readme updated and plotting functions tweaked with legend title

R/percent_located_disasters.R

@@ -58,7 +58,8 @@ percent_located_disasters <- function(.,
       ggplot2::xlab("Geocoding") +
       ggplot2::ylab("Percent of Disasters") +
       ggplot2::ggtitle("Percent of Disasters Geocoded") +
-      ggplot2::coord_flip()
+      ggplot2::coord_flip() +
+      ggplot2::theme(legend.title = ggplot2::element_blank())
   }
   else {
     return(perc_df)

R/percent_located_locations.R

@@ -34,6 +34,7 @@ percent_located_locations <- function(.,
     dplyr::mutate(percent = 100*count/sum(count)) %>%
     dplyr::mutate(coords_nonexistent = forcats::as_factor(coords_nonexistent)) %>%
     dplyr::mutate(coords_nonexistent = forcats::fct_recode(coords_nonexistent, "Geocode Failed" = "TRUE", "Geocode Success"  = "FALSE"))
+
   if (plot_result){
     perc_df %>%
       ggplot2::ggplot(mapping = ggplot2::aes(x = coords_nonexistent, y = percent, fill = coords_nonexistent))+
@@ -42,7 +43,8 @@ percent_located_locations <- function(.,
       ggplot2::xlab("Geocoding") +
       ggplot2::ylab("Percent of Locations") +
       ggplot2::ggtitle("Percent of Locations Geocoded") +
-      ggplot2::coord_flip()
+      ggplot2::coord_flip()+
+      ggplot2::theme(legend.title = ggplot2::element_blank())
   }
   else {
     return(perc_df)

README.Rmd

@@ -22,7 +22,7 @@ The goal of weed is to make the analysis of EM-DAT and related datasets easier,
 
 ## Pre Requisites
 
-Installation of the following packages
+Installation of the following packages : 
     readxl,
     dplyr,
     magrittr,
@@ -34,7 +34,7 @@ Installation of the following packages
     purrr,
     tidyr,
     forcats,
-    ggplot2
+    ggplot2.
 
 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).
 
@@ -71,8 +71,11 @@ locationized_data %>%
 ```
 
 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?
@@ -90,9 +93,17 @@ locationized_data %>%
 
 # 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 = "rammkripa")
+  geocode(geonames_username = dummy_name)
 geocoded_data %>%
   select(`Dis No`, Location,location_word, lat, lng)
 ```

README.md

@@ -13,9 +13,9 @@ this package\!
 
 ## Pre Requisites
 
-Installation of the following packages readxl, dplyr, magrittr,
+Installation of the following packages : readxl, dplyr, magrittr,
 tidytext, stringr, tibble, geonames, countrycode, purrr, tidyr, forcats,
-ggplot2
+ggplot2.
 
 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
@@ -64,10 +64,14 @@ locationized_data %>%
 #> 6 2020-0164… West Pokot, Elgeyo Marakwet, Kisu… kisumu        116.343  365.935
 ```
 
-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?
+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
 
@@ -83,9 +87,14 @@ locationized_data %>%
 
 # 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
 geocoded_data <- locationized_data %>%
-  geocode(geonames_username = "rammkripa")
+  geocode(geonames_username = dummy_name)
 geocoded_data %>%
   select(`Dis No`, Location,location_word, lat, lng)
 #> # A tibble: 6 x 5