Add dplyr and tibble :: in vignettes

vignettes/debit.Rmd

@@ -51,7 +51,7 @@ As in `grim()`, the mean needs to be a string. (The same is true for the SD.) Th
 If you want to test more than a handful of cases, the recommended way is to enter them into a data frame and to run `debit_map()` on the data frame. Below are the example data from Heathers and Brown's (2019) Table 1. A useful way to enter such data is to copy them from a PDF file and paste them into `tibble::tribble()`, which is available via scrutiny:
 
 ```{r}
-flying_pigs <- tibble(
+flying_pigs <- tibble::tibble(
     x  = runif(5, 0.2, 1) %>% round(2) %>% restore_zeros(),
     sd = runif(5, 0, 0.3) %>% round(2) %>% restore_zeros(),
     n = 1000

vignettes/grim.Rmd

@@ -59,7 +59,7 @@ If you want to GRIM-test more than a handful of cases, the recommended way is to
 Copy summary data from a PDF file and paste them into `tibble::tribble()`, which is available via scrutiny:
 
 ```{r}
-flying_pigs1 <- tribble(
+flying_pigs1 <- tibble::tribble(
   ~x,
 "8.97",
 "2.61",
@@ -69,7 +69,7 @@ flying_pigs1 <- tribble(
 "10.46",
 "7.39"
 ) %>% 
-  mutate(n = 28)
+  dplyr::mutate(n = 28)
 ```
 
 Use RStudio's multiple cursors to draw quotation marks around all the `x` values, and to set commas at the end. See `vignette("wrangling")`, section *With copy and paste*, if you are not sure how to do that.
@@ -87,7 +87,7 @@ The `x` and `n` columns are the same as in the input. By default, the number of
 If a mean is composed of multiple items, set the `items` parameter to that number. Below are hypothetical means of a three-items scale. With the single-item default, half of these are wrongly flagged as inconsistent:
 
 ```{r, error=TRUE}
-jpap_1 <- tribble(
+jpap_1 <- tibble::tribble(
    ~x,
   "5.90",
   "5.71",
@@ -96,7 +96,7 @@ jpap_1 <- tribble(
   "4.61",
   "5.24",
 ) %>% 
-  mutate(n = 40)
+  dplyr::mutate(n = 40)
 
 jpap_1 %>% 
   grim_map()  # default is wrong here!
@@ -112,7 +112,7 @@ jpap_1 %>%
 It is also possible to include an `items` column in the data frame instead:
 
 ```{r, error=TRUE}
-jpap_2 <- tribble(
+jpap_2 <- tibble::tribble(
    ~x,    ~items,
   "6.92",  1,
   "3.48",  1,
@@ -134,7 +134,7 @@ An underappreciated strength of GRIM is testing percentages. Since these are act
 Both `grim()` and `grim_map()` have a `percent` parameter which, if set to `TRUE`, divides the `x` values by 100 and increases the decimal count by two, so that percentages can be tested just like means:
 
 ```{r}
-jpap_3 <- tribble(
+jpap_3 <- tibble::tribble(
   ~x,     ~n,
   "32.5",  438,
   "35.6",  455,
@@ -246,7 +246,7 @@ These columns are ---
 There is a specialized visualization function for GRIM test results, `grim_plot()`:
 
 ```{r, error=TRUE, fig.width=6, fig.height=5.5}
-jpap_5 <- tribble(
+jpap_5 <- tibble::tribble(
   ~x,        ~n,
   "7.19",    28,
   "4.56",    34,
@@ -357,7 +357,7 @@ More precisely, they went from a 20/20 group split to a 19/21 split, then to an
 Instead of going about this manually, call `grim_map_total_n()`, followed by `audit_total_n()` for summarizing the results. It will find two further plausible scenarios in which both means are consistent; more on that below.
 
 ```{r}
-df <- tibble(x1 = "4.71", x2 = "5.3", n = 40)
+df <- tibble::tibble(x1 = "4.71", x2 = "5.3", n = 40)
 
 # Detailed results:
 df_tested <- grim_map_total_n(df)

vignettes/grimmer.Rmd

@@ -59,7 +59,7 @@ If you want to GRIMMER-test more than a handful of cases, the recommended way is
 Copy summary data from a PDF file and paste them into `tibble::tribble()`, which is available via scrutiny:
 
 ```{r}
-flying_pigs1 <- tribble(
+flying_pigs1 <- tibble::tribble(
   ~x,   ~sd,
 "8.9",  "2.81",
 "2.6",  "2.05",
@@ -69,7 +69,7 @@ flying_pigs1 <- tribble(
 "10.4", "2.53",
 "7.3",  "3.14"
 ) %>% 
-  mutate(n = 25)
+  dplyr::mutate(n = 25)
 ```
 
 Use RStudio's multiple cursors to draw quotation marks around all the `x` and `sd` values, and to set commas at the end. See `vignette("wrangling")`, section *With copy and paste*, if you are not sure how to do that.
@@ -91,7 +91,7 @@ The `reason` column says why a set of values was inconsistent. To be GRIMMER-con
 If a mean is composed of multiple items, set the `items` parameter to that number. Below are hypothetical means of a three-items scale. With the single-item default, half of these are wrongly flagged as inconsistent:
 
 ```{r, error=TRUE}
-jpap_1 <- tribble(
+jpap_1 <- tibble::tribble(
    ~x,    ~sd,
   "5.90", "2.19",
   "5.71", "1.42",
@@ -100,7 +100,7 @@ jpap_1 <- tribble(
   "4.61", "1.92",
   "5.24", "2.51",
 ) %>% 
-  mutate(n = 40)
+  dplyr::mutate(n = 40)
 
 jpap_1 %>% 
   grimmer_map()  # default is wrong here!
@@ -116,7 +116,7 @@ jpap_1 %>%
 It is also possible to include an `items` column in the data frame instead:
 
 ```{r, error=TRUE}
-jpap_2 <- tribble(
+jpap_2 <- tibble::tribble(
    ~x,     ~sd,    ~items,
   "6.92",  "2.19",  1,
   "3.48",  "1.42",  1,
@@ -125,7 +125,7 @@ jpap_2 <- tribble(
   "4.04",  "1.92",  3,
   "4.50",  "2.51",  3,
 ) %>% 
-  mutate(n = 30)
+  dplyr::mutate(n = 30)
 
 jpap_2 %>% 
   grimmer_map()
@@ -176,7 +176,7 @@ These columns are ---
 GRIMMER does not currently have a dedicated visualization function in scrutiny. However, `grim_plot()` will accept the output of `grimmer_map()` just as well as that from `grim_map()`:
 
 ```{r, fig.width=6, fig.height=5.5}
-jpap_5 <- tribble(
+jpap_5 <- tibble::tribble(
   ~x,      ~sd,    ~n,
   "7.19",  "1.19",  54,
   "4.56",  "2.56",  66,

vignettes/wrangling.Rmd

@@ -54,7 +54,7 @@ Perhaps not all R users know that RStudio features an option for multiple cursor
 You should then get something like this:
 
 ```{r}
-flights1 <- tribble(
+flights1 <- tibble::tribble(
   ~x,
 "8.97",
 "2.61",
@@ -70,7 +70,7 @@ All that's missing is the sample size. Add it either via another `tribble()` col
 
 ```{r}
 flights1 <- flights1 %>% 
-  mutate(n = 28)
+  dplyr::mutate(n = 28)
 
 flights1
 ```
@@ -111,7 +111,7 @@ vec %>%
 A convenient way to restore trailing zeros in a data frame is `restore_zeros_df()`. By default, it operates on all columns that are coercible to numeric (factors don't count):
 
 ```{r}
-iris <- as_tibble(iris)
+iris <- tibble::as_tibble(iris)
 iris %>% 
   restore_zeros_df(width = 3)
 ```
@@ -130,7 +130,7 @@ iris %>%
 With summary data copied or extracted from PDF (see above), you might encounter values presented like `5.22 (0.73)`. Instead of manually teasing them apart, call `split_by_parens()`:
 
 ```{r}
-flights2 <- tribble(
+flights2 <- tibble::tribble(
   ~drone,           ~selfpilot,
   "0.09 (0.21)",    "0.19 (0.13)",
   "0.19 (0.28)",    "0.53 (0.10)",