assorted typos

Author: maneesha

episodes/01-starting-with-data.Rmd

@@ -88,7 +88,7 @@ read.csv(file = "data/inflammation-01.csv", header = FALSE)
 The expression `read.csv(...)` is a [function call](../learners/reference.md#function-call) that asks R to run the function `read.csv`.
 
 `read.csv` has two [arguments](../learners/reference.md#argument): the name of the file we want to read, and whether the first line of the file contains names for the columns of data.
-The filename needs to be a character string (or [string](../learners/reference.md#string)for short), so we put it in quotes.
+The filename needs to be a character string (or [string](../learners/reference.md#string) for short), so we put it in quotes.
 Assigning the second argument, `header`, to be `FALSE` indicates that the data file does not have column headers.
 We'll talk more about the value `FALSE`, and its converse `TRUE`, in lesson 04.
 In case of our `inflammation-01.csv` example, R auto-generates column names in the sequence `V1` (for "variable 1"), `V2`, and so on, until `V40`.
@@ -334,7 +334,7 @@ dim(dat)
 
 This tells us that our data frame, `dat`, has `r nrow(dat)` rows and `r ncol(dat)` columns.
 
-If we want to get a single value from the data frame, we can provide an [index](../learners/reference.md#index)in square brackets.
+If we want to get a single value from the data frame, we can provide an [index](../learners/reference.md#index) in square brackets.
 The first number specifies the row and the second the column:
 
 ```{r selecting data frame elements}
@@ -466,7 +466,7 @@ sd(dat[, 7])
 
 ## Forcing Conversion
 
-Note that R may return an error when you attempt to perform similar calculations on subsetted *rows*of data frames.
+Note that R may return an error when you attempt to perform similar calculations on subsetted *rows* of data frames.
 This is because some functions in R automatically convert the object type to a numeric vector, while others do not (e.g. `max(dat[1, ])` works as expected, while `mean(dat[1, ])` returns `NA` and a warning).
 You get the expected output by including an explicit call to `as.numeric()`, e.g. `mean(as.numeric(dat[1, ]))`.
 By contrast, calculations on subsetted *columns* always work as expected, since columns of data frames are already defined as vectors.

episodes/02-func-R.Rmd

@@ -266,7 +266,7 @@ z
 center(z, 3)
 ```
 
-That looks right, so let's try center on our real data.
+That looks right, so let's try `center` on our real data.
 We'll center the inflammation data from day 4 around 0:
 
 ```{r}

episodes/03-loops-R.Rmd

@@ -92,7 +92,7 @@ print_words(best_practice[-6])
 
 ## Not Available
 
-R has has a special variable, `NA`, for designating missing values that are **N**ot **A**vailable in a data set.
+R has a special variable, `NA`, for designating missing values that are **N**ot **A**vailable in a data set.
 See `?NA` and [An Introduction to R][na] for more details.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::

episodes/04-cond.Rmd

@@ -280,7 +280,7 @@ When `use_boxplot` is set to `FALSE`, `plot_dist` will instead plot a histogram
 As before, if the length of the vector is shorter than `threshold`, `plot_dist` will create a stripchart.
 A histogram is made with the `hist` command in R.
 
-```{r conditional-challenge-hist, fig.alt=c("A grey unlabeled boxplot chart showing the distrubution values between 2 and 9 with a mean at 6.", "A grey unlabeled histogram showing bimodal distribution between 2 and 9 with peaks at 2 and 6.", "A mostly blank strip chart showing five points at 3, 4, 6, 7, and 9"), echo=-1}
+```{r conditional-challenge-hist, fig.alt=c("A grey unlabeled boxplot chart showing the distribution of values between 2 and 9 with a mean at 6.", "A grey unlabeled histogram showing bimodal distribution between 2 and 9 with peaks at 2 and 6.", "A mostly blank strip chart showing five points at 3, 4, 6, 7, and 9"), echo=-1}
 plot_dist <- function(x, threshold, use_boxplot = TRUE) {
    if (length(x) > threshold && use_boxplot) {
     boxplot(x)

episodes/06-best-practices-R.Rmd

@@ -52,7 +52,7 @@ library(reshape)
 library(vegan)
 ```
 
-Another way you can be explicit about the requirements of your code and improve it's reproducibility is to limit the "hard-coding" of the input and output files for your script.
+Another way you can be explicit about the requirements of your code and improve its reproducibility is to limit the "hard-coding" of the input and output files for your script.
 If your code will read in data from a file, define a variable early in your code that stores the path to that file.
 For example
 
@@ -111,7 +111,7 @@ It's easy to annotate and mark your code using `#` or `#-`to set off sections of
 For example, it's often helpful when writing code to separate the function definitions.
 If you create only one or a few custom functions in your script, put them toward the top of your code.
 If you have written many functions, put them all in their own .
-R file and then` source` those files. `source` will define all of these functions so that your code can make use of them as needed.
+R file and then `source` those files. `source` will define all of these functions so that your code can make use of them as needed.
 
 ```{r source_ex, eval=FALSE}
 source("my_genius_fxns.R")

episodes/08-making-packages-R.Rmd

@@ -100,7 +100,7 @@ We will use the [devtools] and [roxygen2] packages, which make creating packages
 Both can be installed from CRAN like this:
 
 ```{r, eval=FALSE}
-install.packages(c("devtools", "roxygen2"))  # installations can be `c`ombined
+install.packages(c("devtools", "roxygen2"))  # installations can be combined
 library("devtools")
 library("roxygen2")
 ```

episodes/10-supp-addressing-data.Rmd

@@ -32,7 +32,7 @@ There are three main ways for addressing data inside R objects.
 - By logical vector
 - By name
 
-Lets start by loading some sample data:
+Let's start by loading some sample data:
 
 ```{r readData}
 dat <- read.csv(file = 'data/sample.csv', header = TRUE, stringsAsFactors = FALSE)
@@ -49,7 +49,7 @@ Using factors in R is covered in a separate lesson.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-Lets take a look at this data.
+Let's take a look at this data.
 
 ```{r classDat}
 class(dat)
@@ -63,8 +63,8 @@ We can compactly display the internal structure of a data frame using the  struc
 str(dat)
 ```
 
-The `str` function tell us that the data has 100 rows and 9 columns.
-It is also tell us that the data frame is made up of character `chr`, integer `int` and `numeric` vectors.
+The `str` function tells us that the data has 100 rows and 9 columns.
+It is also tells us that the data frame is made up of character `chr`, integer `int` and `numeric` vectors.
 
 ```{r headDat}
 head(dat)

episodes/11-supp-read-write-csv.Rmd

@@ -28,12 +28,12 @@ library(svglite)
 
 The most common way that scientists store data is in Excel spreadsheets.
 While there are R packages designed to access data from Excel spreadsheets (e.g., gdata, RODBC, XLConnect, xlsx, RExcel), users often find it easier to save their spreadsheets in [comma-separated values](reference.html#comma-separated-values-csv) files (CSV) and then use R's built in functionality to read and manipulate the data.
-In this short lesson, we'll learn how to read data from a .csv and write to a new .csv, and explore the [arguments](../learners/reference.md#argument) that allow you read and write the data correctly for your needs.
+In this short lesson, we'll learn how to read data from a .csv and write to a new .csv, and explore the [arguments](../learners/reference.md#argument) that allow you to read and write the data correctly for your needs.
 
 ### Read a .csv and Explore the Arguments
 
 Let's start by opening a .csv file containing information on the speeds at which cars of different colors were clocked in 45 mph zones in the four-corners states (`car-speeds.csv`).
-We will use the built in `read.csv(...)` [function call](../learners/reference.md#function-call), which reads the data in as a data frame, and assign the data frame to a variable (using `<-`) so that it is stored in R's memory.
+We will use the built in `read.csv(...)` [function call](../learners/reference.md#function-call), which reads the data in as a data frame, and assigns the data frame to a variable (using `<-`) so that it is stored in R's memory.
 Then we will explore some of the basic arguments that can be supplied to the function.
 First, open the RStudio project containing the scripts and data you were working on in episode 'Analyzing Patient Data'.
 

episodes/12-supp-factors.Rmd

@@ -33,8 +33,8 @@ Factors can be ordered or unordered and are an important class for statistical a
 Factors are stored as integers, and have labels associated with these unique integers.
 While factors look (and often behave) like character vectors, they are actually integers under the hood, and you need to be careful when treating them like strings.
 
-Once created, factors can only contain a pre-defined set values, known as *levels*.
-By default, R always sorts*levels*in alphabetical order.
+Once created, factors can only contain a pre-defined set of values, known as *levels*.
+By default, R always sorts *levels* in alphabetical order.
 For instance, if you have a factor with 2 levels:
 
 :::::::::::::::::::::::::::::::::::::::::  callout
@@ -57,7 +57,7 @@ levels(sex)
 nlevels(sex)
 ```
 
-Sometimes, the order of the factors does not matter, other times you might want to specify the order because it is meaningful (e.g., "low", "medium", "high") or it is required by particular type of analysis.
+Sometimes, the order of the factors does not matter, other times you might want to specify the order because it is meaningful (e.g., "low", "medium", "high") or it is required by a particular type of analysis.
 Additionally, specifying the order of the levels allows us to compare levels:
 
 ```{r, error=TRUE}

episodes/13-supp-data-structures.Rmd

@@ -363,7 +363,7 @@ mdat[2, 3]
 
 In R lists act as containers.
 Unlike atomic vectors, the contents of a list are not restricted to a single mode and can encompass any mixture of data types.
-Lists are sometimes called generic vectors, because the elements of a list can by of any type of R object, even lists containing further lists.
+Lists are sometimes called generic vectors, because the elements of a list can be of any type of R object, even lists containing further lists.
 This property makes them fundamentally different from atomic vectors.
 
 A list is a special type of vector.
@@ -461,18 +461,18 @@ If the elements of a list are named, they can be referenced by the `$` notation
 A data frame is a very important data type in R.
 It's pretty much the *de facto* data structure for most tabular data and what we use for statistics.
 
-A data frame is a *special type of list* where every element of the list has same length (i.e. data frame is a "rectangular" list).
+A data frame is a *special type of list* where every element of the list has the same length (i.e. data frame is a "rectangular" list).
 
 Data frames can have additional attributes such as `rownames()`, which can be useful for annotating data, like `subject_id` or `sample_id`.
 But most of the time they are not used.
 
 Some additional information on data frames:
 
 - Usually created by `read.csv()` and `read.table()`, i.e. when importing the data into R.
-- Assuming all columns in a data frame are of same type, data frame can be converted to a matrix with data.matrix() (preferred) or as.matrix(). Otherwise type coercion will be enforced and the results may not always be what you expect.
+- Assuming all columns in a data frame are of the same type, data frame can be converted to a matrix with data.matrix() (preferred) or as.matrix(). Otherwise type coercion will be enforced and the results may not always be what you expect.
 - Can also create a new data frame with `data.frame()` function.
 - Find the number of rows and columns with `nrow(dat)` and `ncol(dat)`, respectively.
-- Rownames are often automatically generated and look like 1, 2, ..., n. Consistency in numbering of rownames may not be honored when rows are reshuffled or subset.
+- Row names are often automatically generated and look like 1, 2, ..., n. Consistency in numbering of rownames may not be honored when rows are reshuffled or subset.
 
 ### Creating Data Frames by Hand
 
@@ -518,7 +518,7 @@ dat[["y"]]
 dat$y
 ```
 
-The following table summarizes the one-dimensional and two-dimensional data structures in R in relation to diversity of data types they can contain.
+The following table summarizes the one-dimensional and two-dimensional data structures in R in relation to the diversity of data types they can contain.
 
 | Dimensions | Homogenous    | Heterogeneous |
 | ---------- | ------------- | ------------- |
@@ -528,7 +528,7 @@ The following table summarizes the one-dimensional and two-dimensional data stru
 :::::::::::::::::::::::::::::::::::::::::  callout
 
 Lists can contain elements that are themselves muti-dimensional (e.g. a lists can contain data frames or another type of objects).
-Lists can also contain elements of any length, therefore list do not necessarily have to be "rectangular".
+Lists can also contain elements of any length, therefore lists do not necessarily have to be "rectangular".
 However in order for the list to qualify as a data frame, the length of each element has to be the same.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
@@ -537,7 +537,7 @@ However in order for the list to qualify as a data frame, the length of each ele
 
 ## Column Types in Data Frames
 
-Knowing that data frames are lists, can columns be of different type?
+Knowing that data frames are lists, can columns be of different types?
 
 What type of structure do you expect to see when you explore the structure of the `PlantGrowth` data frame?
 Hint: Use `str()`.