README.Rmd

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# WEED (Wrangler for Emergency Events Database)

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The goal of weed is to make the analysis of EM-DAT and related datasets easier, with most of the pre-processing abstracted away by functions in this package!

## Pre Requisites

Installation of the following packages : 
    readxl,
    dplyr,
    magrittr,
    tidytext,
    stringr,
    tibble,
    geonames,
    countrycode,
    purrr,
    tidyr,
    forcats,
    ggplot2,
    sf,
    rgeos.
    
You also need a geonames user account if you intend to use the geocoding functionality of this package. Info on how to get one for free is available [here](https://www.geonames.org/manual.html).

## Installation

You can install the development version from [GitHub](https://github.com/rammkripa/weed) with:

``` r
# install.packages("devtools")
devtools::install_github("rammkripa/weed")
```
# Example

This is a basic example which shows a common ``weed`` workflow:

```{r example, include=FALSE}
library(weed)
library(tidyverse)
```

# Loading the Data
```{r ex2}
em <- read_emdat("/Users/ramkripa/Desktop/Tk2.xlsx", file_data = TRUE)
```

# Locationizing the Data
```{r ex3}
locationized_data <- em$disaster_data %>%
  tail() %>%
  split_locations(column_name = "Location") %>%
  head()
locationized_data %>%
  select(`Dis No`, Location,location_word, Latitude, Longitude, uncertain_location_specificity)
```

There are two problems with the Dataset as it exists here.

1. Half of our observations, even in this toy dataset, don't have Lat/Long data

2. The Lat/Long here is blatantly wrong.

Lat > 90?
Long > 360? 
How is this possible?

So, we must recode this Lat/Long data

# Solving Problem 1: Our locations have very little Lat/Long data

```{r ex4}
locationized_data %>%
  percent_located_locations(lat_column = "Latitude",
                            lng_column = "Longitude")
```


# Solving Problem 2: Geocoding the Locationized Data

A reminder that you need a geonames username to access this feature of the ``weed`` package.

More info available [here](https://www.geonames.org/manual.html).

```{r preex5, include = FALSE}
dummy_name = "rammkripa"
```

```{r ex5}
geocoded_data <- locationized_data %>%
  geocode(geonames_username = dummy_name)
geocoded_data %>%
  select(`Dis No`, Location,location_word, lat, lng)
```

Side note: These Lat/Long data look much better than before, given that Kenya is close to the equator!

# How effective was our geocoding?

```{r ex6}
geocoded_data %>%
  percent_located_locations()
```

```{r ex62}
geocoded_data %>%
  percent_located_disasters()
```

# Check if the locations are in a Lat Long Box!!

```{r ex77}
geocoded_data <- geocoded_data %>%
  located_in_box(top_left_lat = 0, 
                 top_left_lng = 35, 
                 bottom_right_lat = -6, 
                 bottom_right_lng = 40) 
geocoded_data %>%
  select(`Dis No`, Location,location_word, lat, lng, in_box)
```

# Check if Locations are in Shapefile!

```{r preex87, include = FALSE}
s_file_name = "~/Desktop/Projects/emdat_proj/shape_data/SH_mask.shp"
```

```{r ex87}
geocoded_data %>%
  located_in_shapefile(shapefile_name = s_file_name) %>%
  select(`Dis No`, Location, location_word, lat, lng, in_box, in_shape)
```



# Want to re-nest the location data?

```{r ex9} 
geocoded_data %>%
  nest_locations() %>%
  select(`Dis No`, location_data)
```