revert use of gt for now

content/articles/2018-10-dplyr-0-8-0-release-candidate.Rmarkdown

@@ -20,45 +20,44 @@ knitr::opts_chunk$set(collapse = T, comment = "#>")
 options(tibble.print_min = 4L, tibble.print_max = 4L)
 library(dplyr)
 # devtools::install_github("rstudio/gt@colorize")
-library(gt)
-
-show_groups <- function(tbl) {
-  # Get a named list of column labels
-  col_labels <-
-    lapply(names(tbl), function(name) {
-      x <- tbl[[name]]
-      class_x <- class(x)
-
-      value <- if (class_x == "factor") {
-        paste0("fctr<", paste0(levels(x), collapse = ","), ">")
-      } else {
-        paste0("<", class(x), ">")
-      }
-      paste0(name, " ", value)
-    }) %>%
-    magrittr::set_names(names(tbl))
-
-  # Create the gt table
-  gt(tbl) %>%
-    cols_label(.list = col_labels) %>%
-    cols_color_gradient_n(
-      column = "x",
-    	breaks = c(0, 3),
-    	colors = c("lightblue", "steelblue")
-    ) %>%
-    cols_color_manual(
-      column = "f1", values = c("a", "b", "c"),
-      colors = c("cornsilk", "bisque", "goldenrod")
-    ) %>% 
-    cols_color_manual(
-      column = "f2", values = c("d", "e", "f"),
-      colors = c("pink", "pink2", "pink3")
-    ) %>% 
-    cols_color_gradient_n(
-      column = "n", breaks = c(0, 1),
-      colors = c("red", "white")
-    ) 
-}
+# library(gt)
+# show_groups <- function(tbl) {
+#   # Get a named list of column labels
+#   col_labels <-
+#     lapply(names(tbl), function(name) {
+#       x <- tbl[[name]]
+#       class_x <- class(x)
+#       
+#       value <- if (class_x == "factor") {
+#         paste0("fctr<", paste0(levels(x), collapse = ","), ">")
+#       } else {
+#         paste0("<", class(x), ">")
+#       }
+#       paste0(name, " ", value)
+#     }) %>%
+#     magrittr::set_names(names(tbl))
+#   
+#   # Create the gt table
+#   gt(tbl) %>%
+#     cols_label(.list = col_labels) %>%
+#     cols_color_gradient_n(
+#       column = "x",
+#     	breaks = c(0, 3),
+#     	colors = c("lightblue", "steelblue")
+#     ) %>%
+#     cols_color_manual(
+#       column = "f1", values = c("a", "b", "c"),
+#       colors = c("cornsilk", "bisque", "goldenrod")
+#     ) %>% 
+#     cols_color_manual(
+#       column = "f2", values = c("d", "e", "f"),
+#       colors = c("pink", "pink2", "pink3")
+#     ) %>% 
+#     cols_color_gradient_n(
+#       column = "n", breaks = c(0, 1),
+#       colors = c("red", "white")
+#     ) 
+# }
 ```
 
 A new release of dplyr (0.8.0) is on the horizon, and since it is a major release, we'd love for the 
@@ -95,77 +94,46 @@ match the observed data. This closes the epic issue
 Let's illustrate the new algorithm with the [count()](https://dplyr.tidyverse.org/reference/tally.html) 
 function:
 
-```{r, results = "hide"}
+```{r}
 df <- tibble(
   f1 = factor(c("a", "a", "a", "b", "b"), levels = c("a", "b", "c")), 
   f2 = factor(c("d", "e", "d", "e", "f"), levels = c("d", "e", "f")), 
   x  = c(1, 1, 1, 2, 2), 
   y  = 1:5
 )
-```
-
-```{r, echo = FALSE}
-show_groups(df)
-```
-
-```{r, results = "hide"}
+df
 df %>% 
   count(f1)
 ```
 
-```{r, echo = FALSE}
-df %>% 
-  count(f1) %>% 
-  show_groups()
-```
-
 Where previous versions of `dplyr` would have created only two groups (for levels `a` and `b`), 
 it now creates one group per level, and the group related to the level `c` just happens to be 
 empty. 
 
 Groups are still made to match the data on other types of columns:
 
-```{r, results = "hide"}
+```{r}
 df %>% 
   count(x)
 ```
 
-```{r, echo = FALSE}
-df %>% 
-  count(x) %>% 
-  show_groups()
-```
-
 Expansion of groups for factors happens at each step of the grouping, so if we group
 by `f1` and `f2` we get 9 groups, 
 
-```{r, results = "hide"}
+```{r}
 df %>% 
   count(f1, f2)
 ```
 
-```{r, echo = FALSE}
-df %>% 
-  count(f1, f2) %>% 
-  show_groups()
-```
-
-
 When factors and non factors are involved in the grouping, the number of 
 groups depends on the order. At each level of grouping, factors are always expanded
 to one group per level, but non factors only create groups based on observed data. 
 
-```{r, results = "hide"}
+```{r}
 df %>% 
   count(f1, x)
 ```
 
-```{r, echo = FALSE}
-df %>% 
-  count(f1, x) %>% 
-  show_groups()
-```
-
 
 In this example, we group by `f1` then `x`. At the first layer, grouping on `f1` creates
 two groups. Each of these grouops is then subdivided based on the values of the second 
@@ -176,17 +144,11 @@ The last group, associated with the level `c` of the factor `f1` is empty, and
 consequently has no values for the vector `x`. In that case, `group_by()` uses 
 `NA`. 
 
-```{r, results = "hide"}
+```{r}
 df %>% 
   count(x, f1)
 ```
 
-```{r, echo = FALSE}
-df %>% 
-  count(x, f1) %>% 
-  show_groups()
-```
-
 When we group by `x` then `f1` we initially split the data according to `x` which 
 gives 2 groups. Each of these two groups is then further divided in 3 groups, 
 i.e. one for each level of `f1`.