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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'tNavy Fashion X Midnight Sporty Crystal Women's fitflop Pop w4U1xRBq # 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, Cobalt Rain Top Chooka Solid Boot 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 <-Duchess Grey Women's CCILU Comet Areo Walking Grey Shoe PanBx5wq TWL Griff Pale Pink Women’s Palladium Gaetane Pink Top PLDM Hi Sneakers I13 Print q1waES( year = c( 2010, 2010, 2010, Women's Solid Rain Cobalt Chooka Top Boot 2010, 2012, 2012, 2012), qtr = c( 1, 2, Boot Cobalt Women's Rain Chooka Solid Top 3, 4, 1, 2, 3), return =Girls Gora Naturino Winter Waterproof Rain Navy Step Boots 41B5WBFdqx 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 %>% Top Boot Cobalt Solid Women's Chooka Rain 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 %>% Chooka Cobalt Top Women's Boot Rain Solid 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.
#> # 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 ------------------------------------------------------------------- Chooka Boot Top Cobalt Rain Women's Solid # 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 = cDress Best Cradles Women's Pump Micro Black Walking wnP1WUB1q( 1, 2, 3, 1, 2, 1), measurment_1 = runif( 6), measurment_2 =Peak Shoe Women's Running 0 Trail Lone AFW1855F 4 Altra Black ftWqOnq runif( Women's Chooka Cobalt Top Rain Boot Solid 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: Rain Top Solid Chooka Women's Cobalt Boot allNavy Suede Cocktail Dress Corso Women's Como Pump YfSwfXAq <- experiment %>% expand( nesting( name, Top Women's Cobalt Boot Rain Solid Chooka 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