vignette 2

README.md

@@ -8,7 +8,7 @@
 
 # tectonicr
 
-**tectonicr** is a free and open-source **R** package for modeling and analyzing the direction of the maximum horizontal stress (SHmax) based on the empirical link between the direction of intraplate stress and the direction of the relative motion of neighboring plates (Wdowinski, 1998; Stephan et al., 2023). The following methods are available:
+`tectonicr` is a free and open-source **R** package for modeling and analyzing the direction of the maximum horizontal stress (SHmax) based on the empirical link between the direction of intraplate stress and the direction of the relative motion of neighboring plates (Wdowinski, 1998; Stephan et al., 2023). The following methods are available:
 
 - **Direction of the plate boundary forces**: `PoR_shmax()` gives the predicted stress field adjacent to a plate boundary, calculated using the relative plate motion of the neighboring plates using the function `model_shmax()`. The goodness-of-fit can be statistically tested by e.g. `norm_chisq()`, `circular_dispersion()` ,`rayleigh_test()`, and `confidence_interval()`.
 - **Distance to plate boundary**: `distance_from_pb()` gives the distance between the stress data point and the plate boundary measured along the stress trajectories.
@@ -21,14 +21,14 @@
 
 ## Prerequisites
 You must have R installed on your system (see http://r-project.org). To install 
-**tectonicr** from CRAN, type the following code at the R command line prompt:
+`tectonicr` from CRAN, type the following code at the R command line prompt:
 
 ```
 install.packages("tectonicr")
 ```
 
 ## Installation
-The most recent development version of **tectonicr** is available from Github 
+The most recent development version of `tectonicr` is available from Github 
 and can be installed on your system as follows:
 
 ```
@@ -75,7 +75,7 @@ found a bug, please file it
 [here](https://github.com/tobiste/tectonicr/issues) with minimal code to
 reproduce the issue.
 
-## How to cite `tectonicr`
+## How to cite tectonicr
 When referencing this package, please cite 
 
 Stephan, T., Enkelmann, E., and Kroner, U. (2023). Analyzing the horizontal orientation of the crustal stress adjacent to plate boundaries. *Scientific Reports*, *13*(1). DOI: [10.1038/s41598-023-42433-2](https://doi.org/10.1038/s41598-023-42433-2).

vignettes/E_interpolation.Rmd

@@ -14,25 +14,22 @@ editor_options:
     wrap: 72
 ---
 
-# ```{r, include = FALSE}
-# knitr::opts_chunk$set(
-#   collapse = TRUE,
-#   comment = "#>"
-# )
-# ```
-
-```{r, child="children/chunk_options.txt"}
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+ collapse = TRUE,
+ comment = "#>"
+)
 ```
 
 This vignette teaches you how to spatially interpolate stress fields and 
 display the lateral patterns of stress anomalies.
 
-```{r setup, echo=TRUE}
+```{r setup}
 library(tectonicr)
 library(ggplot2) # load ggplot library
 ```
 
-```{r load_data, echo=TRUE}
+```{r load_data}
 data("san_andreas")
 
 data("cpm_models")
@@ -48,15 +45,15 @@ Spatial interpolation of stress data is based on the aforementioned metrics (the
 algorithm is a modified version of the MATLAB script 'stress2grid' by Ziegler 
 and Heidbach (2017).
 
-```{r interpolation, echo=TRUE}
+```{r interpolation}
 mean_SH <- stress2grid(san_andreas, gridsize = 1, R_range = seq(50, 350, 100))
 ```
 
 The default settings apply quality and inverse distance weighting of the mean, 
 as well as a 25% cut-off for the standard deviation. 
 
 The data can now be visualized:  
-```{r plot, echo=TRUE, warning=FALSE, message=FALSE, eval=FALSE}
+```{r plot, warning=FALSE, message=FALSE}
 trajectories <- eulerpole_loxodromes(x = por, n = 40, cw = FALSE)
 ggplot(mean_SH) +
   borders(fill = "grey80") +
@@ -72,7 +69,7 @@ ggplot(mean_SH) +
   ) +
   facet_wrap(~R)
 ```
-![](interpolation.png)
+<!--  ![](interpolation.png) -->
 
 ### PoR coordinate system
 
@@ -83,11 +80,11 @@ constant no matter the distance between the data points. Assuming that the
 stress field is sourced by the plate boundary force, the model-based 
 interpolation allows more reliable results for areas close to plate boundaries.
 
-```{r interpolation_PoR, eval=TRUE}
+```{r interpolation_PoR}
 mean_SH_PoR <- PoR_stress2grid(san_andreas, PoR = por, gridsize = 1, R_range = seq(50, 350, 100))
 ```
 
-```{r plot2, echo=TRUE, warning=FALSE, message=FALSE, eval=FALSE}
+```{r plot2, warning=FALSE, message=FALSE}
 ggplot(mean_SH_PoR) +
   borders(fill = "grey80") +
   geom_sf(data = trajectories, lty = 2) +
@@ -102,7 +99,7 @@ ggplot(mean_SH_PoR) +
   ) +
   facet_wrap(~R)
 ```
-![](interpolation_PoR.png)
+<!--  ![](interpolation_PoR.png) -->
 
 ## Rasterize the interpolation
 
@@ -112,7 +109,7 @@ was performed in the PoR CRS, the interpolated azimuths are additionally given
 in the transformed azimuths. This allows to easily calculate misfits from 
 predicted directions:
 
-```{r compact, echo = TRUE}
+```{r compact}
 mean_SH_PoR_reduced <- mean_SH_PoR |>
   compact_grid() |>
   dplyr::mutate(cdist = circular_distance(azi.PoR, 135))
@@ -125,7 +122,7 @@ the grid is not composed of equally spaced grid cells in the geographic CRS. To
 rasterize such grids, we can, e.g., use Voronoi cells from the `ggforce` 
 package.
 
-```{r voronoi, echo=TRUE, warning=FALSE, message=FALSE, eval=FALSE}
+```{r voronoi, warning=FALSE, message=FALSE}
 ggplot(mean_SH_PoR_reduced) +
   ggforce::geom_voronoi_tile(
     aes(lon, lat, fill = cdist),
@@ -141,7 +138,8 @@ ggplot(mean_SH_PoR_reduced) +
   scale_alpha("Standard deviation", range = c(1, .25)) +
   coord_sf(xlim = range(san_andreas$lon), ylim = range(san_andreas$lat))
 ```
-![](interpolation_voronoi.png)
+<!-- ![](interpolation_voronoi.png) -->
+
 The map highlights **stress anomalies** which show misfits to the direction of 
 tested plate boundary force.
 
@@ -156,14 +154,14 @@ dispersion (`compact_grid(x, 'dispersion')`).
 > It is recommended to calculate the kernel dispersion on PoR transformed data to 
 avoid angle distortions due to projections.
 
-```{r kernel_disp, fig.width = small_width, fig.height = small_height}
+```{r kernel_disp}
 san_andreas_por <- san_andreas 
 san_andreas_por$azi <- PoR_shmax(san_andreas, por, "right")$azi.PoR
 san_andreas_por$prd <- 135
 san_andreas_kdisp <- kernel_dispersion(san_andreas_por, gridsize = 1, R_range = seq(50, 350, 100))
 san_andreas_kdisp <- compact_grid(san_andreas_kdisp, 'dispersion')
 
-ggplot(san_andreas_kdisp) +
+p <- ggplot(san_andreas_kdisp) +
   ggforce::geom_voronoi_tile(
     aes(lon, lat, fill = stat),
     max.radius = .7, normalize = FALSE
@@ -177,6 +175,7 @@ ggplot(san_andreas_kdisp) +
   ) +
   scale_alpha("Standard deviation", range = c(1, .25)) +
   coord_sf(xlim = range(san_andreas$lon), ylim = range(san_andreas$lat))
+print(p)
 ```