Index updates per editor (#163)

05-descriptive-analysis.Rmd

@@ -67,7 +67,7 @@ recs_des <- recs_2020 %>%
 We discuss many different types of descriptive analyses in this chapter. However, it is important to know what type of data we are working with and which statistics are appropriate. In survey data, we typically consider data as one of four main types:
 
   * \index{Categorical data|(}\index{Nominal data|see {Categorical data}}Categorical/nominal data: variables with levels or descriptions that cannot be ordered, such as the region of the country (North, South, East, and West)\index{Categorical data|)}
-  * \index{Oridnal data|(}Ordinal data: variables that can be ordered, such as those from a Likert scale (strongly disagree, disagree, agree, and strongly agree)\index{Oridnal data|(}
+  * \index{Ordinal data|(}Ordinal data: variables that can be ordered, such as those from a Likert scale (strongly disagree, disagree, agree, and strongly agree)\index{Ordinal data|)}
   * \index{Discrete data|(}Discrete data: variables that are counted or measured, such as number of children\index{Discrete data|)}
   * \index{Continuous data|(}Continuous data: variables that are measured and whose values can lie anywhere on an interval, such as income\index{Continuous data|)}
 

08-communicating-results.Rmd

@@ -436,7 +436,7 @@ anes_des_gtsum5 %>%
 
 ### Charts and plots
 
-\index{Plots|(} \index{Charts|see {Plots}} \index{ggplot|see {Plots}} \index{Graphs| see {Plots}}
+\index{Plots|(} \index{Charts|see {Plots }} \index{ggplot|see {Plots }} \index{Graphs| see {Plots }}
 Survey analysis can yield an abundance of printed summary statistics and models. Even with the most careful analysis, interpreting the results can be overwhelming. This is where charts and plots play a key role in our work. By transforming complex data into a visual representation, we can recognize patterns, relationships, and trends with greater ease.
 
 R has numerous packages for creating compelling and insightful charts. In this section, we focus on {ggplot2}, a member of the {tidyverse} collection of packages. Known for its power and flexibility, {ggplot2} is an invaluable tool for creating a wide range of data visualizations [@ggplot2wickham].

09-reproducible-data.Rmd

@@ -73,7 +73,7 @@ A package is made up of a collection of functions. If we find ourselves sharing
 
 ## Version control with Git
 
-\index{Version control|(} \index{Git| see {Version control}}
+\index{Version control|(} \index{Git| see {Version control }}
 Often, a survey analysis project produces a lot of code. Keeping track of the latest version can become challenging, as files evolve throughout a project. If a team of analysts is working on the same script, someone may use an outdated version, resulting in incorrect results or redundant work.
 
 Version control systems like Git can help alleviate these pains. Git is a system that tracks changes in files. We can use Git to follow code evaluation and manage asynchronous work. With Git, it is easy to see any changes made in a script, revert changes, and resolve differences between code versions (called conflicts).