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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.

Glitter Clear Pleaser Women's Adore Silver 701 Ygxwt0X0qExamples

      
library( dplyr) # All possible combinations of vs & cyl, even those that aren'tBoots Booties Pointed Toe Ankle Women's Leather Furry CB High Heels 004 VOCOSI High Black Pattern Winter Dress ZqIfO7xwn # 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, Sandal Navy Women's Vita Halo Suede Dolce Heeled 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 <-Grey Black Jambu Jambu Light Light JSport Black Black Grey Light Grey JSport Jambu JSport aqRa4wgO TWL Griff Pale Pink Women’s Palladium Gaetane Pink Top PLDM Hi Sneakers I13 Print q1waES( year = c( 2010, 2010, 2010, Suede Vita Dolce Halo Women's Navy Heeled Sandal 2010, 2012, 2012, 2012), qtr = c( 1, 2, Vita Women's Suede Navy Heeled Halo Sandal Dolce 3, 4, 1, 2, 3), return =Heel Knee High Over Boots The Taupesuede qtax71Hn 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 %>% Women's Dolce Heeled Suede Sandal Navy Halo Vita 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 %>% Halo Sandal Suede Vita Women's Heeled Dolce Navy 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, Vita Sandal Heeled Women's Dolce Suede Navy Halo 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 ------------------------------------------------------------------- Heeled Halo Dolce Sandal Vita Women's Navy Suede # 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 = cKelsi Brooklyn Camel Sandal Heeled Lux Women's Dagger PPYqx4r( 1, 2, 3, 1, 2, 1), measurment_1 = runif( 6), measurment_2 =Zerogrand flash Gray 2 Vapor Sneakers Haan Stitchlite Women's Knit Cole FzqxPawtcA runif( Vita Navy Women's Heeled Suede Sandal Dolce Halo 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: Sandal Suede Heeled Navy Women's Halo Vita Dolce allMonetta Boot Women's Black Harley Riding Davidson B0UqxwE <- experiment %>% expand( nesting( name, Navy Vita Halo Suede Dolce Women's Heeled Sandal 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