Merge pull request #523 from yjunechoe/docs-tidyselect

tidyselect in readme and vignettes

R/incorporate.R

@@ -83,7 +83,7 @@
 #'     info = "Time-based values (e.g., `Sys.time()`)."
 #'   ) %>%
 #'   info_columns(
-#'     columns = "date",
+#'     columns = date,
 #'     info = "The date part of `date_time`. ((CALC))"
 #'   ) %>%
 #'   info_section(

R/info_add.R

@@ -331,15 +331,15 @@ info_tabular <- function(
 #' # R statement
 #' informant %>% 
 #'   info_columns(
-#'     columns = "date_time",
+#'     columns = date_time,
 #'     info = "*info text* 1."
 #'   ) %>%
 #'   info_columns(
-#'     columns = "date",
+#'     columns = date,
 #'     info = "*info text* 2."
 #'   ) %>%
 #'   info_columns(
-#'     columns = "item_count",
+#'     columns = item_count,
 #'     info = "*info text* 3. Statistics: {snippet_1}."
 #'   ) %>%
 #'   info_columns(
@@ -409,7 +409,7 @@ info_tabular <- function(
 #'     info = "Time-based values (e.g., `Sys.time()`)."
 #'   ) %>%
 #'   info_columns(
-#'     columns = "date",
+#'     columns = date,
 #'     info = "The date part of `date_time`. ((CALC))"
 #'   )
 #' ```
@@ -609,11 +609,11 @@ info_columns <- function(
 #' informant <-
 #'   informant %>%
 #'   info_columns(
-#'     columns = "item_revenue",
+#'     columns = item_revenue,
 #'     info = "Revenue reported in USD."
 #'   ) %>%
 #'   info_columns(
-#'     columns = "acquisition",
+#'     columns = acquisition,
 #'     `top list` = "{top5_aq}"
 #'   ) %>%
 #'   info_snippet(
@@ -1013,7 +1013,7 @@ info_section <- function(
 #' # R statement
 #' informant %>% 
 #'   info_columns(
-#'     columns = "date_time",
+#'     columns = date_time,
 #'     `Latest Date` = "The latest date is {latest_date}."
 #'   ) %>%
 #'   info_snippet(
@@ -1076,7 +1076,7 @@ info_section <- function(
 #'     info = "Time-based values (e.g., `Sys.time()`)."
 #'   ) %>%
 #'   info_columns(
-#'     columns = "date",
+#'     columns = date,
 #'     info = "The date part of `date_time`. ((CALC))"
 #'   ) %>%
 #'   info_section(
@@ -1264,7 +1264,7 @@ info_snippet <- function(
 #'     label = "An example."
 #'   ) %>% 
 #'   info_columns(
-#'     columns = "f",
+#'     columns = f,
 #'     `Items` = "This column contains {values_f}."
 #'   ) %>%
 #'   info_snippet(
@@ -1495,7 +1495,7 @@ snip_list <- function(
 #'     label = "An example."
 #'   ) %>% 
 #'   info_columns(
-#'     columns = "d",
+#'     columns = d,
 #'     `Stats` = "Stats (fivenum): {stats_d}."
 #'   ) %>%
 #'   info_snippet(
@@ -1574,7 +1574,7 @@ snip_stats <- function(
 #'     label = "An example."
 #'   ) %>% 
 #'   info_columns(
-#'     columns = "a",
+#'     columns = a,
 #'     `Lowest Value` = "Lowest value is {lowest_a}."
 #'   ) %>%
 #'   info_snippet(
@@ -1648,7 +1648,7 @@ snip_lowest <- function(column) {
 #'     label = "An example."
 #'   ) %>% 
 #'   info_columns(
-#'     columns = "a",
+#'     columns = a,
 #'     `Highest Value` = "Highest value is {highest_a}."
 #'   ) %>%
 #'   info_snippet(

R/object_ops.R

@@ -205,7 +205,7 @@
 #'     info = "Time-based values."
 #'   ) %>%
 #'   info_columns(
-#'     columns = "date",
+#'     columns = date,
 #'     info = "The date part of `date_time`."
 #'   ) %>%
 #'   incorporate()
@@ -680,7 +680,7 @@ x_read_disk <- function(
 #'     info = "Time-based values."
 #'   ) %>%
 #'   info_columns(
-#'     columns = "date",
+#'     columns = date,
 #'     info = "The date part of `date_time`."
 #'   ) %>%
 #'   incorporate()

README.md

@@ -141,14 +141,14 @@ dplyr::tibble(
     b = c(6, 1, 0, 6,  0, 7)
   ) %>%
   col_vals_between(
-    vars(a), 1, 9,
+    a, 1, 9,
     na_pass = TRUE
   ) %>%
   col_vals_lt(
-    vars(c), 12,
+    c, 12,
     preconditions = ~ . %>% dplyr::mutate(c = a + b)
   ) %>%
-  col_is_numeric(vars(a, b))
+  col_is_numeric(c(a, b))
 ```
 
     Error: Exceedance of failed test units where values in `c` should have been < `12`.
@@ -169,17 +169,17 @@ dplyr::tibble(
     b = c(6, 1, 0, 6,  0, 7)
   ) %>%
   col_vals_between(
-    vars(a), 1, 9,
+    a, 1, 9,
     na_pass = TRUE,
     actions = warn_on_fail()
   ) %>%
   col_vals_lt(
-    vars(c), 12,
+    c, 12,
     preconditions = ~ . %>% dplyr::mutate(c = a + b),
     actions = warn_on_fail()
   ) %>%
   col_is_numeric(
-    vars(a, b),
+    c(a, b),
     actions = warn_on_fail()
   )
 ```
@@ -267,19 +267,19 @@ informant <-
     `README` pages. Column names are `a` and `b`. ((Cool stuff))"
   ) %>%
   info_columns(
-    columns = "a",
+    columns = a,
     info = "This column has an `NA` value. [[Watch out!]]<<color: red;>>"
   ) %>%
   info_columns(
-    columns = "a",
+    columns = a,
     info = "Mean value is `{a_mean}`."
   ) %>%
   info_columns(
-    columns = "b",
+    columns = b,
     info = "Like column `a`. The lowest value is `{b_lowest}`."
   ) %>%
   info_columns(
-    columns = "b",
+    columns = b,
     info = "The highest value is `{b_highest}`."
   ) %>%
   info_snippet(

vignettes/INFO-1.Rmd

@@ -71,7 +71,7 @@ informant <-
     description = "This table is included in the **pointblank** pkg."
   ) %>%
   info_columns(
-    columns = "date_time",
+    columns = date_time,
     info = "This column is full of timestamps."
   ) %>%
   info_section(
@@ -97,7 +97,7 @@ The `info_tabular()` function adds information to the **TABLE** section. We use
 
 ```{r eval=FALSE}
 informant %>% 
-  info_tabular(`🔄 updates` = "This table is not regularly updated.")
+  info_tabular("🔄 updates" = "This table is not regularly updated.")
 ```
 
 <div style="text-align: center;"><img src="https://silly-jackson-b3dec8.netlify.app/informant_report_4.png"
@@ -128,40 +128,40 @@ informant_pp <-
     label = "The `penguins` dataset from the **palmerpenguins** 📦."
   ) %>% 
   info_columns(
-    columns = "species",
+    columns = species,
     `ℹ️` = "A factor denoting penguin species (*Adélie*, *Chinstrap*, and *Gentoo*)."
   ) %>%
   info_columns(
-    columns = "island",
+    columns = island,
     `ℹ️` = "A factor denoting island in Palmer Archipelago, Antarctica
     (*Biscoe*, *Dream*, or *Torgersen*)."
   ) %>%
   info_columns(
-    columns = "bill_length_mm",
+    columns = bill_length_mm,
     `ℹ️` = "A number denoting bill length"
   ) %>%
   info_columns(
-    columns = "bill_depth_mm",
+    columns = bill_depth_mm,
     `ℹ️` = "A number denoting bill depth"
   ) %>%
   info_columns(
-    columns = "flipper_length_mm",
+    columns = flipper_length_mm,
     `ℹ️` = "An integer denoting flipper length"
   ) %>%
   info_columns(
     columns = ends_with("mm"),
     `ℹ️` = "(in units of millimeters)."
   ) %>%
   info_columns(
-    columns = "body_mass_g",
+    columns = body_mass_g,
     `ℹ️` = "An integer denoting body mass (grams)."
   ) %>%
   info_columns(
-    columns = "sex",
+    columns = sex,
     `ℹ️` = "A factor denoting penguin sex (`\"female\"`, `\"male\"`)."
   ) %>%
   info_columns(
-    columns = "year",
+    columns = year,
     `ℹ️` = "The study year (e.g., `2007`, `2008`, `2009`)."
   )
 

vignettes/INFO-2.Rmd

@@ -44,7 +44,7 @@ The `small_table` dataset is associated with the `informant` as the target table
 informant <- 
   informant %>%
   info_columns(
-    columns = vars(d),
+    columns = d,
     info = "This column contains fairly large numbers (much larger than
     those numbers in column `a`. The mean value is {mean_d}, which is
     far greater than any number in that other column."
@@ -76,7 +76,7 @@ informant <-
     fn = ~ . %>% .$d %>% mean() %>% round(1)
   ) %>%
     info_columns(
-    columns = vars(d),
+    columns = d,
     info = "This column contains fairly large numbers (much larger than
     those numbers in column `a`. The mean value is {mean_d}, which is
     far greater than any number in that other column."
@@ -181,11 +181,11 @@ informant_pp <-
     label = "The `penguins` dataset from the **palmerpenguins** 📦."
   ) %>% 
   info_columns(
-    columns = "species",
+    columns = species,
     `ℹ️` = "A factor denoting penguin species ({species_snippet})."
   ) %>%
   info_columns(
-    columns = "island",
+    columns = island,
     `ℹ️` = "A factor denoting island in Palmer Archipelago, Antarctica
     ({island_snippet})."
   ) %>%
@@ -216,7 +216,7 @@ This also works for numeric values. Let's use `snip_list()` to provide a text sn
 informant_pp <-
   informant_pp %>%
   info_columns(
-    columns = "year",
+    columns = year,
     `ℹ️` = "The study year ({year_snippet})."
   ) %>%
   info_snippet(
@@ -244,24 +244,24 @@ We can get the lowest and highest values from a column and inject those formatte
 informant_pp <-
   informant_pp %>%
   info_columns(
-    columns = "bill_length_mm",
+    columns = bill_length_mm,
     `ℹ️` = "A number denoting bill length"
   ) %>%
   info_columns(
-    columns = "bill_depth_mm",
+    columns = bill_depth_mm,
     `ℹ️` = "A number denoting bill depth (in the range of
     {min_depth} to {max_depth} millimeters)."
   ) %>%
   info_columns(
-    columns = "flipper_length_mm",
+    columns = flipper_length_mm,
     `ℹ️` = "An integer denoting flipper length"
   ) %>%
   info_columns(
     columns = matches("length"),
     `ℹ️` = "(in units of millimeters)."
   ) %>%
   info_columns(
-    columns = "flipper_length_mm",
+    columns = flipper_length_mm,
     `ℹ️` = "Largest observed is {largest_flipper_length} mm."
   ) %>%
   info_snippet(
@@ -338,7 +338,7 @@ We can take portions of text and present them as labels. These will help you cal
 informant_pp <-
   informant_pp %>%
   info_columns(
-    columns = vars(body_mass_g), 
+    columns = body_mass_g, 
     `ℹ️` = "An integer denoting body mass."
   ) %>%
   info_columns(
@@ -396,7 +396,7 @@ Continuing with our **palmerpenguins** reporting, we'll add some more *info text
 informant_pp <-
   informant_pp %>%
   info_columns(
-    columns = vars(sex), 
+    columns = sex, 
     `ℹ️` = "A [[factor]]<<text-decoration: underline;>> 
     denoting penguin sex (female or male)."
   ) %>%