add sectioning

episodes/clean-data.Rmd

@@ -100,9 +100,8 @@ sim_ebola_data$case_id <- as.character(sim_ebola_data$case_id)
 ```
 
 ```{r}
-sim_ebola_data <- cleanepi::check_subject_ids(sim_ebola_data, 
-                                              target_columns = "case_id",
-                                              range = c(0, 15000))
+sim_ebola_data <- cleanepi::check_subject_ids(sim_ebola_data,
+                                              target_columns = "case_id")
 ```
 
 Note that our simulated  dataset does contain duplicated subject IDS.
@@ -112,11 +111,9 @@ Note that our simulated  dataset does contain duplicated subject IDS.
 Certainly an epidemic dataset contains date columns for different events, such as the date of infection, date of symptoms onset, ..etc, and these dates can come in different date forms, and it good practice to unify them. The `{cleanepi}` package provides functionality for converting date columns in epidemic datasets into ISO format, ensuring consistency across the different date columns. Here's how you can use it on our simulated dataset:
 
 ```{r}
-sim_ebola_data <- cleanepi::standardize_dates(
-  sim_ebola_data, 
-  target_columns = c("date_onset", "date_sample")
-)
-
+sim_ebola_data <- cleanepi::standardize_dates(sim_ebola_data,
+                                              target_columns = c("date_onset",
+                                                                 "date_sample"))
 utils::head(sim_ebola_data)
 ```
 
@@ -129,15 +126,13 @@ The `convert_to_numeric()` function in `{cleanepi}` does such conversion as illu
 Note that this function makes call of functions from the `{numberize}` package.
 
 ```{r}
-sim_ebola_data <- cleanepi::convert_to_numeric(sim_ebola_data, 
+sim_ebola_data <- cleanepi::convert_to_numeric(sim_ebola_data,
                                                target_columns = "age")
 utils::head(sim_ebola_data)
 ```
 
 ## Epidemiology related operations
-
-In addition to common data cleansing tasks, such as those discussed in the above section, the `{cleanepi}` package offers additional functionalities tailored specifically for processing and analyzing outbreak and epidemic data. This section covers some of these specialized tasks.
-
+In addition to common data cleansing tasks, such as those discussed in the previous section, the {cleanepi} package offers additional functionalities tailored specifically for processing and analyzing outbreak and epidemic data. This section covers some of these specialized tasks.
 ### Dictionary-based substitution
 
 ### Calculating age at different time scales