high res png files

01-software-modeling.Rmd

@@ -65,7 +65,7 @@ For example, large scale measurements of RNA have been possible for some time us
 
 An early method for evaluating such issues were probe-level models, or PLMs [@bolstad2004]. A statistical model would be created that accounted for the known differences in the data, such as the chip, the RNA sequence, the type of sequence, and so on. If there were other, unknown factors in the data, these effects would be captured in the model residuals. When the residuals were plotted by their location on the chip, a good quality chip would show no patterns. When a problem did occur, some sort of spatial pattern would be discernible. Often the type of pattern would suggest the underlying issue (e.g., a fingerprint) and a possible solution (wipe off the chip and rescan, repeat the sample, etc.). Figure \@ref(fig:software-descr-examples)(a) shows an application of this method for two microarrays taken from @Gentleman2005. The images show two different color values; areas that are darker are where the signal intensity was larger than the model expects while the lighter color shows lower than expected values. The left-hand panel demonstrates a fairly random pattern while the right-hand panel exhibits an undesirable artifact in the middle of the chip. 
 
-```{r software-descr-examples, echo = FALSE, fig.cap = "Two examples of how descriptive models can be used to illustrate specific patterns", out.width = '80%', dev = "png", fig.height = 8, warning = FALSE, message = FALSE}
+```{r software-descr-examples, echo = FALSE, fig.cap = "Two examples of how descriptive models can be used to illustrate specific patterns", out.width = '80%', fig.height = 8, warning = FALSE, message = FALSE}
 load("RData/plm_resids.RData")
 
 resid_cols <- RColorBrewer::brewer.pal(8, "Set1")[1:2]

16-dimensionality-reduction.Rmd

@@ -317,7 +317,6 @@ bean_rec_trained %>%
 ```
 
 ```{r bean-pca, ref.label = "dimensionality-pca"}
-#| dev = "png", 
 #| echo = FALSE,
 #| fig.height = 7,
 #| fig.cap = "First two principal component scores for the bean validation set, colored by class",
@@ -360,7 +359,6 @@ bean_rec_trained %>%
 ```
 
 ```{r bean-pls, ref.label = "dimensionality-pls"}
-#| dev = "png", 
 #| fig.height = 7,
 #| echo = FALSE,
 #| fig.cap = "First two PLS component scores for the bean validation set, colored by class",
@@ -398,7 +396,6 @@ bean_rec_trained %>%
 ```
 
 ```{r bean-ica, ref.label = "dimensionality-ica"}
-#| dev = "png", 
 #| echo = FALSE,
 #| fig.height = 7,
 #| fig.cap = "First two ICA component scores for the bean validation set, colored by class",
@@ -436,7 +433,6 @@ bean_rec_trained %>%
 
 ```{r bean-umap}
 #| echo = FALSE,
-#| dev = "png",
 #| fig.height = 5,
 #| fig.width = 10.1,
 #| fig.cap = "The first two UMAP component scores for the bean validation set, colored by class. Results are shown for supervised and unsupervised versions.",

DESCRIPTION

@@ -62,6 +62,7 @@ Imports:
     probably,
     pscl,
     purrr,
+    ragg,
     ranger,
     recipes (>= 0.1.16),
     rlang,
@@ -88,7 +89,6 @@ Imports:
     xgboost,
     yardstick
 Remotes:
-    tidymodels/censored,
     tidymodels/learntidymodels
 biocViews: mixOmics
 Encoding: UTF-8

_common.R

@@ -3,11 +3,17 @@ options(dplyr.print_min = 6, dplyr.print_max = 6)
 options(cli.width = 85)
 options(crayon.enabled = FALSE)
 
+library(ragg)
+
 knitr::opts_chunk$set(
   comment = "#>",
   collapse = TRUE,
   fig.align = 'center',
-  tidy = FALSE
+  tidy = FALSE,
+  # see https://www.tidyverse.org/blog/2020/08/taking-control-of-plot-scaling/#the-solution
+  dev = "agg_png",
+  dev.args = list(res = 300, units = "in"),
+  fig.ext = "png"
 )
 
 

extras/ames_sf.R

@@ -120,8 +120,7 @@ plain_ames <-
   ggplot() +
   xlim(ames_x) +
   ylim(ames_y) + 
-  theme_vo
-<id() + 
+  theme_void() + 
   theme(legend.position = "none") +
   geom_sf(data = ia_roads, aes(geometry = geometry), alpha = .1) +
   geom_point(
@@ -251,7 +250,7 @@ mitchell <-
 # mitchell_box +  plot_spacer() + mitchell +  plot_layout(widths = c(2, 0.1, 3))
 
 # guide inset in plot
-agg_png("mitchell.png", width = 480 * mitchell_ratio * 2, height = 480 * 2, res = 200)
+agg_png("mitchell.png", width = 480 * mitchell_ratio * 3, height = 480 * 3, res = 300, scaling = 1)
 print(mitchell)
 print(mitchell_box, vp = viewport(0.8, 0.27, width = 0.3 * ames_ratio, height = 0.3))
 dev.off()
@@ -317,7 +316,7 @@ timberland <-
   scale_shape_manual(values = ames_pch)
 
 # guide inset in plot
-agg_png("timberland.png", width = 480 * timberland_ratio)
+agg_png("timberland.png", width = 480 * timberland_ratio, res = 300, scaling = 1/3)
 print(timberland)
 print(timberland_box, vp = viewport(0.85, 0.2, width = 0.3 * ames_ratio, height = 0.3))
 dev.off()
@@ -378,7 +377,7 @@ dot_rr <-
   ) 
 
 # guide inset in plot
-agg_png("dot_rr.png", width = 480 * dot_rr_ratio)
+agg_png("dot_rr.png", width = 480 * dot_rr_ratio, res = 300, scaling = 1/3)
 print(dot_rr)
 print(dot_rr_box, vp = viewport(0.5, 0.26, width = 0.45 * ames_ratio, height = 0.45))
 dev.off()
@@ -440,7 +439,7 @@ crawford <-
   ) 
 
 # guide inset in plot
-agg_png("crawford.png", width = 480 * crawford_ratio)
+agg_png("crawford.png", width = 480 * crawford_ratio, res = 300, scaling = 1/3)
 print(crawford)
 print(crawford_box, vp = viewport(0.5, 0.2, width = 0.35 * ames_ratio, height = 0.35))
 dev.off()
@@ -523,7 +522,7 @@ northridge <-
 # northridge_box +  plot_spacer() + northridge +  plot_layout(widths = c(2, 0.1, 3))
 
 # guide inset in plot
-agg_png("northridge.png", width = 480 * northridge_ratio)
+agg_png("northridge.png", width = 480 * northridge_ratio, res = 300, scaling = 1/3)
 print(northridge)
 print(northridge_box, vp = viewport(0.85, 0.21, width = 0.35 * ames_ratio, height = 0.35))
 dev.off()

extras/iowa_highway.prj

@@ -0,0 +1 @@
+GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],TOWGS84[0,0,0,0,0,0,0],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.01745329251994328,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]