Import data

## # A tibble: 27 × 5
##    number film            release_date        run_time film_rating
##     <dbl> <chr>           <dttm>              <chr>    <chr>      
##  1      1 Toy Story       1995-11-22 00:00:00 81       G          
##  2      2 A Bug's Life    1998-11-25 00:00:00 95       G          
##  3      3 Toy Story 2     1999-11-24 00:00:00 92       G          
##  4      4 Monsters, Inc.  2001-11-02 00:00:00 92       G          
##  5      5 Finding Nemo    2003-05-30 00:00:00 100      G          
##  6      6 The Incredibles 2004-11-05 00:00:00 115      PG         
##  7      7 Cars            2006-06-09 00:00:00 117      G          
##  8      8 Ratatouille     2007-06-29 00:00:00 111      G          
##  9      9 WALL-E          2008-06-27 00:00:00 98       G          
## 10     10 Up              2009-05-29 00:00:00 96       PG         
## # ℹ 17 more rows

Apply the following dplyr verbs to your data

Filter rows

## # A tibble: 1 × 5
##   number film      release_date        run_time film_rating
##    <dbl> <chr>     <dttm>              <chr>    <chr>      
## 1      1 Toy Story 1995-11-22 00:00:00 81       G
## # A tibble: 13 × 5
##    number film                release_date        run_time film_rating
##     <dbl> <chr>               <dttm>              <chr>    <chr>      
##  1      1 Toy Story           1995-11-22 00:00:00 81       G          
##  2      2 A Bug's Life        1998-11-25 00:00:00 95       G          
##  3      3 Toy Story 2         1999-11-24 00:00:00 92       G          
##  4      4 Monsters, Inc.      2001-11-02 00:00:00 92       G          
##  5      5 Finding Nemo        2003-05-30 00:00:00 100      G          
##  6      7 Cars                2006-06-09 00:00:00 117      G          
##  7      8 Ratatouille         2007-06-29 00:00:00 111      G          
##  8      9 WALL-E              2008-06-27 00:00:00 98       G          
##  9     11 Toy Story 3         2010-06-18 00:00:00 103      G          
## 10     12 Cars 2              2011-06-24 00:00:00 106      G          
## 11     14 Monsters University 2013-06-21 00:00:00 104      G          
## 12     18 Cars 3              2017-06-16 00:00:00 102      G          
## 13     21 Toy Story 4         2019-06-21 00:00:00 100      G

Arrange rows

## # A tibble: 27 × 5
##    number film            release_date        run_time film_rating
##     <dbl> <chr>           <dttm>              <chr>    <chr>      
##  1      1 Toy Story       1995-11-22 00:00:00 81       G          
##  2      2 A Bug's Life    1998-11-25 00:00:00 95       G          
##  3      3 Toy Story 2     1999-11-24 00:00:00 92       G          
##  4      4 Monsters, Inc.  2001-11-02 00:00:00 92       G          
##  5      5 Finding Nemo    2003-05-30 00:00:00 100      G          
##  6      6 The Incredibles 2004-11-05 00:00:00 115      PG         
##  7      7 Cars            2006-06-09 00:00:00 117      G          
##  8      8 Ratatouille     2007-06-29 00:00:00 111      G          
##  9      9 WALL-E          2008-06-27 00:00:00 98       G          
## 10     10 Up              2009-05-29 00:00:00 96       PG         
## # ℹ 17 more rows
## # A tibble: 27 × 5
##    number film                release_date        run_time film_rating
##     <dbl> <chr>               <dttm>              <chr>    <chr>      
##  1      5 Finding Nemo        2003-05-30 00:00:00 100      G          
##  2     21 Toy Story 4         2019-06-21 00:00:00 100      G          
##  3     23 Soul                2020-12-25 00:00:00 100      PG         
##  4     18 Cars 3              2017-06-16 00:00:00 102      G          
##  5     22 Onward              2020-03-06 00:00:00 102      PG         
##  6     11 Toy Story 3         2010-06-18 00:00:00 103      G          
##  7     14 Monsters University 2013-06-21 00:00:00 104      G          
##  8     19 Coco                2017-11-22 00:00:00 105      PG         
##  9     12 Cars 2              2011-06-24 00:00:00 106      G          
## 10      8 Ratatouille         2007-06-29 00:00:00 111      G          
## # ℹ 17 more rows

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