fix: resolved errors in the new get-rm-stats and tidy-env

Author: sidohaakma

R/get-rm-stats.R

@@ -9,7 +9,7 @@
 #' @param id_var name of id variable in df
 #' @param age_var name of age variable in df
 #' @param conns connection object for DataSHIELD backends
-#' 
+#'
 #' @return a tibble containing the following columns:
 #'
 #' min_age: 5th percentile of age
@@ -23,7 +23,7 @@
 #' @export
 dh.getRmStats <- function(df = NULL, outcome = NULL, id_var = NULL, age_var = NULL, conns = NULL) {
   variable <- perc_5 <- perc_95 <- cohort <- min_age <- max_age <- valid_n <- NULL
-  
+
   if (is.null(df)) {
     stop("Please provide the name of a datashield dataframe")
   }
@@ -47,7 +47,7 @@ dh.getRmStats <- function(df = NULL, outcome = NULL, id_var = NULL, age_var = NU
   ## ---- First get overall stats for some of the easy ones -------------------------------------------
   stats <- dh.getStats(
     df = df,
-    vars = c(outcome, age_var), 
+    vars = c(outcome, age_var),
     conns = conns
   )
 
@@ -77,7 +77,7 @@ dh.getRmStats <- function(df = NULL, outcome = NULL, id_var = NULL, age_var = NU
     X.name = "data$weight",
     INDEX.names = "id_fact",
     FUN.name = "N",
-    newobj = "id_summary", 
+    newobj = "id_summary",
     datasources = conns
   )
 
@@ -87,49 +87,49 @@ dh.getRmStats <- function(df = NULL, outcome = NULL, id_var = NULL, age_var = NU
     mutate(combined = rowSums(.)) %>%
     pivot_longer(
       cols = everything(),
-      names_to = "cohort", 
+      names_to = "cohort",
       values_to = "n_participants"
     )
-    
-    ## ---- Median number of weight measurements per child ----------------------------------------
 
-  # We can use the ds.quantileMean function with the object we created above to get the 
+  ## ---- Median number of weight measurements per child ----------------------------------------
+
+  # We can use the ds.quantileMean function with the object we created above to get the
   # median number of measurements per child.
 
   ds.asNumeric("id_summary$N", "id_summary_num", datasources = conns)
 
-quants <- DSI::datashield.aggregate(conns, as.symbol("quantileMeanDS(id_summary_num)"))
+  quants <- DSI::datashield.aggregate(conns, as.symbol("quantileMeanDS(id_summary_num)"))
 
   weight_med_iqr <- quants %>%
-  bind_rows(.id = "cohort") %>%
-  select(cohort, "5%", "50%", "95%") %>%
-  rename(n_meas_med = "50%", n_meas_5 = "5%", n_meas_95 = "95%")
+    bind_rows(.id = "cohort") %>%
+    select(cohort, "5%", "50%", "95%") %>%
+    rename(n_meas_med = "50%", n_meas_5 = "5%", n_meas_95 = "95%")
 
   ## Get the combined version using weighted sum
-  lengths <- DSI::datashield.aggregate(conns, call('lengthDS', "id_summary_num"))
+  lengths <- DSI::datashield.aggregate(conns, call("lengthDS", "id_summary_num"))
   numNAs <- DSI::datashield.aggregate(conns, "numNaDS(id_summary_num)")
-  
-valid_n <- list(lengths, numNAs) %>% pmap(~.x - .y)
 
-weights <- unlist(valid_n) / sum(unlist(valid_n))
+  valid_n <- list(lengths, numNAs) %>% pmap(~ .x - .y)
 
-weighted_quant <- list(quants, weights) %>% pmap(~.x * .y) 
+  weights <- unlist(valid_n) / sum(unlist(valid_n))
 
-sum_quant <- weighted_quant %>%
-pmap(function(...){
-  sum(c(...))
-}) %>%
-bind_rows %>%
-rename(n_meas_med = "50%", n_meas_5 = "5%", n_meas_95 = "95%") %>%
-mutate(cohort = "combined") %>%
-select(cohort, n_meas_med, n_meas_5, n_meas_95)
+  weighted_quant <- list(quants, weights) %>% pmap(~ .x * .y)
+
+  sum_quant <- weighted_quant %>%
+    pmap(function(...) {
+      sum(c(...))
+    }) %>%
+    bind_rows() %>%
+    rename(n_meas_med = "50%", n_meas_5 = "5%", n_meas_95 = "95%") %>%
+    mutate(cohort = "combined") %>%
+    select(cohort, n_meas_med, n_meas_5, n_meas_95)
 
-quant_out <- bind_rows(weight_med_iqr, sum_quant)
+  quant_out <- bind_rows(weight_med_iqr, sum_quant)
 
-## ---- Create final output -------------------------------------------------------------------
-   out <- left_join(age_ranges, outcome_n, by = "cohort") %>%
-   left_join(., n_subjects, by = "cohort") %>%
-   left_join(., quant_out, by = "cohort")
+  ## ---- Create final output -------------------------------------------------------------------
+  out <- left_join(age_ranges, outcome_n, by = "cohort") %>%
+    left_join(., n_subjects, by = "cohort") %>%
+    left_join(., quant_out, by = "cohort")
 
-return(out)
+  return(out)
 }

R/tidy-env.R

@@ -17,7 +17,7 @@
 #'
 #' @export
 dh.tidyEnv <- function(obj = NULL, type = c("remove", "keep"), conns = NULL) {
-  . <- NULL
+  . <- n_meas_5 <- n_meas_95 <- n_meas_med <- NULL
 
   if (is.null(obj)) {
     stop("Please specify one or more objects to remove from environment")
@@ -62,7 +62,7 @@ dh.tidyEnv <- function(obj = NULL, type = c("remove", "keep"), conns = NULL) {
       bind_rows()
 
     vars_tibble %>% pmap(function(cohort, value) {
-      ds.rm(x.name = value, datasources = conns[cohort])
+      ds.rm(x.names = value, datasources = conns[cohort])
     })
   }
 }