change name of pivot_wider_name_first

Author: wjschne

NAMESPACE

@@ -31,7 +31,7 @@ export(is_numeric_like)
 export(italic_md)
 export(num_pad)
 export(p2stars)
-export(pivot_wider_name_value)
+export(pivot_wider_name_first)
 export(pretty_widths)
 export(separate_star_column)
 export(star_balance)

R/tables.R

@@ -1628,7 +1628,7 @@ pretty_widths <- function(
 #' @inheritParams tidyr::pivot_wider
 #' @return data.frame
 #' @export
-pivot_wider_name_value <- function(
+pivot_wider_name_first <- function(
     data,
     ...,
     id_cols = NULL,

man/pivot_wider_name_value.Rd man/pivot_wider_name_first.Rdman/pivot_wider_name_value.Rd renamed to man/pivot_wider_name_first.Rd

@@ -441,7 +441,7 @@ d_star
 apa_flextable(d_star)
 ```
 
-## pivot_wider_name_value
+## pivot_wider_name_first
 
 
 
@@ -460,7 +460,7 @@ d |>
   # deselect t, df, and p
   select(-c(t,df, p)) |> 
   # restructure data
-  pivot_wider_name_value(names_from = Model, 
+  pivot_wider_name_first(names_from = Model, 
                       values_from = c(b, beta)) |> 
   # add break column between 2 models
   add_break_columns(`Model 1_beta`) |> 
@@ -577,10 +577,10 @@ ggplot2::diamonds |>
     aes(label = paste0(p, " (", n, ")")),
     position = position_fill(vjust = .5),
     size.unit = "pt",
-    size = 15 * .8,
+    size = 14 * .8,
     color = "gray10"
   ) +
-  theme_minimal(base_family = "Roboto Condensed", base_size = 15) +
+  theme_minimal(base_family = "Roboto Condensed", base_size = 14) +
   scale_y_continuous(
     "Cumulative Proportion",
     expand = expansion(c(0, .025)),

vignettes/apa7.qmd

@@ -394,7 +394,7 @@ d <- d |>
          .by = c(Variable, name)) |>
   mutate(`Full sample_n` = sum(n),
          .by = c(Variable, Group)) |>
-  pivot_wider_name_value(values_from = c(n , `%`)) |> 
+  pivot_wider_name_first(values_from = c(n , `%`)) |> 
   apa7::add_break_columns(ends_with("%")) |>
   relocate(`Full sample_n`, .after = last_col()) |>
   mutate(
@@ -670,7 +670,7 @@ d <- d |>
     100 * `n` / sum(`n`), 
     accuracy = .01),
     .by = Source) |>
-  pivot_wider_name_value(values_from = c(`n`, `%`)) |>
+  pivot_wider_name_first(values_from = c(`n`, `%`)) |>
   relocate(chi, .after = dplyr::last_col()) |>
   rename(`&chi;^2^(2)` = chi) |> 
   mutate(across(-Source, align_chr)) |> 
@@ -774,7 +774,7 @@ d <- d |>
   mutate(Age = paste0(Age, "-year-olds"), 
          Cohens_d = signs(abs(Cohens_d), 
                                  accuracy = .001)) |> 
-  pivot_wider_name_value(
+  pivot_wider_name_first(
     values_from = c(M, SD), 
     names_from = Age) |> 
   select(`Logistic Parameter`, 
@@ -1122,7 +1122,7 @@ d <- d |>
       summarise(M = mean(x),
                 SD = sd(x), 
                 .by = c(Group, Time)) |> 
-      pivot_wider_name_value(
+      pivot_wider_name_first(
         names_from = Group,
         values_from = c(M, SD)) |> 
       mutate(Time = paste("Time", Time))
@@ -1472,7 +1472,7 @@ d_performance <- apa_performance_comparison(
   pivot_wider(names_from = Model)
 
 d_parameters |> 
-  pivot_wider_name_value(
+  pivot_wider_name_first(
     id_cols = Variable, 
     names_from = Model, 
     values_from = 3:5) |> 

vignettes/articles/_APAManualTables.qmd

@@ -102,26 +102,28 @@ The flextable package's `tabulator` function has considerable power in creating
 Here I calculate the means, standard deviations, and sample sizes within each cell of a contingency table.
 
 ```{r}
-warpbreaks |>
+d_tension <- warpbreaks |>
   summarise(
-    Mean = mean(breaks, na.rm = TRUE),
-    stdev = sd(breaks, na.rm = TRUE),
-    sample_size = sum(!is.na(breaks)),
+    m = mean(breaks, na.rm = TRUE),
+    sd = sd(breaks, na.rm = TRUE),
+    n = n(),
     .by = c(wool, tension)
   ) %>%
   rename(Wool = wool) |>
   mutate(
     tension = factor(
       tension,
       labels = paste(c("Low", "Medium", "High"), "Tension")
-    )) |>
+    )) 
+  
+d_tension |> 
   flextable::tabulator(
-    rows = "Wool",
+    rows = c("Wool"),
     columns = "tension",
-    M = as_paragraph(Mean),
-    SD = as_paragraph(stdev),
-    n = as_paragraph(sample_size)
-  ) |>
+    M = as_paragraph(m),
+    SD = as_paragraph(sd),
+    N = as_paragraph(n)
+  ) |> 
   flextable::as_flextable() |>
   italic(i = 2, part = "header") |>
   align(j = 1, align = "center") |> 
@@ -130,33 +132,18 @@ warpbreaks |>
 
 # Processing Data Yourself
 
-What if you want it to look a little different? Unfortunately, you might have to do some of the heavy lifting yourself. The `tidyr::pivot_wider` function can save a lot of time, if you know how to use the `names_glue` argument to get the column names in the right order and `names_vary` argument to get the columns in the right order.
+What if you want it to look a little different? Unfortunately, you might have to do some of the heavy lifting yourself. The `pivot_wider_name_first` function is a wrapper around `tidyr::pivot_wider` that prints and sorts column names as "name_variable" instead of "variable_name". I wanted a shortcut because I frequently needed to make a shortcut for setting `names_glue = "{tension}_{.value}"` and `names_vary = "slowest"` when `names_from = "tension"`.
 
 A subtle difference in this version of the table is that the columns are decimal aligned, which is evident in the standard deviation column.
 
-Also, whereas previously the Wool column header was middle aligned, it is now top aligned, which is consistent with comparable examples in the *APA Style Manual*. I am not sure if this is a real rule in APA style, and I actually prefer middle alignment for situations like this.
+Also, whereas previously the "stub header" (Wool column) was middle aligned, it is now top aligned, which is consistent with comparable examples in the *APA Style Manual*. I am not sure if this is a real rule in APA style, and I actually prefer middle alignment for situations like this.
 
 ```{r warpbreaks}
-warpbreaks %>%
-  summarise(
-    M = mean(breaks, na.rm = TRUE),
-    SD = sd(breaks, na.rm = TRUE),
-    n = sum(!is.na(breaks)),
-    .by = c(wool, tension)
-  ) %>%
-  rename(Wool = wool) %>%
-  mutate(
-    tension = factor(
-      tension,
-      levels = c("L", "M", "H"),
-      labels = paste(c("Low", "Medium", "High"), "Tension")
-    )
-  ) |>
-  pivot_wider(
+d_tension |> 
+  rename(M = m, SD = sd) |> 
+  pivot_wider_name_first(
     values_from = c(M, SD, n),
-    names_from = tension,
-    names_glue = "{tension}_{.value}",
-    names_vary = "slowest"
+    names_from = tension
   ) |>
   add_break_columns(ends_with("_n"), omit_last = TRUE) |>
   apa_flextable(column_formats = column_formats(accuracy = .1)) |>