---
title: "Introduction"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r echo=FALSE, include=FALSE}
library(data.table)
library(magrittr)
```

# csfmt_rts_data_v1

csfmt_rts_data_v1 (`vignette("csfmt_rts_data_v1", package = "cstidy")`) is a data format for real-time surveillance.

```{r}
d <- cstidy::generate_test_data()
cstidy::set_csfmt_rts_data_v1(d)

# Looking at the dataset
d[]
```

## Smart assignment

`csfmt_rts_data_v1` does smart assignment for time and geography.

When the **variables in bold** are assigned using `:=`, the listed variables will be automatically imputed.

**location_code**:

- granularity_geo
- country_iso3

**isoyear**:

- granularity_time
- isoweek
- isoyearweek
- season
- seasonweek
- calyear
- calmonth
- calyearmonth
- date

**isoyearweek**:

- granularity_time
- isoyear
- isoweek
- season
- seasonweek
- calyear
- calmonth
- calyearmonth
- date

**date**:

- granularity_time
- isoyear
- isoweek
- isoyearweek
- season
- seasonweek
- calyear
- calmonth
- calyearmonth

```{r}
d <- cstidy::generate_test_data()[1:5]
cstidy::set_csfmt_rts_data_v1(d)

# Looking at the dataset
d[]

# Smart assignment of time columns (note how granularity_time, isoyear, isoyearweek, date all change)
d[1,isoyearweek := "2021-01"]
d

# Smart assignment of time columns (note how granularity_time, isoyear, isoyearweek, date all change)
d[2,isoyear := 2019]
d

# Smart assignment of time columns (note how granularity_time, isoyear, isoyearweek, date all change)
d[4:5,date := as.Date("2020-01-01")]
d

# Smart assignment fails when multiple time columns are set
d[1,c("isoyear","isoyearweek") := .(2021,"2021-01")]
d

# Smart assignment of geo columns
d[1,c("location_code") := .("norge")]
d

# Collapsing down to different levels, and healing the dataset 
# (so that it can be worked on further with regards to real time surveillance)
d[, .(deaths_n = sum(deaths_n), location_code = "norge"), keyby=.(granularity_time)] %>%
  cstidy::set_csfmt_rts_data_v1(create_unified_columns = FALSE) %>%
  print()

# Collapsing to different levels, and removing the class csfmt_rts_data_v1 because
# it is going to be used in new output/analyses
d[, .(deaths_n = sum(deaths_n), location_code = "norge"), keyby=.(granularity_time)] %>%
  cstidy::remove_class_csfmt_rts_data() %>%
  print()
```

## Summary

We need a way to easily summarize the data structure of a dataset.

```{r}
cstidy::generate_test_data() %>%
  cstidy::set_csfmt_rts_data_v1() %>%
  summary()
```

## Identifying data structure of one column

We need a way to easily summarize the data structure of one column inside a dataset.

```{r}
cstidy::generate_test_data() %>%
  cstidy::set_csfmt_rts_data_v1() %>%
  cstidy::identify_data_structure("deaths_n") %>%
  plot()
```
