---
title: "Recode AJS outbreak data"
output: 
  word_document:
    keep_md: true
---

# Introduction to this template

This is a template which can be used to recode data from non-DHIS2 formats to be 
able to use the template for creating a report for an AJS outbreak. 
This will produce a word document which can be kept as a log to show how the data 
was changed. 

- There are sections for reading, reformatting and cleaning data  
- For a more detailed explanation of this template, please visit https://github.com/R4EPI/sitrep/  
- Feedback and suggestions are welcome at the [GitHub issues page](https://github.com/R4EPI/sitrep/issues)

- Text within <! > will not show in your final document. These comments are used
to explain the template. You can delete them if you want.

<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This comment will not show up when you knit the document.
A comment with a title with slashes indicates a name of a code chunk.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

## Installing and loading required packages 
<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/// setup \\\
--------------------------------------------------------------------------------

Several packages are required for different aspects of  analysis with *R*. 
You will need to install these before starting. 

These packages can be quite large and may take a while to download in the
field. If you have access to a USB key with these packages, it makes sense to
copy and paste the packages into your computer's R package library 
(run the command .libPaths() to see the folder path). 

For help installing packages, please visit https://github.com/R4EPI/sitrep/
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->


```{r setup, include=FALSE, results='hide', message=FALSE, warning=FALSE}

## You need to activate code chunks by changing eval to true below

## hide all code chunks in the output, but show errors
knitr::opts_chunk$set(eval = FALSE,       # YOU NEED TO CHANGE THIS TO TRUE
                      echo = TRUE,        # show all code chunks in output
                      error = TRUE,       # show errors if they appear, but don't stop
                      warning = TRUE,     # show warnings if they appear, but don't stop
                      message = TRUE      # show messages if they appear, but don't stop
                      )



# Ensures the package "pacman" is installed
if (!require("pacman")) {
     install.packages("pacman") }

# Install and load required packages for this template
pacman::p_load(
  knitr,           # create output docs
  here,            # find your files
  rio,             # read in data
  dplyr,           # clean/shape data
  tidyr,           # clean/shape data
  sitrep,          # MSF field epi functions
  janitor          # clean data
)

```



<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/// read_data \\\
--------------------------------------------------------------------------------

This section is for importing data not from DHIS2. 
There are several options below depending on whether you the file type you are 
importing. Uncomment the code you need. 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

```{r read_data, warning = FALSE, message = FALSE}

## Excel file ------------------------------------------------------------------
## to read in a specific sheet use "which"
linelist_raw <- rio::import(here::here("Data", "linelist.xlsx"), which = "Sheet1")



## Excel file -- Specific range ------------------------------------------------
## you can specify a range in an excel sheet.
# linelist_raw <- rio::import(here::here("Data", "linelist.xlsx"), range = "B2:J102")



## Excel file with password ----------------------------------------------------
## use this section if your Excel has a password.

# install.packages(c("excel.link", "askpass"))
# library(excel.link)

# linelist_raw <- xl.read.file(here::here("Data", "linelist.xlsx"),
#                              xl.sheet = "Sheet1",
#                              password = askpass::askpass(prompt = "please enter file password"))


## CSV file --------------------------------------------------------------------
# linelist_raw <- rio::import(here::here("Data", "linelist.csv"))



## Stata data file -------------------------------------------------------------
# linelist_raw <- rio::import(here::here("Data", "linelist.dat"))
```



<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/// view_data_dict \\\
--------------------------------------------------------------------------------

You will need to clean your dataset so that it matches the expected linelist 
structure.

Specifically, you will need to: 
[ ] Recode your variable names to match the dictionary
[ ] Recode variable contents to match the dictionary


This step shows you the data dictionary. The data dictionary has variable names
in the data_element_shortname column. Possible values for each variable are
specified in "Code" and "Name" columns. 
You will need to recode to the "values_short" content. 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

```{r view_data_dict, warning = FALSE, message = FALSE}

## get MSF standard dictionary 
recode_dict <- msf_dict("AJS", compact = FALSE) %>% 
  ## only keep variables and values
  select("variable" = data_element_shortname, 
         "values_short" = option_code, 
         "values_long" = option_name)

## browse dictionary
View(recode_dict)

```


<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/// Clean column names  \\\
--------------------------------------------------------------------------------
This step fixes the column names. This is done in two steps:
1) Use the clean_names function from janitor to automatically standardise column 
    names as per good coding practice (lower case, remove spaces and punctuation)
2) Manually clean column names to match linelist standard
    a) Use the function msf_dict_rename_helper() to create a template based on the 
        standard dictionary. This will copy a rename command  to your clipboard.
    b)Paste the result below and edit your code so that your column names are 
      renamed. Be careful! You still need to be aware of what each variable means 
      and what values it takes. If there are any variables that are in the MSF 
      dictionary that are not in your data set, then you should comment them out, 
      but be aware that some analyses may not run because of this. 
    
Note example code at the bottom of this chunk. 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

```{r clean_column_names, warning = FALSE, message = FALSE}

## 1) Automatically clean column names -----------------------------------------

## make a copy of your orginal dataset and name it linelist_cleaned
linelist_cleaned <- linelist_raw
 
## define clean variable names using clean_names from the janitor package. 
linelist_cleaned <- janitor::clean_names(linelist_cleaned)



## 2) Manually clean column names to match linelist standard---------------------
# 2A) Create template code for renaming your columns

msf_dict_rename_helper("AJS")

# 2B) Paste the result below and your column names to the matching variable.


## PASTE HERE


## Below is an EXAMPLE for changing a few specific names. function. In this
## example, we have the columns "gender" and "age" that we want to rename as
## "sex" and "age_years". 
## The formula for this is rename(data, NEW_NAME = OLD_NAME).

# linelist_cleaned <- rename(linelist_cleaned, 
#                            sex_id          = gender,
#                             adm1_residence  = region)

```


<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/// browse_data \\\
--------------------------------------------------------------------------------

You'll want to look at your data. Here are a few ways you can explore.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

```{r browse_data, eval = FALSE}

## view your whole dataset interactively (in an excel style format)
View(linelist_cleaned)

## view unique values contained in variables 
## you can run this for any column -- just replace the column name
unique(linelist_cleaned$sex)

## use the dfSummary function in combination with view
## note that view is not capitalised with this package
summarytools::dfSummary(linelist_cleaned) %>%
  summarytools::view()
```



<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/// recode_factor_vars \\\
--------------------------------------------------------------------------------

This chunk is for recoding factor (categorical) variables. 
You will need to edit this section to recode the values in your dataset to suit 
the values in the expected linelist format. You can look at the data dictionary 
object (`recode_dict`) and the outputs from the `browse_data` chunk to write 
the correct code. 


The chunk shows an initial example, showing you how to fix mispellings in 
the geographic variable `patient_origin`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->

```{r recode_factor_vars}

## Recode character variables
## This step shows how to fix misspellings in the geographic region variable.
## Ideally, you want these values to match and population data!
linelist_cleaned <- linelist_cleaned %>%
  mutate(patient_origin = case_when(
    patient_origin == "Valliages D"       ~ "Village D",
    patient_origin == "VillageD"          ~ "Village D",
    patient_origin == "Town C"            ~ "Village C",
    TRUE ~ as.character(patient_origin))
  )

```



<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/// recode_numeric_vars \\\ 
--------------------------------------------------------------------------------

This chunk will help recode numeric variables. 
The example shown below is for restructing age columns from 
three (numerical columns for year, month, age) to two (age_num and age_unit).

You will need to add to it based on your dataset by comparing to the variables 
in the standard data dictionary. 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
```{r recode_numeric_vars}

## split one age variable in to three 
## the dictionary has all those less than 2 years of age recorded in months
## and those less than 2 months recorded in days
## If you have a numeric variable for age ('age_num') and an associated 
## categorical variable for the unit 'age_unit' (days, month, years), recode like this 
study_data_cleaned <- study_data_cleaned %>% 
  mutate(
    age_years = case_when(
      age_num >=2 & age_unit == "Year" ~ age_num, 
      age_num >= 24 & age_unit == "Month" ~ as.numeric(age_num / 12),
      age_num >= 730 & age_unit == "Day" ~ as.numeric(age_num/365),
      TRUE ~ NA), 
    age_months = case_when(
      age_num >=2 & age_unit == "Month" ~ age_num, 
      age_num >=62 & age_num <730 & age_unit == "Day" ~ as.numeric(age_num / 30), 
      TRUE ~ NA),
    age_days = case_when(
      age_num <62 & age_unit == "Day" ~ age_num, 
      age_num <2 & age_unit == "Month" ~ as.numeric(age_num * 30),
      TRUE ~ NA)
  )
```


<!-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/// save_recoded_data \\\
--------------------------------------------------------------------------------

Save your recoded dataset as an Excel. 
This automatically names your file "linelist_recoded_DATE", where DATE is the
current date.
You can now use this data with analysis template. 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -->
```{r save_cleaned_data}
# rio::export(linelist_cleaned, here::here("Data", str_glue("linelist_recoded_{Sys.Date()}.xlsx")))
```

