fix lintr checks

episodes/clean-data.Rmd

@@ -127,7 +127,10 @@ Note that, our simulated Ebola does not contain duplicated nor constant rows or
 In addition to the regularities, raw data can contain missing values that may be encoded by different strings, including the empty. To ensure robust analysis, it is a good practice to replace all missing values by `NA` in the entire dataset. Below is a code snippet demonstrating how you can achieve this in `{cleanepi}`:
 
 ```{r}
-sim_ebola_data <- cleanepi::replace_missing_values(sim_ebola_data, na_strings = "")
+sim_ebola_data <- cleanepi::replace_missing_values(
+  data = sim_ebola_data,
+  na_strings = ""
+)
 ```
 
 ### Validating subject IDs
@@ -136,7 +139,7 @@ Each entry in the dataset represents a subject and should be distinguishable by
 
 
 ```{r}
-sim_ebola_data <- 
+sim_ebola_data <-
   cleanepi::check_subject_ids(
     data = sim_ebola_data,
     target_columns = "case_id",

episodes/describe-cases.Rmd

@@ -198,11 +198,11 @@ library("ggplot2")
 library("tracetheme")
 
 # Plot daily incidence data
-base::plot(dialy_incidence_data) + 
+base::plot(dialy_incidence_data) +
   ggplot2::labs(
     x = "Time (in days)",
     y = "Dialy cases"
-  ) + 
+  ) +
   tracetheme::theme_trace()
 ``` 
 
@@ -211,11 +211,11 @@ base::plot(dialy_incidence_data) +
 
 # Plot weekly incidence data
 
-base::plot(weekly_incidence_data) + 
+base::plot(weekly_incidence_data) +
   ggplot2::labs(
     x = "Time (in days)",
     y = "weekly cases"
-  ) + 
+  ) +
   tracetheme::theme_trace()
 ``` 
 

episodes/read-cases.Rmd

@@ -130,7 +130,7 @@ library("RSQLite")
 
 # Create a temporary SQLite database in memory
 db_con <- DBI::dbConnect(
-  drv = RSQLite::SQLite(), 
+  drv = RSQLite::SQLite(),
   dbname = ":memory:"
 )
 

episodes/simple-analysis.Rmd

@@ -60,17 +60,17 @@ df <- base::subset(
 # uses the incidence function from the incidence2 package to compute the
 # incidence data
 df_incid <- incidence2::incidence(
-  df, 
-  date_index = "date", 
+  df,
+  date_index = "date",
   groups = "sex"
 )
 
 # fit a curve to the incidence data. The model chosen is the negative binomial
 # distribution with a significance level (alpha) of 0.05.
-fitted_curve_nb <- 
+fitted_curve_nb <-
   i2extras::fit_curve(
-    df_incid, 
-    model = "negbin", 
+    df_incid,
+    model = "negbin",
     alpha = 0.05
   )
 
@@ -89,13 +89,13 @@ Repeat the above analysis using Poisson distribution?
 :::::::::::::::::::::::: solution 
 
 ```{r, warning=FALSE, message=FALSE}
-fitted_curve_poisson <- 
+fitted_curve_poisson <-
   i2extras::fit_curve(
     x = df_incid,
     model = "poisson",
     alpha = 0.05
   )
-  
+
 base::plot(fitted_curve_poisson, angle = 45) +
   ggplot2::labs(x = "Date", y = "Cases")
 ```