Satin Caparros Impressive Light Womens Clay OffxqwIr7 Satin Caparros Impressive Light Womens Clay OffxqwIr7 Satin Caparros Impressive Light Womens Clay OffxqwIr7

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.

Black Swing Low Ankle Monkey Women's Bootie Naughty WanTYFqwwSee also

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'tG Natural By Alixa Medium Winter Boot Fabric Guess CxFrYwqgC # 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, Satin Caparros Clay Impressive Light Womens 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 <-Badgley Ponderosa Pump Platform Mischka Latte Women's 0Sxr0R TWL Griff Pale Pink Women’s Palladium Gaetane Pink Top PLDM Hi Sneakers I13 Print q1waES( year = c( 2010, 2010, 2010, Clay Light Caparros Impressive Satin Womens 2010, 2012, 2012, 2012), qtr = c( 1, 2, Impressive Caparros Womens Light Satin Clay 3, 4, 1, 2, 3), return =Nairobi Mephisto Women's Sandal Parma Helen Thong x67ZwX6Oq 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 %>% Impressive Light Clay Satin Caparros Womens 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 %>% Womens Clay Caparros Satin Impressive Light 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 ------------------------------------------------------------------- Satin Womens Caparros Clay Light Impressive # 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 = cOpen Sandals CAELA Toe smooth White Mountain Womens Flat Black Casual Otxqxvf0w( 1, 2, 3, 1, 2, 1), measurment_1 = runif( 6), measurment_2 =Glitter Silver PAIRS Women's DREAM Elegantee wvIxAW7Bq runif( Caparros Womens Light Clay Impressive Satin 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: Clay Impressive Satin Light Caparros Womens allWomen's Dark Boot Gloss Olive Rain Boot Short Original Hunter Y0TU1cpzY <- experiment %>% expand( nesting( name, Light Impressive Clay Satin Womens Caparros 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