diff --git a/docs/articles/exploring-imputed-values.html b/docs/articles/exploring-imputed-values.html index 0fcc807b..d9658e42 100644 --- a/docs/articles/exploring-imputed-values.html +++ b/docs/articles/exploring-imputed-values.html @@ -96,7 +96,7 @@

Exploring Imputed Values

Nicholas Tierney

-

2018-08-31

+

2018-09-03

Source: vignettes/exploring-imputed-values.Rmd diff --git a/docs/articles/getting-started-w-naniar.html b/docs/articles/getting-started-w-naniar.html index 50f51cc1..83343508 100644 --- a/docs/articles/getting-started-w-naniar.html +++ b/docs/articles/getting-started-w-naniar.html @@ -96,7 +96,7 @@

Getting Started with naniar

Nicholas Tierney

-

2018-08-31

+

2018-09-03

Source: vignettes/getting-started-w-naniar.Rmd diff --git a/docs/articles/naniar-visualisation.html b/docs/articles/naniar-visualisation.html index 381d7578..709a0f8e 100644 --- a/docs/articles/naniar-visualisation.html +++ b/docs/articles/naniar-visualisation.html @@ -96,7 +96,7 @@

Gallery of Missing Data Visualisations

Nicholas Tierney

-

2018-08-31

+

2018-09-03

Source: vignettes/naniar-visualisation.Rmd diff --git a/docs/articles/replace-with-na.html b/docs/articles/replace-with-na.html index 0c3f23e1..900bed36 100644 --- a/docs/articles/replace-with-na.html +++ b/docs/articles/replace-with-na.html @@ -96,7 +96,7 @@

Replacing values with NA

Nicholas Tierney

-

2018-08-31

+

2018-09-03

Source: vignettes/replace-with-na.Rmd diff --git a/docs/articles/special-missing-values.html b/docs/articles/special-missing-values.html index 62c22a96..bbc5da98 100644 --- a/docs/articles/special-missing-values.html +++ b/docs/articles/special-missing-values.html @@ -96,7 +96,7 @@

Special Missing Values

Nicholas Tierney

-

2018-08-31

+

2018-09-03

Source: vignettes/special-missing-values.Rmd @@ -107,7 +107,7 @@

2018-08-31

Data sometimes have special missing values to indicate specific reasons for missingness. For example, “9999” is sometimes used in weather data, say for for example, the Global Historical Climate Network (GHCN) data), to indicate specific types of missingness, such as instrument failure.

You might be interested in creating your own special missing values so that you can mark specific, known reasons for missingness. For example, an individual dropping out of a study, known instrument failure in weather instruments, or for values being censored in analysis. In these cases, the data is missing, but we have information about why it is missing. Coding these cases as NA would cause us to lose this valuable information. Other stats programming languages like STATA, SAS, and SPSS have this capacity, but currently R does not. So, we need a way to create these special missing values.

-

We can use recode_shadoe to recode missingness by recoding the special missing value as something like NA_reason. naniar records these values in the shadow part of nabular data, which is a special dataframe that contains missingness information.

+

We can use recode_shadow to recode missingness by recoding the special missing value as something like NA_reason. naniar records these values in the shadow part of nabular data, which is a special dataframe that contains missingness information.

This vignette describes how to add special missing values using the recode_shadow() function. First we consider some terminology to explain these ideas, if you are not familiar with the workflows in naniar.

@@ -200,8 +200,7 @@

Under the hood, this special missing value is recoded as a new factor level in the shadow matrix, so that every column is aware of all possible new values of missingness.

Some examples of using recode_shadow in a workflow will be discussed in more detail in the near future, for the moment, here is a recommended workflow:

diff --git a/vignettes/special-missing-values.Rmd b/vignettes/special-missing-values.Rmd index 2c430724..02203a55 100644 --- a/vignettes/special-missing-values.Rmd +++ b/vignettes/special-missing-values.Rmd @@ -20,7 +20,7 @@ Data sometimes have special missing values to indicate specific reasons for miss You might be interested in creating your own special missing values so that you can mark specific, known reasons for missingness. For example, an individual dropping out of a study, known instrument failure in weather instruments, or for values being censored in analysis. In these cases, the data is missing, but we have information about _why_ it is missing. Coding these cases as `NA` would cause us to lose this valuable information. Other stats programming languages like STATA, SAS, and SPSS have this capacity, but currently `R` does not. So, we need a way to create these special missing values. -We can use `recode_shadoe` to recode missingness by recoding the special missing value as something like `NA_reason`. `naniar` records these values in the `shadow` part of `nabular` data, which is a special dataframe that contains missingness information. +We can use `recode_shadow` to recode missingness by recoding the special missing value as something like `NA_reason`. `naniar` records these values in the `shadow` part of `nabular` data, which is a special dataframe that contains missingness information. This vignette describes how to add special missing values using the `recode_shadow()` function. First we consider some terminology to explain these ideas, if you are not familiar with the workflows in `naniar`. @@ -96,7 +96,7 @@ To summarise, to use `recode_shadow`, the user provides the following informatio * A variable that they want to effect (`recode_shadow(var = ...)`) * A condition that they want to implement (`.where(condition ~ ...)`) -* A suffix for the new type of missing value (`.where(condition ~ suffix)` +* A suffix for the new type of missing value (`.where(condition ~ suffix)`) Under the hood, this special missing value is recoded as a new factor level in the shadow matrix, so that every column is aware of all possible new values of missingness.