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Pull cur_column() from across()

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Bryan Roessler
2024-09-16 15:07:07 -04:00
parent 4045b31543
commit 37743b1f5e
1 changed files with 11 additions and 15 deletions
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26
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -789,43 +789,39 @@ filter_and_print_non_finite <- function(df, vars_to_check, print_vars) {
filter_data_for_plots <- function(df, variables, missing = TRUE, limits_map = NULL) { filter_data_for_plots <- function(df, variables, missing = TRUE, limits_map = NULL) {
# Initialize lists to store lm lines # Print missing data and out-of-range data separately
lm_lines <- list()
# Check for missing and out-of-range data
for (variable in variables) { for (variable in variables) {
y_var_sym <- sym(variable) y_var_sym <- sym(variable)
# Print missing data if requested
if (missing) { if (missing) {
missing_data <- df %>% filter(is.na(!!y_var_sym)) missing_data <- df %>% filter(is.na(!!y_var_sym))
if (nrow(missing_data) > 0) { if (nrow(missing_data) > 0) {
message("Filtering missing data for variable ", variable, " for plotting:") message("Missing data for variable ", variable, ":")
print(head(missing_data, 10)) # Print only the first 10 rows to avoid too much output print(missing_data)
} }
} }
# Print out-of-range data if limits_map is provided
if (!is.null(limits_map)) { if (!is.null(limits_map)) {
# Get y-limits for the variable
ylim_vals <- limits_map[[variable]] ylim_vals <- limits_map[[variable]]
# Identify out-of-range data and print it
out_of_range_data <- df %>% filter( out_of_range_data <- df %>% filter(
!is.na(!!y_var_sym) & !is.na(!!y_var_sym) &
(!!y_var_sym < min(ylim_vals, na.rm = TRUE) | !!y_var_sym > max(ylim_vals, na.rm = TRUE)) (!!y_var_sym < min(ylim_vals, na.rm = TRUE) | !!y_var_sym > max(ylim_vals, na.rm = TRUE))
) )
if (nrow(out_of_range_data) > 0) { if (nrow(out_of_range_data) > 0) {
message("Filtering out-of-range data for variable ", variable, " for plotting:") message("Out-of-range data for variable ", variable, ":")
print(head(out_of_range_data, 10)) # Print only the first 10 rows print(out_of_range_data)
} }
} }
} }
# Apply filtering across all variables in one step using if_any and if_all # Filter data by checking if all variables are within the specified limits
if (!is.null(limits_map)) { if (!is.null(limits_map)) {
df_filtered <- df %>% df_filtered <- df %>%
filter(if_all(all_of(variables), ~ !is.na(.))) %>% filter(if_all(all_of(variables), ~ !is.na(.))) %>% # Check for non-NA values
filter(if_all(all_of(variables), filter(if_all(all_of(variables), ~ . >= limits_map[[cur_column()]][1] & . <= limits_map[[cur_column()]][2]))
~ between(., limits_map[[cur_column()]][1], limits_map[[cur_column()]][2])
))
} else { } else {
df_filtered <- df %>% filter(if_all(all_of(variables), ~ !is.na(.))) df_filtered <- df %>% filter(if_all(all_of(variables), ~ !is.na(.)))
} }