```{r setup}
library(tidyverse)
```


## Exercise 01
Download files **IM.csv** and **GP.csv** that you will need for exercise. These are participants responses on two questionnaires with each participant identified by their ID (`Participant` in _IM.csv_ and `Respondent` in _GP.csv_), `Condition` (which experimental group they belong to), and their `Gender`. Read both tables and join them so that there no missing values in the table (some participants are missing in `GP.csv`, so there are _three_ joins that can do this, which one will you pick?). Use a Tidyverse function for that. Then, turn `Condition` and `Gender` into factors, so that for `Condition` levels are `"control"` (`1`) and `"game"` (`2`) and for `Gender` levels are `"female"` (`1`) and `"male"` (`2`).
```{r exercise 01}

```

## Exercise 02
Repeat exercise 1 but use `merge()` function
```{r exercise 02}

```

## Exercise 03
Create two tables that need to be joined by a single key column. In the first table, Use [rnorm()](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/Normal.html) function to generate normally distributed data with mean of 175 and standard deviation of 7.6 for column `Height`. In the second table, use same normal distribution but with mean of 80 and standard deviation of 10 for column `Weight`. WWhen fill in key column for both tables, do it so that _inner_ and _right_ join would give the same final table but _left_ and _full_ would give you a longer one (test this explicitly!). After joining two tables, plot `Height` against `Weight` and superimpose linear regression fit.
```{r exercise 03}
```


## Exercise 04.
Preprocess _GP.csv_ data. Split the computations below into two pipelines. One that loads and pre-processes the data (steps 1-4). Another one that produces a summary and stores it into a different table (steps 5-6). Advice, implement it one step at a time, checking the table and making sure that you get expected results before piping it and adding the next operation.

1. Read the file, make sure you specify column types.
2. Convert `Condition` column to a factor with "game" (1) and "control" (2).
3. Pivot all `GP..` columns. You should get a table with five columns: `Respondent`, `Condition`, `Gender`, `Item` (column where original column names go), and `Response` (a column where original go). Hint, you can use slicing `:` to specify the range of columns or [starts_with()](https://tidyselect.r-lib.org/reference/starts_with.html) function to specify a common prefix. Try both approaches.
4. Convert `Response` column to a factor assuming a seven-point scale, the levels are
  1. Not at all
  2. Slightly
  3. Somewhat
  4. Moderately
  5. Quite a bit
  6. Considerably
  7. Very much
5. Count responses for each item and condition.
6. Convert counts to proportion of responses for each item and condition.

```{r exercise 04}

```

## Exercise 05
Repeat exercise 4 but use `reshape()` function instead of `pivot_longer()`.
```{r exercise 05}

```

## Exercise 06
Plot results from the previous exercise to compare conditions visually. Use `Response` for x-axis, `Proportion` for y-axis, `Condition` for color, think about how facet the plot.
```{r exercise 06}

```

## Exercise 07
Use `complete()` to fill in missing combinations of Condition, Item, and Response so that both count and proportion columns are zero. Replot the updated table using code from the previous exercise.
```{r exercise 07}

```

## Exercise 08

Pivot _adaptation_ data to a wide format that is easier for humans to read. Use `pivot_wider()`. This should be a single pipeline.

1. Read the file (don't forget to specify column types)
2. Computer `Psame` proportion of same responses given number of total responses.
3. Pivot the table wider, think about your id columns. Also try without specifying any and see what you get.
4. Compute an average stability across all probes and put it into a new `Average` column. You can do it "by hand" or you can use [rowSums()](https://stat.ethz.ch/R-manual/R-devel/library/base/html/colSums.html) to compute it. In the latter case, you need to use `.` to refer to the table inside the `mutate()` function and you will need to normalize it by the number of probes to get an average instead of the sum.
5. Pipe it to the output, using [knitr::kable()](https://bookdown.org/yihui/rmarkdown-cookbook/kable.html).

```{r exercise 08}

```

## Exercise 09
Repeat exercise 8 but use `reshape()` function.
```{r exercise 09}

```

## Exercise 10
Using _adaptation_ data, create group average summary as a square 5×4 table with a single row per _Prime_ and four columns for _Probe_ plus a column that says which prime the row corresponds to. As a value for each cell, we want to code _median ± MAD_. Use `?median` and `?mad` in the console for information.
```{r exercise 10}

```
