Fixed some Grammar

chapters/01_part_getting_started/04_speaking_r.Rmd

@@ -26,11 +26,11 @@ seq(from = 2, to = 100, by = 2)
 
 Question: We keep talking about "speaking" to R, but when you speak to R using the R language, who are you actually speaking to?
 
-Well, you are speaking to something called the **R interpreter**. The R interpreter takes the commands we've written in the R language, sends them to our computer to do the actual work (e.g., get the mean of a set of numbers), and then translates the results of that work back to us in a form that we humans can understand (e.g., the mean is 25.5). At this stage, one of the key concepts for you to understand about the R language is that is **extremely literal!** Understanding the literal nature of R is important because it will be the underlying cause of a lot of errors in our R code. 
+Well, you are speaking to something called the **R interpreter**. The R interpreter takes the commands we've written in the R language, sends them to our computer to do the actual work (e.g., get the mean of a set of numbers), and then translates the results of that work back to us in a form that we humans can understand (e.g., the mean is 25.5). At this stage, one of the key concepts for you to understand about the R language is that it is **extremely literal!** Understanding the literal nature of R is important because it will be the underlying cause of a lot of errors in our R code. 
 
 ## Errors
 
-No matter what I write next, you are going to get errors in your R code. We still get errors in my R code every single time we write R code. However, our hope is that this section will help you begin to understand _why_ you are getting errors when you get them and provide us with a common language for discussing errors.
+No matter what I write next, you are going to get errors in your R code. We still get errors in our R code every single time we write R code. However, our hope is that this section will help you begin to understand _why_ you are getting errors when you get them and provide us with a common language for discussing errors.
 
 So, what exactly do we mean when we say that the R interpreter is extremely literal? Well, in the Navigating RStudio chapter, we already told you that R is a **case sensitive** language. Again, that means that uppercase x (X) and lowercase x (x) are different things to R. So, if you assign 2 to lowercase x (`x <- 2`). And then later ask R to tell what number you stored in upper case X; you will get an error (`Error: object 'X' not found`).
 
@@ -83,7 +83,7 @@ But how do we know what the first, second, and third arguments to a function are
 knitr::include_graphics("img/01_part_getting_started/03_navigating_rstudio/help.png")
 ```
 
-In the "Usage" section of the documentation for the `seq()` function, we can see that all of the arguments that the `seq()` function accepts. These documentation files are a little cryptic until you get used to them but look directly underneath the part that says "## Default S3 method." There, it tells us that the `seq()` function understands the `from`, `to`, `by`, `length.out`, `along.with`, and `...` arguments. The `from` argument is first argument to the `seq()` function because it is listed there first, the `to` argument is second argument to the `seq()` function because it is listed there second, and so on. It is really that simple. Therefore, when we type `seq(2, 100, 2)`, R automatically translates it to `seq(from = 2, to = 100, by = 2)`. And this is called passing values to function arguments by position.
+In the "Usage" section of the documentation for the `seq()` function, we can see that all of the arguments that the `seq()` function accepts. These documentation files are a little cryptic until you get used to them but look directly underneath the part that says "## Default S3 method." There, it tells us that the `seq()` function understands the `from`, `to`, `by`, `length.out`, `along.with`, and `...` arguments. The `from` argument is the first argument to the `seq()` function because it is listed there first, the `to` argument is the second argument to the `seq()` function because it is listed there second, and so on. It is really that simple. Therefore, when we type `seq(2, 100, 2)`, R automatically translates it to `seq(from = 2, to = 100, by = 2)`. And this is called passing values to function arguments by position.
 
 <p class="note"> 🗒**Side Note:** As an aside, we can view the documentation for any function by typing `?function name` into the R console and then pressing the enter/return key. For example, we can type `?seq` to view the documentation for the `seq()` function.</p>
 
@@ -145,7 +145,7 @@ x <- 2
 x
 ```
 
-In this code chunk, "# Store the value 2 in the variable x" and "# Print the contents of x to the screen" are both examples of comments. Notice that they both start with the pound or hash sign (#). The R interpreter will ignore anything on the _current line_ that comes after the hash sign. A carriage return (new line) ends the comment. However, comments don't have to be written on their own line. They can also be written on the same line as R code as long as put them after the R code, like this:
+In this code chunk, "# Store the value 2 in the variable x" and "# Print the contents of x to the screen" are both examples of comments. Notice that they both start with the pound or hash sign (#). The R interpreter will ignore anything on the _current line_ that comes after the hash sign. A carriage return (new line) ends the comment. However, comments don't have to be written on their own line. They can also be written on the same line as R code as long as they are put after the R code, like this:
 
 ```{r}
 x <- 2 # Store the value 2 in the variable x
@@ -162,15 +162,15 @@ Most beginning R programmers underestimate the importance of comments. In the si
 
 In addition to being a functional programming language, R is also a type of programming language called an [open source](https://en.wikipedia.org/wiki/Open-source_software) programming language. For our purposes, this has two big advantages. First, it means that R is **FREE!** Second, it means that smart people all around the world get to develop new **packages** for the R language that can do cutting edge and/or very niche things.
 
-That second advantage is probably really confusing if this is not a concept you are already familiar with. For example, when you install Microsoft Word on your computer all the code that makes that program work is owned and Maintained by the Microsoft corporation. If you need Word to do something that it doesn’t currently do, your only option is to make a feature request on Microsoft’s website. Microsoft may or may not every get around to fulfilling that request.
+That second advantage is probably really confusing if this is not a concept you are already familiar with. For example, when you install Microsoft Word on your computer, all the code that makes that program work is owned and Maintained by the Microsoft Corporation. If you need Word to do something that it doesn’t currently do, your only option is to make a feature request on Microsoft’s website. Microsoft may or may not ever get around to fulfilling that request.
 
 R works a little differently. When you downloaded R from the CRAN website, you actually downloaded something called **Base R**. Base R is maintained by the R Core Team. However, anybody – _even you_ – can write your own code (called packages) that add new functions to the R syntax. Like all functions, these new functions allow you to _do_ things that you can't do (or can't do as easily) with Base R.
 
 An analogy that I really like here is used by Ismay and Kim in [ModernDive](https://moderndive.com/1-getting-started.html#packages).
 
 > A good analogy for R packages is they are like apps you can download onto a mobile phone. So R is like a new mobile phone: while it has a certain amount of features when you use it for the first time, it doesn’t have everything. R packages are like the apps you can download onto your phone from Apple’s App Store or Android’s Google Play. @Ismay2019-iw
 
-So, when you get a new smart phone it comes with apps for making phone calls, checking email, and sending text messages. But, what if you want to listen to music on Spotify? You may or may not be able to do that through your phone's web browser, but it's way more convenient and powerful to download and install the Spotify app.
+So, when you get a new smartphone it comes with apps for making phone calls, checking email, and sending text messages. But, what if you want to listen to music on Spotify? You may or may not be able to do that through your phone's web browser, but it's way more convenient and powerful to download and install the Spotify app.
 
 In this course, we will make extensive use of packages developed by people and teams outside of the R Core Team. In particular, we will use a number of related packages that are collectively known as the [Tidyverse](https://www.tidyverse.org/). One of the most popular packages in the tidyverse collection (and one of the most popular R packages overall) is called the `dplyr` package for data management.