New parameter peak added to read_access() function. Closes #17.

r-package/NEWS.md

@@ -4,8 +4,12 @@
 
 # aopdata v0.1.0 (dev)
 
+
+**Major changes**
+* New parameter `peak` added to `read_access()` function. Closes #17.
+
 **Minor changes**
-* Downloads two or more cities at the same time. Closes #3
+* Downloads two or more cities at the same time. Closes #3.
 
 
 

r-package/R/read_access.R

@@ -10,10 +10,14 @@
 #'             `city="all"`, results for all cities are loaded.
 #' @param mode Character. A transport mode. Modes available include
 #'             'public_transport', 'bicycle', or 'walk' (the default).
+#' @param peak Logical. If `TRUE` (the default), returns accessibility estimates
+#'             during peak time, between 6am and 8am. If `FALSE`, returns
+#'             accessibility during off-peak, between 2pm and 4am. This argument
+#'             only takes effect when `mode = public_transport`.
 #' @param year Numeric. A year number in YYYY format. Default set to 2019, the
 #'             only year currently available.
-#' @param geometry Logical. if FALSE (the default), returns a regular data.table
-#'                 of aop data. if TRUE, returns a an `sf data.frame` with simple
+#' @param geometry Logical. If `FALSE` (the default), returns a regular data.table
+#'                 of aop data. If `TRUE`, returns a an `sf data.frame` with simple
 #'                 feature geometry of spatial hexagonal grid H3. See details in
 #'                 \link{read_grid}.
 #' @param showProgress Logical. Defaults to `TRUE` display progress bar
@@ -31,7 +35,7 @@
 #' all <- read_access(city = 'all', mode = 'public_transport', year = 2019)
 
 #'}
-read_access <- function(city, mode = 'walk', year = 2019, geometry = FALSE, showProgress = TRUE){
+read_access <- function(city, mode = 'walk', peak = TRUE, year = 2019, geometry = FALSE, showProgress = TRUE){
 
   # Get metadata with data url addresses
   temp_meta <- select_metadata(t='access',
@@ -45,15 +49,23 @@ read_access <- function(city, mode = 'walk', year = 2019, geometry = FALSE, show
   # download files
   aop_df <- download_data(file_url, progress_bar = showProgress)
 
-  # without spatial data, return df
+  # peak Vs off-peak
+  if(peak==TRUE){
+                 aop_df <- subset(aop_df, peak == 1)
+                } else {
+                 aop_df <- subset(aop_df, peak == 0)
+                }
+
+  # with Vs without spatial data
   if(geometry == FALSE){
+                        # return df
                         return(aop_df)
-                        }
 
-  # with spatial data, return sf
-  if(geometry == TRUE){
+                        } else {
+
+                        # return sf
                         aop_sf <- read_grid(city=city, showProgress=showProgress)
                         aop <- aop_spatial_join(aop_df, aop_sf)
                         return(aop)
                         }
-}
+  }

r-package/prep_data/vignette_map.R

@@ -0,0 +1,72 @@
+
+library(aopdata)
+library(sf)
+library(ggplot2)
+library(data.table)
+
+# download aop data
+df <- aop::read_access(city='bho',
+                       mode='public_transport',
+                      year=2019,
+                      geometry = T)
+
+access_peak <- subset(df, peak==1)
+
+# plot map accessibility
+ggplot() +
+  geom_sf(data=df, aes(fill=CMATT60), color=NA, alpha=.9) +
+  scale_fill_viridis_c(option = "inferno", labels=scales::percent) +
+  labs(title='Proportion of jobs accessible', fill="Accessibility",
+       subtitle='by public transport in less than 60 min.') +
+  theme_void()
+
+
+# plot map Schools
+ggplot() +
+  geom_sf(data=df, aes(fill=S001), color=NA, alpha=.7) +
+  scale_fill_viridis_c(option = "inferno") +
+  labs(title='Spatial distribution of public schools', fill="N. of schools") +
+  theme_void()
+
+ggplot() +
+  geom_sf(data=df, aes(fill=df$S001), color=NA, alpha=.7) +
+  scale_fill_viridis_c(option = "inferno") +
+  labs(title='Spatial distribution of public schools', fill="N. of schools") +
+  theme_void()
+
+
+
+# Download
+df2 <- aop::read_access(city='bho',
+                       mode='public_transport',
+                       year=2019)
+
+
+ggplot() +
+  geom_boxplot(data=subset(df2, !is.na(R003)),
+               aes(x = factor(R003), y=CMAET30, color=factor(R003))) +
+  scale_color_brewer(palette = 'RdBu') +
+  labs(title='Distribution of the proportion of jobs accessible', color="Income\ndecile",
+       subtitle='by public transport in less than 30 min. by income decile',
+       x='Income decile', y="Accessibility") +
+  scale_x_discrete(labels=c("D1 Poorest", paste0('D', 2:9), "D10 Wealthiest")) +
+  scale_y_continuous(labels = scales::percent) +
+  theme_minimal()
+
+weight=P001
+summary(df2$P001)
+
+
+
+
+
+
+
+library(aopdata)
+library(data.table)
+
+df <- aop::read_access(city='all', mode='walk', year=2019)
+
+df[, round(weighted.mean(x = CMATT30, w=P001),3), by=name_muni]
+
+