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remove filtered #431

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merged 2 commits into from
Jul 26, 2022
Merged

remove filtered #431

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c9efd56
remove filtered
Jul 22, 2022
7660363
Merge branch 'main' into 76_remove_filtered@main
Jul 26, 2022
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58 changes: 29 additions & 29 deletions R/tm_missing_data.R
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Original file line number Diff line number Diff line change
Expand Up @@ -348,32 +348,32 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
teal.code::chunks_push(..., chunks = common_stack)
}

anl_name <- paste0(dataname, "_FILTERED")
anl_filtered <- datasets$get_data(dataname, filtered = TRUE)
assign(anl_name, anl_filtered)
anl_name <- dataname
anl <- datasets$get_data(dataname, filtered = TRUE)
assign(anl_name, anl)
teal.code::chunks_reset(chunks = common_stack)
group_var <- input$group_by_var

if (!is.null(selected_vars()) && length(selected_vars()) != ncol(anl_filtered)) {
if (!is.null(selected_vars()) && length(selected_vars()) != ncol(anl)) {
common_stack_push(
id = "ANL_FILTERED_selection_call",
id = "ANL_selection_call",
expression = substitute(
expr = ANL_FILTERED <- anl_name[, selected_vars], # nolint
expr = ANL <- anl_name[, selected_vars], # nolint
env = list(anl_name = as.name(anl_name), selected_vars = selected_vars())
)
)
} else {
common_stack_push(
id = "ANL_FILTERED_call",
expression = substitute(expr = ANL_FILTERED <- anl_name, env = list(anl_name = as.name(anl_name))) # nolint
id = "ANL_call",
expression = substitute(expr = ANL <- anl_name, env = list(anl_name = as.name(anl_name))) # nolint
)
}

if (input$summary_type == "By Variable Levels" && !is.null(group_var) && !(group_var %in% selected_vars())) {
common_stack_push(
id = "ANL_FILTERED_group_var_call",
id = "ANL_group_var_call",
substitute(
expr = ANL_FILTERED[[group_var]] <- anl_name[[group_var]], # nolint
expr = ANL[[group_var]] <- anl_name[[group_var]], # nolint
env = list(group_var = group_var, anl_name = as.name(anl_name))
)
)
Expand Down Expand Up @@ -531,7 +531,7 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
summary_stack_push(
id = "ANL_na_column_call",
expression = substitute(
expr = ANL_FILTERED[[new_col_name]] <- ifelse(rowSums(is.na(ANL_FILTERED)) > 0, NA, FALSE), # nolint
expr = AN[[new_col_name]] <- ifelse(rowSums(is.na(ANL)) > 0, NA, FALSE), # nolint
env = list(new_col_name = new_col_name)
)
)
Expand All @@ -540,7 +540,7 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
summary_stack_push(
id = "analysis_vars_call",
expression = substitute(
expr = analysis_vars <- setdiff(colnames(ANL_FILTERED), data_keys),
expr = analysis_vars <- setdiff(colnames(ANL), data_keys),
env = list(data_keys = data_keys())
)
)
Expand All @@ -551,13 +551,13 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
expr = summary_plot_obs <- data_frame_call[, analysis_vars] %>%
dplyr::summarise_all(list(function(x) sum(is.na(x)))) %>%
tidyr::pivot_longer(tidyselect::everything(), names_to = "col", values_to = "n_na") %>%
dplyr::mutate(n_not_na = nrow(ANL_FILTERED) - n_na) %>%
dplyr::mutate(n_not_na = nrow(ANL) - n_na) %>%
tidyr::pivot_longer(-col, names_to = "isna", values_to = "n") %>%
dplyr::mutate(isna = isna == "n_na", n_pct = n / nrow(ANL_FILTERED) * 100),
dplyr::mutate(isna = isna == "n_na", n_pct = n / nrow(ANL) * 100),
env = list(data_frame_call = if (!inherits(datasets$get_data(dataname, filtered = TRUE), "tbl_df")) {
quote(tibble::as_tibble(ANL_FILTERED))
quote(tibble::as_tibble(ANL))
} else {
quote(ANL_FILTERED)
quote(ANL)
})
)
)
Expand Down Expand Up @@ -646,13 +646,13 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
)
summary_stack_push(
id = "ndistinct_subjects_call",
expression = quote(ndistinct_subjects <- dplyr::n_distinct(ANL_FILTERED[, parent_keys]))
expression = quote(ndistinct_subjects <- dplyr::n_distinct(ANL[, parent_keys]))
)

summary_stack_push(
id = "summary_plot_patients_call",
expression = quote(
summary_plot_patients <- ANL_FILTERED[, c(parent_keys, analysis_vars)] %>%
summary_plot_patients <- ANL[, c(parent_keys, analysis_vars)] %>%
dplyr::group_by_at(parent_keys) %>%
dplyr::summarise_all(anyNA) %>%
tidyr::pivot_longer(cols = !tidyselect::all_of(parent_keys), names_to = "col", values_to = "anyna") %>%
Expand Down Expand Up @@ -762,7 +762,7 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
teal.code::chunks_push(
id = "combination_cutoff_call",
expression = quote({
combination_cutoff <- ANL_FILTERED %>%
combination_cutoff <- ANL %>%
dplyr::mutate_all(is.na) %>%
dplyr::group_by_all() %>%
dplyr::tally() %>%
Expand Down Expand Up @@ -956,8 +956,8 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
# Add common code into this stack
teal.code::chunks_push_chunks(common_code_chunks(), chunks = table_stack)

# extract the ANL_FILTERED dataset for use in further validation
anl_filtered <- teal.code::chunks_get_var("ANL_FILTERED", chunks = table_stack)
# extract the ANL dataset for use in further validation
anl <- teal.code::chunks_get_var("ANL", chunks = table_stack)

validate(need(input$count_type, "Please select type of counts"))
if (!is.null(input$group_by_var)) {
Expand All @@ -969,7 +969,7 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
validate(
need(
is.null(group_var) ||
nrow(unique(anl_filtered[, group_var])) < 100,
nrow(unique(anl[, group_var])) < 100,
"Please select group-by variable with fewer than 100 unique values"
)
)
Expand All @@ -979,10 +979,10 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
vars <- unique(variables_select, group_var)
count_type <- input$count_type # nolint (local variable is assigned and used)

if (!is.null(selected_vars()) && length(selected_vars()) != ncol(anl_filtered)) {
if (!is.null(selected_vars()) && length(selected_vars()) != ncol(anl)) {
variables <- selected_vars() # nolint (local variable is assigned and used)
} else {
variables <- colnames(anl_filtered)
variables <- colnames(anl)
}

summ_fn <- if (input$count_type == "counts") {
Expand All @@ -997,7 +997,7 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
id = "summary_data_call_1",
expression = substitute(
expr = {
summary_data <- ANL_FILTERED %>%
summary_data <- ANL %>%
dplyr::mutate(group_var_name := forcats::fct_explicit_na(as.factor(group_var_name), "NA")) %>%
dplyr::group_by_at(group_var) %>%
dplyr::filter(group_var_name %in% group_vals)
Expand All @@ -1019,11 +1019,11 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
table_stack_push(
id = "summary_data_call_1",
expression = substitute(
expr = summary_data <- ANL_FILTERED %>%
expr = summary_data <- ANL %>%
dplyr::summarise_all(summ_fn) %>%
tidyr::pivot_longer(tidyselect::everything(),
names_to = "Variable",
values_to = paste0("Missing (N=", nrow(ANL_FILTERED), ")")
values_to = paste0("Missing (N=", nrow(ANL), ")")
) %>%
dplyr::mutate(`Variable label` = create_cols_labels(Variable), .after = Variable),
env = list(summ_fn = summ_fn)
Expand Down Expand Up @@ -1065,15 +1065,15 @@ srv_missing_data <- function(id, datasets, reporter, dataname, plot_height, plot
by_subject_stack_push(
id = "analysis_vars_call",
expression = substitute(
expr = analysis_vars <- setdiff(colnames(ANL_FILTERED), data_keys),
expr = analysis_vars <- setdiff(colnames(ANL), data_keys),
env = list(data_keys = data_keys())
)
)

by_subject_stack_push(
id = "summary_plot_patients_call",
expression = quote({
summary_plot_patients <- ANL_FILTERED[, c(parent_keys, analysis_vars)] %>%
summary_plot_patients <- ANL[, c(parent_keys, analysis_vars)] %>%
dplyr::group_by_at(parent_keys) %>%
dplyr::mutate(id = dplyr::cur_group_id()) %>%
dplyr::ungroup() %>%
Expand Down
6 changes: 3 additions & 3 deletions R/tm_outliers.R
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Original file line number Diff line number Diff line change
Expand Up @@ -845,10 +845,10 @@ srv_outliers <- function(id, datasets, reporter, outlier_var,
all_categories <- lapply(
unique(anl[[categorical_var]]),
function(x) {
anl_filtered <- anl %>% dplyr::filter(get(categorical_var) == x)
anl <- anl %>% dplyr::filter(get(categorical_var) == x)
anl_outlier2 <- ANL_OUTLIER %>% dplyr::filter(get(categorical_var) == x)
ecdf_df <- anl_filtered %>%
dplyr::mutate(y = stats::ecdf(anl_filtered[[outlier_var]])(anl_filtered[[outlier_var]]))
ecdf_df <- anl %>%
dplyr::mutate(y = stats::ecdf(anl[[outlier_var]])(anl[[outlier_var]]))

dplyr::left_join(
ecdf_df,
Expand Down