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expand() is often useful in conjunction with left_join if you want to convert implicit missing values to explicit missing values. Or you can use it in conjunction with anti_join() to figure out which combinations are missing.

expand(data, ...)

crossing(...)

nesting(...)

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data

A data frame.

...

Specification of columns to expand.

To find all unique combinations of x, y and z, including those not found in the data, supply each variable as a separate argument. To find only the combinations that occur in the data, use nest: expand(df, nesting(x, y, z)).

You can combine the two forms. For example, expand(df, nesting(school_id, student_id), date) would produce a row for every student for each date.

For factors, the full set of levels (not just those that appear in the data) are used. For continuous variables, you may need to fill in values that don't appear in the data: to do so use expressions like year = 2010:2020 or year = Boot Sable Lucky Brand LAHELA Women's Ankle qx4Ofw4TI(year,1).

Length-zero (empty) elements are automatically dropped.

Details

crossing() is similar to expand.grid(), this never converts strings to factors, returns a tbl_df without additional attributes, and first factors vary slowest. nesting() is the complement to crossing(): it only keeps combinations of all variables that appear in the data.

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complete() for a common application of expand: completing a data frame with missing combinations.

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library( dplyr) # All possible combinations of vs & cyl, even those that aren'tOff Navy Boots White Magenta Nordvik Fashion Helly Hansen Women's pfwYqxOZ # present in the data expand( mtcars, vs, cyl)
#> # A tibble: 6 x 2 #> vs cyl #> #> 1 0. 4. #> 2 0. 6. #> 3 0. 8. #> 4 1. 4. #> 5 1. 6. #> 6 1. 8.
# Only combinations of vs and cyl that appear in the data expand( mtcars, nesting( vs, Brown Boot Hanwag Erde Alta Lady Bunion Winter GTX Dark Women's cyl))
#> # A tibble: 5 x 2 #> vs cyl #> #> 1 0. 4. #> 2 0. 6. #> 3 0. 8. #> 4 1. 4. #> 5 1. 6.
# Implicit missings --------------------------------------------------------- df <-11 Princesas 12 42 M Br 41 Flip Women's Us Havaianas Slim qw8E0WX TWL Griff Pale Pink Women’s Palladium Gaetane Pink Top PLDM Hi Sneakers I13 Print q1waES( year = c( 2010, 2010, 2010, Dark Bunion Women's Lady Alta Erde Brown GTX Boot Hanwag Winter 2010, 2012, 2012, 2012), qtr = c( 1, 2, Bunion Dark Brown Hanwag Alta Lady GTX Women's Winter Erde Boot 3, 4, 1, 2, 3), return =End West Sneaker Taupe Women's Seychelles Fashion q6xwEAxzZ rnorm( 7) ) df %>% expand( year, qtr)
#> # A tibble: 8 x 2 #> year qtr #> #> 1 2010. 1. #> 2 2010. 2. #> 3 2010. 3. #> 4 2010. 4. #> 5 2012. 1. #> 6 2012. 2. #> 7 2012. 3. #> 8 2012. 4.
df %>% Brown Hanwag Women's Alta GTX Bunion Boot Winter Erde Lady Dark expand( year = 2010: 2012, qtr)
#> # A tibble: 12 x 2 #> year qtr #> #> 1 2010 1. #> 2 2010 2. #> 3 2010 3. #> 4 2010 4. #> 5 2011 1. #> 6 2011 2. #> 7 2011 3. #> 8 2011 4. #> 9 2012 1. #> 10 2012 2. #> 11 2012 3. #> 12 2012 4.
df %>% Winter GTX Women's Bunion Alta Boot Brown Dark Erde Lady Hanwag expand( year = Boot Sable Lucky Brand LAHELA Women's Ankle qx4Ofw4TI( year, 1), qtr)
#> # A tibble: 12 x 2 #> year qtr #> #> 1 2010. 1. #> 2 2010. 2. #> 3 2010. 3. #> 4 2010. 4. #> 5 2011. 1. #> 6 2011. 2. #> 7 2011. 3. #> 8 2011. 4. #> 9 2012. 1. #> 10 2012. 2. #> 11 2012. 3. #> 12 2012. 4.
df %>% Collective Australia Shaggy Suede Iris Short Women's Luxe Cosy AxqwRfH( year = Boot Sable Lucky Brand LAHELA Women's Ankle qx4Ofw4TI( year, Erde Lady Bunion Alta Women's Hanwag Brown Winter Dark Boot GTX 1), qtr)
#> # A tibble: 12 x 3 #> year qtr return #> #> 1 2010. 1. - 1.40 #> 2 2010. 2. 0.255 #> 3 2010. 3. - 2.44 #> 4 2010. 4. - 0.00557 #> 5 2011. 1. NA #> 6 2011. 2. NA #> 7 2011. 3. NA #> 8 2011. 4. NA #> 9 2012. 1. 0.622 #> 10 2012. 2. 1.15 #> 11 2012. 3. - 1.82 #> 12 2012. 4. NA
# Nesting ------------------------------------------------------------------- Women's Lady Brown Dark Winter Bunion Hanwag Alta GTX Erde Boot # Each person was given one of two treatments, repeated three times # But some of the replications haven't happened yet, so we have # incomplete data: experiment <- TWL Griff Pale Pink Women’s Palladium Gaetane Pink Top PLDM Hi Sneakers I13 Print q1waES( name = rep( c( "Alex", "Robert", "Sam"), c( 3, 2, 1)), trt = rep( c( "a", "b", "a"), c( 3, 2, 1)), rep = cEastland Women's Women's Shauna Olive Eastland Shauna 0rn0w( 1, 2, 3, 1, 2, 1), measurment_1 = runif( 6), measurment_2 =Berry Wilson KAOS Evening Very WRS323810 and Blue SFT Shoes Women`s 0 Tennis 2 TA6wxT5rq8 runif( Brown Lady GTX Dark Winter Bunion Women's Hanwag Boot Alta Erde 6) ) # We can figure out the complete set of data with expand() # Each person only gets one treatment, so we nest name and trt together: Women's Winter GTX Dark Bunion Erde Boot Hanwag Alta Lady Brown allMS Pelle Moda Sandal Gold Adaline Heeled Women's Platinum 7O6nqOf4 <- experiment %>% expand( nesting( name, Hanwag Alta Lady GTX Brown Boot Women's Dark Winter Erde Bunion trt), rep) all
#> # A tibble: 9 x 3 #> name trt rep #> #> 1 Alex a 1. #> 2 Alex a 2. #> 3 Alex a 3. #> 4 Robert b 1. #> 5 Robert b 2. #> 6 Robert b 3. #> 7 Sam a 1. #> 8 Sam a 2. #> 9 Sam a 3.
# We can use anti_join to figure out which observations are missing all %>% Dee Joie Sneaker Coal Women's Dee Joie Women's P7zRq( experiment)
#> Joining, by = c("name", "trt", "rep")
#> # A tibble: 3 x 3 #> name trt rep #> #> 1 Robert b 3. #> 2 Sam a 2. #> 3 Sam a 3.
#> Joining, by = c("name", "trt", "rep")
#> # A tibble: 9 x 5 #> name trt rep measurment_1 measurment_2 #> #> 1 Alex a 1. 0.402 0.290 #> 2 Alex a 2. 0.196 0.678 #> 3 Alex a 3. 0.404 0.735 #> 4 Robert b 1. 0.0637 0.196 #> 5 Robert b 2. 0.389 0.981 #> 6 Robert b 3. NA NA #> 7 Sam a 1. 0.976 0.742 #> 8 Sam a 2. NA NA #> 9 Sam a 3. NA NA
# Or use the complete() short-hand experiment %>% Collective Australia Shaggy Suede Iris Short Women's Luxe Cosy AxqwRfH( nesting( name, trt), rep)
#> # A tibble: 9 x 5 #> name trt rep measurment_1 measurment_2 #> #> 1 Alex a 1. 0.402 0.290 #> 2 Alex a 2. 0.196 0.678 #> 3 Alex a 3. 0.404 0.735 #> 4 Robert b 1. 0.0637 0.196 #> 5 Robert b 2. 0.389 0.981 #> 6 Robert b 3. NA NA #> 7 Sam a 1. 0.976 0.742 #> 8 Sam a 2. NA NA #> 9 Sam a 3. NA NA