add lecture 8

08-freestyle.Rmd

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 # Freestyle
 
->  ... in which we venture into the unknown to show an example data
-  analysis.
+>  ... in which we learn about ANOVA, explore ggplot extensions and
+  build and interactive web application with Shiny.
 
-::: { .alert .alert-info}
-These are just the notes for the lecture that
-will be filled during the recording.
-Check back later to the the completed script.
+::: {.video-container}
+<iframe class="video" src="https://www.youtube.com/embed/gZ0u6b_1ej4?vq=hd1080" allowfullscreen></iframe>
 :::
 
+## Setup
+
+Today we are using quite a bunch of packages.
+In the lecture these are of course introduced one by one,
+but because it is a good habit to have all your imports
+and dependencies near the top of your analysis they
+show up here already.
+
+```{r}
+library(multcomp)
+library(patchwork)
+library(gganimate)
+library(palmerpenguins)
+library(broom)
+library(tidyverse)
+```
+
 ## ANOVA
 
+Let's get started.
+ANOVA stands for Analysis of Variance.
+We start with our familiar penguins dataset and ask
+the question: "Is there a difference in bill length between the species?"
+
+```{r}
+penguins %>% 
+  ggplot(aes(species, bill_length_mm)) +
+  geom_boxplot(outlier.color = NA) +
+  geom_jitter(alpha = 0.2, width = 0.2)
+```
+
+Optically, it certainly looks like it.
+We could run a bunch of t-tests to compare between the groups,
+which would be 3 in total (Adelie–Chinstrap, Adelie–Gentoo, Gentoo–Chinstrap).
+And then we would have to correct for multiple testing.
+At this point, only running one of tests (like Adelie vs. Chinstrap)
+because it looks so promising is not an option!
+Looking at the data visually is a form of comparison,
+so we would be cheating if we formed our hypothesis only after
+looking at the data.
+
+**ANOVA** is a way of comparing multiple groups and tells us, if there
+is a difference at all between the groups.
+It does not however tell us, between which groups the difference exists,
+just that there is an overall difference.
+In order to get p-values for direct comparisons between the groups
+we run a **post-hoc** (Latin for "after the fact") on our anova result.
+
+First I am sliding in a little extra code chunk, because later down
+the line we might want to compare all groups to e.g. a baseline or
+control group (see the section about Dunnet below) and this baseline
+is decided to be the first factor-level of the grouping variable.
+With `fct_relevel` we can move a lovel to the first (or any other)
+position.
+
+```{r}
+penguins <- penguins %>% 
+  mutate(species = fct_relevel(species, "Gentoo"))
+```
+
+Not we create an anova fit with `aov`.
+
+```{r}
+anova_fit <- aov(bill_length_mm ~ species, data = penguins)
+anova_fit
+```
+
+And explore if further with various functions.
+
+```{r}
+summary(anova_fit)
+```
+
+```{r}
+tidy(anova_fit)
+```
+
+### Tukey Post-Hoc Test
+
+The most straighforward post-hoc test, which is already built into R,
+is "Tukey's Honest Significant Differences",
+which compares all groups with all other groups.
+
+```{r}
+TukeyHSD(anova_fit) %>% 
+  tidy()
+```
+
+### Dunnet Post-Hoc Test
+
+If we have a control group we use Dunnet's test to compare all other
+groups to the control group.
+This means we have to do less comparisons and end up with a higher statistical power.
+
+Unfortunately, Dunnet is not built into R, but we can get the necessary
+functions from the `multcomp` package.
+When loading the `multcomp` package we have to be careful
+to load it before the `tidyverse`!
+Because `multcomp` also loads a bunch of other packages and one
+of them brings it's own `select` function, so loading it after
+the `tidyverse` would overwrite tidyverse `select` and make our life very hard.
+
+```{r}
+glht(anova_fit, mcp(species = "Dunnet")) %>% 
+  tidy()
+```
+
+Note, that this package can also do Tukey's test by changing "Dunnet"
+to "Tukey".
+
 ## ggplot extensions
 
-## Build Apps with Shiny
+In order to plot these nice significance stars on top of of our
+comparisons let me first introduce you to ggplot extensions:
+
+<https://exts.ggplot2.tidyverse.org/>
+
+This is just a collection of packages that extend ggplot in some shape or form
+or simply work very well with it.
+
+### `ggsignif`, `ggpubr`
+
+One of these is `ggsignif`, which we can use to add comparison
+brackets to our plot.
+
+```{r}
+penguins %>% 
+  ggplot(aes(species, bill_length_mm)) +
+  geom_boxplot(outlier.color = NA) +
+  geom_jitter(alpha = 0.2, width = 0.2) +
+  ggsignif::geom_signif(
+    comparisons = list(c("Gentoo", "Adelie")),
+    annotations = c("hello")
+  )
+```
+
+`ggsignif` can also run it's own comparisons, which is
+a wilcoxon rank sum test by default, but especially
+for statistical tests I prefer to run them myself first
+before trusting more packages.
+Additionally, we have of course a different type of test (ANOVA),
+and we already have our values, so we use it just to manually
+add text to the annotation:
+
+```{r}
+stars <- function(p) {
+  case_when(
+    p <= 0.001 ~ "***",
+    p <= 0.01  ~ "**",
+    p <= 0.05  ~ "*",
+    TRUE       ~ "ns"
+  )
+}
+
+dunnet <- glht(anova_fit, mcp(species = "Dunnet")) %>% 
+  tidy() %>% 
+  mutate(contrast = str_split(contrast, " - "),
+         stars    = stars(adj.p.value))
+
+plt <- penguins %>% 
+  ggplot(aes(species, bill_length_mm)) +
+  geom_boxplot(outlier.color = NA) +
+  geom_jitter(alpha = 0.2, width = 0.2) +
+  ggsignif::geom_signif(
+    comparisons = dunnet$contrast,
+    annotations = dunnet$stars,
+    y_position = c(60, 65)
+  )
+
+plt
+```
+
+
+### `patchwork`
+
+I also saved the plot to a variable and will now create a second
+one just to show you the `patchwork` package,
+which can combine plots into neat layouts:
+
+```{r}
+plt2 <- ggplot(penguins, aes(bill_length_mm, bill_depth_mm, color = species)) +
+  geom_point()
+
+((plt | plt2) / plt2 ) + plot_layout(guides = 'collect')
+```
+
+### `ggfortify`
+
+`ggfortiy` might not strictly be necessary but I want to mention it
+to talk about `autoplot`.
+`autoplot` is in `ggplot2` already by default.
+It creates automatic plot for certain objects, like models for example,
+or an anova result.
+`ggfortify` makes more objects have the ability to generate an autoplot.
+
+```{r}
+library(ggfortify)
+autoplot(anova_fit)
+```
+
+I want to mention this here because it has an important implication
+for communication.
+There are two purposes too plots.
+The first purpose is to generate insight for you, the data analyst.
+Autoplots are often very helpful for this.
+But the second purpose is to communicate your findings to a reader!
+It takes time and effort to refine a plot from something that generated
+and insight for you after having spent a lot of time with your data
+to also generating insights for a reader that sees the data for the first time.
+Too many people stop at the first step and publish their plots
+as is, without thinking much about the reader.
+Keep this in mind as you advance in your scientific career.
+
+> Communication is key!
+  Both code and plots are means of communicating.
+
+### `esquisse`
+
+Two more cool packages: The `esquisse` package let's you
+create ggplots interactively with a gui!
+But make sure to always save the code this created to ensure
+reproducibility!
+
+### `gganimate`
+
+And `gganimate` extends `ggplot` with the ability to use time as a dimension:
+
+```{r}
+gapminder::gapminder %>% 
+  ggplot(aes(gdpPercap, lifeExp, size = pop, color = country)) +
+  geom_point() +
+  scale_x_log10() +
+  # facet_wrap(~year) +
+  transition_time(year) +
+  scale_color_manual(values = gapminder::country_colors) +
+  guides(color = "none",
+         size = "none")
+```
+
+## Build Apps with `shiny`
+
+You can find the code written during the lecture in: `example-app/`.
+
+## Exercises
+
+### The Whole Deal
+
+Data analysis takes practice.
+For this last exercise you get the option to show what you
+have learned on a fresh dataset.
+But people have different interests, so I am leaving
+the choice open.
+
+Choose a dataset from the TidyTuesday project:
+
+<https://github.com/rfordatascience/tidytuesday#datasets>
+
+and write your data analysis report.
+Aim to write down some questions you have about the
+topic and answer them with the available data.
+Explore it with plots and tables and try
+to end up with 1 or 2 high quality plots
+that clearly communicate you findings.
+Above all, have fun!
 
 ## Feedback
 
-I will send round a link with a feedback form.
-It is anonymous.
+I will send round a link for a feedback form.
+It is anonymous.

_bookdown_files/08-freestyle_cache/html/__packages

@@ -0,0 +1,20 @@
+base
+mvtnorm
+survival
+MASS
+TH.data
+multcomp
+patchwork
+ggplot2
+gganimate
+palmerpenguins
+broom
+tidyverse
+tibble
+tidyr
+readr
+purrr
+dplyr
+stringr
+forcats
+ggfortify