appliedepi · pitmonticone · Jun 28, 2022
diff --git a/bookdown/basics.Rmd b/bookdown/basics.Rmd
@@ -529,7 +529,7 @@ library(tidyverse)
 
 Think of R as your personal library: When you download a package your library gains a book of functions, but each time you want to use a function in that book, you must borrow that book from your library.  
 
-For clarity in this handbook, functions are usually preceeded by the name of their package using the `::` symbol in the following way:  
+For clarity in this handbook, functions are usually preceded by the name of their package using the `::` symbol in the following way:  
 
 `package_name::function_name()`  
 
@@ -542,7 +542,7 @@ linelist <- rio::import("linelist.xlsx", which = "Sheet1")
 ```
 
 **Dependencies**  
-Packages often depend on other packages, and these are called "dependencies". When a package is installed from CRAN, it will typically also install its dependenices.  
+Packages often depend on other packages, and these are called "dependencies". When a package is installed from CRAN, it will typically also install its dependencies.  
 
 
 

diff --git a/contribution_guide_05-02-2021.Rmd b/contribution_guide_05-02-2021.Rmd
@@ -111,7 +111,7 @@ In your code, please maintain [tidyverse style](https://style.tidyverse.org/) as
   * Bullets are nice and easy to read  
 * Explanations should not rely on websites - users may be offline; links can be included but should not be required viewing  
 * Prioritize using packages and functions that are *stable*, *commonly-used*, and do not require additional downloads  
-* Use lots of `# comments` to explain the code to a beginner - be concise and previse please!  
+* Use lots of `# comments` to explain the code to a beginner - be concise and precise please!  
 * When using a function for the first time in a page, indicate its package in the accompanying text, or in code like `dplyr::mutate()`  
 * Make use of magrittr pipes and dplyr verbs whenever practical  
 * When making assignments, use `<-`, NOT `=`  
@@ -216,7 +216,7 @@ knitr::include_graphics(here::here("images", "Git_console.png"))
 ```  
 Please note the buttons circled as they will be referenced later (from left to right):  
 
-* Button to begin "commiting" your changes to your branch (will open a new window)  
+* Button to begin "committing" your changes to your branch (will open a new window)  
 * Arrows to PULL (update your local version of the branch with any changes to made your branch by others) and to PUSH (send any completed commits stored in your local version of the branch to the remote/Github version of your branch)  
 * The Git tab in RStudio  
 * Button to create a NEW branch of whichever version is listed to the right. **You almost always want to branch off of the master (after you PULL to update the master first)**.  

diff --git a/new_pages/age_pyramid.Rmd b/new_pages/age_pyramid.Rmd
@@ -13,7 +13,7 @@ knitr::include_graphics(here::here("images", "likert.png"))
 Demographic pyramids are useful to show distributions of age and gender. Similar code can be used to visualize the results of Likert-style survey questions (e.g. "Strongly agree", "Agree", "Neutral", "Disagree", "Strongly disagree"). In this page we cover the following:  
 
 * Fast & easy pyramids using the **apyramid** package  
-* More customizeable pyramids using `ggplot()`  
+* More customizable pyramids using `ggplot()`  
 * Displaying "baseline" demographics in the background of the pyramid  
 * Using pyramid-style plots to show other types of data (e.g responses to **Likert-style** survey questions)  
 

diff --git a/new_pages/basics.Rmd b/new_pages/basics.Rmd
@@ -73,7 +73,7 @@ There are many PDF "cheatsheets" available on the [RStudio website](https://rstu
 -   Factors with **forcats** package\
 -   Dates and times with **lubridate** package\
 -   Strings with **stringr** package\
--   iterative opertaions with **purrr** package\
+-   iterative operations with **purrr** package\
 -   Data import\
 -   Data transformation cheatsheet with **dplyr** package\
 -   R Markdown (to create documents like PDF, Word, Powerpoint...)\
@@ -153,7 +153,7 @@ To see which versions of R, RStudio, or packages were used when this Handbook as
 #### TinyTex {.unnumbered}
 
 TinyTex is a custom LaTeX distribution, useful when trying to produce PDFs from R.\
-See <https://yihui.org/tinytex/> for more informaton.
+See <https://yihui.org/tinytex/> for more information.
 
 To install TinyTex from R:
 
@@ -406,7 +406,7 @@ my_function <- function( ARGUMENTS HERE ){ CODE HERE }
 
 <!-- * `contacts_each = 5` The number contacts enumerated for each case   -->
 
-<!-- * `time_case = 0.5`  Number of hours to complete a case investigaton by phone   -->
+<!-- * `time_case = 0.5`  Number of hours to complete a case investigation by phone   -->
 
 <!-- * `time_contact = 0.25`  Number of hours to complete a contact follow-up by phone   -->
 

diff --git a/new_pages/characters_strings.Rmd b/new_pages/characters_strings.Rmd
@@ -47,7 +47,7 @@ pacman::p_load(
 ### Import data  {.unnumbered}  
 
 
-In this page we will occassionally reference the cleaned `linelist` of cases from a simulated Ebola epidemic. If you want to follow along, <a href='https://github.com/appliedepi/epirhandbook_eng/raw/master/data/case_linelists/linelist_cleaned.rds' class='download-button'>click to download the "clean" linelist</a> (as .rds file). Import data with the `import()` function from the **rio** package (it handles many file types like .xlsx, .csv, .rds - see the [Import and export] page for details).  
+In this page we will occasionally reference the cleaned `linelist` of cases from a simulated Ebola epidemic. If you want to follow along, <a href='https://github.com/appliedepi/epirhandbook_eng/raw/master/data/case_linelists/linelist_cleaned.rds' class='download-button'>click to download the "clean" linelist</a> (as .rds file). Import data with the `import()` function from the **rio** package (it handles many file types like .xlsx, .csv, .rds - see the [Import and export] page for details).  
 
 ```{r, echo=F}
 # import the linelist into R
@@ -401,7 +401,7 @@ paste0("Regional hospital needs ", n_beds, " beds and ", n_masks, " masks.")
 <!-- ======================================================= -->
 ### Change case {.unnumbered}
 
-Often one must alter the case/capitalization of a string value, for example names of jursidictions. Use `str_to_upper()`, `str_to_lower()`, and `str_to_title()`, from **stringr**, as shown below:  
+Often one must alter the case/capitalization of a string value, for example names of jurisdictions. Use `str_to_upper()`, `str_to_lower()`, and `str_to_title()`, from **stringr**, as shown below:  
 
 ```{r}
 str_to_upper("California")

diff --git a/new_pages/cleaning.Rmd b/new_pages/cleaning.Rmd
@@ -1224,7 +1224,7 @@ table(linelist$age_cat, useNA = "always")
 
 You can adjust how the labels are displayed with `separator = `. The default is "-"  
 
-You can adjust how the top numbers are handled, with the `ceiling = ` arguemnt. To set an upper cut-off set `ceiling = TRUE`. In this use, the highest break value provided is a "ceiling" and a category "XX+" is not created. Any values above highest break value (or to `upper = `, if defined) are categorized as `NA`. Below is an example with `ceiling = TRUE`, so that there is no category of XX+ and values above 70 (the highest break value) are assigned as NA.  
+You can adjust how the top numbers are handled, with the `ceiling = ` argument. To set an upper cut-off set `ceiling = TRUE`. In this use, the highest break value provided is a "ceiling" and a category "XX+" is not created. Any values above highest break value (or to `upper = `, if defined) are categorized as `NA`. Below is an example with `ceiling = TRUE`, so that there is no category of XX+ and values above 70 (the highest break value) are assigned as NA.  
 
 ```{r}
 # With ceiling set to TRUE

diff --git a/new_pages/collaboration.Rmd b/new_pages/collaboration.Rmd
@@ -114,15 +114,15 @@ install *Git* from <https://git-scm.com/downloads>.**
 ### Install an interface (optional but recommended) {.unnumbered}
 
 Git has its own language of commands, which can be typed into a command
-line terminal. However, there are many clients/interfaces and as non-developpers, in your
+line terminal. However, there are many clients/interfaces and as non-developers, in your
 day-to-day use, you will rarely _need_ to interact with Git directly and 
 interface usually provide nice visualisation tools for file modifications or branches. 
 
 Many options exist, on all OS, from beginner friendly to more complex ones. 
 Good options for beginners include the RStudio Git pane and 
 [Github Desktop](https://desktop.github.com/), which we will showcase in 
 this chapter.
-Intermediate (more powerfull, but more complex) options include Source Tree, 
+Intermediate (more powerful, but more complex) options include Source Tree, 
 Gitkracken, Smart Git and others.
 
 Quick explanation on [Git clients](-%09https:/happygitwithr.com/git-client.html#git-client).
@@ -145,7 +145,7 @@ You may be offered to set-up two-factor authentication with an app on
 your phone. Read more in the Github [help
 documents](https://docs.github.com/en/github/authenticating-to-github/securing-your-account-with-two-factor-authentication-2fa).
 
-If you use Github Desktop, you can enter your Gitub credentials after
+If you use Github Desktop, you can enter your Github credentials after
 installation following these
 [steps](https://docs.github.com/en/desktop/installing-and-configuring-github-desktop/authenticating-to-github).
 If you don't do it know, credentials will be asked later when you try to

diff --git a/new_pages/contact_tracing.Rmd b/new_pages/contact_tracing.Rmd
@@ -2,7 +2,7 @@
 # Contact tracing { }
 
 
-This page demonstrates descriptive analysis of contact tracing data, addessing some key considerations and approaches unique to these kinds of data.  
+This page demonstrates descriptive analysis of contact tracing data, addressing some key considerations and approaches unique to these kinds of data.  
 
 This page references many of the core R data management and visualisation competencies covered in other pages (e.g. data cleaning, pivoting, tables, time-series analyses), but we will highlight examples specific to contact tracing that have been useful for operational decision making. For example, this includes visualizing contact tracing follow-up data over time or across geographic areas, or producing clean Key Performance Indicator (KPI) tables for contact tracing supervisors.