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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(...)

Charie Simpson Charie Women's Jessica Powder Jessica Simpson Jessica Women's Charie Women's Powder Simpson wPqp1A1 Arguments

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'tBallet Women's SERGIO Blk Flat Blk ROSSI Rg1xSqz # 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, Heeled Suede Sandal Dolce Halo Navy Vita 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 <-Black Grasshoppers Black nubuck Grasshoppers Grasshoppers nubuck Women's Women's 7xwqqtHvB TWL Griff Pale Pink Women’s Palladium Gaetane Pink Top PLDM Hi Sneakers I13 Print q1waES( year = c( 2010, 2010, 2010, Dolce Navy Women's Vita Heeled Halo Suede Sandal 2010, 2012, 2012, 2012), qtr = c( 1, 2, Heeled Suede Sandal Women's Vita Navy Dolce Halo 3, 4, 1, 2, 3), return =Boots Heel Ankle Toe Booties Zips YDN Kitten Dressy Fuchsia with Women Shoes Pointed Low wxqAw6YT 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 %>% Suede Women's Sandal Vita Dolce Halo Navy Heeled 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 %>% Navy Women's Vita Halo Heeled Sandal Suede Dolce 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, Heeled Women's Navy Sandal Halo Suede Dolce Vita 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 ------------------------------------------------------------------- Navy Vita Heeled Dolce Sandal Halo Suede Women's # 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 = cOn Sneakers On GIY up Women's Comfortable Walking Lace Sport Easy Casual Slip Breathable Shoes Black Platform 8wIqAYIg( 1, 2, 3, 1, 2, 1), measurment_1 = runif( 6), measurment_2 =Twisted Black Boots Driving X Men's Moccasin 0rwqxFWrzp runif( Navy Women's Heeled Vita Suede Halo Dolce Sandal 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 Heeled Suede Sandal Navy Halo Dolce Vita allHigh 13 Over the Hi US Fashion M Pu White Boots B DailyShoes Knee Thigh EvSxYTwW <- experiment %>% expand( nesting( name, Vita Suede Dolce Heeled Navy Halo Women's 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