Update intro_to_r.Rmd (#121)

5 typos

01_intro_to_r/intro_to_r.Rmd

@@ -96,7 +96,7 @@ The Arbuthnot data set refers to the work of Dr. John Arbuthnot, an 18<sup>th</s
 arbuthnot
 ```
 
-This command does display the data for us, however, printing the whole dataset in the console is not that useful. One advantage of RStudio is that it comes with a built-in data viewer. The *Environment* tab (in the upper right pane) lists the objects in your environment. Clicking on the name `arbuthnot` will open up a *Data Viewer* tab next to your R Markdown file, which provides an alternative display of the data set. This display should feel similar to viewing data in Excel, where you are able to scroll through the dataset to inspect it. However, unlike Excel, you **will not** be able to edit the data in this tab. Once you are done viewing the data, You can close this tab by clicking on the `x` in the upper left hand corner.
+This command does display the data for us, however, printing the whole dataset in the console is not that useful. One advantage of RStudio is that it comes with a built-in data viewer. The *Environment* tab (in the upper right pane) lists the objects in your environment. Clicking on the name `arbuthnot` will open up a *Data Viewer* tab next to your R Markdown file, which provides an alternative display of the data set. This display should feel similar to viewing data in Excel, where you are able to scroll through the dataset to inspect it. However, unlike Excel, you **will not** be able to edit the data in this tab. Once you are done viewing the data, you can close this tab by clicking on the `x` in the upper left hand corner.
 
 When inspecting the data, you should see four columns of numbers and 82 rows. Each row represents a different year that Arbuthnot collected data. The first entry in each row is the row number (an index we can use to access the data from individual years if we want), the second is the year, and the third and fourth are the numbers of boys and girls baptized that year, respectively. Use the scrollbar on the right side of the console window to examine the complete data set.
 
@@ -199,7 +199,7 @@ arbuthnot <- arbuthnot %>%
   mutate(total = boys + girls)
 ```
 
-This code has a lot of new pieces to it, so let's break it down. In the first line we are doing two things, (1) adding a new `total` column to this updated data frame, and (2) overwriting the existing `arbutnot` data frame with an updated data frame that includes the new `total` column. We are able to chain these two processes together using the **piping** (`%>%`) operator. The piping operator takes the output of the previous expression and "pipes it" into the first argument of the next expression.
+This code has a lot of new pieces to it, so let's break it down. In the first line we are doing two things, (1) adding a new `total` column to this updated data frame, and (2) overwriting the existing `arbuthnot` data frame with an updated data frame that includes the new `total` column. We are able to chain these two processes together using the **piping** (`%>%`) operator. The piping operator takes the output of the previous expression and "pipes it" into the first argument of the next expression.
 
 To continue our analogy with mathematical functions, `x %>% f(y)` is equivalent to `f(x, y)`. Connecting `arbuthnot` and `mutate(total = boys + girls)` with the pipe operator is the same as typing `mutate(arbuthnot, total = boys + girls)`, where `arbuthnot` becomes the first argument included in the `mutate()` function.
 
@@ -307,7 +307,7 @@ These data come from reports by the Centers for Disease Control. You can learn m
 
 That was a short introduction to R and RStudio, but we will provide you with more functions and a more complete sense of the language as the course progresses.
 
-In this course we will be using the suite of R packages from the **tidyverse**. The book [R For Data Science](https://r4ds.had.co.nz/) by Grolemund and Wickham is a fantastic resource for data analysis in R with the tidyverse. If you are Goggling for R code, make sure to also include these package names in your search query. For example, instead of Goggling "scatterplot in R", Goggle "scatterplot in R with the tidyverse".
+In this course we will be using the suite of R packages from the **tidyverse**. The book [R For Data Science](https://r4ds.had.co.nz/) by Grolemund and Wickham is a fantastic resource for data analysis in R with the tidyverse. If you are Googling for R code, make sure to also include these package names in your search query. For example, instead of Googling "scatterplot in R", Google "scatterplot in R with the tidyverse".
 
 These may come in handy throughout the semester: