Remove a step from an existing design

remove_step(...)

Arguments

...

bare (unquoted) names of step(s) to remove from a design

Details

see modify_design for details.

Examples

my_population <- declare_population(N = 100, noise = rnorm(N)) my_potential_outcomes <- declare_potential_outcomes(Y_Z_0 = noise, Y_Z_1 = noise + rnorm(N, mean = 2, sd = 2)) my_assignment <- declare_assignment(m = 50) my_assignment_2 <- declare_assignment(m = 25) design <- declare_design(my_population, my_potential_outcomes, my_assignment) design
#> #> Design Summary #> #> Step 1 (population): my_population --------------------------------------------- #> #> N = 100 #> #> Added variable: ID #> N_missing N_unique #> 0 100 #> #> Added variable: noise #> min median mean max sd N_missing N_unique #> -2.67 0.07 0.14 2.67 0.99 0 100 #> #> Step 2 (potential outcomes): my_potential_outcomes ----------------------------- #> #> Added variable: Y_Z_0 #> min median mean max sd N_missing N_unique #> -2.67 0.07 0.14 2.67 0.99 0 100 #> #> Added variable: Y_Z_1 #> min median mean max sd N_missing N_unique #> -4.43 2.05 2.07 7.69 2.53 0 100 #> #> Step 3 (assignment): my_assignment --------------------------------------------- #> #> Added variable: Z #> 0 1 NA #> Frequency 50 50 0 #> Proportion 0.50 0.50 0.00 #> #> Added variable: Z_cond_prob #> 0.5 NA #> Frequency 100 0 #> Proportion 1.00 0.00 #>
modify_design(design, replace_step(my_assignment_2, replace = my_assignment))
#> #> Design Summary #> #> Step 1 (population): my_population --------------------------------------------- #> #> N = 100 #> #> Added variable: ID #> N_missing N_unique #> 0 100 #> #> Added variable: noise #> min median mean max sd N_missing N_unique #> -2.44 0.05 -0.00 2.24 0.92 0 100 #> #> Step 2 (potential outcomes): my_potential_outcomes ----------------------------- #> #> Added variable: Y_Z_0 #> min median mean max sd N_missing N_unique #> -2.44 0.05 -0.00 2.24 0.92 0 100 #> #> Added variable: Y_Z_1 #> min median mean max sd N_missing N_unique #> -4.09 2.14 2.20 7.72 1.99 0 100 #> #> Step 3 (assignment): my_assignment_2 ------------------------------------------- #> #> Added variable: Z #> 0 1 NA #> Frequency 75 25 0 #> Proportion 0.75 0.25 0.00 #> #> Added variable: Z_cond_prob #> 0.25 0.75 NA #> Frequency 25 75 0 #> Proportion 0.25 0.75 0.00 #>
modify_design(design, add_step(dplyr::mutate(income = noise^2), after = my_assignment))
#> #> Design Summary #> #> Step 1 (population): my_population --------------------------------------------- #> #> N = 100 #> #> Added variable: ID #> N_missing N_unique #> 0 100 #> #> Added variable: noise #> min median mean max sd N_missing N_unique #> -2.18 -0.10 -0.12 2.19 0.97 0 100 #> #> Step 2 (potential outcomes): my_potential_outcomes ----------------------------- #> #> Added variable: Y_Z_0 #> min median mean max sd N_missing N_unique #> -2.18 -0.10 -0.12 2.19 0.97 0 100 #> #> Added variable: Y_Z_1 #> min median mean max sd N_missing N_unique #> -4.08 1.77 1.69 7.66 2.06 0 100 #> #> Step 3 (assignment): my_assignment --------------------------------------------- #> #> Added variable: Z #> 0 1 NA #> Frequency 50 50 0 #> Proportion 0.50 0.50 0.00 #> #> Added variable: Z_cond_prob #> 0.5 NA #> Frequency 100 0 #> Proportion 1.00 0.00 #> #> Step 4 (declare step): dplyr::mutate(income = noise^2) ------------------------- #> #> Added variable: income #> min median mean max sd N_missing N_unique #> 0.00 0.49 0.95 4.78 1.18 0 100 #>
modify_design(design, add_step(dplyr::mutate(income = noise^2), before = my_assignment))
#> #> Design Summary #> #> Step 1 (population): my_population --------------------------------------------- #> #> N = 100 #> #> Added variable: ID #> N_missing N_unique #> 0 100 #> #> Added variable: noise #> min median mean max sd N_missing N_unique #> -2.71 0.11 0.07 2.49 0.92 0 100 #> #> Step 2 (potential outcomes): my_potential_outcomes ----------------------------- #> #> Added variable: Y_Z_0 #> min median mean max sd N_missing N_unique #> -2.71 0.11 0.07 2.49 0.92 0 100 #> #> Added variable: Y_Z_1 #> min median mean max sd N_missing N_unique #> -2.55 2.57 2.42 8.21 2.07 0 100 #> #> Step 3 (declare step): dplyr::mutate(income = noise^2) ------------------------- #> #> Added variable: income #> min median mean max sd N_missing N_unique #> 0.00 0.51 0.85 7.34 1.16 0 100 #> #> Step 4 (assignment): my_assignment --------------------------------------------- #> #> Added variable: Z #> 0 1 NA #> Frequency 50 50 0 #> Proportion 0.50 0.50 0.00 #> #> Added variable: Z_cond_prob #> 0.5 NA #> Frequency 100 0 #> Proportion 1.00 0.00 #>
modify_design(design, remove_step(my_assignment))
#> #> Design Summary #> #> Step 1 (population): my_population --------------------------------------------- #> #> N = 100 #> #> Added variable: ID #> N_missing N_unique #> 0 100 #> #> Added variable: noise #> min median mean max sd N_missing N_unique #> -2.51 -0.20 -0.16 2.84 1.00 0 100 #> #> Step 2 (potential outcomes): my_potential_outcomes ----------------------------- #> #> Added variable: Y_Z_0 #> min median mean max sd N_missing N_unique #> -2.51 -0.20 -0.16 2.84 1.00 0 100 #> #> Added variable: Y_Z_1 #> min median mean max sd N_missing N_unique #> -3.01 1.94 1.97 6.14 2.00 0 100 #>