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--- r_workshop1 [2018/10/06 21:27]
mariehbrice [Workshop 1: Introduction to R]
+++ r_workshop1 [2021/10/13 16:02] (current)
lsherin
@@ Line 1: / Line 1: @@
+<WRAP group>
+<WRAP centeralign>
+<WRAP important>
+<wrap em> __MAJOR UPDATE__ </wrap>
+<wrap em> As of Fall 2021, this wiki has been discontinued and is no longer being actively developed. </wrap>
+<wrap em> All updated materials and announcements for the QCBS R Workshop Series are now housed on the [[https://r.qcbs.ca/workshops/r-workshop-01/|QCBS R Workshop website]]. Please update your bookmarks accordingly to avoid outdated material and/or broken links. </wrap>
+<wrap em> Thank you for your understanding, </wrap>
+<wrap em> Your QCBS R Workshop Coordinators. </wrap>
+</WRAP>
+</WRAP>
+<WRAP clear></WRAP>
 ======= QCBS R Workshops =======
@@ Line 12: / Line 29: @@
 **Summary:** In this introductory R Workshop you will learn what R open-source statistical software is, why you should absolutely start using it, and all the first steps to help you get started in R. We will show you how R can act as a calculator, teach you about the various types of objects in R, show you how to use functions and load packages, and find all the resources you need to get help. If any of this sounds obscure, don’t worry! By the end of this workshop you’ll know what all these words mean!
-**Link to new [[https://qcbsrworkshops.github.io/Workshops/workshop01/workshop01-en/workshop01-en.html|Rmarkdown presentation]]**
+**Link to new [[https://qcbsrworkshops.github.io/workshop01/workshop01-en/workshop01-en.html|Rmarkdown presentation]]**
 //Please try it and tell the R workshop coordinators what you think!//
 Link to old [[http://prezi.com/ygatqgksbaso/qcbs-r-workshop-1/|Prezi presentation]]
-===== Installing R and R Studio =====
-Before you can effectively start this workshop, you will need to install the R and R Studio softwares on your computer. For R, go to http://www.r-project.org/ and click on download. You will have to select a mirror site (usually one close to you) and select your platform (OS X, Windows or Linux). Download the file and launch it to complete the installation. You don't have to change the default settings.
+Download the R [[https://github.com/QCBSRworkshops/workshop01/blob/dev/workshop01-en/ReferenceScriptWorkshop1.R|script]] for this lesson.
+===== Installing R and RStudio =====
-To install R Studio, go to http://www.rstudio.com/ and select R Studio from the Products tab. Click on the open source version of R Studio Desktop and select your platform to download it. Launch the file you just downloaded to complete the installation. Again, you can keep the default settings.
+Before you can effectively start this workshop, you will need to install the R and RStudio softwares on your computer. For R, go to http://www.r-project.org/ and click on download. You will have to select a mirror site (usually one close to you) and select your platform (OS X, Windows or Linux). Download the file and launch it to complete the installation. You don't have to change the default settings.
-===== What is R? =====
+To install RStudio, go to http://www.rstudio.com/ and select RStudio from the Products tab. Click on the open source version of RStudio Desktop and select your platform to download it. Launch the file you just downloaded to complete the installation. Again, you can keep the default settings.
-R is an open-source programming language designed for statistical analysis, data analysis and data visualization.
-===== Why use R? =====
+===== Learning Objectives =====
+  - Understand what R and RStudio are;
+  - Use R as a calculator;
+  - Manipulate objects in R;
+  - Install, use an R package and get help.
-{{::operating_systems.png?50|}}  {{::open_source.png?100|}} {{::the_why_r_plot_2.png?200|}}
+===== Understand what R and RStudio are =====
-R is open-source!
+==== What is R? ====
-This means that it is free, and constantly being updated and improved.
+----
+  * R is an open source programming language and environment.
+  * It is designed for data analysis, graphical display and data simulations.
+  * It is one of the world's leading statistical programming environment.
+==== Why use R? ====
+----
-R is compatible with all major operating systems, so you can exchange R work with people from all kinds of backgrounds, all over the world, using all kinds of different computer set ups.
+  * R is **open-source**! This means that it is free, and constantly being updated and improved.
+  * It is **compatible**. R works on most existing operating systems.
+  * R can help you create tables, produce graphs and do your statistics, all within the same program. So with R, there is no need to use more than one program to manage data for your publications. Everything can happen in one single program.
+  * More and more scientists are using R every year. This means that its capacities are constantly growing and will continue to increase over time. This also means that there is a big online community of people that can help with any problems you have in R.
-R can help you create tables, produce graphs and do your statistics, all within the same program. So with R, there is no need to use more than one program to manage data for your publications. Everything can happen in one single program.
+{{::operating_systems.png?50|}}  {{::open_source.png?100|}} {{::the_why_r_plot_2.png?200|}}
-More and more scientists are using R every year. This means that its capacities are constantly growing and will continue to increase over time. This also means that there is a big online community of people that can help with any problems you have in R.
+==== Using RStudio ====
+-----
-==== Using R Studio ====
+RStudio is an integrated development environment (IDE) for R.  Basically, it's a place where you can easily use the R language, visualize tables and figures and even run all your statistical analyses. We recommend using it instead of the traditional command line as it provides great visual aid and a number of useful tools that you will learn more about over the course of this workshop.
-R Studio is an integrated development environment (IDE) for R.  Basically, it's a place where you can easily use the R language, visualize tables and figures and even run all your statistical analyses. We recommend using it instead of the traditional command line as it provides great visual aid and a number of useful tools that you will learn more about over the course of this workshop.
-\\
 -----
 == CHALLENGE 1 ==
-Open R Studio
+Open RStudio
-{{:logo_rstudio.jpg?200|R Studio logo}}
+{{:logo_rstudio.jpg?200|RStudio logo}}
------
-\\
-Note for Windows users: If the restriction:"unable to write on disk" appears when you try to open R-Studio, right-click on your R-Studio icon and chose:"Execute as administrator" to open the program.
+**Note for Windows users**: If the restriction "unable to write on disk" appears when you try to open R-Studio, right-click on your R-Studio icon and chose:"Execute as administrator" to open the program.
+----
+**The console**
-When you open R studio, the first thing that you see to the left of the screen is the "console". This is where we will be working for the rest of this Introduction to R workshop. Text in the console typically looks like this:
+When you open RStudio, the first thing that you see to the left of the screen is the "console". This is where we will be working for the rest of this Introduction to R workshop.
+{{:Rstudioconsole_fleche.png?800|RStudio console}}
+Text in the console typically looks like this:
 <file rsplus| Illustrating R console input and output>
-> input
+> output
-[1] output
+[1] "This is the output"
 </file>
@@ Line 69: / Line 99: @@
 </file>
-==== R as a calculator =====
+===== 2. Use R as a calculator ======
 The first thing to know about the R console is that you can use it as a calculator.
 <file rsplus| Addition>
@@ Line 82: / Line 113: @@
 <file rsplus| Multiplication>
-> 2*2
+> 2 * 2
 [1] 4
 </file>
 <file rsplus| Division>
-> 8/2
+> 8 / 2
 [1] 4
 </file>
@@ Line 95: / Line 126: @@
 [1] 8
 </file>
+\\
+-----
 == CHALLENGE 2 ==
-Complete the following skill testing question in the R Studio console: 2+16x24-56
+Use R to calculate the following skill testing question: 2 + 16 x 24 - 56
-++Challenge 2: Solution| \\ ''> 2+16*24-56\\
+++Challenge 2: Solution| \\ ''> 2 + 16 * 24 - 56\\
 [1] 330'' ++ \\
+-----
 == CHALLENGE 3 ==
-Complete the following skill testing question in the R Studio console.
+Use R to calculate the following skill testing question: \\
-Pay attention to the order of operations when thinking about this question.
++ 16 x 24 - 56 / (2 + 1) - 457
-+16x24-56/(2+1)-457
-++Challenge 3: Solution| \\ ''> 2+16*24-56/(2+1)-457\\
+//Pay attention to the order of operations when thinking about this question!//
+++Challenge 3: Solution| \\ ''> 2 + 16 * 24 - 56 / (2 + 1) - 457\\
 [1] -89.66667'' ++ \\
 Note that R //always// follows the order of priorities.
-\\
 -----
 **R TIP**
@@ Line 123: / Line 155: @@
 {{::arrow_keys.png?100|Use arrow keys to go back to previous commands.}}
 -----
-\\
 == CHALLENGE 4 ==
 What is the area of a circle with a radius of 5 cm?
-++Challenge 4: Solution| \\ Recall : $A_{circle} = \pi r^2$ \\ ''> 3.1416*5^2\\
+++Challenge 4: Solution| \\ Recall : $A_{circle} = \pi r^2$ \\ ''> 3.1416 * 5^2\\
 [1] 78.54''\\
-R also has many built in constants like pi.\\
+R also has many built-in constants like pi.\\
-''> pi*5^2\\
+''> pi * 5^2\\
 [1] 78.54''
 ++ \\
-===== The concept of object in R =====
+===== 3. Manipulate objects in R =====
+==== Object ====
+-----
 You have learned so far how to use R as a calculator to obtain various numerical values. However, it can get tiresome to always write the same code down in the R console, especially if you have to use some values repeatedly. This is where the concept of object becomes useful.
-R is an object-oriented programming language. What this means is that we can allocate a name to values we've created to save them in our workspace. An object is composed of three parts: 1) a value we're interested in, 2) an identifier and 3) the assignment operator. The value can be almost anything we want: a number, the result of a calculation, a string of characters, a data frame, a plot or a function. The identifier is the name you assign to the value. Whenever you want to refer to this value, you simply type the identifier in the R console and R will return its value. Identifiers can include only letters, numbers, periods and underscores, and should always begin with a letter. The assignment operator resembles an arrow (''%%<-%%'') and is used to link the value to the identifier. The following code clarifies these ideas:
+R is an object-oriented programming language. What this means is that we can allocate a name to values we've created to save them in our workspace. An object is composed of three parts: 1) a value we're interested in, 2) an identifier and 3) the assignment operator.
+  - The value can be almost anything we want: a number, the result of a calculation, a string of characters, a data frame, a plot or a function.
+  - The identifier is the name you assign to the value. Whenever you want to refer to this value, you simply type the identifier in the R console and R will return its value. Identifiers can include only letters, numbers, periods and underscores, and should always begin with a letter.
+  - The assignment operator resembles an arrow (''%%<-%%'') and is used to link the value to the identifier.
+The following code clarifies these ideas:
 <file rsplus| Illustrating the concept of object>
-#Let's create an object called mean.x.
+# Let's create an object called mean_x.
-#The # symbol is used in R to indicate comments. It is not processed by R.
+# The # symbol is used in R to indicate comments. It is not processed by R.
-#It is important to add comments to code so that it can be understood and used by other people.
+# It is important to add comments to code so that it can be understood and used by other people.
-mean.x <- (2+6)/2
+mean_x <- (2 + 6) / 2
-#Typing its name will return its value.
+# Typing its name will return its value.
-mean.x
+> mean_x
-#! [1]  4
+[1]  4
 </file>
-Here, ''(2+6)/2'' is the value you want to save as an object. The identifier ''mean.x'' is assigned to this value. Typing ''mean.x'' returns the value of the calculation (//i.e.// 4). You have to be scrupulous when typing the identifier because R is case-sensitive: writing ''mean.x'' is not the same as writing ''MEAN.X''. You can see that the assignment operator ''%%<-%%'' creates an explicit link between the value and the identifier. It always points from the value to the identifier. Note that it is also possible to use the equal sign ''='' as the assignment operator but [[http://stackoverflow.com/questions/1741820/assignment-operators-in-r-and | it is preferable not]] to because it is also used for other operations in R, which can cause problems when using it for assignment. Finally, imagine that the operator ''%%<-%%'' and ''%%=%%'' follow their own order of priorities.
+Here, ''(2 + 6) / 2'' is the value you want to save as an object. The identifier ''mean_x'' is assigned to this value. Typing ''mean_x'' returns the value of the calculation (//i.e.// 4). You have to be scrupulous when typing the identifier because R is case-sensitive: writing ''mean_x'' is not the same as writing ''MEAN_X''. You can see that the assignment operator ''%%<-%%'' creates an explicit link between the value and the identifier. It always points from the value to the identifier. Note that it is also possible to use the equal sign ''='' as the assignment operator but [[http://stackoverflow.com/questions/1741820/assignment-operators-in-r-and | it is preferable not]] to because it is also used for other operations in R, which can cause problems when using it for assignment. Finally, imagine that the operator ''%%<-%%'' and ''%%=%%'' follow their own order of priorities.
 <file rsplus| Order of priorities with assignment operator and equal sign >
@@ Line 165: / Line 205: @@
 </file>
+----
+=== Good practices in R code ===
+**Name**
+  * Try having short and explicit names for your variables. Naming a variable ''var''  is not very informative.
+  * Use an underscore (''_''), or a dot (''.'') to separate words within a name and try to be consistent!
+  * Avoid using names of existing functions and variables (e.g., ''c'', ''table'', ''T'', etc.)
------
+**Space**
-**R TIP**
+  - Add spaces around all operators (''='', ''+'', ''-'', ''<-'', etc.) to make the code more readable.
+  - Always put a space after a comma, and never before (like in regular English).
-Try choosing explicit names for your objects. It is good practice and allows you to understand quickly what the object represents. Naming an object ''variable'' or ''data'' isn't very informative!
------
-\\
 -----
 == CHALLENGE 5 ==
-> Create an object with a value of 1 + 1.718282 (Euler's number) and name it ''euler.value''
+> Create an object with a value of 1 + 1.718282 (Euler's number) and name it ''euler_value''
 ++++
 Challenge 5: Solution|
 <code rsplus>
-euler.value <- 1 + 1.718282
+> euler_value <- 1 + 1.718282
 </code>
 ++++
------
-\\
 -----
 == CHALLENGE 6 ==
@@ Line 194: / Line 236: @@
 ''unexpected symbol in %%"%%your object name%%"%%''.
 ++++
------
-\\
 -----
 **R TIP**
-Using the Tab key allows auto-completion of names. It speeds up command entering and avoids spelling errors. For example, if you type ''eu'' and then press tab, you will see a list of objects or functions beginning with ''eu''. Select ''euler.value'' (the object we just created) and press enter. The ''euler.value'' identifier now appears at the command line.
+Using the Tab key allows auto-completion of names. It speeds up command entering and avoids spelling errors. For example, if you type ''eu'' and then press tab, you will see a list of objects or functions beginning with ''eu''. Select ''euler_value'' (the object we just created) and press enter. The ''euler_value'' identifier now appears at the command line.
+==== Types of data structures in R ====
 -----
-\\
+Using R to analyze your data is an important aspect of this software. Data comes in different forms and can be grouped in distinct categories. Depending on the nature of the values enclosed inside your data or object, R classifies them accordingly. The following figure illustrates common objects found in R.
-==== Types of data structures in R ====
-Using R to analyse your data is an important aspect of this software. Data comes in different forms and can be grouped in distinct categories. Depending on the nature of the values enclosed inside your data or object, R classifies them accordingly. The following figure illustrates common objects found in R.
 {{ :: imageobjr.png?500 |Types of objects in R}}
-The first object is a vector. It is one of the most common objects in R. A vector is an entity consisting of a list of related values. All values in a vector must be the same mode. The main modes in R are numeric, character and logical. Numeric vectors are made of numbers only. Character vectors include text strings or a mix of text strings and other modes. You need to use ''%%""%%'' to delimit elements in a character vector. Logical vectors include ''TRUE/FALSE'' entries only. A vector with a single value (usually a constant) is called an atomic vector.
+The first object is a **vector**. It is one of the most common objects in R. A vector is an entity consisting of a list of related values. All values in a vector must be the same mode. The main modes in R are **numeric**, **character** and **logical**. Numeric vectors are made of numbers only. Character vectors include text strings or a mix of text strings and other modes. You need to use ''%%""%%'' to delimit elements in a character vector. Logical vectors include ''TRUE/FALSE'' entries only. A vector with a single value (usually a constant) is called an atomic vector.
-Before we look at how to create different types of vectors, let's have a look at the generic method of creating vectors. If you recall what you have just learned, you will first have to identify some value you want to put in a vector and then link it to an identifier with the assignment operator (//i.e.// create an object). When you have more than one value in a vector, you need a way to tell R to group all these values to create a vector. The trick here is to use the ''c'' function. Don't worry, you will learn about functions pretty soon in one of the following sections. For now, just remember to put your values  between parentheses next to letter ''c'' in this format: ''vector.name %%<-%% c(value1, value2, value3, ...)''. The function ''c()'' means combine or concatenate. It is a quick and easy function so remember it!
+Before we look at how to create different types of vectors, let's have a look at the generic method of creating vectors. If you recall what you have just learned, you will first have to identify some value you want to put in a vector and then link it to an identifier with the assignment operator (//i.e.// create an object). When you have more than one value in a vector, you need a way to tell R to group all these values to create a vector. The trick here is to use the ''c()'' function. Don't worry, you will learn about functions pretty soon in one of the following sections. For now, just remember to put your values between parentheses next to letter ''c()'' in this format: ''vector.name %%<-%% c(value1, value2, value3, ...)''. The function ''c()'' means combine or concatenate. It is a quick and easy function so remember it!
+Now that you know the generic method to create a vector in R, let's have a look at how to create different types of vectors.
+<file rsplus| Creating vectors in R>
+# Create a numeric vector with the c (which means combine or concatenate) function.
+# We will learn about functions soon!
+> num_vector <- c(1, 4, 3, 98, 32, -76, -4)
+# Create a character vector. Always use "" to delimit text strings!
+> char_vector <- c("blue", "red", "green")
+# Create a logical or boolean vector. Don't use "" or R will consider this as text strings.
+> bool_vector <- c(TRUE, TRUE, FALSE)
+#It is also possible to use abbreviations for logical vectors.
+> bool_vector2 <- c(T, T, F)
+</file>
 -----
 == CHALLENGE 7 ==
-> Create a vector containing the first five odd numbers (starting from 1) and name it odd.n.
+> Create a vector containing the first five odd numbers (starting from 1) and name it odd_n.
 ++++Challenge 7: Solution|
 <code rsplus>
-odd.n <- c(1,3,5,7,9)
+> odd_n <- c(1, 3, 5, 7, 9)
 </code>
 ++++
 -----
-\\
+**R TIP**
-Now that you know the generic method to create a vector in R, let's have a look at how to create different types of vectors.
-<file rsplus| Creating vectors in R>
-#Create a numeric vector with the c (which means combine or concatenate) function.
-#We will learn about functions soon!
-num.vector<-c(1,2,5,3,6,-2,4)
-#Create a character vector. Always use "" to delimit text strings!
-col.vector<-c("blue","red","green")
-#Create a logical vector. Don't use "" or R will consider this as text strings.
-logic.vector<-c(TRUE,TRUE,FALSE)
-#It is also possible to use abbreviations for logical vectors.
-logic.vector2<-c(T,T,F)
-</file>
-\\
------
-**Truc R**
-Use ''dput'' function to obtain the reverse, //i.e.// the content of an object formatted as a vector. //e.g.// :
+Use ''dput()'' function to obtain the reverse, //i.e.// the content of an object formatted as a vector. //e.g.// :
 <code rsplus>
-> odd <- c(1, 3, 5, 7, 9)
+> odd_n <- c(1, 3, 5, 7, 9)
-> odd
+> odd_n
 [1] 1 3 5 7 9
-> dput(odd)
+> dput(odd_n)
 c(1, 3, 5, 7, 9)
+# The output can be copied-pasted to create a new object by using the structure() function
+> structure(c(1, 3, 5, 7, 9))
 </code>
-This demonstration might not be that convincing, but keep in mind that it can be very useful when you're manipulating data. The result returned by R with ''%%dput%%'' can be copied-pasted to create a new object, since it's already formatted for R. On the contrary, the answer that R gives when typing ''odd'' is not directly usable since it's not in a ''c()'' function and that the numbers are not separated by commas.
+This demonstration might not be that convincing, but keep in mind that it can be very useful when you're manipulating data. These functions are really useful to provide a reproducible example for a question on [[https://stackoverflow.com/|Stack Overflow]] for instance (see one more application in the part about data frames)!
 -----
+What you have learned previously about calculations is also valid for vectors: vectors can be used for calculations. The only difference is that when a vector has more than 1 element, the operation is applied on all elements of the vector. The following example clarifies this.
-What you have learned previously is also valid for vectors: vectors can be used for calculations. The only difference is that when a vector has more than 1 element, the operation is applied on all elements of the vector. The following example clarifies this.
 <file rsplus| Calculations with vectors>
-#Create two numeric vectors.
+# Create two numeric vectors.
-x <- 1:5
+> x <- 1:5
-#An equivalent form is: x <- c(1:5).
-y <- 6
-#Remember that the : symbol, when used with numbers, is the sequence operator.
-#It tells R to create a series of numbers increasing by 1.
-#Equivalent to this is x <- c(1,2,3,4,5)
-#Let's sum both vectors.
-#6 is added to all elements of the x vector.
-x + y
-#! [1]  7 8 9 10 11
-#Let's multiply x by itself.
-x * x
-#! [1]  1 4 9 16 25
-#It is the same thing as using exponents:
-x^2
-#! [1]  1 4 9 16 25
-</file>
-Another important type of object you will use regularly is the data frame. A data frame is a group of vectors of the same length (//i.e.// the same number of elements). Columns are always variables and rows are observations, cases, sites or replicates. Different modes can be saved in different columns (but always the same mode in a column). It is in this format that ecological data are usually stored. The following example shows a fictitious dataset representing 4 sites where soil pH and number of plant species were recorded. There is also a "Treatment" variable (fertilised or not). Let's have a look at the creation of a data frame.
+# An equivalent form is: x <- c(1:5).
+> y <- 6
+# Remember that the : symbol, when used with numbers, is the sequence operator.
+# It tells R to create a series of numbers increasing by 1.
+# Equivalent to this is x <- c(1,2,3,4,5)
+# Let's sum both vectors.
+# 6 is added to all elements of the x vector.
+> x + y
+[1]  7 8 9 10 11
+# Let's multiply x by itself.
+> x * y
+[1]  6 12 18 24 30
+</file>
+----
+Another important type of object you will use regularly is the data frame. A data frame is a group of vectors of the same length (//i.e.// the same number of elements). Columns are always variables and rows are observations, cases, sites or replicates. Different modes can be saved in different columns (but always the same mode in a column). It is in this format that ecological data are usually stored. The following example shows a fictitious dataset representing 4 sites where soil pH and the number of plant species were recorded. There is also a "Treatment" variable (fertilized or not). Let's have a look at the creation of a data frame.
-^ Site_ID ^ soil.pH ^ num.sp ^ Treatment ^
+^ site_id ^ soil_pH ^ num_sp ^ treatment ^
 | A1.01 | 5.6 | 17 | Fertilised |
 | A1.02 | 7.3 | 23 | Fertilised |
@@ Line 286: / Line 328: @@
 <file rsplus| Creating a data frame>
-#We first start by creating vectors.
+# We first start by creating vectors.
-Site_ID<-c("A1.01","A1.02","B1.01","B1.02")
+> site_id <- c("A1.01", "A1.02", "B1.01", "B1.02")
-soil.pH<-c(5.6,7.3,4.1,6.0)
+> soil_pH <- c(5.6, 7.3, 4.1, 6.0)
-num.sp<-c(17,23,15,7)
+> num_sp <- c(17, 23, 15, 7)
-Treatment<-c("Fert","Fert","No.Fert","No.Fert")
+> treatment <- c("Fert", "Fert", "No_fert", "No_fert")
-#We then combine them to create a data frame with the data.frame function.
-my.first.df<-data.frame(Site_ID,soil.pH,num.sp,Treatment)
+# We then combine them to create a data frame with the data.frame function.
-#Visualise it!
+my_df <- data.frame(site_id, soil_pH, num_sp, treatment)
-my.first.df
+# Visualise it!
+my_df
 </file>
+\\
+-----
+** R TIP**
+Here the ''dput()'' function in another example.
+<code rsplus>
+> dput(my_df)
+structure(list(site_id = c("A1.01", "A1.02", "B1.01", "B1.02"
+), soil_pH = c(5.6, 7.3, 4.1, 6), num_sp = c(17, 23, 15, 7),
+treatment = c("Fert", "Fert", "No_fert", "No_fert")),
+class = "data.frame", row.names = c(NA, -4L))
+# It's possible to rebuild the initial data frame (with some associated metadata as the class of variables) by copying and pasting the previous output:
+> structure(list(site_id = c("A1.01", "A1.02", "B1.01", "B1.02"
+), soil_pH = c(5.6, 7.3, 4.1, 6), num_sp = c(17, 23, 15, 7),
+treatment = c("Fert", "Fert", "No_fert", "No_fert")),
+class = "data.frame", row.names = c(NA, -4L))
+</code>
+-----
 Other types of objects include matrices, arrays and lists. A matrix is similar to a data frame except that all cells in the matrix must be the same mode. An array is similar to a matrix but can have more than two dimensions. Arrays are usually used for advanced computation like numerical simulations and permutation tests. A list is an aggregation of various types of objects. For example, a list could include a vector, a data frame and a matrix in the same object.
-===== Indexing objects in R =====
+==== Indexing objects in R ====
+----
+=== Indexing a vector ===
 Typing an object's name in R returns the complete object. But what if our object is a huge data frame with millions of entries? It can easily become confusing to identify specific elements of an object. R allows us to extract only part of an object. This is called indexing. We specify the position of values we want to extract from an object with brackets ''[ ]''. The following code illustrates the concept of indexation with vectors.
 <file rsplus| Indexing a vector>
-#Let's first create a numeric and a character vector.
+# Let's first create a numeric and a character vector.
-#There is no need to do this again if you already did it in the previous exercise!
+# There is no need to do this again if you already did it in the previous exercise!
-num.vector<-c(1,2,5,3,6,-2,4)
+> odd_n <- c(1, 3, 5, 7, 9)
-col.vector<-c("blue","red","green")
-#Extract the third element of the numeric vector.
+# Extract the second element of the numeric vector.
-num.vector[3]
+> odd_n[2]
-#! [1]  5
+[1]  3
-#Extract all but the third element of the numeric vector.
-num.vector[-3]
+# Extract the second and fourth elements of the numeric vector.
-#! [1]  1  2  3  6  -2  4
+> odd_n[c(2, 4)]
-#Extract the first and third elements of the character vector.
+[1]  3 7
-col.vector[c(1,3)]
-#! [1]  "blue"  "green"
+# Extract all but the two first elements of the numeric vector.
-#Extract the first and fourth elements of the character vector.
+> odd_n[c(-1, -2)]
-#There is no fourth value in this vector so R returns a null value (i.e. NA)
+[1] 5 7 9
-#NA stands for 'Not available'.
-col.vector[c(1,4)]
+# If you select a position that is not in the numeric vector
-#! [1]  "blue"  NA
+> odd_n[c(1,6)]
-#Extract all values from the numeric vector greater than 5.
+[1] 1 NA
-num.vector[num.vector>5]
+# There is no sixth value in this vector so R returns a null value (i.e. NA)
-#! [1]  6
+# NA stands for 'Not available'.
-#Extract all elements of the character vector corresponding exactly to "blue".
-#Note the use of the double equal sign ==.
+# You can use logical statement to select values.
-col.vector[col.vector=="blue"]
+> odd_n[odd_n > 4]
-#! [1]  "blue"
+[1] 5 7 9
+# Extract all elements of the character vector corresponding exactly to "blue".
+> char_vecteur[char_vecteur == "blue"]
+[1]  "bleu"
+# Note the use of the double equal sign ==.
 </file>
-\\
 -----
 == CHALLENGE 8 ==
-> a) Extract the 4th value of the ''num.vector'' vector.
+> a) Extract the 4th value of the ''num_vector'' vector.
-> b) Extract the 1st and 3rd values of the ''num.vector'' vector.
+> b) Extract the 1st and 3rd values of the ''num_vector'' vector.
-> c) Extract all values of the ''num.vector'' vector excluding the 2nd and 4th values.
+> c) Extract all values of the ''num_vector'' vector excluding the 2nd and 4th values.
 >
 ++++Challenge 8a: Indexing vectors|
 <code rsplus>
-num.vector[4]
+> num_vector[4]
-#! [1] 3
+[1] 3
 </code>
 ++++
 ++++Challenge 8b: Indexing vectors|
 <code rsplus>
-num.vector[c(1,3)]
+> num_vector[c(1, 3)]
-#! [1] 1  5
+[1] 1  5
 </code>
 ++++
 ++++Challenge 8c: Indexing vectors|
 <code rsplus>
-num.vector[c(-2,-4)]
+> num_vector[c(-2, -4)]
-#! [1] 1  5  6  -2  4
+[1] 1  5  6  -2  4
 </code>
 ++++
------
-\\
 -----
 == CHALLENGE 9 ==
@@ Line 362: / Line 429: @@
 >Explore the difference between these 2 lines of code:
 <file rsplus| Differences between codes>
-col.vector == "blue"
+> char_vector == "blue"
-col.vector[col.vector == "blue"]
+> char_vector[char.vector == "blue"]
 </file>
 ++++Challenge 9: Differences between codes|
-In the first line of code you test a logical statement. For each entry in the "col.vector" vector, R checks whether the entry is exactly equal to "blue" or not and returns a TRUE/FALSE answer. The next subsection introduces you to logical statements. In the second line of code you ask R to extract all values within the "col.vector" vector that are exactly equal to "blue". It is also possible to extract the "blue" value by assigning a logical value to each element of the vector. Of course, you have to know the position of the "blue" value inside the vector.
+In the first line of code you test a logical statement. For each entry in the "char_vector" vector, R checks whether the entry is exactly equal to "blue" or not and returns a TRUE/FALSE answer. The next subsection introduces you to logical statements. In the second line of code you ask R to extract all values within the "char_vector" vector that are exactly equal to "blue". It is also possible to extract the "blue" value by assigning a logical value to each element of the vector. Of course, you have to know the position of the "blue" value inside the vector.
 <file rsplus| Extracting values with logical indexing>
-col.vector[c(TRUE, FALSE, FALSE)]
+> char_vector[c(TRUE, FALSE, FALSE)]
-#! [1] "blue"
+[1] "blue"
-#We specify which value is true,
+# We specify which value is true,
-#i.e. the value we want R to return (the first one)
+# i.e. the value we want R to return (the first one)
-#which corresponds to "blue".
+# which corresponds to "blue".
 </file>
 ++++
 -----
-\\
+=== Indexing a data frame ===
-For data frames, the concept of indexation is similar, but we usually have to specify two dimensions: the row and column numbers. The R syntax is\\ ''dataframe[row number, column number]''. Here are a few examples of data frame indexation. Note that the first four operations are also valid for indexing matrices.
+For data frames, the concept of indexation is similar, but we usually have to specify two dimensions: the row and column numbers. The R syntax is
+''dataframe[row number, column number]''.
+Here are a few examples of data frame indexation. Note that the first four operations are also valid for indexing matrices.
 <file rsplus| Indexing a data frame>
-#Let's reuse the data frame we created earlier (my.first.df)
+# Let's reuse the data frame we created earlier (my_df)
-#Extract the 1st row of the data frame
+# Extract the 1st row of the data frame
-my.first.df[1,]
+> my_df[1, ]
-#Extract the 3rd columm
-my.first.df[,3]
-#Extract the 2nd element of the 4th column
-my.first.df[2,4]
-#Extract lines 2 to 4
-my.first.df[c(2:4),]
-#Extract the "Site ID" column by referring directly to its name
-#The dollar sign ($) allows such an operation!
-my.first.df$Site_ID
-#Extract the "Site ID" and "Soil pH" variables
-my.first.df[,c("Site_ID","soil.pH")]
-</file>
-\\
------
-== CHALLENGE 10 ==
-> a) Extract the ''num.sp'' column from ''my.first.df'' and multiply its values by the first four values of the ''num.vec'' vector.\\
-> b) After that, write a statement that checks if the values you obtained are greater than 25. Refer to challenge 9 to complete this challenge.\\
->
-++++ Challenge 10a: Indexing and multiplying|
-<code rsplus>
-my.first.df$num.sp * num.vector[c(1:4)]
-#! [1] 17 46 75 21
-</code>
-++++
-++++ Challenge 10b: Logical statement|
+# Extract the 3rd columm
-<code rsplus>
+> my_df[, 3]
-(my.first.df$num.sp * num.vector[c(1:4)]) > 25
-#! [1] FALSE TRUE TRUE FALSE
+# Extract the 2nd element of the 4th column
-</code>
+> my_df[2, 4]
-++++
------
+# Extract lines 2 to 4
-\\
+> my_df[c(2,4), ]
+# Extract the "site_id" column by referring directly to its name
+# The dollar sign ($) allows such an operation!
+> my_df$site_id
+# Extract the "site_id" and "soil_pH" variables
+> my_df[, c("site_id","soil_pH")]
+</file>
+----
 ==== A quick note on logical statements ====
-Challenge 9 and 10 briefly introduced R's possibility to test logical statements, //i.e.// to evaluate whether a statement is true or false. You can compare objects with the following logical operators:
+R gives you the possibility to test logical statements, //i.e.// to evaluate whether a statement is true or false. You can compare objects with the following logical operators:
 ^ Operator ^ Description ^
@@ Line 433: / Line 489: @@
 <file rsplus| Testing logical statements>
-#First, let's create two vectors for comparison.
+# First, let's create two vectors for comparison.
-x2 <- c(1:5)
+> x2 <- c(1:5)
-y2 <- c(1,2,-7,4,5)
+> y2 <- c(1, 2, -7, 4, 5).
-#Let's verify if the elements in x2 are greater or equal to 3.
-#R returns a TRUE/FALSE value for each element (in order).
+# Let's verify if the elements in x2 are greater or equal to 3.
-x2 >= 3
+# R returns a TRUE/FALSE value for each element (in order).
-#! [1] FALSE FALSE TRUE TRUE TRUE
+> x2 >= 3
-#Let's see if the elements of x2 are exactly equal to those of y2.
+[1] FALSE FALSE TRUE TRUE TRUE
-x2 == y2
-#! [1] TRUE TRUE FALSE TRUE TRUE
+# Let's see if the elements of x2 are exactly equal to those of y2.
-#Is 3 not equal to 4? Of course!
+> x2 == y2
-!= 4
+[1] TRUE TRUE FALSE TRUE TRUE
-#! [1] TRUE
-#Let's see which values in x2 are greater than 2 but smaller than 5.
+# Is 3 not equal to 4? Of course!
-#You have to write x2 twice.
+> 3 != 4
-#If you write x2 > 2 & < 5, you will get an error message.
+[1] TRUE
-x2 > 2 & x2 < 5
-#! [1] FALSE FALSE TRUE TRUE FALSE
+# Let's see which values in x2 are greater than 2 but smaller than 5.
+# You have to write x2 twice.
+# If you write x2 > 2 & < 5, you will get an error message.
+> x2 > 2 & x2 < 5
+[1] FALSE FALSE TRUE TRUE FALSE
 </file>
+\\
+-----
+== CHALLENGE 10 ==
+> a) Extract the ''num_sp'' column from ''my_df'' and multiply its values by the first four values of the ''num_vec'' vector.\\
+> b) After that, write a statement that checks if the values you obtained are greater than 25. Refer to challenge 9 to complete this challenge.\\
+>
+++++ Challenge 10a: Indexing and multiplying|
+<code rsplus>
+> my_df$num.sp * num.vector[c(1:4)]
+[1] 17  92  45 686
+# or
+> my_df[, 3] * num_vector[c(1:4)]
+[1]  17  92  45 686
-===== Functions =====
+</code>
+++++
+++++ Challenge 10b: Logical statement|
+<code rsplus>
+> (my_df$num.sp * num.vector[c(1:4)]) > 25
+[1] FALSE TRUE TRUE FALSE
+</code>
+++++
+===== Functions =====
+----
 Most of the time with R, you will need to use functions to do what you want.
@@ Line 463: / Line 546: @@
 To perform the function call you will need entry values called **arguments** (or sometimes parameters).
-After performing its operations, the function will then give you a return **value**.
+After performing its operations, the function will then give you a **return value**.
 The command also must be structured properly, following the "grammar rules" of the R language (syntax).
@@ Line 470: / Line 553: @@
 ''//function_name//**(**arg1**,** arg2**, ...)**''
-Ex:
+Here an example:
 <file rsplus| Function syntax>
-sum(1, 2)
+> sum(1, 2)
 </file>
 Arguments are **values** and the instructions the function needs to run.
@@ Line 480: / Line 562: @@
 <file rsplus| Objects as arguments>
-a <- 3
+> a <- 3
-b <- 4
+> b <- 5
-sum(a, b)
+> sum(a, b)
-#! [1] 7
+[1] 8
 </file>
-On the last line, the output that appears is the **return value** of the function. In this case, it is the sum of ''a'' and ''b'', 7.\\
+On the last line, the output that appears is the **return value** of the function. In this case, it is the sum of ''a'' and ''b'', 8.\\
-\\
 -----
@@ Line 502: / Line 583: @@
 ++++Challenge 11a: Calling functions|
 <file rsplus Challenge11a>
-a <- 1:5
+> a <- 1:5
-b <- 2
+> b <- 2
-result_add <- a + b
+> result_add <- a + b
-result_sum <- sum(a, b)
+> result_sum <- sum(a, b)
 </file>
 <code rsplus>
-result_add
+> result_add
-#! [1]  3 4 5 6 7
+[1]  3 4 5 6 7
 </code>
 The operation on the vector adds 2 to each element. The result is a **vector**. \\
 <code rsplus>
-result_sum
+> result_sum
-#! [1] 17
+[1] 17
 </code>
 The function ''sum()'' adds all values of ''a'' and ''b''. It is the same as doing 1 + 2 + 3 + 4 + 5 + 2. The result is a **number**.\\
@@ Line 522: / Line 603: @@
 ++++Challenge 11b: Calling functions|
 <code rsplus>
-sum(result_sum, 5)
+> sum(result_sum, 5)
-#! [1] 22
+[1] 22
 </code>
 ++++
 -----
-\\
+=== Arguments ===
 Arguments each have a **name** that can be provided during a function call.\\
@@ Line 536: / Line 617: @@
 <file rsplus| Argument name>
-log(8, base=2)
+log(8, base = 2)
 </file>
-\\
 -----
 == CHALLENGE 12 ==
@@ Line 546: / Line 625: @@
 <file rsplus| Challenge 12>
-a <- 1:100
+> a <- 1:100
-b <- a^2
+> b <- a^2
-plot(a, b)
+> plot(a, b)
-plot(b, a)
+> plot(b, a)
-plot(x=a, y=b)
+> plot(x = a, y = b)
-plot(y=b, x=a)
+> plot(y = b, x = a)
 </file>
@@ Line 564: / Line 643: @@
 Plots the graph of ''a'' as a function of ''b''.The argument names are not provided, the order of the arguments matters.
-<code rsplus>plot(x=a, y=b)</code>
+<code rsplus>plot(x = a, y = b)</code>
 {{:pltxayb.png?200|}}\\
 Plots the graph of ''b'' as a function of ''a'', same as plot(a, b).
-<code rsplus>plot(y=b, x=a)</code>
+<code rsplus>plot(y = b, x = a)</code>
 {{:plotybxa.png?200|}}\\
 Plots the graph of ''b'' as a function of ''a''. The argument names are provided, the order of the arguments does not matter.
 ++++
 -----
-\\
 As a reference, here is a list of some of the most common R functions:
@@ Line 583: / Line 661: @@
 help (or ?), help.search (or ??), help.start
 </code>
-\\
-==== Packages ====
+===== 4. Install, use an R package and get help =====
+==== Package ====
+-----
 Packages are a grouping of functions and/or datasets that share a similar theme. Ex : statistics, spatial analysis, plotting...
@@ Line 594: / Line 672: @@
 They are usually available through the Comprehensive R Archive Network (CRAN)[[http://cran.r-project.org/web/packages/]]
-Currently, more than 5877 package are publicly available.
+Currently, more than 16000 packages are publicly available.
 To install packages on your computer, use the function ''install.packages()''
 <code rsplus>
-install.packages("ggplot2")
+> install.packages("ggplot2")
 </code>
@@ Line 606: / Line 684: @@
 <code rsplus>
-qplot(1:10, 1:10)
+> qplot(1:10, 1:10)
 </code>
@@ Line 616: / Line 694: @@
 <code rsplus>
-library("ggplot2")
+> library("ggplot2")
-qplot(1:10, 1:10)
+> qplot(1:10, 1:10)
 </code>
-Now the function is found and the execution of our code leads to the following graph:\\
+Now the function is found and the execution of our code leads to the following graph:
-{{::qplot.png?200|}}\\
+{{::qplot.png?200|}}
 It is good practice to unload packages once we are done with them because it might conflict with other packages. Unloading a package is done with the ''detach()'' function and by specifying that it is a package:
 <file rsplus| Unloading a package>
-detach(package:ggplot2)
+> detach(package:ggplot2)
 </file>
-===== Getting help and additional resources =====
-==== Getting help with functions ====
+==== Getting help ====
+-----
+=== Getting help with functions ===
 We've seen so far that R is really great and offers us a lot of functions to work with. Among all these functions, there are probably some that can do what we want.
@@ Line 640: / Line 720: @@
 <code rsplus Searching for a function>
-??sequence
+> ??sequence
 </code>
@@ Line 651: / Line 731: @@
   * On the right, we have the description of the function
-Usually, the functions have name that are representative of what they do. This makes it easier to use them. Remember this if you ever start to write your own functions!
+Usually, functions have names that are representative of what they do. This makes it easier to use. Remember this if you ever start to write your own functions!
 Here the result that interests us is ''base::seq'', i.e. the function ''seq'' that can be found in the ''base'' package and that generates sequences.\\
-//Note//: the ''base'' package contains basic functions that load with R when you launch it and are therefore always available.
+**Note**: the ''base'' package contains basic functions that load with R when you launch it and are therefore always available.
+-----
+=== Help page of functions ===
 We will use the ''seq()'' function to generate our sequence. But how does it work? What does it do? How should I use it?
@@ Line 664: / Line 745: @@
 <code rsplus Finding help>
-?seq
+> ?seq
 </code>
@@ Line 681: / Line 762: @@
   * **See Also**: Related functions that can sometimes be of use, especially when searching for the correct function for our needs.
   * **Examples**: Some examples on how to use the function(s)
-\\
 -----
 == Challenge 13 ==
@@ Line 695: / Line 774: @@
 ++++ Challenge 13a |
 <code rsplus>
-seq(from=0, to=10, by=2)
+> seq(from = 0, to = 10, by = 2)
-#! [1] 0 2 4 6 8 10
+[1] 0 2 4 6 8 10
 </code>
@@ Line 702: / Line 781: @@
 <code rsplus>
-seq(0, 10, 2)
+> seq(0, 10, 2)
-#! [1] 0 2 4 6 8 10
+[1] 0 2 4 6 8 10
 </code>
 ++++
@@ Line 709: / Line 788: @@
 ++++ Challenge 13b |
 <code rsplus>
-numbers <- c(4, 55, 6, 22, 3)
+> numbers <- c(2, 4, 22, 6, 26)
-sort(numbers, decreasing=TRUE)
+> sort(numbers, decreasing = TRUE)
-#! [1]  55 22 6 4 3
+[1]  26 22  6  4  2
 </code>
 ++++
 -----
-\\
+=== Getting help on the Web ===
-==== Getting help on the Web ====
 Usually, your best source of information will be your favorite search engine (Google, Bing, Yahoo, etc.)
@@ Line 727: / Line 804: @@
   * Learn to read discussion forums. Chances are other people already had the same problem and asked about it. Create your account on forums where questions about R are often asked like [[http://stats.stackexchange.com|stackexchange]].
   * Don't hesitate to search again with different keywords!
-\\
 -----
@@ Line 745: / Line 820: @@
 d) ''ls'' \\
 ++++
------
+===== Additional resources =====
-\\
-==== Some useful books on R ====
-Dalgaard, P. - Introductory Statistics with R.\\
-Zuur, A.F., Ieno, E.N. & Meesters, E. - A Beginner's Guide to R.\\
-Crawley, M. - The R Book.\\
-Everitt, B.S. & Hothorn, T. - A Handbook of Statistical Analyses Using R.\\
-Kabacoff, R.I. - R in Action.
-==== Some useful websites ====
+=== Cheat Sheets ===
-http://stats.stackexchange.com/ \\
-https://www.zoology.ubc.ca/~schluter/R/ \\
-http://www.statmethods.net/ \\
-http://www.rseek.org/ \\
-http://www.cookbook-r.com/ \\
-http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf
-==== R script reference ====
+[[https://www.rstudio.com/resources/cheatsheets/|https://www.rstudio.com/resources/cheatsheets/]]
+These cheat sheets are also directly available in RStudio (Help -> Cheat Sheets)
+----
+=== A few books ===
+  * R for Dummies
+  * A Handbook of Statistical Analyses using R
+  * R in Action
+  * Introductory Statistics in R
+  * A Beginner's Guide to R
+  * The R Book
+----
+=== A few useful links ===
+  * [[http://r4ds.had.co.nz/index.html|http://r4ds.had.co.nz/index.html]]
+  * [[https://cran.r-project.org/doc/manuals/r-release/R-intro.html|https://cran.r-project.org/doc/manuals/r-release/R-intro.html]]
+  * [[http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf|http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf]]
+  * [[http://statmethods.net/|http://statmethods.net/]]
+  * [[https://support.rstudio.com/hc/en-us/categories/200035113-Documentation|https://support.rstudio.com/hc/en-us/categories/200035113-Documentation]]
+  * [[http://cookbook-r.com/|http://cookbook-r.com/]]
+==== R script ====
+-----
 Want to revise/practice the material seen here at home?
-[[{}{ :referencescriptworkshop1.r }|Download R script]]
+[[{}{ wiki:workshop01-en.r }|Download R script]]