Only attach tidyexplain in README

Calls to tidyr and dplyr are now prefixed with package name. This lets us use the README as a test to make sure all namespace issues are fixed. We can roll this back when the package is more stable.

README.Rmd

   fig.path = "man/figures/tidyexplain-",
   cache = TRUE
 )
-library(dplyr)
 library(tidyexplain)
 set_font_size(11, 26)
 ```
 ## Mutating Joins
 
 ```{r intial-dfs}
-x <- data_frame(
+x <- dplyr::data_frame(
   id = 1:3,
   x = paste0("x", 1:3)
 )
 
-y <- data_frame(
+y <- dplyr::data_frame(
   id = (1:4)[-3],
   y = paste0("y", (1:4)[-3])
 )
 
 
 ```{r}
-inner_join(x, y, by = "id")
+dplyr::inner_join(x, y, by = "id")
 ```
 
 ### Left Join
 
 
 ```{r}
-left_join(x, y, by = "id")
+dplyr::left_join(x, y, by = "id")
 ```
 
 ### Left Join (Extra Rows in y)
 > ... If there are multiple matches between `x` and `y`, all combinations of the matches are returned.
 
 ```{r left-join-extra}
-y_extra <- bind_rows(y, data_frame(id = 2, y = "y5"))
+y_extra <- dplyr::bind_rows(y, dplyr::data_frame(id = 2, y = "y5"))
 y_extra # has multiple rows with the key from `x`
 
 animate_left_join(x, y_extra, by = "id")
 ```
 
 ```{r}
-left_join(x, y_extra, by = "id")
+dplyr::left_join(x, y_extra, by = "id")
 ```
 
 ### Right Join
 
 
 ```{r}
-right_join(x, y, by = "id")
+dplyr::right_join(x, y, by = "id")
 ```
 
 ### Full Join
 
 
 ```{r}
-full_join(x, y, by = "id")
+dplyr::full_join(x, y, by = "id")
 ```
 
 ## Filtering Joins
 
 
 ```{r}
-semi_join(x, y, by = "id")
+dplyr::semi_join(x, y, by = "id")
 ```
 
 ### Anti Join
 
 
 ```{r}
-anti_join(x, y, by = "id")
+dplyr::anti_join(x, y, by = "id")
 ```
 
 ## Set Operations
 
 ```{r intial-dfs-so}
-x <- data_frame(
+x <- dplyr::data_frame(
   x = c(1, 1, 2),
   y = c("a", "b", "a")
 )
-y <- data_frame(
+y <- dplyr::data_frame(
   x = c(1, 2),
   y = c("a", "b")
 )
 
 
 ```{r}
-union(x, y)
+dplyr::union(x, y)
 ```
 
 
 ```{r union-y-x}
 animate_union(y, x)
 
-union(y, x)
+dplyr::union(y, x)
 ```
 
 ### Union All
 
 
 ```{r}
-union_all(x, y)
+dplyr::union_all(x, y)
 ```
 
 
 
 
 ```{r}
-intersect(x, y)
+dplyr::intersect(x, y)
 ```
 
 ### Set Difference
 
 
 ```{r}
-setdiff(x, y)
+dplyr::setdiff(x, y)
 ```
 
 
 ```{r setdiff-y-x}
 animate_setdiff(y, x)
 
-setdiff(y, x)
+dplyr::setdiff(y, x)
 ```
 
 ## Tidy Data and `gather()`, `spread()` functionality
 you organize your data into tidy data.
 
 ```{r}
-library(tidyr)
-
-long <- data_frame(
+long <- dplyr::data_frame(
   year = c(2010, 2011, 2010, 2011, 2010, 2011),
   person = c("Alice", "Alice", "Bob", "Bob", "Charlie", "Charlie"),
   sales = c(105, 110, 100, 97, 90, 95)
 )
-wide <- data_frame(
+wide <- dplyr::data_frame(
   year = 2010:2011, 
   Alice = c(105, 110), 
   Bob = c(100, 97), 
 ```
 
 ```{r}
-gather(wide, key = "person", value = "sales", -year)
+tidyr::gather(wide, key = "person", value = "sales", -year)
 ```
 
 ### Spread
 ```
 
 ```{r}
-spread(long, key = "person", value = "sales")
+tidyr::spread(long, key = "person", value = "sales")
 ```
 
 

README.md

 ## Mutating Joins
 
 ``` r
-x <- data_frame(
+x <- dplyr::data_frame(
   id = 1:3,
   x = paste0("x", 1:3)
 )
 
-y <- data_frame(
+y <- dplyr::data_frame(
   id = (1:4)[-3],
   y = paste0("y", (1:4)[-3])
 )
 ![](man/figures/tidyexplain-inner-join-1.gif)<!-- -->
 
 ``` r
-inner_join(x, y, by = "id")
+dplyr::inner_join(x, y, by = "id")
 #> # A tibble: 2 x 3
 #>      id x     y    
 #>   <int> <chr> <chr>
 ![](man/figures/tidyexplain-left-join-1.gif)<!-- -->
 
 ``` r
-left_join(x, y, by = "id")
+dplyr::left_join(x, y, by = "id")
 #> # A tibble: 3 x 3
 #>      id x     y    
 #>   <int> <chr> <chr>
 > of the matches are returned.
 
 ``` r
-y_extra <- bind_rows(y, data_frame(id = 2, y = "y5"))
+y_extra <- dplyr::bind_rows(y, dplyr::data_frame(id = 2, y = "y5"))
 y_extra # has multiple rows with the key from `x`
 #> # A tibble: 4 x 2
 #>      id y    
 ![](man/figures/tidyexplain-left-join-extra-1.gif)<!-- -->
 
 ``` r
-left_join(x, y_extra, by = "id")
+dplyr::left_join(x, y_extra, by = "id")
 #> # A tibble: 4 x 3
 #>      id x     y    
 #>   <dbl> <chr> <chr>
 ![](man/figures/tidyexplain-right-join-1.gif)<!-- -->
 
 ``` r
-right_join(x, y, by = "id")
+dplyr::right_join(x, y, by = "id")
 #> # A tibble: 3 x 3
 #>      id x     y    
 #>   <int> <chr> <chr>
 ![](man/figures/tidyexplain-full-join-1.gif)<!-- -->
 
 ``` r
-full_join(x, y, by = "id")
+dplyr::full_join(x, y, by = "id")
 #> # A tibble: 4 x 3
 #>      id x     y    
 #>   <int> <chr> <chr>
 ![](man/figures/tidyexplain-semi-join-1.gif)<!-- -->
 
 ``` r
-semi_join(x, y, by = "id")
+dplyr::semi_join(x, y, by = "id")
 #> # A tibble: 2 x 2
 #>      id x    
 #>   <int> <chr>
 ![](man/figures/tidyexplain-anti-join-1.gif)<!-- -->
 
 ``` r
-anti_join(x, y, by = "id")
+dplyr::anti_join(x, y, by = "id")
 #> # A tibble: 1 x 2
 #>      id x    
 #>   <int> <chr>
 ## Set Operations
 
 ``` r
-x <- data_frame(
+x <- dplyr::data_frame(
   x = c(1, 1, 2),
   y = c("a", "b", "a")
 )
-y <- data_frame(
+y <- dplyr::data_frame(
   x = c(1, 2),
   y = c("a", "b")
 )
 ![](man/figures/tidyexplain-union-1.gif)<!-- -->
 
 ``` r
-union(x, y)
+dplyr::union(x, y)
 #> # A tibble: 4 x 2
 #>       x y    
 #>   <dbl> <chr>
 
 ``` r
 
-union(y, x)
+dplyr::union(y, x)
 #> # A tibble: 4 x 2
 #>       x y    
 #>   <dbl> <chr>
 ![](man/figures/tidyexplain-union-all-1.gif)<!-- -->
 
 ``` r
-union_all(x, y)
+dplyr::union_all(x, y)
 #> # A tibble: 5 x 2
 #>       x y    
 #>   <dbl> <chr>
 ![](man/figures/tidyexplain-intersect-1.gif)<!-- -->
 
 ``` r
-intersect(x, y)
+dplyr::intersect(x, y)
 #> # A tibble: 1 x 2
 #>       x y    
 #>   <dbl> <chr>
 ![](man/figures/tidyexplain-setdiff-1.gif)<!-- -->
 
 ``` r
-setdiff(x, y)
+dplyr::setdiff(x, y)
 #> # A tibble: 2 x 2
 #>       x y    
 #>   <dbl> <chr>
 
 ``` r
 
-setdiff(y, x)
+dplyr::setdiff(y, x)
 #> # A tibble: 1 x 2
 #>       x y    
 #>   <dbl> <chr>
 you organize your data into tidy data.
 
 ``` r
-library(tidyr)
-
-long <- data_frame(
+long <- dplyr::data_frame(
   year = c(2010, 2011, 2010, 2011, 2010, 2011),
   person = c("Alice", "Alice", "Bob", "Bob", "Charlie", "Charlie"),
   sales = c(105, 110, 100, 97, 90, 95)
 )
-wide <- data_frame(
+wide <- dplyr::data_frame(
   year = 2010:2011, 
   Alice = c(105, 110), 
   Bob = c(100, 97), 
 ![](man/figures/tidyexplain-gather-1.gif)<!-- -->
 
 ``` r
-gather(wide, key = "person", value = "sales", -year)
+tidyr::gather(wide, key = "person", value = "sales", -year)
 #> # A tibble: 6 x 3
 #>    year person  sales
 #>   <int> <chr>   <dbl>
 ![](man/figures/tidyexplain-spread-1.gif)<!-- -->
 
 ``` r
-spread(long, key = "person", value = "sales")
+tidyr::spread(long, key = "person", value = "sales")
 #> # A tibble: 2 x 4
 #>    year Alice   Bob Charlie
 #>   <dbl> <dbl> <dbl>   <dbl>