markdown source builds

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Source  : 54fc9ab
Branch  : main
Author  : Christian Knudsen <christian.knudsen@gmail.com>
Time    : 2025-03-07 11:07:09 +0000
Message : Merge pull request #47 from chrbknudsen/main

så er vi vist tæt på målet

Author: actions-user

fig/theming-rendered-background-1.png

@@ -1,7 +1,7 @@
 "file" "checksum" "built" "date"
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@@ -10,6 +10,9 @@
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+"episodes/saving.Rmd" "1f0fb37b3a3459f098b42dba856797bb" "site/built/saving.md" "2025-03-07"
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fig/theming-rendered-theme_bw-1.png

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+---
+title: "Saving and exporting"
+teaching: 5
+exercises: 0
+---
+
+::::questions
+- "How can I save the plots?"
+::::
+
+::::objectives
+- "Learn how to save your plots in different formats"
+::::
+
+
+
+
+
+
+## It would be nice to be able to save the plot.
+
+Saving a plot can be done directly from the plot pane in
+RStudio
+![Saving from Rstudio](../fig/08-saving-plot.png)
+
+
+ggplot2 also includes a function for saving the last plot
+you made. This function will save it as "myimage.png" in your
+current directory. The image will be 800x600 pixels (px) in size, and with a resolution of 300 dpi.
+
+
+``` r
+ggsave("myimage.png", width = 800, 
+       height = 600, units = "px",
+       dpi = 300)
+```
+
+However, this does not look very nice:
+![Nasty looking](../fig/myimage.png)
+The points are too big for the plot!
+
+Be prepared for a lot of fiddling about with your plots if
+you want to use `ggsave()`. 
+
+Adjusting size, and getting af nice image is often easier 
+adjusting the size of the plot pane directly in RStudio. 
+
+The saved image will reflect what you see on the screen.
+
+## There is more than JPG and PNG in the world!
+JPG is a popular format for saving images. It produces nice,
+small files. PNG is also a popular format for images.
+
+By default, ggsave is able to recognize the extension you
+give your file name (.png in the example above), and save 
+to these formats:
+
+* eps
+* ps
+* tex
+* pdf
+* jpeg/jpg
+* tiff
+* png
+* bmp
+* svg
+* wmf (only on windows)
+
+We would like to recommend the "svg" format. That format is a "vector-based" format
+that you can scale to any size you want.
+
+::::keypoints:
+- "The easiest way to adjust the size of your saved plots is by adjusting the plot window in RStudio"
+::::

fig/theming-rendered-theming-1.png

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+---
+title: "Theming"
+teaching: 10
+exercises: 5
+---
+
+:::: questions
+- "How can I make the plot look good?"
+- "How do I get rid of that grey background?"
+- "How do I get rid of the gridlines?"
+::::
+
+::::objectives
+- "Learn to use different themes"
+- "Learn to adjust the appearance of specific parts of the plot"
+::::
+
+
+
+
+
+### The THEME_FUNCTIONs
+
+Every part of the plot can be changed. The grey background might be annoying
+The gridlines might be confusing.
+
+These non-data components of the plots can be controlled using the family of `theme`
+functions:
+
+
+``` r
+ggplot(diamonds, aes(carat, price)) +
+  geom_point() +
+  theme_bw()
+```
+
+<img src="fig/theming-rendered-theme_bw-1.png" style="display: block; margin: auto;" />
+More exists:
+
+``` error
+Error in default | bw: operations are possible only for numeric, logical or complex types
+```
+
+:::: callout
+## Notice the pattern?
+
+A general pattern of function names in ggplot2 can be seen.
+
+Themes are named "theme_" and then the name of the theme. We saw the
+same pattern with the scale functions: "scale_" and then the axis, followed
+by what we did to the axis, eg: "scale_y_log10"
+
+::::
+
+### Even more theming
+
+*Every* element in the plot can be controlled. The `theme()`  function is the
+way to do that:
+
+
+``` r
+diamonds %>% 
+  ggplot(aes(carat, price)) +
+  geom_point() +
+  theme_dark() +
+  theme(axis.text.y = element_text(angle= 45))
+```
+
+<img src="fig/theming-rendered-theming-1.png" style="display: block; margin: auto;" />
+
+Angling the labels in a plot can be good for readability. However the actual
+way to do it can be a bit more involved as you see above. Read the help for
+theme to get at complete list of things that can be changed. There are 97 
+things in total.
+
+Also note, that we can add theming on top of previous theming. Here we begin
+with a built-in theme that we like, and change the parts we want to change. 
+
+Finally note, that the order is important:
+
+
+
+``` r
+diamonds %>% 
+  ggplot(aes(carat, price)) +
+  geom_point() +
+  theme_dark() +
+  theme(axis.text.y = element_text(angle= 45))
+```
+and 
+
+``` r
+diamonds %>% 
+  ggplot(aes(carat, price)) +
+  geom_point() +
+  theme(axis.text.y = element_text(angle= 45)) +
+  theme_dark() 
+```
+
+Will not give the same result. `theme_dark` has a setting for the way the text
+on the y-axis is shown, and will overwrite the changes done before calling it.
+
+Most of the `elements` of the plot need to be defined in a special way. If we 
+want the "theme" a text element, we set the `axis.text` to be an `element_text()` 
+function with specific arguments to specify *what* we want to do. For the background
+of the plot we are changing a rectangular object `element_rect`, and can set the background
+color like this: 
+
+
+``` r
+diamonds %>% 
+  ggplot(aes(carat, price)) +
+  geom_point() +
+  theme(axis.text.y = element_text(angle= 45),
+        panel.background = element_rect(fill = "red"))
+```
+
+<img src="fig/theming-rendered-background-1.png" style="display: block; margin: auto;" />
+Note that we are not setting the `plot.background`, as that would change the 
+background of the entire plot, rather than the background of the actual 
+area on which we are plotting.
+
+::::keypoints
+- "EVERYTHING in the plot can be customized"
+::::

md5sum.txt

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+---
+title: "Whats next?"
+teaching: 10
+exercises: 5
+---
+
+::::questions
+- "What is the next step in learning to plot?"
+::::
+
+::::objectives
+- "Provide tips on where to locate data for plotting"
+- "Provide tips for finding inspiration for plotting"
+::::
+
+
+
+
+
+## What should I do next?
+
+First of all: If you do not have data you want to visualize 
+already. Find some!
+
+[Kaggle](https://www.kaggle.com/datasets) host competitions 
+in machine learning. For the use of those competitions, they
+give access to a lot of interesting datasets to work with.
+With more than 200.000 datasets at time of writing, it can
+be a bit overwhelming, so consider looking at the "[Data Visualization](https://www.kaggle.com/datasets?fileType=csv&tags=13208-Data+Visualization)" category. The link provided only shows datasets saved as CSV-files, and has only about 2.500 datasets.
+
+## Play around!
+
+ggplot2 comes with a lot of functionality. This is the
+list of build-in geoms in ggplot2:
+
+
+``` output
+ [1] "geom_abline"            "geom_area"              "geom_bar"              
+ [4] "geom_bin_2d"            "geom_bin2d"             "geom_blank"            
+ [7] "geom_boxplot"           "geom_col"               "geom_contour"          
+[10] "geom_contour_filled"    "geom_count"             "geom_crossbar"         
+[13] "geom_curve"             "geom_density"           "geom_density_2d"       
+[16] "geom_density_2d_filled" "geom_density2d"         "geom_density2d_filled" 
+[19] "geom_dotplot"           "geom_errorbar"          "geom_errorbarh"        
+[22] "geom_freqpoly"          "geom_function"          "geom_hex"              
+[25] "geom_histogram"         "geom_hline"             "geom_jitter"           
+[28] "geom_label"             "geom_line"              "geom_linerange"        
+[31] "geom_map"               "geom_path"              "geom_point"            
+[34] "geom_pointrange"        "geom_polygon"           "geom_qq"               
+[37] "geom_qq_line"           "geom_quantile"          "geom_raster"           
+[40] "geom_rect"              "geom_ribbon"            "geom_rug"              
+[43] "geom_segment"           "geom_sf"                "geom_sf_label"         
+[46] "geom_sf_text"           "geom_smooth"            "geom_spoke"            
+[49] "geom_step"              "geom_text"              "geom_tile"             
+[52] "geom_violin"            "geom_vline"            
+```
+ggplot2 is also build as an extensible package, making 
+it relatively easy to build extensions, that does things
+that ggplot2 is not able to on its own. [This page](https://exts.ggplot2.tidyverse.org/) contains a collection of these.
+
+
+## Get some inspiration!
+
+The website/book [Fundamentals of Data Visualization](https://clauswilke.com/dataviz/) is a great resource for tips, tricks and thinking about visualizations, 
+especially the [directory of visualizations](https://clauswilke.com/dataviz/directory-of-visualizations.html). Note, however, that the author does not provide examples of the code you need to write to make the plots.
+
+There exist an online challenge, [#30DayChartChallenge](https://30daychartchallenge.org/), that 
+challenges you to create a data visualization on a certain topic each day of april. That can be a bit of a mouthful and 
+we are not going to participate ourselves. But! The collection 
+of visualizations from 2022 is humongous, and a great place
+to find ideas.
+
+
+## Other online courses
+
+[EdX](https://www.edx.org/search?q=ggplot) offers a multitude of interesting courses - here we link to their ggplot-related courses.
+
+Codecademy also offers free courses (and a certificate of completion if you pay) [This course](https://www.codecademy.com/learn/learn-ggplot-2) offers
+a more indepth introduction to ggplot2.
+
+## Extensions
+
+ggplot2 is build for extensions. And there are many.
+
+* (Hack for ggplot)[https://teunbrand.github.io/ggh4x/] A packages with utilities for doing stuff on the edge of what ggplot is designed for.
+
+
+::::keypoints
+- "ggplot2 is extensible - a LOT of extensions are available"
+::::