recap_lab04.qmd

---
title: "Recap Lab 4"
author: "Leon Eyrich Jessen"
format:
  revealjs:
    embed-resources: true
    theme: moon
    slide-number: c/t
    width: 1600
    height: 900
    mainfont: avenir
    logo: images/r4bds_logo_small.png
    footer: "R for Bio Data Science"
---



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# A Few Meta Points...

```{r}
#| echo: false
#| eval: true
#| message: false

library("tidyverse")
SPE_ENV <- read_tsv(file = "data/SPE_ENV.tsv")
targets <- c("Methanobacteria", "Clostridia", "Actinobacteria",
             "Sphingobacteria", "Anaerolineae")
SPE_ENV_targets <- filter(SPE_ENV, Taxa %in% targets)
```


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## Paths

:::: {.columns}

::: {.column width="50%"}

![](images/paths_19.png)
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::: {.column width="50%"}
![](images/paths_20.png)
:::

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- The `.Rproj`-file defines your project root, i.e. everything is relative to where this file is
- In your `.Qmd`-file you need to think about where files you need are
- To load something from `data`, you need to specify `data/my_data.csv`
- To load from `_raw` in `data`, you specify `data/_raw/my_raw_data.xlsx`



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## Qmd 

- Load libraries *before* calling needed functions
- Do a `Run All` check (Menu above Quarto doc $\rightarrow$ `Run` $\rightarrow$ `Run All`)
- When getting errors, make sure *read* the error message

![](images/paths_21.png){fig-align="center"}


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## Group work

:::: {.columns}

::: {.column width="50%"}

- Each student *must* have a and work in a project on the RStudio Cloud Server

- Each student *must* to actively write code (no one has ever gotten good at football by watching others play $\rightarrow$ Learning by actively doing)

- Make sure to talk together in your groups, you *will* learn something by explaining

- Group work **is** a Learning Objective element in this course $\rightarrow$ Outlook to collaboratice bio data science!

:::

::: {.column width="50%"}

![](images/group_work_meme.png){fig-align="center"}

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# Recap



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## Task 1 - Recreate the following plot

:::: {.columns}

::: {.column width="50%"}
```{r}
#| echo: true
#| eval: true

pl <- ggplot(data = SPE_ENV,
             mapping = aes(x = OTU_Count,
                           y = Taxa,
                           fill = site)) +
  geom_boxplot()
```
:::

::: {.column width="50%"}
```{r}
#| echo: false
#| eval: true

pl
```
:::

::::



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## Task 2 - Recreate the following plot
:::: {.columns}

::: {.column width="50%"}
```{r}
#| echo: true
#| eval: true

pl <- ggplot(data = SPE_ENV,
             mapping = aes(x = site,
                           y = Temp)) +
  geom_boxplot()
```
:::

::: {.column width="50%"}
```{r}
#| echo: false
#| eval: true

pl
```
:::

::::



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## Task 3 - Recreate the following plot
:::: {.columns}

::: {.column width="50%"}
```{r}
#| echo: true
#| eval: true

pl <- ggplot(data = SPE_ENV,
             mapping = aes(x = Temp,
                           y = pH,
                           colour = site)) +
  geom_point()
```
:::

::: {.column width="50%"}
```{r}
#| echo: false
#| eval: true

pl
```
:::

::::



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## Task 4 - Recreate the following plot
:::: {.columns}

::: {.column width="50%"}
```{r}
#| echo: true
#| eval: true

pl <- ggplot(data = SPE_ENV,
             mapping = aes(x = Temp,
                           y = pH,
                           colour = site,
                           label = Samples)) +
  geom_label(size = 3)
```
:::

::: {.column width="50%"}
```{r}
#| echo: false
#| eval: true

pl
```
:::

::::



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## Task 5 - Recreate the following plot
:::: {.columns}

::: {.column width="50%"}
```{r}
#| echo: true
#| eval: true

pl <- ggplot(data = SPE_ENV,
             mapping = aes(x = NH4,
                           fill = site)) +
  geom_density()
```
:::

::: {.column width="50%"}
```{r}
#| echo: false
#| eval: true

pl
```
:::

::::



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## Task 5 - Recreate the following plot - OR
:::: {.columns}

::: {.column width="50%"}
```{r}
#| echo: true
#| eval: true

pl <- pl +
  scale_x_log10()
```

*Note this syntax and think about what it means*

:::

::: {.column width="50%"}
```{r}
#| echo: false
#| eval: true

pl
```
:::

::::



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## Task 6 - Recreate the following plot
:::: {.columns}

::: {.column width="50%"}
```{r}
#| echo: true
#| eval: true

pl <- ggplot(data = SPE_ENV,
             mapping = aes(x = CODt,
                           y = CODs,
                           colour = site)) +
  geom_point() +
  geom_smooth(method = "lm")
```
:::

::: {.column width="50%"}
```{r}
#| echo: false
#| eval: true

pl
```
:::

::::



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## Task 7 - Recreate the following plot
:::: {.columns}

::: {.column width="50%"}
```{r}
#| echo: true
#| eval: true

pl <- ggplot(data = SPE_ENV,
             mapping = aes(x = Taxa,
                           y = Samples,
                           fill = OTU_Count)) +
  geom_tile() +
  scale_fill_gradient2(midpoint = 10000,
                       low = "blue",
                       mid = "white",
                       high = "red") +
  theme_classic(base_size = 8) +
  theme(legend.position = "bottom",
        axis.text.x = element_text(angle = 45,
                                   hjust = 1))

```
:::

::: {.column width="50%"}
```{r}
#| echo: false
#| eval: true
#| fig-width: 7
#| fig-height: 7
#| fig-align: center

pl
```
:::

::::



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## Task 8 - Recreate the following plot
:::: {.columns}

::: {.column width="50%"}
```{r}
#| echo: true
#| eval: true

library("ggridges")
pl <- ggplot(data = SPE_ENV_targets,
             mapping = aes(x = OTU_Count,
                           y = Taxa,
                           fill = Taxa)) +
  geom_density_ridges(alpha = 0.5) +
  scale_fill_viridis_d() +
  labs(x = "OTU Count",
       y = "Taxa Identified",
       title = "OTU Count Distribution for 5 Taxa Stratified on Site",
       caption = "Data from doi.org/10.1111/1751-7915.12334") +
  theme_minimal(base_family = "Avenir",
                base_size = 12) +
  theme(legend.position = "bottom") +
  facet_wrap(vars(site),
             ncol = 2)
```

:::

::: {.column width="50%"}
```{r}
#| echo: false
#| eval: true

pl
```
:::

::::



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## Task 9 - <span style="color: red;">GROUP ASSIGNMENT</span>

Let us take a look at this years submissions...