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Refactor data filtering

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This commit is contained in:
Bryan Roessler
2024-09-16 21:10:22 -04:00
parent 9d23540157
commit d5141d04c4
1 changed files with 9 additions and 34 deletions
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41
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -155,9 +155,7 @@ update_gene_names <- function(df, sgd_gene_list) {
return(df)
}
# Calculate summary statistics for all variables
calculate_summary_stats <- function(df, variables, group_vars = c("OrfRep", "conc_num", "conc_num_factor")) {
# Summarize the variables within the grouped data
summary_stats <- df %>%
group_by(across(all_of(group_vars))) %>%
@@ -166,18 +164,17 @@ calculate_summary_stats <- function(df, variables, group_vars = c("OrfRep", "con
across(all_of(variables), list(
mean = ~mean(., na.rm = TRUE),
median = ~median(., na.rm = TRUE),
max = ~ ifelse(all(is.na(.)), NA, max(., na.rm = TRUE)),
min = ~ ifelse(all(is.na(.)), NA, min(., na.rm = TRUE)),
max = ~ifelse(all(is.na(.)), NA, max(., na.rm = TRUE)),
min = ~ifelse(all(is.na(.)), NA, min(., na.rm = TRUE)),
sd = ~sd(., na.rm = TRUE),
se = ~ ifelse(all(is.na(.)), NA, sd(., na.rm = TRUE) / sqrt(sum(!is.na(.)) - 1))
se = ~ifelse(all(is.na(.)), NA, sd(., na.rm = TRUE) / sqrt(sum(!is.na(.)) - 1))
), .names = "{.fn}_{.col}")
)
# print(summary_stats)
# Prevent .x and .y suffix issues by renaming columns
# Create a cleaned version of df that doesn't overlap with summary_stats
cols_to_exclude <- unique(c(variables, group_vars))
df_cleaned <- df %>%
select(-any_of(setdiff(names(summary_stats), group_vars))) # Avoid duplicate columns in the final join
select(-any_of(cols_to_exclude))
# Join the stats back to the original dataframe
df_with_stats <- left_join(df_cleaned, summary_stats, by = group_vars)
@@ -205,22 +202,8 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
AUC = df %>% filter(conc_num_factor == 0) %>% pull(sd_AUC) %>% first()
)
stats <- df %>%
group_by(OrfRep, Gene, num, conc_num, conc_num_factor) %>%
summarise(
N = sum(!is.na(L)),
NG = sum(NG, na.rm = TRUE),
DB = sum(DB, na.rm = TRUE),
SM = sum(SM, na.rm = TRUE),
across(all_of(variables), list(
mean = ~mean(., na.rm = TRUE),
median = ~median(., na.rm = TRUE),
max = ~ifelse(all(is.na(.)), NA, max(., na.rm = TRUE)),
min = ~ifelse(all(is.na(.)), NA, min(., na.rm = TRUE)),
sd = ~sd(., na.rm = TRUE),
se = ~ifelse(sum(!is.na(.)) > 1, sd(., na.rm = TRUE) / sqrt(sum(!is.na(.)) - 1), NA)
), .names = "{.fn}_{.col}")
)
stats <- calculate_summary_stats(df, variables,
group_vars = c("OrfRep", "Gene", "num", "conc_num", "conc_num_factor"))$summary_stats
stats <- df %>%
group_by(OrfRep, Gene, num) %>%
@@ -873,14 +856,6 @@ filter_data <- function(df, variables, nf = FALSE, missing = FALSE, adjust = FAL
avg_zscore_cols <- paste0("Avg_Zscore_", variables)
z_lm_cols <- paste0("Z_lm_", variables)
# # Optional: Validate that the expected columns exist in the dataframe
# expected_cols <- c(avg_zscore_cols, z_lm_cols, variables)
# missing_cols <- setdiff(expected_cols, names(df))
# if (length(missing_cols) > 0) {
# stop("The following expected columns are missing from the dataframe: ",
# paste(missing_cols, collapse = ", "))
# }
# Adjust NAs if 'adjust' is TRUE
if (adjust) {
if (verbose) message("Replacing NA with 0.001 for Avg_Zscore_ and Z_lm_ columns.")