Merge pull request #41 from bedapub/cran_slow_vignettes

cran remove slow vignettes + remove |> pipe

.Rbuildignore

@@ -23,3 +23,4 @@
 ^vignettes/cached/.*\.Rmd$
 # large vignette excluded on CRAN
 ^vignettes/plate_scoring_examples.Rmd$
+^vignettes/plate_layouts.Rmd$

R/all_equal_df.R

@@ -25,9 +25,9 @@ all_equal_df <- function(df1, df2) {
   df2 <- df2[colnames(df1)]
 
   # convert factors to characters
-  df1 <- df1 |>
+  df1 <- df1 %>%
     dplyr::mutate(dplyr::across(dplyr::where(is.factor), as.character))
-  df2 <- df2 |>
+  df2 <- df2 %>%
     dplyr::mutate(dplyr::across(dplyr::where(is.factor), as.character))
 
   # order by all columns

R/batch_container.R

@@ -383,7 +383,7 @@ BatchContainer <- R6::R6Class("BatchContainer",
           }
           v
         }
-      ) |>
+      ) %>%
         purrr::flatten_dbl()
       assertthat::assert_that(length(res) >= length(scoring))
       assertthat::assert_that(

tests/testthat/test-scoring-names.R

@@ -2,7 +2,7 @@ samples <- data.frame(i = 1:384, x = rnorm(384))
 
 bc <- BatchContainer$new(
   dimensions = c(row = 16, column = 24)
-) |>
+) %>%
   assign_in_order(samples)
 
 

vignettes/NCS22_talk.Rmd

@@ -33,18 +33,18 @@ It uses a subset of the `longitudinal_subject_samples` dataset.
 ```{r get_data, include = TRUE}
 data("longitudinal_subject_samples")
 
-dat <- longitudinal_subject_samples |>
-  filter(Group %in% 1:5, Week %in% c(1, 4)) |>
+dat <- longitudinal_subject_samples %>% 
+  filter(Group %in% 1:5, Week %in% c(1, 4)) %>%
   select(SampleID, SubjectID, Group, Sex, Week)
 
 # for simplicity: remove two subjects that don't have both visits
-dat <- dat |>
+dat <- dat %>%
   filter(SubjectID %in%
-    (dat |> count(SubjectID) |> filter(n == 2) |> pull(SubjectID)))
+    (dat %>% count(SubjectID) %>% filter(n == 2) %>% pull(SubjectID)))
 
 
-subject_data <- dat |>
-  select(SubjectID, Group, Sex) |>
+subject_data <- dat %>%
+  select(SubjectID, Group, Sex) %>%
   unique()
 ```
 
@@ -56,7 +56,7 @@ This is the experiment design:
 
 ```{r, fig.width= 4, fig.height=3, echo = FALSE}
 data("plate_effect_example")
-plate_effect_example |>
+plate_effect_example %>%
   ggplot() +
   aes(x = column, y = row, fill = treatment, alpha = log_conc) +
   geom_tile() +
@@ -74,7 +74,7 @@ plate_effect_example |>
 These are the readouts:
 
 ```{r, fig.width= 4, fig.height=5, echo = FALSE}
-p1 <- plate_effect_example |>
+p1 <- plate_effect_example %>%
   ggplot() +
   aes(x = column, y = row, fill = readout) +
   geom_tile() +
@@ -86,9 +86,9 @@ p1 <- plate_effect_example |>
   scale_fill_viridis_c() +
   ggtitle("Readout")
 
-p2 <- plate_effect_example |>
-  filter(treatment == "control") |>
-  mutate(column = as.numeric(column)) |>
+p2 <- plate_effect_example %>%
+  filter(treatment == "control") %>%
+  mutate(column = as.numeric(column)) %>%
   ggplot() +
   aes(x = column, y = readout, color = row) +
   geom_point() +
@@ -114,15 +114,15 @@ set.seed(17) # gives `bad` random assignment
 
 bc <- BatchContainer$new(
   dimensions = list("batch" = 3, "location" = 11)
-) |>
+) %>%
   assign_random(subject_data)
 ```
 
 Gone wrong: Random distribution of 31 grouped subjects into 3 batches 
 turns out unbalanced: 
 
 ```{r, fig.width= 3, fig.height=3, echo = FALSE}
-bc$get_samples() |>
+bc$get_samples() %>%
   ggplot(aes(x = batch, fill = Group)) +
   geom_bar() +
   labs(y = "subject count")
@@ -169,7 +169,7 @@ set.seed(17) # gives `bad` random assignment
 ```{r}
 bc <- BatchContainer$new(
   dimensions = list("batch" = 3, "location" = 11)
-) |>
+) %>%
   assign_random(subject_data)
 ```
 
@@ -179,11 +179,11 @@ bc <- BatchContainer$new(
 ```{r, fig.width= 5.5, fig.height=3, echo = FALSE}
 cowplot::plot_grid(
   plotlist = list(
-    bc$get_samples() |>
+    bc$get_samples() %>%
       ggplot(aes(x = batch, fill = Group)) +
       geom_bar() +
       labs(y = "subject count"),
-    bc$get_samples() |>
+    bc$get_samples() %>%
       ggplot(aes(x = batch, fill = Sex)) +
       geom_bar() +
       labs(y = "subject count")
@@ -198,18 +198,18 @@ bc$get_samples()
 
 ```{r, echo=FALSE}
 bind_rows(
-  head(bc$get_samples(), 3) |>
+  head(bc$get_samples(), 3) %>%
     mutate(across(everything(), as.character)),
   tibble(
     batch = "...",
     location = " ...",
     SubjectID = "...",
     Group = "...", Sex = "..."
   ),
-  tail(bc$get_samples(), 3) |>
+  tail(bc$get_samples(), 3) %>%
     mutate(across(everything(), as.character))
-) |>
-  gt::gt() |>
+) %>%
+  gt::gt() %>%
   gt::tab_options(
     table.font.size = 11,
     data_row.padding = 0.1
@@ -250,11 +250,11 @@ bc <- optimize_design(
 ```{r, fig.width= 8, fig.height=3, echo = FALSE}
 cowplot::plot_grid(
   plotlist = list(
-    bc$get_samples() |>
+    bc$get_samples() %>%
       ggplot(aes(x = batch, fill = Group)) +
       geom_bar() +
       labs(y = "subject count"),
-    bc$get_samples() |>
+    bc$get_samples() %>%
       ggplot(aes(x = batch, fill = Sex)) +
       geom_bar() +
       labs(y = "subject count"),
@@ -267,18 +267,18 @@ cowplot::plot_grid(
 
 ```{r, echo=FALSE}
 bind_rows(
-  head(bc$get_samples(), 3) |>
+  head(bc$get_samples(), 3) %>%
     mutate(across(everything(), as.character)),
   tibble(
     batch = "...",
     location = " ...",
     SubjectID = "...",
     Group = "...", Sex = "..."
   ),
-  tail(bc$get_samples(), 3) |>
+  tail(bc$get_samples(), 3) %>%
     mutate(across(everything(), as.character))
-) |>
-  gt::gt() |>
+) %>%
+  gt::gt() %>%
   gt::tab_options(
     table.font.size = 11,
     data_row.padding = 0.1
@@ -315,7 +315,7 @@ set.seed(4)
 
 bc <- BatchContainer$new(
   dimensions = list("plate" = 3, "row" = 4, "col" = 6)
-) |>
+) %>%
   assign_in_order(dat)
 ```
 
@@ -496,7 +496,7 @@ see vignette `invivo_study_design` for the full story.
 
 
 ```{r, fig.width=4.0, fig.hight = 5.0, echo = FALSE}
-layout <- crossing(row = 1:9, column = 1:12) |>
+layout <- crossing(row = 1:9, column = 1:12) %>%
   mutate(Questions = "no")
 layout$Questions[c(
   16, 17, 18, 19, 20, 21,

vignettes/plate_layouts.Rmd

@@ -131,7 +131,7 @@ We can look at the trace objects for each internal `optimize_design` run,
 returned from the wrapper function.
 
 ```{r fig.width=6, fig.height=3}
-bc$scores_table() |>
+bc$scores_table() %>%
   ggplot(aes(step, value, color = score)) +
   geom_line() +
   geom_point() +
@@ -328,7 +328,7 @@ cowplot::plot_grid(
 ```
 
 ```{r fig.width=5.5, fig.height=2}
-bc$scores_table() |>
+bc$scores_table() %>%
   ggplot(aes(step, value, color = score)) +
   geom_line() +
   geom_point() +
@@ -556,7 +556,7 @@ cowplot::plot_grid(
 
 
 ```{r fig.width=6, fig.height=4}
-bc$scores_table() |>
+bc$scores_table() %>%
   ggplot(aes(step, value, color = score)) +
   geom_line() +
   geom_point() +