---
title: "Vignette >Basic usage<"
author: "Clemens Schmid"
date: "March 2017"
output: rmarkdown::html_vignette
bibliography: ../inst/REFERENCES.bib
vignette: >
  %\VignetteIndexEntry{Vignette >Basic usage<}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# Load libraries

```{r, message=FALSE}
library(mortAAR)
library(magrittr)
```

# Make test data available

Data from four neolithic gallery graves in central Germany [@czarnetzki_menschlichen_1966].

```{r}
td <- gallery_graves
```

Inspect the data. Show the first ten rows of the data set:

```{r, echo=FALSE, results='asis'}
td %>% head(., n = 10) %>% knitr::kable()
```

# Clean up data

Replace: "?" with `NA` values.

```{r}
td %>% replace(td == "?", NA) -> td
```

```{r, echo=FALSE, results='asis'}
td %>% head(., n = 10) %>% knitr::kable()
```

Translate "inf_I", "inf_I" and "juv" into numeric age ranges [@martin_lehrbuch_1928, pp. 580].

```{r}
td <- td %>% 
  replace(td == "inf_I",  "0-6") %>%
  replace(td == "inf_II", "7-13") %>%
  replace(td == "juv",    "14-19")
```

```{r, echo=FALSE, results='asis'}
td %>% head(., n = 10) %>% knitr::kable()
```

Remove rows that do not have age information.

```{r}
td <- td %>%
  dplyr::filter(!is.na(age))
```

```{r, echo=FALSE, results='asis'}
td %>% head(., n = 10) %>% knitr::kable()
```

Make a decision on individual 139 from Niedertiefenbach with age less or equal 60. 

```{r}
td[td$indnr == "139" & td$site == "Niedertiefenbach", ]$age <- "50-60"
```

```{r, echo=FALSE, results='asis'}
td %>% head(n = 10) %>% knitr::kable()
```

Separate the age range column.

```{r}
td <- td %>%
  tidyr::separate(age, c("from", "to"))
```

```{r, echo=FALSE, results='asis'}
td %>% head(., n = 10) %>% knitr::kable()
```

Adjust variable types.

```{r}
td <- td %>%
  transform(
    from = as.numeric(from),
    to = as.numeric(to)
  )
```

# Analysis preparation

Control the flow of the analysis by exemplifying what the different variables of the input data stand for.

```{r}
# tdlist <- td %>%
#   plyr::dlply("site", identity)

td_prepared <- prep.life.table(
  td, 
  dec = NA, 
  agebeg = "from",
  ageend = "to", 
  group = "site", 
  method = "Standard",
  agerange = "included"
)
```

# Analysis

```{r}
td_result <- td_prepared %>%
  life.table()
```

# Plot

```{r, fig.width=7, fig.height=5}
td_result %>% plot(display = c("qx", "dx", "lx"))
```

```{r, fig.width=7, fig.height=5}
td_result %>% plot(display = c("ex", "rel_popx"))
```

# References