chapter1.Rmd

---
title: 'R and statistics'
description: 'Basics of R, the amazing statistical programming language. Do not be afraid of the art of programming!'
---

## What is R?

```yaml
type: NormalExercise
key: 168121d0f0
lang: r
xp: 100
skills: 1
```

R is a programming language designed for statistical analysis. R is completely open source and all analysis done with R are reproduciple and easy to share. R is flexible, powerful and includes implementations of the latest research. 

Because of these reasons, R is a very popular language among the scientific community. R is however also widely [used](http://blog.revolutionanalytics.com/2014/05/companies-using-r-in-2014.html) for data analysis and visualisations by companies such as Google, Facebook, Twitter and Reaktor. R is perfect for data science.

Researchers and experts often use programming to visualize and explore data and implement new statistical methods. The purpose of this course is to expose you to the basics of programming and data analysis with R.

Follow the instructions below to complete the exercise.

`@instructions`
- Take a peek at the code written on the right in script.R. When executed, the code gives your computer the instructions to (1) load a dataset and (2) produce a graphic.
- Could you tell which lines of code relates to (1) or (2)? A hashtag marks a line as a comment line.
- When you're done pondering, click 'Submit Answer' to execute the code and move on to the next exercise! (You do not need to make any changes to the code in this exercise)

`@hint`
- Simply press 'Submit Answer' when you're ready to move forward.

`@pre_exercise_code`
```{r}
data_url <- "http://www.helsinki.fi/~kvehkala/JYTmooc/JYTOPKYS-data.txt"
students2014 <- read.table(data_url, sep="\t", header=TRUE)

```

`@sample_code`
```{r}
# This is an example of the power of R

# Create a character object holding data location
data_url <- "http://www.helsinki.fi/~kvehkala/JYTmooc/JYTOPKYS-data.txt"

# Load and save data with the read.table() function
students2014 <- read.table(data_url, sep = "\t", header = TRUE)

# Draw a graph of student shoesize and hight using plot()
with(students2014, plot(x = kenka, y = pituus, col = sukup, pch = 20, main = "Scatter plot of student shoesize and height \n(color indicates sex)"))


```

`@solution`
```{r}
# This is an example of the power of R

# Create a character object holding data location
data_url <- "http://www.helsinki.fi/~kvehkala/JYTmooc/JYTOPKYS-data.txt"

# Load and save data with the read.table() function
students2014 <- read.table(data_url, sep = "\t", header = TRUE)

# Draw a graph of student shoesize and hight using plot()
with(students2014, plot(x = kenka, y = pituus, col = sukup, pch = 20, main = "Scatter plot of student shoesize and height \n(color indicates sex)"))

```

`@sct`
```{r}
success_msg("Great! Move on to the first exercise.")
```

---

## Basic tools

```yaml
type: NormalExercise
key: 35ad6c43ec
lang: r
xp: 100
skills: 1
```

On your right you see the R editor area - the script - and below that the R console. The editor area is just a simple text editor where you write code - just like text.

You can first write code to the editor area and then tell R that you want to execute a line of code where your cursor currently is by pressing `Ctrl + Enter` (`Cmd + Enter` on a mac). Input and output will then appear in the console.

It is also possible to write code directly to the console and use `Enter` to execute, but working with the script area is preferred.

Try it!

`@instructions`
- Type "Hello world!" in the editor area. Use quotation marks.
- Press `Ctrl + Enter` to execute your "Hello world!" - code
- Make sure to use a capital "H" and an exclamation mark.
- Click 'Submit Answer' when done.

`@hint`
- Remember to use quotation marks and write the sentence exactly as instructed.

`@pre_exercise_code`
```{r}
# no pec
```

`@sample_code`
```{r}
# This is the R editor!

# A hashtag at the beginning of the line defines the line as a comment

# Write your code below





# Below is the R console, where you will see output
```

`@solution`
```{r}
# This is the R editor!

# A hashtag at the beginning of the line defines the line as a comment

# write your code below
"Hello world!"




# Below is the R console, where you will see output
```

`@sct`
```{r}
test_student_typed("Hello world!", not_typed_msg="Please type 'Hello world!' in the editor.")

test_error()
success_msg("Great work! You have now executed your first line of code :)")
```

---

## Arithmetics

```yaml
type: NormalExercise
key: b0a54af3ff
lang: r
xp: 100
skills: 1
```

Let's continue with something simple. R can do amazing things like scrape websites and draw beautiful graphics but it can also do basic calculations. Consider the following arithmetic operators:

- Addition: `+`
- Subtraction: `-`
- Multiplication: `*`
- Division: `/`
- Exponentiation: `^` (or `**`)   

The `^` operator raises the number to its left to the power of the number to its right: for example `3^2` is 9. With this knowledge, follow the instructions below to complete the exercise.

`@instructions`
- Study and execute the examples in the R script. (Use `Ctrl + Enter` or `Cmd + Enter`)
- Type `2^5` in the editor and calculate 2 to the power 5.
- When done, click 'Submit Answer'.

`@hint`
- If you have trouble accessing the `^` -sign, you can also use double star `**`.

`@pre_exercise_code`
```{r}
# no pec
```

`@sample_code`
```{r}
# An addition
5 + 5 

# A subtraction
5 - 5 

# A multiplication
3 * 5

 # A division
(5 + 5) / 2 

# Exponentiation



```

`@solution`
```{r}
# An addition
5 + 5

# A subtraction
5 - 5 

# A multiplication
3 * 5

 # A division
(5 + 5) / 2 

# Exponentiation
2 ^ 5

```

`@sct`
```{r}
test_output_contains("2^5", incorrect_msg = "The exponentiation example is not correct. Please write `2 ^ 5` on a new line.")

success_msg("Great work! Head over to the next exercise.")
```

---

## Objects

```yaml
type: NormalExercise
key: 856fab934f
lang: r
xp: 100
skills: 1
```

Here's where things start to get interesting. In R you can create and operate on things called *objects*. 
An object is something that can store information such as numerical values and names. Once an object is created, it will be stored in memory and the information it contains will be available to you later.

Objects are created using the assign operator: `<-` (< and -). The value of an object can be printed by typing it's name.

`@instructions`
- Execute the lines that create and operate on `my_character_object` and `my_numeric_object`.
- Assign your name or nickname to `my_name`. Use quotation marks.
- Assign a positive message to yourself to `my_message`. Use quotation marks.
- Click 'Submit Answer'.

`@hint`
- Remember that `Ctrl + Enter` executes a single row.

`@pre_exercise_code`
```{r}
# no pec
```

`@sample_code`
```{r}

# Create an object
my_character_object <- "Hi there!"

# Print the contents of the object
my_character_object

# Create another object
my_numeric_object <- 5 + 5

# Do further calculations with the object
my_numeric_object / 5

# Override the value of an object by assigning a new value to it
my_character_object <- my_numeric_object
my_character_object

# Create character objects my_name and my_message here. Use quotation marks.
my_name <- 
my_message <- 

```

`@solution`
```{r}

# Create an object
my_character_object <- "Hi there!"

# Print the contents of the object
my_character_object

# Create another object
my_numeric_object <- 5 + 5

# Do further calculations with the object
my_numeric_object / 5

# Override the value of an object by assigning a new value to it
my_character_object <- my_numeric_object
my_character_object

# Create character objects my_name and my_message here. Use quotation marks.
my_name <- "Hey You"
my_message <- "You are awsome!"

```

`@sct`
```{r}
test_object("my_name", eval = F, incorrect_msg = "Did you assign your name to `my_name`?")
test_object("my_message", eval = F, incorrect_msg = "Did you assign a message to `my_message`?")

test_error(incorrect_msg = "Did you use quotation marks to assign your name and a message to the objects `my_name` and `my_message`")
success_msg(paste0("Great job ", my_name, ". ", my_message, "!"))
```

---

## Functions

```yaml
type: NormalExercise
key: b81cdce659
lang: r
xp: 100
skills: 1
```

In R you operate on objects using *functions*. A function is a special kind of object that uses other objects as inputs, performs operations, and usually outputs a result. The inputs of a function are called *arguments*. 

A Function is *called* by typing it's name and giving it the necessary arguments inside parenthesis. When a function is called, it will perform an action.

R has many functions ready for you to use. Functions are how you're actually going to do all the magic. Here we will do a little bit of magic using data that will become more familiar to you later on.

`@instructions`
- Study and execute the examples in the R script. Don't worry about the `$` -sign for now, we will get to that later.
- Follow the examples and compute the mean age of students.
- When done, click 'Submit Answer'.

`@hint`
- Use the function `mean()` and the object `student_age` like in the example on line 7 of script.R.

`@pre_exercise_code`
```{r}
students2014 <- read.table("http://www.helsinki.fi/~kvehkala/JYTmooc/JYTOPKYS-data.txt", sep="\t", header=TRUE)

# keep a couple background variables
students2014 <- students2014[,c("sukup","toita","ika","pituus","kenka","kone")]

# recode kone -variable NA values as factor levels
students2014$kone <- addNA(students2014$kone)

# choose rows without missing values
students2014 <- students2014[complete.cases(students2014),]

# integers to numeric
students2014$ika <- as.numeric(students2014$ika)
students2014$pituus <- as.numeric(students2014$pituus)
students2014$kenka <- as.numeric(students2014$kenka)

```

`@sample_code`
```{r}
# students2014 data is available

# Create object student_height
student_height <- students2014$pituus

# Compute the average height of the students
mean(student_height)

# Create object student_age
student_age <- students2014$ika

# Compute the average age of the students




```

`@solution`
```{r}
# students2014 data is available

# Create object student_height
student_height <- students2014$pituus

# Compute the average height of the students
mean(student_height)

# Create object student_age
student_age <- students2014$ika

# Compute the average age of the students
mean(student_age)



```

`@sct`
```{r}
test_output_contains("mean(student_age)", incorrect_msg = "Please use the `mean()` function to compute the mean of student_age.")

test_error()
success_msg("Excellent! You are making great progress.")
```

---

## Good arguments

```yaml
type: NormalExercise
key: 88d1d679eb
lang: r
xp: 100
skills: 1
```

During this course you will operate on many of R's ready-made functions. As we already saw, functions take objects as their arguments and then perform actions using the objects. Using a function is called *calling* it.

Functions usually have more than one possible argument. Some of them can have default values and others need to be specified. The arguments have names, which can (and often should) be used when specifying their values. The recommended style is to specify all but the first argument by name, when calling a function.

`@instructions`
- Execute the example codes where the function `head()` is used to explore the first couple observations in the `students2014` dataset.
- Using the assign operator `<-`, create object `first_ten_students` by calling `head()` with `n = 10`.

`@hint`
- Directly assign the output of `head()` to `first_ten_students` to create the object
- Remember to separate the arguments with a comma
- The answer is of the form: `first_ten_students <- head(arguments_here)`

`@pre_exercise_code`
```{r}
# load data from web
students2014 <- read.table("http://www.helsinki.fi/~kvehkala/JYTmooc/JYTOPKYS-data.txt", sep="\t", header=TRUE)
# keep a couple background variables
students2014 <- students2014[,c("sukup","toita","ika","pituus","kenka","kone")]
# recode kone variables missing values as factor levels
students2014$kone <- addNA(students2014$kone)
# keep only rows without missing values
students2014 <- students2014[complete.cases(students2014),]
# integers to numeric
students2014$ika <- as.numeric(students2014$ika)
students2014$pituus <- as.numeric(students2014$pituus)
students2014$kenka <- as.numeric(students2014$kenka)

```

`@sample_code`
```{r}
# students2014 is available

# Use the function head() on the object students2014
head(students2014, n = 4)

# Argument 'n' of head() has a default value 6
head(students2014)

# These calls are identical. Try them.
head(students2014, n = 3) # recommended style
head(students2014, 3)     # style usually not recommended
head(n = 3, students2014) # style not recommended

# Save the first ten observations of the data
first_ten_students <- 


```

`@solution`
```{r}
# students2014 is available

# Use the function head() on the object students2014
head(students2014, n = 4)

# Argument 'n' of head() has a default value 6
head(students2014)

# These calls are identical. Try them.
head(students2014, n = 3) # recommended style
head(students2014, 3)     # style usually not recommended
head(n = 3, students2014) # style not recommended

first_ten_students <- head(students2014, n = 10)


```

`@sct`
```{r}
test_error(incorrect_msg = "Your code seems to produce an error. Did you create the object `first_ten_students` as instructed?")

test_object("first_ten_students")

success_msg("Great job!")
```

---

## students2014

```yaml
type: MultipleChoiceExercise
key: 8d2100f40a
lang: r
xp: 50
skills: 1
```

We will be doing data science using a dataset that was collected in 2014 from the students of the Faculty of Social Sciences in the University of Helsinki. 

The students filled out [this short questionnaire](http://www.helsinki.fi/~kvehkala/JYTmooc/ASSIST-2014-FI-0-20140903.pdf). Please go ahead and read both pages of the pdf in the above link and then come back and answer the question below.

How many numbered sections does the questionnaire have?

`@possible_answers`
- two
- four
- seven
- ten
- fourteen

`@hint`
- if the link does not work, the url is:  http://www.helsinki.fi/~kvehkala/JYTmooc/ASSIST-2014-FI-0-20140903.pdf

`@pre_exercise_code`
```{r}
# pre exercise code here
```

`@sct`
```{r}
# submission correctness tests
msg1 <- msg3 <- msg4 <- msg5 <- "Sorry, not quite."
msg2 <- "Yes, correct!"
test_mc(correct = 2, feedback_msgs = c(msg1, msg2, msg3, msg4, msg5))

# Final message the student will see upon completing the exercise
success_msg("Great work! Next time we'll explore the data :)")
```