update site

public/pkgdown.yml

@@ -6,7 +6,7 @@ articles:
   codebook: codebook.html
   employment-and-underemployment: employment-and-underemployment.html
   lfsclean: lfsclean.html
-last_built: 2023-08-15T15:33Z
+last_built: 2023-08-15T15:37Z
 urls:
   reference: https://stapm.gitlab.io/r-packages/lfsclean/reference
   article: https://stapm.gitlab.io/r-packages/lfsclean/articles

vignettes/lfsclean.Rmd

@@ -9,15 +9,21 @@ vignette: >
 
 The **inputs** are the raw LFS data files obtained from the [UK Data Service](https://ukdataservice.ac.uk/). __These must be the tab delimited versions of the data, not Stata or SPSS__, and all individual tab delimited files needed must be placed together in a single directory. 
 
-A typical workflow for using the package looks as follows, with the read data function for each individual year contained within a global cleaning function which applies each cleaning function in turn. The collection of global cleaning functions for each year are then wrapped in a function to combine years of data.
+A typical workflow for using the package looks as follows, with the read data function for each individual year contained within a global cleaning function which applies each cleaning function in turn. The collection of global cleaning functions for each year are then wrapped in a function to combine years of data into a single data table for output.
+
+### Quarterly cross-sectional datasets
 
 The `lfsclean()` wrapper function is used to process multiple quarterly cross-sectional LFS data files into a single dataset of cleaned variables.
 
 ```{r workflow, message = FALSE, eval = FALSE}
 
-#### read in and clean raw data using the package
+## Load the package and other useful libraries
+
+library(lfsclean)
+library(tidyverse)
+library(ggthemes)
+library(zoo)
 
-####################
 ## input arguments
 
 root  <- "C:/"
@@ -41,10 +47,19 @@ lfs_data <- lfsclean(root = root,
 
 ```
 
+### Longitudinal datasets
+
 The `lfsclean_5q()` wrapper function is used to process the raw five-quarter longitudinal data. The arguments are simular to those used for the quarterly data cleaning function. `year` in this case refers to the calendar year in which the first wave of the longitudinal data was interviewed e.g. setting year equal to 2020 would read in, clean, and combine the four longitudinal datasets which began in January-March, April-June, July-September, and October-December of 2020.
 
 ```{r workflow-long, message = FALSE, eval = FALSE}
 
+## Load the package and other useful libraries
+
+library(lfsclean)
+library(tidyverse)
+library(ggthemes)
+library(zoo)
+
 ## input arguments
 
 root  <- "U:/"