# Exercises: Basics

## 1. Data Types and Basic Operations

For introductory concepts, please see [this course](https://coding-for-reproducible-research.github.io/CfRR_Courses/individual_modules/introduction_to_r/basic_commands.html).

You are studying *Drosophila* (fruit flies) in a genetics lab. Each of four vials contains a different number of flies after a week of growth.

You've created a vector recording all the counts.

```
# Please run the following code:
flies <- c(23, 17, 28, 21)
```

After counting them, you are now ready to analyse them.

* Check what type of data flies contains using:`typeof(flies)`.
* Calculate the total number of flies across all vials. Hint: Use `help.search("sum")` to find helpful functions (commands).
* Compute the average (mean) number of flies.
* Find how many vials have fewer than the average number of flies.
* Suppose each vial can hold up to 30 flies. Create a new vector called `remaining_space` showing how many flies could still fit in each vial.


```
# help.search() lets you look for R functions or topics by keyword when you don’t know the exact function name.

# Your code here:


```

Just for fun: use `summary(flies)` function and notice the output. 

## 2. Data Frames and Subsetting

For data frames and subsetting, please see [this course](https://coding-for-reproducible-research.github.io/CfRR_Courses/individual_modules/introduction_to_r/data_types.html).

You measured expression levels for three genes in four *Drosophila* samples. The data frame looks like this: 

```
# Please run the code below
expression <- data.frame(
  Gene = c("CEP290", "GATA2", "IFT190"),
  Sample1 = c(12.4, 7.8, 15.3),
  Sample2 = c(13.1, 8.6, 14.9),
  Sample3 = c(11.8, 9.0, 15.7),
  Sample4 = c(12.9, 8.3, 16.1)
)

```

Tasks:

* Use `head()` and `str()` to inspect the data frame.
* Use `names()` or `colnames()` to see the column names..
* Extract all expression values for the gene `IFT190`.
* Extract expression values from `Sample2` only.
* Extract the expression value of `CEP290` in Sample3 (hint: you can use row and column indexing, e.g. `expression[row, column])`.
* Create a new data frame containing only `GATA2` and `IFT190` rows.
* Add a new column called `Average` showing the mean expression for each gene across all samples.
* Which gene has the highest average expression? (Use `which.max()`.)
* Use `subset()` to extract all rows where the average expression is greater than 12.

```
# Your code here

```


## 3. Basic Visualisation

For basic visualisation plots, please see [this course](https://coding-for-reproducible-research.github.io/CfRR_Courses/individual_modules/introduction_to_r/plots.html).

You measured the wing lengths (in millimetres) of 20 fruit flies from a single population and recorded them: 

```
# Please run the code below
# Wing length measurements (mm)
wing_lengths <- c(2.3, 2.5, 2.4, 2.6, 2.2, 2.8, 2.4, 2.5, 2.7, 2.6, 2.3, 2.9, 2.8, 2.7, 2.6, 2.4, 2.5, 2.3, 2.6, 2.7)

```


* Create a histogram of `wing_lengths` (hint: use `help(hist)` for more info).
* Create a boxplot of `wing_lengths` (hint: use `help(boxplot)` for more info).

```
# Your code here


```

* Generate a new set of measurements for a second population `wing_lengths_2`.
* Create side-by-side boxplots comparing the two populations. 

```
# Your code here


```

## 4. Control Structures and Functions

For controls structures syntax, please see [this course](https://coding-for-reproducible-research.github.io/CfRR_Courses/individual_modules/introduction_to_r/control_flow.html).

For functions basic syntax, please see [this course](https://coding-for-reproducible-research.github.io/CfRR_Courses/short_courses/r_functions.html).


You are studying the growth of plants under different light conditions.
After one week, you measured the growth (in cm) of 10 plants.

```
# Please run the code below
# Plant growth in cm after one week
growth <- c(3.5, 5.2, 2.8, 4.6, 5.9, 6.3, 3.1, 4.9, 2.7, 5.5)

```

* Loop through each plant’s growth value and print a message in the format: "Plant X grew X cm". There are two options: (1) loop directly over values; (2) loop using an index - uses `(i in 1:length(growth)`.

```
# Run the code below and observe what happens:

# Create a vector
numbers <- c(10, 20, 30)

# Option 1
for (i in numbers) {
  print(i)
}

# Option 2
for (i in 1:length(numbers)) {
  print(i)
}


```


```
# Your code here


```

* Loop through each plant’s growth value and print a message `Plant X: grew well` if growth is `>=` 4.5 cm and `Plant X: poor growth` otherwise.


```
# Your code here


```


* Write a function called `good_growth()` that:
 * Takes a vector of growth measurements and a threshold value as input.
 * Returns the number of plants that grew above the threshold.
Example:
`good_growth(growth, 4.5)` should return 6.

```
# Your code here


```

* Bonus challenge:
 * Modify your function so it also prints a short summary: `6 out of 10 plants grew above 4.5 cm.`

```
# Your code here


```