# Reshape database from wide format to long format This function automates the process of converting a wide-format database (i.e., a database in which intercorrelations between construct pairs define the columns, such that there are multiple columns of correlations) to a long-format database (i.e., a database with just one column of correlations). The meta-analysis functions in `psychmeta` work best with long-format databases, so this function can be a helpful addition to one's workflow when data are organized in a wide format. ```r reshape_wide2long( data, common_vars = NULL, es_design = NULL, n_design = NULL, other_design = NULL, es_name = "rxyi", missing_col_action = c("warn", "ignore", "stop") ) ``` ## Arguments - `data`: Database of data for use in a meta-analysis in "wide" format. - `common_vars`: String vector of column names relevant to all variables in data. - `es_design`: p x p matrix containing the names of columns of intercorrelations among variables in the lower triangle of the matrix. - `n_design`: Scalar sample-size column name or a p x p matrix containing the names of columns of sample sizes the lower triangle of the matrix. - `other_design`: A matrix with variable names on the rows and names of long-format variables to create on the columns. Elements of this matrix must be column names of `data`. - `es_name`: Name of the effect size represented in `data`. - `missing_col_action`: Character scalar indicating how missing columns should be handled. Options are: "warn", "ignore", and "stop" ## Returns A long-format database ## Examples ```r n_params = c(mean = 150, sd = 20) rho_params <- list(c(.1, .3, .5), c(mean = .3, sd = .05), rbind(value = c(.1, .3, .5), weight = c(1, 2, 1))) rel_params = list(c(.7, .8, .9), c(mean = .8, sd = .05), rbind(value = c(.7, .8, .9), weight = c(1, 2, 1))) sr_params = c(list(1, 1, c(.5, .7))) sr_composite_params = list(1, c(.5, .6, .7)) wt_params = list(list(c(1, 2, 3), c(mean = 2, sd = .25), rbind(value = c(1, 2, 3), weight = c(1, 2, 1))), list(c(1, 2, 3), c(mean = 2, sd = .25), rbind(value = c(1, 2, 3), weight = c(1, 2, 1)))) ## Simulate with wide format ## Not run: data <- simulate_r_database(k = 10, n_params = n_params, rho_params = rho_params, rel_params = rel_params, sr_params = sr_params, sr_composite_params = sr_composite_params, wt_params = wt_params, var_names = c("X", "Y", "Z"), format = "wide")$statistics ## End(Not run) ## Define values to abstract from the data object common_vars <- "sample_id" es_design <- matrix(NA, 3, 3) var_names <- c("X", "Y", "Z") es_design[lower.tri(es_design)] <- c("rxyi_X_Y", "rxyi_X_Z", "rxyi_Y_Z") rownames(es_design) <- colnames(es_design) <- var_names n_design <- "ni" other_design <- cbind(rxxi = paste0("parallel_rxxi_", var_names), ux_local = paste0("ux_local_", var_names), ux_external = paste0("ux_external_", var_names)) rownames(other_design) <- var_names ## Reshape the data to "long" format reshape_wide2long(data = data, common_vars = common_vars, es_design = es_design, n_design = n_design, other_design = other_design) ```

reshape_wide2long function