updated readme

R/tidyr_helpers.R

     frame_labels <- c(sequence[["operation"]], sequence[["reverse_operation"]])
 
     title_string <- "{ifelse(transitioning, previous_state, ifelse(grepl('gather', next_state), 'Wide', 'Long'))}"
+
+    tl <- 2
+    sl <- 1
+
   } else {
     xshift <- 2
 
     )
     title_string <- "{gsub('\\\\) [a-zA-Z]+$', ')', previous_state)}"
 
+    tl <- length(unique(anim_df$.frame)) * 2
+    sl <- 1
   }
 
   frame_levels <- anim_df$.frame %>% unique()
                            labels = frame_labels))
 
   if (export == "gif") {
-    animate_plot(anim_df, title = title_string) #...
+    animate_plot(anim_df, title = title_string, transition_length = tl, state_length = sl) #...
   } else if (export == "first") {
     static_plot(state_start) #....
   } else if (export == "last") {

README.md

 ## Set Operations
 
 ``` r
-
-x <- tibble::tribble(
-  ~x,    ~y,
-  "1",  "a",
-  "1",  "b",
-  "2",  "a"
+x <- data_frame(
+  x = c(1, 1, 2),
+  y = c("a", "b", "a")
 )
-
-y <- tibble::tribble(
-  ~x, ~y,
-  "1",  "a",
-  "2",  "b"
+y <- data_frame(
+  x = c(1, 2),
+  y = c("a", "b")
 )
 
 animate_union(x, y, export = "first")
 ``` r
 x
 #> # A tibble: 3 x 2
-#>   x     y    
-#>   <chr> <chr>
-#> 1 1     a    
-#> 2 1     b    
-#> 3 2     a
+#>       x y    
+#>   <dbl> <chr>
+#> 1     1 a    
+#> 2     1 b    
+#> 3     2 a
 y 
 #> # A tibble: 2 x 2
-#>   x     y    
-#>   <chr> <chr>
-#> 1 1     a    
-#> 2 2     b
+#>       x y    
+#>   <dbl> <chr>
+#> 1     1 a    
+#> 2     2 b
 ```
 
 ### Union
 ``` r
 union(x, y)
 #> # A tibble: 4 x 2
-#>   x     y    
-#>   <chr> <chr>
-#> 1 2     b    
-#> 2 2     a    
-#> 3 1     b    
-#> 4 1     a
+#>       x y    
+#>   <dbl> <chr>
+#> 1     2 b    
+#> 2     2 a    
+#> 3     1 b    
+#> 4     1 a
 ```
 
 ``` r
 
 union(y, x)
 #> # A tibble: 4 x 2
-#>   x     y    
-#>   <chr> <chr>
-#> 1 2     a    
-#> 2 1     b    
-#> 3 2     b    
-#> 4 1     a
+#>       x y    
+#>   <dbl> <chr>
+#> 1     2 a    
+#> 2     1 b    
+#> 3     2 b    
+#> 4     1 a
 ```
 
 ### Union All
 ``` r
 union_all(x, y)
 #> # A tibble: 5 x 2
-#>   x     y    
-#>   <chr> <chr>
-#> 1 1     a    
-#> 2 1     b    
-#> 3 2     a    
-#> 4 1     a    
-#> 5 2     b
+#>       x y    
+#>   <dbl> <chr>
+#> 1     1 a    
+#> 2     1 b    
+#> 3     2 a    
+#> 4     1 a    
+#> 5     2 b
 ```
 
 ### Intersection
 ``` r
 intersect(x, y)
 #> # A tibble: 1 x 2
-#>   x     y    
-#>   <chr> <chr>
-#> 1 1     a
+#>       x y    
+#>   <dbl> <chr>
+#> 1     1 a
 ```
 
 ### Set Difference
 ``` r
 setdiff(x, y)
 #> # A tibble: 2 x 2
-#>   x     y    
-#>   <chr> <chr>
-#> 1 1     b    
-#> 2 2     a
+#>       x y    
+#>   <dbl> <chr>
+#> 1     1 b    
+#> 2     2 a
 ```
 
 ``` r
 
 setdiff(y, x)
 #> # A tibble: 1 x 2
-#>   x     y    
-#>   <chr> <chr>
-#> 1 2     b
+#>       x y    
+#>   <dbl> <chr>
+#> 1     2 b
 ```
 
 ## Tidy Data and `gather()`, `spread()` functionality
 
+[Tidy data](http://r4ds.had.co.nz/tidy-data.html#tidy-data-1) follows
+the following three rules:
+
+1.  Each variable has its own column.
+2.  Each observation has its own row.
+3.  Each value has its own cell.
+
+Many of the tools in the [tidyverse](https://tidyverse.org) expect data
+to be formatted as a tidy dataset and the
+[tidyr](https://tidyr.tidyverse.org) package provides functions to help
+you organize your data into tidy data.
+
 ``` r
 long <- data_frame(
   year = c(2010, 2011, 2010, 2011, 2010, 2011),