tarsus_length",
-          "day_14_weight*day_14_tarsus_length"
+  tribble(
+    ~response_variable_name,
+    "euc_sdlgs_all",
+    "euc_sdlgs>50cm",
+    "euc_sdlgs0_2m",
+    "small*0.25+medium*1.25+large*2.5",
+    "euc_sdlgs50cm_2m",
+    "average.proportion.of.plots.containing.at.least.one.euc.seedling.of.any.size",
+    "day_14_weight/(day_14_tarsus_length^2)",
+    "day_14_weight/day_14_tarsus_length",
+    "day_14_weight*day_14_tarsus_length"
   )
 
 # Analyst Constructed Variables
-all_constructed_vars <- 
-  ManyEcoEvo %>% 
-    pull(data, dataset) %>% 
-    list_rbind(names_to = "dataset") %>% 
-    filter(str_detect(response_variable_type, "constructed")) %>% 
-    distinct(response_variable_name) %>% 
-    drop_na() %>% 
-    arrange()
+all_constructed_vars <-
+  ManyEcoEvo %>%
+  pull(data, dataset) %>%
+  list_rbind(names_to = "dataset") %>%
+  filter(str_detect(response_variable_type, "constructed")) %>%
+  distinct(response_variable_name) %>%
+  drop_na() %>%
+  arrange()
 
 by <- join_by(response_variable_name)
 
-all_constructed_vars %>% 
-  semi_join(ManyEcoEvo_constructed_vars, by) %>% 
-  mutate(included_in_yi = TRUE) %>% 
-  bind_rows(
-    {
-      all_constructed_vars %>% 
-        anti_join(ManyEcoEvo_constructed_vars, by) %>% 
-        mutate(included_in_yi = FALSE)
-    }
-  ) %>% 
-  knitr::kable(col.names = c("Constructed Variable", 
-                             "Included in $y_i$ meta-analysis?"), 
-               format = "markdown")
+all_constructed_vars %>%
+  semi_join(ManyEcoEvo_constructed_vars, by) %>%
+  mutate(included_in_yi = TRUE) %>%
+  bind_rows({
+    all_constructed_vars %>%
+      anti_join(ManyEcoEvo_constructed_vars, by) %>%
+      mutate(included_in_yi = FALSE)
+  }) %>%
+  knitr::kable(
+    col.names = c(
+      "Constructed Variable",
+      "Included in $y_i$ meta-analysis?"
+    ),
+    format = "markdown"
+  )
 ```
 
 </details>
@@ -104,3 +108,4 @@ all_constructed_vars %>%
 | day_14_tarsus_length_deviation                                               | FALSE                            |
 | residual_day14_weight                                                        | FALSE                            |
 | residual_day_14_weight_males                                                 | FALSE                            |
+| euc_sdlgs\>2m                                                                | FALSE                            |