R/make_possible_data.R
In lilymedina/gbiqqtools: Tools for generalized BIQQ
Defines functions
make_possible_data
Documented in
make_possible_data
#' Make data for multi-step strategy
#'
#' Creates a database of possible data from a data strategy.
#' Users can gather additional data on variables specified via \code{vars} for any possible cases in the model ("any"). Or they can
#' gather data in all cases within a given dataset ("within"). Or they can specify the subset of cases for which within-case data should be collected (e.g. "Y == 1").
#'
#' @param model A causal model object generated by \code{make_model}.
#' @param given A data.frame with observations.
#' @param N An integer. Number of variables to seek.
#' @param within Logical. Whether to seek variables within existing data.
#' @param condition A list of character strings indicating for which cases data should be gathered. Options are: (i) to gather additional data on variables specified via \code{vars} for any possible cases in the model ("any"), (ii) to gather data in all cases within a given dataset ("within"), or (iii) to specify the subset of cases for which within-case data should be collected (e.g. "Y == 1").
#' @param vars A character vector. Variables to be sought or NA. If NA \code{make_possible_data} gathers data on all variables containing NA for the specified data strategy.
#' @export
#' @return A dataset
#' @examples
#' library(dplyr)
#' model <- make_model("X->M->Y") %>%
#' set_restrictions(causal_type_restrict = "Y[M=1]<Y[M=0] | M[X=1]<M[X=0] ") %>%
#' set_parameter_matrix()
#' df <- data.frame(X = c(0,0,0,1,1,1), M = NA, Y = c(0,0,1,0,1,1))
#' given <- summarize_data(model, df)[, -2]
#'
#' # Look for data on M for all possible cases in the given data
#' make_possible_data(model, N = 2)
#' make_possible_data(model, given, vars = list("M"), within = TRUE, N = 2)
#'
#' # Not possible:
#' make_possible_data(model, given, vars = "M", within = TRUE, N = 7)
#'
#' # Within conditions
#' make_possible_data(model, given, within = TRUE, N = 2, condition = "X==1 & Y==1", vars = "M")
#' make_possible_data(model, given, within = TRUE, N = 3, condition = "Y==1", vars = "M")
#' make_possible_data(model, given, within = TRUE, condition = "X == 1 | Y == 1", vars = "M")
#'
#' # Look for data on K but not M
#' model <- make_model("X->M->Y <-K") %>%
#' set_parameter_matrix()
#' df <- data.frame(X = c(0,0,1,1,1), K = NA, M = NA, Y = c(0,0,0,1,1))
#' given <- summarize_data(model, df)[, -2]
#' make_possible_data(model, given, within = TRUE, N = 1, vars = "K")
#'
#' # Look for data on M when X = 1 and Y = 0
#' make_possible_data(model,
#' given,
#' condition = "X == 1 & Y == 0",
#' vars ="M")
#'
#' model <- make_model("X->M->Y") %>%
#' set_parameter_matrix()
#' make_possible_data(model,
#' given = NULL,
#' N = list(3,1),
#' within = FALSE,
#' condition = list(TRUE, "X == 1 & Y == 0"),
#' vars = list(c("X", "Y"), "M"))
make_possible_data <- function(model,
given = NULL,
N = list(1),
within = FALSE,
condition = list(TRUE),
vars = list(NULL)) {
if(!is.null(given)) if(!identical(names(given), c("event", "count"))){
stop("'given' df should have two columns: event and count")}
if(!identical(length(condition), length(vars), length(N)) )
stop("N, cases and vars must have the same length")
g_df <- gbiqq:::make_possible_data_single(model,
given = given,
within = within,
N = N[[1]],
condition = condition[[1]],
vars = vars[[1]] )
if(length(N) == 1){
attr(g_df, "possible_data_args") <- list(N = N,within = within, condition = condition, vars = vars)
return(g_df)
}
for (i in 2:length(N)){
possible <- data.frame(select(g_df, event), count = 1)
possible <- simulate_data(model, data_events = possible)
possible<- possible[with(possible, eval(parse(text = condition[[i]]))),]
possible <- gbiqq:::collapse_data(possible, model)
use_this <- g_df[g_df$event %in% possible$event[possible$count>0],] >= N[[i]]
if(nrow(use_this) > 1) use_this <- apply(use_this, 2, any)
use_data <- g_df[,use_this]
skip <- data.frame(dplyr::select(g_df, event, strategy), g_df[,!use_this ])
if(ncol(skip)>2) names(skip)[3:ncol(skip)] <- names(g_df)[!use_this]
# Avoid running make_possible_single if there are possible data with enough space to alocate N
if(all(!use_this[3:ncol(g_df)])){
message(paste0("Not enough space to allocate N = ",N[[i]], " in step ",i))
return(g_df)
}
out <- lapply(3:ncol(use_data), function(s) {
use_data <- use_data[,c(1,s)]
names(use_data) <- c("event", "count")
data_single <- make_possible_data_single(model,
given = use_data,
within = TRUE,
N = N[[i]],
condition = condition[[i]],
vars = vars[[i]])
colnames(data_single)[3:ncol(data_single)] <- paste0(s-2, "-", colnames(data_single)[3:ncol(data_single)])
data_single
})
# out <- lapply(1:length(out), function(n_s){ out1 <- out[[n_s]]
# colnames(out1)[3:ncol(out1)] <- paste0(n_s, "-", colnames(out1)[3:ncol( out1)])
# out1 })
# rbind not working yet since output is of different length
# x <- do.call("rbind", out)
# x <- t(t(x)[!duplicated(t(x)),])
# given <- cbind(given[,1:2], x)
out <- Reduce(function(x, y) merge(x, y, by = c("event", "strategy"), all = TRUE), out)
out <- merge( skip, out, by = c("event", "strategy"), all = TRUE)
g_df <- dplyr:::mutate_if(out, is.numeric, ~replace(., is.na(.), 0))
}
# out2 <- lapply(1:length(out2), function(n_s){
# out1 <- out2[[n_s]]
# colnames(out1)[3:ncol(out1)] <- paste0(n_s, "-", colnames(out1)[3:ncol( out1)])
# out1 })
#given <- Reduce(function(x, y) merge(x, y, by = c("event", "strategy"), all = TRUE), out2, right = TRUE)
#given <- given[,!duplicated(t(given))]
#given
return(g_df)
}
#' Make possible data for a single strategy step
#'
#' Creates a database of possible data from a data strategy.
#' Users can gather additional data on variables specified via \code{vars} for any possible cases in the model ("any"). Or they can
#' gather data in all cases within a given dataset ("within"). Or they can specify the subset of cases for which within-case data should be collected (e.g. "Y == 1").
#'
#' @param model A causal model as created by \code{make_model}
#' @param given A data frame in compact form with first column indicating event type and second column indicating number of events of that type.
#' @param N Number of variables to seek
#' @param within logical Whether to seek variables within existing data
#' @param condition A list of character strings indicating for which cases data should be gathered. Options are: (i) to gather additional data on variables specified via \code{vars} for any possible cases in the model ("any"), (ii) to gather data in all cases within a given dataset ("within"), or (iii) to specify the subset of cases for which within-case data should be collected (e.g. "Y == 1").
#' @param vars Variables to be sought or NA. If NA \code{make_possible_data} gathers data on all variables containing NA for the specified data strategy.
#' @export
#' @return A dataset
#' @examples
#' library(dplyr)
#' model <- make_model("X->M->Y") %>%
#' set_restrictions(causal_type_restrict = "Y[M=1]<Y[M=0] | M[X=1]<M[X=0] ") %>%
#' set_parameter_matrix()
#' df <- data.frame(X = c(0,0,0,1,1,1), M = NA, Y = c(0,0,1,0,1,1))
#' given <- summarize_data(model, df)[, -2]
#'
#' # Look for data on M for all possible cases in the given data
#' make_possible_data_single(model, N = 2)
#' make_possible_data_single(model, given = given, vars = "M", within = TRUE, N = 2)
#' make_possible_data_single(model, given = given,
#' within = TRUE, vars = "M",
#' N = 2,
#' condition = "X==1 & Y==1")
#'
#' model <- make_model("X -> M -> Y <- K") %>%
#' set_restrictions(causal_type_restrict = "(Y[M=1, K= .]<Y[M=0, K= .]) | M[X=1]<M[X=0] ") %>%
#' set_parameter_matrix()
#' given <- data.frame(X = c(0,0,0,1,1,1), K = NA, M = NA, Y = c(0,0,1,0,1,1)) %>%
#' collapse_data(model = model)
#' make_possible_data_single(model, given = given,
#' within = TRUE,
#' N = 2,
#' condition = "X==1",
#' vars = "M")
#'
#' make_possible_data_single(model, given = given,
#' within = TRUE,
#' N = 2,
#' condition = "X==1 & Y==1",
#' vars = c("M", "K"))
#'
#'model <- make_model("X->M->Y") %>%
#' set_restrictions(causal_type_restrict = "Y[M=1]<Y[M=0] | M[X=1]<M[X=0]") %>%
#' set_parameter_matrix()
#' given <- data.frame(X = c(0,0,0,1,1,1), M = NA, Y = c(0,0,1,0,1,1)) %>%
#' collapse_data(model)
#' make_possible_data_single(model,
#' given = given,
#' within = TRUE,
#' vars = "M",
#' N = 1,
#' condition = "X==1 & Y==1")
#' make_possible_data_single(model,
#' given = given,
#' within = TRUE,
#' vars = "M",
#' N = 1,
#' condition = "X==1")
make_possible_data_single <- function(model,
given = NULL,
N = 1,
within = FALSE,
condition = TRUE,
vars = NULL) {
if(is.null(vars) & within) stop("Please specify vars to be examined")
if(within & is.null(given)) stop("If 'within' is specified 'given' must be provided")
if(!within){possible_data <- gbiqq:::all_possible(model, N, vars)}
if(within){
if(is.null(given)) stop("given not provided, but 'within' requested")
all_event_types <- dplyr::select(summarize_data(model, all_data_types(model)), event, strategy)
possible <- get_max_possible_data(model)
possible <- possible[with(possible, eval(parse(text = condition))),]
possible <- gbiqq:::collapse_data(possible, model)
A_w <- get_likelihood_helpers(model)$A_w
# What is the set of types in which we can seek new data
acceptable_bucket <- (A_w %*% possible[,"count"])>0
acceptable_bucket <-rownames(acceptable_bucket)[acceptable_bucket]
all_buckets <- given
all_buckets$capacity <- all_buckets$count
all_buckets$capacity[!(given$event %in% acceptable_bucket)] <-0
if(sum(all_buckets$capacity) < N) {message("Not enough space to allocate N"); return(given)}
strategies <- as.matrix(partitions::blockparts(all_buckets$capacity, N))
colnames(strategies) <- 1:ncol(strategies)
all_buckets <- cbind(all_buckets, strategies)
# This function goes through a bucket strategy and generates all possible datasets that could be produced by the strategy
get_results_from_strategy <- function(strategy){
buckets <- all_buckets[all_buckets$capacity>0 ,]
buckets <- buckets[buckets[,strategy]>0,]
data_list <- lapply(1:nrow(buckets), function(j) fill_bucket(model, buckets, vars, row = j, column = strategy))
b_names <- sapply(1:nrow(buckets), function(b_i) (all_buckets$event %in% buckets$event[b_i])*(ncol(data_list[[b_i]])-2))
addresses <- do.call(rbind, lapply(apply(b_names, 2, perm),function(b)data.frame(b)+1))
addresses <- apply(addresses, 1, paste, collapse = ".")
strategy_results <- Reduce(function(x, y) merge(x, y, by = "event", all = TRUE), data_list)
out <- merge(given, strategy_results, by = "event", all = TRUE )
out <- dplyr::mutate_at(out, vars(-c("event", "count")), list(~ dplyr::coalesce(., count)))
colnames(out)[3:ncol(out)] <- paste0(strategy-3, "-",addresses)
# Hack --avoid column names duplicates
dups <- colnames(out)[duplicated(colnames(out))]
l_dups <- length(dups)
if(l_dups > 0) colnames(out)[duplicated(colnames(out))] <- paste0(dups, "_",seq_len(l_dups))
out[,-2]
}
# Run over all strategies
all_strategies <- sapply(4:ncol(all_buckets), function(s) get_results_from_strategy(s), simplify = FALSE)
all_strategies <- Reduce(function(x, y) merge(x, y, by = "event", all = TRUE), all_strategies)
all_strategies <- dplyr:::mutate_if(all_strategies, is.numeric, ~replace(., is.na(.), 0))
possible_data <- merge(select(given, event), all_strategies, by = "event", all = TRUE)
# Add strategies
possible_data <- merge(all_event_types, possible_data, by = "event")
possible_data <- dplyr:::mutate_if(possible_data, is.numeric, ~replace(., is.na(.), 0))
}
return(possible_data)
}
lilymedina/gbiqqtools documentation built on Nov. 4, 2019, 4:32 p.m.
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Defines functions make_possible_data
Documented in make_possible_data
#' Make data for multi-step strategy
#'
#' Creates a database of possible data from a data strategy.
#' Users can gather additional data on variables specified via \code{vars} for any possible cases in the model ("any"). Or they can
#' gather data in all cases within a given dataset ("within"). Or they can specify the subset of cases for which within-case data should be collected (e.g. "Y == 1").
#'
#' @param model A causal model object generated by \code{make_model}.
#' @param given A data.frame with observations.
#' @param N An integer. Number of variables to seek.
#' @param within Logical. Whether to seek variables within existing data.
#' @param condition A list of character strings indicating for which cases data should be gathered. Options are: (i) to gather additional data on variables specified via \code{vars} for any possible cases in the model ("any"), (ii) to gather data in all cases within a given dataset ("within"), or (iii) to specify the subset of cases for which within-case data should be collected (e.g. "Y == 1").
#' @param vars A character vector. Variables to be sought or NA. If NA \code{make_possible_data} gathers data on all variables containing NA for the specified data strategy.
#' @export
#' @return A dataset
#' @examples
#' library(dplyr)
#' model <- make_model("X->M->Y") %>%
#' set_restrictions(causal_type_restrict = "Y[M=1]<Y[M=0] | M[X=1]<M[X=0] ") %>%
#' set_parameter_matrix()
#' df <- data.frame(X = c(0,0,0,1,1,1), M = NA, Y = c(0,0,1,0,1,1))
#' given <- summarize_data(model, df)[, -2]
#'
#' # Look for data on M for all possible cases in the given data
#' make_possible_data(model, N = 2)
#' make_possible_data(model, given, vars = list("M"), within = TRUE, N = 2)
#'
#' # Not possible:
#' make_possible_data(model, given, vars = "M", within = TRUE, N = 7)
#'
#' # Within conditions
#' make_possible_data(model, given, within = TRUE, N = 2, condition = "X==1 & Y==1", vars = "M")
#' make_possible_data(model, given, within = TRUE, N = 3, condition = "Y==1", vars = "M")
#' make_possible_data(model, given, within = TRUE, condition = "X == 1 | Y == 1", vars = "M")
#'
#' # Look for data on K but not M
#' model <- make_model("X->M->Y <-K") %>%
#' set_parameter_matrix()
#' df <- data.frame(X = c(0,0,1,1,1), K = NA, M = NA, Y = c(0,0,0,1,1))
#' given <- summarize_data(model, df)[, -2]
#' make_possible_data(model, given, within = TRUE, N = 1, vars = "K")
#'
#' # Look for data on M when X = 1 and Y = 0
#' make_possible_data(model,
#' given,
#' condition = "X == 1 & Y == 0",
#' vars ="M")
#'
#' model <- make_model("X->M->Y") %>%
#' set_parameter_matrix()
#' make_possible_data(model,
#' given = NULL,
#' N = list(3,1),
#' within = FALSE,
#' condition = list(TRUE, "X == 1 & Y == 0"),
#' vars = list(c("X", "Y"), "M"))
make_possible_data <- function(model,
given = NULL,
N = list(1),
within = FALSE,
condition = list(TRUE),
vars = list(NULL)) {
if(!is.null(given)) if(!identical(names(given), c("event", "count"))){
stop("'given' df should have two columns: event and count")}
if(!identical(length(condition), length(vars), length(N)) )
stop("N, cases and vars must have the same length")
g_df <- gbiqq:::make_possible_data_single(model,
given = given,
within = within,
N = N[[1]],
condition = condition[[1]],
vars = vars[[1]] )
if(length(N) == 1){
attr(g_df, "possible_data_args") <- list(N = N,within = within, condition = condition, vars = vars)
return(g_df)
}
for (i in 2:length(N)){
possible <- data.frame(select(g_df, event), count = 1)
possible <- simulate_data(model, data_events = possible)
possible<- possible[with(possible, eval(parse(text = condition[[i]]))),]
possible <- gbiqq:::collapse_data(possible, model)
use_this <- g_df[g_df$event %in% possible$event[possible$count>0],] >= N[[i]]
if(nrow(use_this) > 1) use_this <- apply(use_this, 2, any)
use_data <- g_df[,use_this]
skip <- data.frame(dplyr::select(g_df, event, strategy), g_df[,!use_this ])
if(ncol(skip)>2) names(skip)[3:ncol(skip)] <- names(g_df)[!use_this]
# Avoid running make_possible_single if there are possible data with enough space to alocate N
if(all(!use_this[3:ncol(g_df)])){
message(paste0("Not enough space to allocate N = ",N[[i]], " in step ",i))
return(g_df)
}
out <- lapply(3:ncol(use_data), function(s) {
use_data <- use_data[,c(1,s)]
names(use_data) <- c("event", "count")
data_single <- make_possible_data_single(model,
given = use_data,
within = TRUE,
N = N[[i]],
condition = condition[[i]],
vars = vars[[i]])
colnames(data_single)[3:ncol(data_single)] <- paste0(s-2, "-", colnames(data_single)[3:ncol(data_single)])
data_single
})
# out <- lapply(1:length(out), function(n_s){ out1 <- out[[n_s]]
# colnames(out1)[3:ncol(out1)] <- paste0(n_s, "-", colnames(out1)[3:ncol( out1)])
# out1 })
# rbind not working yet since output is of different length
# x <- do.call("rbind", out)
# x <- t(t(x)[!duplicated(t(x)),])
# given <- cbind(given[,1:2], x)
out <- Reduce(function(x, y) merge(x, y, by = c("event", "strategy"), all = TRUE), out)
out <- merge( skip, out, by = c("event", "strategy"), all = TRUE)
g_df <- dplyr:::mutate_if(out, is.numeric, ~replace(., is.na(.), 0))
}
# out2 <- lapply(1:length(out2), function(n_s){
# out1 <- out2[[n_s]]
# colnames(out1)[3:ncol(out1)] <- paste0(n_s, "-", colnames(out1)[3:ncol( out1)])
# out1 })
#given <- Reduce(function(x, y) merge(x, y, by = c("event", "strategy"), all = TRUE), out2, right = TRUE)
#given <- given[,!duplicated(t(given))]
#given
return(g_df)
}
#' Make possible data for a single strategy step
#'
#' Creates a database of possible data from a data strategy.
#' Users can gather additional data on variables specified via \code{vars} for any possible cases in the model ("any"). Or they can
#' gather data in all cases within a given dataset ("within"). Or they can specify the subset of cases for which within-case data should be collected (e.g. "Y == 1").
#'
#' @param model A causal model as created by \code{make_model}
#' @param given A data frame in compact form with first column indicating event type and second column indicating number of events of that type.
#' @param N Number of variables to seek
#' @param within logical Whether to seek variables within existing data
#' @param condition A list of character strings indicating for which cases data should be gathered. Options are: (i) to gather additional data on variables specified via \code{vars} for any possible cases in the model ("any"), (ii) to gather data in all cases within a given dataset ("within"), or (iii) to specify the subset of cases for which within-case data should be collected (e.g. "Y == 1").
#' @param vars Variables to be sought or NA. If NA \code{make_possible_data} gathers data on all variables containing NA for the specified data strategy.
#' @export
#' @return A dataset
#' @examples
#' library(dplyr)
#' model <- make_model("X->M->Y") %>%
#' set_restrictions(causal_type_restrict = "Y[M=1]<Y[M=0] | M[X=1]<M[X=0] ") %>%
#' set_parameter_matrix()
#' df <- data.frame(X = c(0,0,0,1,1,1), M = NA, Y = c(0,0,1,0,1,1))
#' given <- summarize_data(model, df)[, -2]
#'
#' # Look for data on M for all possible cases in the given data
#' make_possible_data_single(model, N = 2)
#' make_possible_data_single(model, given = given, vars = "M", within = TRUE, N = 2)
#' make_possible_data_single(model, given = given,
#' within = TRUE, vars = "M",
#' N = 2,
#' condition = "X==1 & Y==1")
#'
#' model <- make_model("X -> M -> Y <- K") %>%
#' set_restrictions(causal_type_restrict = "(Y[M=1, K= .]<Y[M=0, K= .]) | M[X=1]<M[X=0] ") %>%
#' set_parameter_matrix()
#' given <- data.frame(X = c(0,0,0,1,1,1), K = NA, M = NA, Y = c(0,0,1,0,1,1)) %>%
#' collapse_data(model = model)
#' make_possible_data_single(model, given = given,
#' within = TRUE,
#' N = 2,
#' condition = "X==1",
#' vars = "M")
#'
#' make_possible_data_single(model, given = given,
#' within = TRUE,
#' N = 2,
#' condition = "X==1 & Y==1",
#' vars = c("M", "K"))
#'
#'model <- make_model("X->M->Y") %>%
#' set_restrictions(causal_type_restrict = "Y[M=1]<Y[M=0] | M[X=1]<M[X=0]") %>%
#' set_parameter_matrix()
#' given <- data.frame(X = c(0,0,0,1,1,1), M = NA, Y = c(0,0,1,0,1,1)) %>%
#' collapse_data(model)
#' make_possible_data_single(model,
#' given = given,
#' within = TRUE,
#' vars = "M",
#' N = 1,
#' condition = "X==1 & Y==1")
#' make_possible_data_single(model,
#' given = given,
#' within = TRUE,
#' vars = "M",
#' N = 1,
#' condition = "X==1")
make_possible_data_single <- function(model,
given = NULL,
N = 1,
within = FALSE,
condition = TRUE,
vars = NULL) {
if(is.null(vars) & within) stop("Please specify vars to be examined")
if(within & is.null(given)) stop("If 'within' is specified 'given' must be provided")
if(!within){possible_data <- gbiqq:::all_possible(model, N, vars)}
if(within){
if(is.null(given)) stop("given not provided, but 'within' requested")
all_event_types <- dplyr::select(summarize_data(model, all_data_types(model)), event, strategy)
possible <- get_max_possible_data(model)
possible <- possible[with(possible, eval(parse(text = condition))),]
possible <- gbiqq:::collapse_data(possible, model)
A_w <- get_likelihood_helpers(model)$A_w
# What is the set of types in which we can seek new data
acceptable_bucket <- (A_w %*% possible[,"count"])>0
acceptable_bucket <-rownames(acceptable_bucket)[acceptable_bucket]
all_buckets <- given
all_buckets$capacity <- all_buckets$count
all_buckets$capacity[!(given$event %in% acceptable_bucket)] <-0
if(sum(all_buckets$capacity) < N) {message("Not enough space to allocate N"); return(given)}
strategies <- as.matrix(partitions::blockparts(all_buckets$capacity, N))
colnames(strategies) <- 1:ncol(strategies)
all_buckets <- cbind(all_buckets, strategies)
# This function goes through a bucket strategy and generates all possible datasets that could be produced by the strategy
get_results_from_strategy <- function(strategy){
buckets <- all_buckets[all_buckets$capacity>0 ,]
buckets <- buckets[buckets[,strategy]>0,]
data_list <- lapply(1:nrow(buckets), function(j) fill_bucket(model, buckets, vars, row = j, column = strategy))
b_names <- sapply(1:nrow(buckets), function(b_i) (all_buckets$event %in% buckets$event[b_i])*(ncol(data_list[[b_i]])-2))
addresses <- do.call(rbind, lapply(apply(b_names, 2, perm),function(b)data.frame(b)+1))
addresses <- apply(addresses, 1, paste, collapse = ".")
strategy_results <- Reduce(function(x, y) merge(x, y, by = "event", all = TRUE), data_list)
out <- merge(given, strategy_results, by = "event", all = TRUE )
out <- dplyr::mutate_at(out, vars(-c("event", "count")), list(~ dplyr::coalesce(., count)))
colnames(out)[3:ncol(out)] <- paste0(strategy-3, "-",addresses)
# Hack --avoid column names duplicates
dups <- colnames(out)[duplicated(colnames(out))]
l_dups <- length(dups)
if(l_dups > 0) colnames(out)[duplicated(colnames(out))] <- paste0(dups, "_",seq_len(l_dups))
out[,-2]
}
# Run over all strategies
all_strategies <- sapply(4:ncol(all_buckets), function(s) get_results_from_strategy(s), simplify = FALSE)
all_strategies <- Reduce(function(x, y) merge(x, y, by = "event", all = TRUE), all_strategies)
all_strategies <- dplyr:::mutate_if(all_strategies, is.numeric, ~replace(., is.na(.), 0))
possible_data <- merge(select(given, event), all_strategies, by = "event", all = TRUE)
# Add strategies
possible_data <- merge(all_event_types, possible_data, by = "event")
possible_data <- dplyr:::mutate_if(possible_data, is.numeric, ~replace(., is.na(.), 0))
}
return(possible_data)
}
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