Merge pull request #18 from opensafely/update-report

Update report

analysis/descriptive/coverage_report_data.R

@@ -285,8 +285,7 @@ plot_data_treated_groups <- data_processed_clean %>%
   summarise(n = sum(n, na.rm = T)) %>%
   group_by(high_risk_cohort, treatment_type) %>%
   mutate(count = ifelse(is.na(n), 0, n),
-         cum_count = cumsum(count),
-         cum_count_redacted =  plyr::round_any(cum_count, 10)) %>%
+         count_redacted =  plyr::round_any(count, 10)) %>%
   select(-n) %>%
   arrange(high_risk_cohort, treatment_type, week)
 
@@ -302,8 +301,7 @@ plot_data_treated_all <- data_processed_clean %>%
   group_by(treatment_type, week) %>%  
   tally() %>%
   mutate(count = ifelse(is.na(n), 0, n),
-         cum_count = cumsum(count),
-         cum_count_redacted =  plyr::round_any(cum_count, 10),
+         count_redacted =  plyr::round_any(count, 10),
          high_risk_cohort = "All") %>%
   select(-n) %>%
   arrange(high_risk_cohort, treatment_type, week) %>%
@@ -327,8 +325,7 @@ plot_data_groups <- data_processed_clean %>%
   summarise(n = sum(n, na.rm = T)) %>%
   group_by(high_risk_cohort) %>%
   mutate(count = ifelse(is.na(n), 0, n),
-         cum_count = cumsum(count),
-         cum_count_redacted =  plyr::round_any(cum_count, 10)) %>%
+         count_redacted =  plyr::round_any(count, 10)) %>%
   select(-n) %>%
   arrange(high_risk_cohort, week)
 
@@ -341,8 +338,7 @@ plot_data_all <- data_processed_clean %>%
   group_by(week) %>%  
   tally() %>%
   mutate(count = ifelse(is.na(n), 0, n),
-         cum_count = cumsum(count),
-         cum_count_redacted =  plyr::round_any(cum_count, 10),
+         count_redacted =  plyr::round_any(count, 10),
          high_risk_cohort = "All") %>%
   select(-n) %>%
   arrange(high_risk_cohort, week) %>%
@@ -353,10 +349,10 @@ print(head(plot_data_all))
 plot_data_prop_treated <- left_join(plot_data_treated_all, plot_data_all, 
                                     by = c("high_risk_cohort", "week")) %>%
   mutate(week = as.Date(week) - 2,
-         Treated = ifelse(is.na(cum_count_redacted.x), 0, cum_count_redacted.x),
-         Total = ifelse(is.na(cum_count_redacted.y), 0, cum_count_redacted.y),
+         Treated = ifelse(is.na(count_redacted.x), 0, count_redacted.x),
+         Total = ifelse(is.na(count_redacted.y), 0, count_redacted.y),
          prop = Treated/Total,
-         prop_redacted = ifelse((Total < threshold | Treated < threshold), NA, round(prop, digits = 2)))
+         prop_redacted = ifelse((Total < threshold | Treated < threshold), NA, round(prop, digits = 4)))
 
 write_csv(plot_data_prop_treated %>% select(elig_start = week, prop_redacted, treatment_type, high_risk_cohort), 
           fs::path(output_dir, "table_prop_treated_redacted.csv"))
@@ -549,7 +545,7 @@ data_flowchart <- non_elig_treated %>%
     values_to="n"
   )  %>%
   mutate(n = ifelse(n < 5 & n > 0, "<5", n),
-         n = ifelse(n != "<5", plyr::round_any(as.numeric(n), 5), n))
+         n = ifelse(n != "<5", plyr::round_any(as.numeric(n), 10), n))
 
 all_treated <-  data_processed_clean %>%
   filter(!is.na(treatment_date),
@@ -621,7 +617,7 @@ data_flowchart2 <- all_treated %>%
     values_to="n"
   )  %>%
   mutate(n = ifelse(n < 5 & n > 0, "<5", n),
-         n = ifelse(n != "<5", plyr::round_any(as.numeric(n), 5), n))
+         n = ifelse(n != "<5", plyr::round_any(as.numeric(n), 10), n))
 
 write_csv(rbind(data_flowchart, data_flowchart2), fs::path(output_dir, "table_non_elig_flowchart_redacted.csv"))
 
@@ -639,7 +635,7 @@ high_risk_cohort_comparison_redacted <- data_processed_clean %>%
   tally() %>%
   arrange(desc(n)) %>%
   mutate(n = ifelse(n < 5, NA, n),
-         n = plyr::round_any(as.numeric(n), 5))
+         n = plyr::round_any(as.numeric(n), 10))
 
 write_csv(high_risk_cohort_comparison_redacted, fs::path(output_dir, "table_non_elig_high_risk_cohort_comparison_redacted.csv"))
 
@@ -652,7 +648,7 @@ all <- data_processed_clean %>%
   tally() %>%
   mutate(high_risk_cohort = "All",
          n = ifelse(n < 5, NA, n),
-         n = plyr::round_any(as.numeric(n), 5)) %>%
+         n = plyr::round_any(as.numeric(n), 10)) %>%
   filter(!is.na(n))
 
 groups <- data_processed_clean %>%
@@ -672,7 +668,7 @@ groups <- data_processed_clean %>%
     values_to = "n"
   ) %>%
   mutate(n = ifelse(n < 5, NA, n),
-         n = plyr::round_any(as.numeric(n), 5)) %>%
+         n = plyr::round_any(as.numeric(n), 10)) %>%
   filter(!is.na(n))
 
 groups2 <- data_processed_clean %>%
@@ -692,7 +688,7 @@ groups2 <- data_processed_clean %>%
     values_to = "n"
   ) %>%
   mutate(n = ifelse(n < 5, NA, n),
-         n = plyr::round_any(as.numeric(n), 5),
+         n = plyr::round_any(as.numeric(n), 10),
          variable = group) %>%
   filter(!is.na(n))
 
@@ -708,7 +704,7 @@ for (i in 1:length(groups_tte)) {
     group_by_all() %>% 
     tally() %>%
     mutate(n = ifelse(n < 5, NA, n),
-           n = plyr::round_any(as.numeric(n), 5),
+           n = plyr::round_any(as.numeric(n), 10),
            group = groups_tte[i]) %>%
     filter(!is.na(n))
 
@@ -745,7 +741,7 @@ all <- data_processed_clean %>%
     values_to = "Count"
   ) %>%
   mutate(Count = ifelse(Count < 5, NA, Count),
-         Count = plyr::round_any(as.numeric(Count), 5)) %>%
+         Count = plyr::round_any(as.numeric(Count), 10)) %>%
   pivot_wider(names_from = treated_status, values_from = Count) %>%
   mutate(treated_tot = data_processed_clean %>% filter(!is.na(treatment_type)) %>% nrow(),
          treated_perc = round(treated/treated_tot*100, digits = 0),

project.yaml

@@ -48,7 +48,6 @@ actions:
       moderately_sensitive:
         html: output/coverage/sa_symptomatic_test.html
 
-
   # Report ----
   report_data:
     run: r:latest analysis/descriptive/coverage_report_data.R