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---
layout: default
title: Concepts & Syntax
nav_order: 7
parent: Workshop Content
has_toc: false
layout: default
title: Concepts & Syntax
nav_order: 7
parent: Workshop Content
has_toc: false
---

# **Concepts and Basic Syntax**
Expand All @@ -13,49 +13,49 @@ has_toc: false
In R, an operator is a special symbol or keyword used to perform operations on one or more values. There are several types of operators in R, including:

{: .list-with-space}
* **Assignment Operators**: These operators are used to assign a value to a variable.
* **Assignment Operators**: These operators are used to assign a value to a variable.
Examples include `<-` and `=`.

* **Arithmetic Operators**: These operators are used to perform basic mathematical operations such as addition, subtraction, multiplication, and division. Examples include
- `+` for addition,
- `-` for subtraction,
- `*` for multiplication,
- `/` for division,
* **Arithmetic Operators**: These operators are used to perform basic mathematical operations such as addition, subtraction, multiplication, and division. Examples include
- `+` for addition,
- `-` for subtraction,
- `*` for multiplication,
- `/` for division,
- and `^` for power.

Type `?Arithmetic` to read the R document in the help tab.


* **Logical Operators**: These operators are used to perform logical operations on values or variables, and return a logical value of TRUE or FALSE. Examples include
- `&` for logical AND,
- `|` for logical OR,
- and `!` for logical NOT.
* **Logical Operators**: These operators are used to perform logical operations on values or variables, and return a logical value of TRUE or FALSE. Examples include
- `&` for logical AND,
- `|` for logical OR,
- and `!` for logical NOT.

Type `?base::Logic` to read the R document in the help tab.

* **Comparison Operators**: These operators are used to compare two values or variables and return a logical value of TRUE or FALSE. Examples include
- `<` for less than,
- `>` for greater than,
- `==` for equal to,
- and `!=` for not equal to.
* **Comparison Operators**: These operators are used to compare two values or variables and return a logical value of TRUE or FALSE. Examples include
- `<` for less than,
- `>` for greater than,
- `==` for equal to,
- and `!=` for not equal to.

Type `?Comparison` to read the R document in the help tab.

* **Miscellaneous Operators**: These include
* the hash sign `#` indicates a comment in the code,
* the colon sign `:` creates a sequence of numbers,
* the square bracket `[]` indexes an object such as a vector,
* the dollar sign `$` accesses a variable from a data frame,
* the percent sign `%` is used for special operators like modulo,
* and the double colon `::` used to access functions or variables from a specific package.
* **Miscellaneous Operators**: These include
* the hash sign `#` indicates a comment in the code,
* the colon sign `:` creates a sequence of numbers,
* the square bracket `[]` indexes an object such as a vector,
* the dollar sign `$` accesses a variable from a data frame,
* the percent sign `%` is used for special operators like modulo,
* and the double colon `::` used to access functions or variables from a specific package.

Understanding how to use these different types of operators is important for writing efficient and effective code in R.

### Practice 1
Compare the magnitude of the following numbers:
* the sum of all integers from 1 to 100,
* 10 to the power of 11,
* 11 to the power of 10.
Compare the magnitude of the following numbers:
* the sum of all integers from 1 to 100,
* 10 to the power of 11,
* 11 to the power of 10.

<details>
<summary>Solution</summary>
Expand All @@ -70,31 +70,31 @@ a < c <br>
## 2. Functions and Packages

### Function
When we calculate the sum of all the integers from 1 to 100, we use a formula to help simplify the calculation. What if we cannot memorize the formula? Does it mean that we have to sum those integers brute force by typing 1 + 2 + 3 + ... all the way up to 100? We can use a built-in function `sum()` with the colon operator `:`.
When we calculate the sum of all the integers from 1 to 100, we use a formula to help simplify the calculation. What if we cannot memorize the formula? Does it mean that we have to sum those integers brute force by typing 1 + 2 + 3 + ... all the way up to 100? We can use a built-in function `sum()` with the colon operator `:`.

Input
{: .label .label-green}
```r
sum(1:100)
```

In R, a **function** is a block of code that performs a specific task and can be called or executed by the user. Functions are an essential part of R programming because they allow you to automate tasks and reuse code. As a R user, you can rely on functions heavily.
In R, a **function** is a block of code that performs a specific task and can be called or executed by the user. Functions are an essential part of R programming because they allow you to automate tasks and reuse code. As a R user, you can rely on functions heavily.

When R is installed, it comes with a default package named `base`, which contains some **built-in functions** that you can use. For example,
When R is installed, it comes with a default package named `base`, which contains some **built-in functions** that you can use. For example,

| `mean()` | Calculates the arithmetic mean of a vector of numbers
| `sd()` | Calculates the standard deviation of a vector of numbers
| `str()` | Displays the structure of an R object
| `table()` | Creates a frequency table of a vector or factor
| `mean()` | Calculates the arithmetic mean of a vector of numbers
| `sd()` | Calculates the standard deviation of a vector of numbers
| `str()` | Displays the structure of an R object
| `table()` | Creates a frequency table of a vector or factor
| `plot()` | Creates a basic plot of data

### Package
As introduced earlier, R is powerful partially because it is extensible by installing additional packages quickly. In R, a **package** is a collection of functions, data, and documentation.
As introduced earlier, R is powerful partially because it is extensible by installing additional packages quickly. In R, a **package** is a collection of functions, data, and documentation.

Packages can be installed using the `install.packages()` function, which downloads the package from a repository and installs it on your system. Once a package is installed, its functions and data can be loaded into the R environment using the `library()` function. You do not need to install a package repeatedly, but you do need to library it again in order to use it every time when you restart the RStudio. If you want to use a function from a package without library it, you can use the double colon operator, for example, `dplyr::filter()`, where `dplyr` is the package name, and `filter()` is a function from the package.

### Practice 2
The `tidyverse` is a collection of popular R packages for data manipulation and visualization. Use the following commands to install and load the `tidyverse` package:
The `tidyverse` is a collection of popular R packages for data manipulation and visualization. Use the following commands to install and load the `tidyverse` package:

Input
{: .label .label-green}
Expand Down Expand Up @@ -122,24 +122,24 @@ library(tidyverse) # Load package

## 3. Data Type, Vector, and Data Frame

### Data Type
### Data Type
In R, there are several **data types** that are commonly used, including:
* Numeric: Represents numbers with decimal points. Can be either integers (e.g., 3, 7) or double/floating-point numbers (e.g., 3.14, 2.5e-3).
* Character: Represents text strings. Enclosed in quotes (e.g., "hello", 'world').
* Factor: Represents categorical data with a fixed set of possible values or levels (e.g., "male", "female").
* Logical: Represents Boolean values (TRUE or FALSE) or logical values (NA, NULL).

The `typeof()` function can help check for the data type of a object. For example, `a`, `b`, and `c` we created in practice 1 are all of the double numberic type.
The `typeof()` function can help check for the data type of a object. For example, `a`, `b`, and `c` we created in practice 1 are all of the double numberic type.

Input
{: .label .label-green}
```r
typeof(a); typeof(b); typeof(c) # check for data type, the semicolon ";" separates multiple statements on the same line of code
```

Each data type has its own properties and functions that can be used to manipulate and analyze data. Data type is an important concept in R because it affects how the data is stored, processed, and analyzed. Choosing the appropriate data type can help you optimize your memory usage, perform the necessary data manipulations, conduct the appropriate statistical analyses, and create effective visualizations of your data.
Each data type has its own properties and functions that can be used to manipulate and analyze data. Data type is an important concept in R because it affects how the data is stored, processed, and analyzed. Choosing the appropriate data type can help you optimize your memory usage, perform the necessary data manipulations, conduct the appropriate statistical analyses, and create effective visualizations of your data.

### Vector
### Vector

A **vector** is a basic data structure that represents a sequence of values of *the same data type*. A vector can be created using the `c()` function, which combines values into a vector. For example, to create a vector base on `a`, `b` and `c`, you can use the following code:

Expand All @@ -155,15 +155,15 @@ To access an element in a vector, you can use the operator `[]` and a number ins

A **data frame** is a two-dimensional tabular data structure that represents a rectangular grid of data, where each row represents an observation and each column represents a variable. Essentially, a data frame is several equal-length vectors - one for each column. The data in each column must be of the same type, while the data in each row can be different types.

R comes with several **built-in data frames**. These data sets can be useful for learning and practicing data manipulation, analysis, and visualization techniques. To name a few,
R comes with several **built-in data frames**. These data sets can be useful for learning and practicing data manipulation, analysis, and visualization techniques. To name a few,

| `iris` | A data frame containing measurements of the length and width of petals and sepals for three species of iris flowers (setosa, versicolor, and virginica).
| `mtcars` | A data frame containing information about 32 automobiles, including miles per gallon (mpg), horsepower (hp), and other variables related to performance and design.
| `airquality` | A data frame containing daily measurements of air quality in New York City in the summer of 1973, including measurements of ozone, particulate matter, and other pollutants.
| `ChickWeight` | A data frame containing information on the weight of chickens over time, along with details on diet, gender, and other factors that may impact growth.
| `CO2` | A data frame containing measurements of carbon dioxide uptake in plants, along with information on factors such as light intensity, temperature, and humidity.
| `iris` | A data frame containing measurements of the length and width of petals and sepals for three species of iris flowers (setosa, versicolor, and virginica).
| `mtcars` | A data frame containing information about 32 automobiles, including miles per gallon (mpg), horsepower (hp), and other variables related to performance and design.
| `airquality` | A data frame containing daily measurements of air quality in New York City in the summer of 1973, including measurements of ozone, particulate matter, and other pollutants.
| `ChickWeight` | A data frame containing information on the weight of chickens over time, along with details on diet, gender, and other factors that may impact growth.
| `CO2` | A data frame containing measurements of carbon dioxide uptake in plants, along with information on factors such as light intensity, temperature, and humidity.

These data sets can be accessed by name in R and can be loaded into memory using the `data()` function. For example, to load the `iris` data frame into memory, you can use `data(iris)`.
These data sets can be accessed by name in R and can be loaded into memory using the `data()` function. For example, to load the `iris` data frame into memory, you can use `data(iris)`.

To access an element in a data frame, you can use the operator `[]` and two numbers inside it indicating the row and column position of the element. For example, to access the element in the second row and fourth column in `iris`, the syntax is `iris[2, 4]`. You can also access a variable in a data frame with the `$` operator, for example, `iris$Sepal.Length` calls out the `Sepal.Length` variable from `iris` dataset.

Expand All @@ -180,29 +180,29 @@ You can import **foreign data** into R as well. The beginners-friendly way is to
The following screenshot shows how to download and import the 2016 and 2021 Census data about the race and gender of judges in Canada [https://abacus.library.ubc.ca/dataset.xhtml?persistentId=hdl:11272.1/AB2/PG2NB4](https://abacus.library.ubc.ca/dataset.xhtml?persistentId=hdl:11272.1/AB2/PG2NB4). The dataset is retrieved from [Abacus](https://abacus.library.ubc.ca/). Try follow the steps to import the data into your RStudio.

<p align="center">
<img src="images/Download.png" width="800" />
<em>Figure 4. Download Data from Abacus</em></p>
<img src="images/Download.png" width="800" />
<em>Figure 4. Download Data from Abacus</em></p>

<p align="center">
<img src="images/Copy_URL.png" width="800" />
<em>Figure 5. Find the Downloaded File and Copy Download Link</em></p>
<img src="images/Copy_URL.png" width="800" />
<em>Figure 5. Find the Downloaded File and Copy Download Link</em></p>

<p align="center">
<img src="images/import.png" width="800" />
<em>Figure 6. Import Dataset through Environment</em></p>
<img src="images/import.png" width="800" />
<em>Figure 6. Import Dataset through Environment</em></p>

<p align="center">
<img src="images/after_update.png" width="800" />
<em>Figure 7. Import Dataset Option 1 Paste URL</em></p>
<img src="images/after_update.png" width="800" />
<em>Figure 7. Import Dataset Option 1 Paste URL</em></p>

<p align="center">
<img src="images/Browse1.png" width="800" />
<em>Figure 8. Import Dataset Option 2 Browse File</em></p>
<img src="images/Browse1.png" width="800" />
<em>Figure 8. Import Dataset Option 2 Browse File</em></p>


## 4. Working Directory

I copied the following code from the Code Preview section on the bottom right corner in Figure 7 when importing data by URL.
I copied the following code from the Code Preview section on the bottom right corner in Figure 7 when importing data by URL.

Input
{: .label .label-green}
Expand All @@ -215,14 +215,14 @@ View(X104526_gbrecs_true)
Using these code, others can easily import the target data in the URL, much easier than following the series of screenshots in practice 3. Such a nuance could also contribute to research transparency and reproducibility. Let's paste these code to a script and save it for future use.

<p align="center">
<img src="images/save_script.png" width="800" />
<em>Figure 9. Save a Script</em></p>
<img src="images/save_script.png" width="800" />
<em>Figure 9. Save a Script</em></p>

When you click the save button, a pop-out window will ask you to specify where to save the script. If we have several files to save, such as plots and datasets, we can avoid them from popping out repeatedly by setting the working directory. **Working directory** is a file path on your computer that sets the default location of any files you read into R, or save out of R. To set the working directory, you can go to the toolbar or use code.

<p align="center">
<img src="images/setwd.png" width="800" />
<em>Figure 10. Set Working Directory</em></p>
<img src="images/setwd.png" width="800" />
<em>Figure 10. Set Working Directory</em></p>

Input
{: .label .label-green}
Expand All @@ -231,7 +231,6 @@ getwd() # Get working directory
setwd() # Set working directory
```


This page is meant to introduce some core concepts and basic syntax in R.
What questions do you have about the terminologies and sytax? Now is a good time for you to share your questions, thoughts and comments.
{: .note}
<p style="color:grey; font-size:14px; padding-top: 2em">
This page is meant to introduce some core concepts and basic syntax in R.
What questions do you have about the terminologies and sytax? Now is a good time for you to share your questions, thoughts and comments.
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