Some small changes to link in the tutorial

vignettes/data_processing.Rmd

@@ -17,7 +17,7 @@ vignette: >
 
 In this tutorial, we will provide a short tutorial about data pre-processing, which involves transforming raw data into the desired format and object for running the Bayesian Hierarchical Model (BHM) in the "fdmr" package. To illustrate the process, we will use COVID-19 infection data as a practical example.
 
-In the COVID-19 tutorial [https://4dmodeller.github.io/fdmr/articles/covid.html](https://4dmodeller.github.io/fdmr/articles/covid.html), we aim to fit a Bayesian spatio-temporal model to predict the COVID-19 infection rates across mainland England over space and time, and investigate the impacts of socioeconomic, demographic and environmental factors on COVID-19 infection. The study region is mainland England, which is partitioned into 6789 Middle Layer Super Output Areas (MSOAs). The raw shapefile of the study region is obtained from [https://geoportal.statistics.gov.uk/datasets](https://geoportal.statistics.gov.uk/datasets/ons::msoa-dec-2011-boundaries-super-generalised-clipped-bsc-ew-v3/explore?location=52.782096%2C-2.465779%2C7.81), which stores the location, shape and attributes of geographic features for the MSOAs.
+In the [COVID-19 tutorial](https://4dmodeller.github.io/fdmr/articles/covid.html), we aim to fit a Bayesian spatio-temporal model to predict the COVID-19 infection rates across mainland England over space and time, and investigate the impacts of socioeconomic, demographic and environmental factors on COVID-19 infection. The study region is mainland England, which is partitioned into 6789 Middle Layer Super Output Areas (MSOAs). The raw shapefile of the study region is obtained from the [ONS Open Geography Portal](https://geoportal.statistics.gov.uk/datasets/ons::msoa-dec-2011-boundaries-super-generalised-clipped-bsc-ew-v3/explore?location=52.782096%2C-2.465779%2C7.81), which stores the location, shape and attributes of geographic features for the MSOAs.
 
 First we load in the shapefile into R, and store it in an object named "sp_data".
 
@@ -38,13 +38,13 @@ proj4string(sp_data)
 sp_data <- sp::spTransform(sp_data, CRS("+proj=longlat +datum=WGS84 +no_defs"))
 ```
 
-In the COVID-19 tutorial, the raw COVID-19 infections data and the related covariate data are obtained from the official UK Government COVID-19 dashboard and the Office for National Statistics (ONS). The data were initially downloaded in a CSV file format, which can be easily imported into R using the "utils::read.csv()" function. For convenience, we saved the raw data in a RDS file. We'll load in the data using the `load_tutorial_data` function. 
+In the COVID-19 tutorial, the raw COVID-19 infections data and the related covariate data are obtained from the official UK Government COVID-19 dashboard and the Office for National Statistics (ONS). The data were initially downloaded in a CSV file format, which can be easily imported into R using the `utils::read.csv()` function. For convenience, we saved the raw data in an RDS file. We'll load in the data using the `load_tutorial_data` function. 
 
 ```{r loadCOVIDat}
 covid19_data <- fdmr::load_tutorial_data(dataset = "covid_preprocess", filename = "covid19_data.rds")
 ```
 
-The type of the object "covid19_data" is a "data.frame". 
+The type of the object "covid19_data" is a `data.frame`. 
 
 
 ```{r classdat}