vignettes with eval = !is.null(df)

Author: rafapereirabr

r-package/vignettes/access_inequality.Rmd

@@ -46,7 +46,7 @@ df <- aopdata::read_access(
 
 ## Inequality in access to job opportunities by income decile
 
-```{r, message = FALSE, eval = FALSE}
+```{r, message = FALSE, eval = !is.null(df)}
 ggplot() +
   geom_boxplot(data=subset(df, !is.na(R003)),
                aes(x = factor(R003), y=CMATT60/1000, color=factor(R003))) +
@@ -57,23 +57,11 @@ ggplot() +
   scale_x_discrete(labels=c("D1 Poorest", paste0('D', 2:9), "D10 Wealthiest")) +
   theme_minimal()
 ```
-```{r, message = FALSE, echo = FALSE}
-if (!is.null(df)) {
-  ggplot() +
-    geom_boxplot(data=subset(df, !is.na(R003)),
-                 aes(x = factor(R003), y=CMATT60/1000, color=factor(R003))) +
-    scale_color_brewer(palette = 'RdBu') +
-    labs(title='Distribution of the number of jobs accessible', color="Income\ndecile",
-         subtitle='by public transport in less than 60 min. by income decile',
-         x='Income decile', y="N. of jobs accessible\n(thousands)") +
-    scale_x_discrete(labels=c("D1 Poorest", paste0('D', 2:9), "D10 Wealthiest")) +
-    theme_minimal()
-}
-```
+
 
 ## Palma ratio
 
-```{r, message = FALSE, eval = FALSE}
+```{r, message = FALSE, eval = !is.null(df)}
 # average access of the wealthiest 10%
 avg_access_10p_wealthiest <- df[ R003==10, weighted.mean(x=CMATT60, w=P001, na.rm=T)]
 
@@ -84,19 +72,7 @@ avg_access_40p_poorest <- df[ R003<=4, weighted.mean(x=CMATT60, w=P001, na.rm=T)
 palma_ratio <- avg_access_10p_wealthiest / avg_access_40p_poorest                
 palma_ratio 
 ```
-```{r, message = FALSE, echo = FALSE}
-if (!is.null(df)) {
-  # average access of the wealthiest 10%
-  avg_access_10p_wealthiest <- df[ R003==10, weighted.mean(x=CMATT60, w=P001, na.rm=T)]
-  
-  # average access of the poorest 40%
-  avg_access_40p_poorest <- df[ R003<=4, weighted.mean(x=CMATT60, w=P001, na.rm=T)]
-  
-  # Palma ratio
-  palma_ratio <- avg_access_10p_wealthiest / avg_access_40p_poorest                
-  palma_ratio
-}
-```
+
 This means that the 10% wealthiest population could access by public transport 
 on average 2.6 times more job opportunites than the 40% 
 poorest people in less than 60 min.
@@ -105,7 +81,7 @@ poorest people in less than 60 min.
 
 ## Inequality in travel time to closes hospital
 
-```{r, message = FALSE, eval = FALSE}
+```{r, message = FALSE, eval = !is.null(df)}
 # replace Inf travel time with 120
 df[, TMISA := fifelse(TMISA==Inf, 120, TMISA)]
 
@@ -124,23 +100,3 @@ ggplot() +
   theme_minimal()
 ```
 
-```{r, message = FALSE, echo = FALSE}
-if (!is.null(df)) {
-  # replace Inf travel time with 120
-  df[, TMISA := fifelse(TMISA==Inf, 120, TMISA)]
-  
-  # calculate average travel time by race
-  df[, .(average = weighted.mean(x=TMISA, w=P001, na.rm=T),
-         white   = weighted.mean(x=TMISA, w=P002, na.rm=T),
-         black   = weighted.mean(x=TMISA, w=P003, na.rm=T))]
-  
-  # calculate average travel time by income
-  temp <- df[, .(average = weighted.mean(x=TMISA, w=P001, na.rm=T)), by=R003]
-  temp <- na.omit(temp)
-  
-  ggplot() + 
-    geom_point(data=temp, aes(y=average, x=factor(R003))) +
-    labs(x='Income decile', y='Avg. travel time to\nclosest hospital') +
-    theme_minimal()
-}
-```

r-package/vignettes/access_maps.Rmd

@@ -20,7 +20,6 @@ knitr::opts_chunk$set(
 ```
 
 
-
 **Load libraries**
 
 ```{r, message = FALSE, eval = TRUE, warning=FALSE}
@@ -46,42 +45,24 @@ df <- aopdata::read_access(
 ```
 
 ## Map access to job opportunities
-```{r, message = FALSE, eval = FALSE}
+```{r, message = FALSE, eval = !is.null(df)}
 ggplot() +
   geom_sf(data=df, aes(fill=CMATT60), color=NA, alpha=.9) +
   scale_fill_viridis_c(option = "inferno", labels = scales::comma) +
   labs(title='Number of jobs accessible', fill="Accessibility",
        subtitle='by public transport in less than 60 min.') +
   theme_void()
 ```
-```{r, message = FALSE, echo = FALSE}
-if (!is.null(df)) {
-  ggplot() +
-    geom_sf(data=df, aes(fill=CMATT60), color=NA, alpha=.9) +
-    scale_fill_viridis_c(option = "inferno", labels = scales::comma) +
-    labs(title='Number of jobs accessible', fill="Accessibility",
-         subtitle='by public transport in less than 60 min.') +
-    theme_void()
-}
-```
+
 
 ## Map access to schools
 
-```{r, message = FALSE, eval = FALSE}
+```{r, message = FALSE, eval = !is.null(df)}
 ggplot() +
   geom_sf(data=df, aes(fill=CMAET30), color=NA, alpha=.9) +
   scale_fill_viridis_c(option = "cividis", labels=scales::comma) +
     labs(title='Number of schools accessible', fill="Accessibility",
        subtitle='by public transport in less than 30 min.', fill="N. of schools") +
   theme_void()
 ```
-```{r, message = FALSE, echo = FALSE}
-if (!is.null(df)) {
-  ggplot() +
-    geom_sf(data=df, aes(fill=CMAET30), color=NA, alpha=.9) +
-    scale_fill_viridis_c(option = "cividis", labels=scales::comma) +
-      labs(title='Number of schools accessible', fill="Accessibility",
-         subtitle='by public transport in less than 30 min.', fill="N. of schools") +
-    theme_void()
-}
-```
+

r-package/vignettes/landuse_maps.Rmd

@@ -45,7 +45,7 @@ df <- aopdata::read_landuse(
 ## Spatial distribution of jobs
 
 
-```{r, message = FALSE, eval = TRUE}
+```{r, message = FALSE, eval = !is.null(df)}
 ggplot() +
   geom_sf(data=df, aes(fill=T001), color=NA, alpha=.9) +
   scale_fill_distiller(palette = "YlOrRd", direction = 1) +
@@ -54,76 +54,41 @@ ggplot() +
 
 ```
 
-```{r, message = FALSE, echo = TRUE}
-if (!is.null(df)) {
-  ggplot() +
-    geom_sf(data=df, aes(fill=T001), color=NA, alpha=.9) +
-    scale_fill_distiller(palette = "YlOrRd", direction = 1) +
-    labs(title='Spatial distribution of jobs', fill="N. of jobs") +
-    theme_void()
-}
-```
 
 
 ## Spatial distribution of schools
 
 In this case below, elementary schools with the column`E003`.
 
-```{r, message = FALSE, eval = FALSE}
+```{r, message = FALSE, eval = !is.null(df)}
 ggplot() +
   geom_sf(data=df, aes(fill=factor(E003)), color=NA, alpha=.9) +
   scale_fill_brewer(palette = "PuBuGn", direction = 1) +
   labs(title='Spatial distribution of elementary schools', fill="N. of schools") +
   theme_void()
 
 ```
-```{r, message = FALSE, echo = FALSE}
-if (!is.null(df)) {  
-  ggplot() +
-    geom_sf(data=df, aes(fill=factor(E003)), color=NA, alpha=.9) +
-    scale_fill_brewer(palette = "PuBuGn", direction = 1) +
-    labs(title='Spatial distribution of elementary schools', fill="N. of schools") +
-    theme_void()
-}
-```
 
 ## Spatial distribution of healthcare
 
 In this example, we mape high-complexity health care facilities (column `S004`).
 
-```{r, message = FALSE, eval = FALSE}
+```{r, message = FALSE, eval = !is.null(df)}
 ggplot() +
   geom_sf(data=df, aes(fill=factor(S004)), color=NA, alpha=.9) +
   scale_fill_brewer(palette = "YlGnBu", direction = 1)+
   labs(title='Spatial distribution of hospitals', fill="N. of hospitals") +
   theme_void()
 ```
 
-```{r, message = FALSE, echo = FALSE}
-if (!is.null(df)) {  
-  ggplot() +
-    geom_sf(data=df, aes(fill=factor(S004)), color=NA, alpha=.9) +
-    scale_fill_brewer(palette = "YlGnBu", direction = 1)+
-    labs(title='Spatial distribution of hospitals', fill="N. of hospitals") +
-    theme_void()
-}
-```
 
 ## Map Centers for social assistance (CRAS)
 
-```{r, message = FALSE, eval = FALSE}
+```{r, message = FALSE, eval = !is.null(df)}
 ggplot() +
   geom_sf(data=df, aes(fill=factor(C001)), color=NA, alpha=.9) +
   scale_fill_brewer(palette = "RdPu", direction = 1)+
   labs(title='Spatial distribution of CRAS facilities', fill="N. of CRAS") +
   theme_void()
 ```
-```{r, message = FALSE, echo = FALSE}
-if (!is.null(df)) {  
-  ggplot() +
-    geom_sf(data=df, aes(fill=factor(C001)), color=NA, alpha=.9) +
-    scale_fill_brewer(palette = "RdPu", direction = 1)+
-    labs(title='Spatial distribution of CRAS facilities', fill="N. of CRAS") +
-    theme_void()
-}
-```
+

r-package/vignettes/population_maps.Rmd

@@ -46,30 +46,21 @@ df <- aopdata::read_population(
 
 ## Map total population
 
-```{r, message = FALSE, eval=FALSE}
+```{r, message = FALSE, eval=!is.null(df)}
 ggplot() +
   geom_sf(data=subset(df, P001>0), aes(fill=P001), color=NA, alpha=.8) +
   scale_fill_distiller(palette = "YlOrRd", direction = 1)+
   labs(title='Population distribution', fill="Total population") +
   theme_void()
 
 ```
-```{r, message = FALSE, echo=FALSE}
-if (!is.null(df)) {
-  ggplot() +
-    geom_sf(data=subset(df, P001>0), aes(fill=P001), color=NA, alpha=.8) +
-    scale_fill_distiller(palette = "YlOrRd", direction = 1)+
-    labs(title='Population distribution', fill="Total population") +
-    theme_void()
-}
-```
 
 
 ## Map population by income levels
 
 Here, we map the spatial distribution population by income decile (column `R003`).
 
-```{r, eval = FALSE}
+```{r, eval = !is.null(df)}
 ggplot() +
   geom_sf(data=subset(df, !is.na(R002)), aes(fill=factor(R003)), color=NA, alpha=.8) +
   scale_fill_brewer(palette = "RdBu") +
@@ -78,22 +69,13 @@ ggplot() +
 
 ```
 
-```{r, message=F, echo=FALSE}
-if (!is.null(df)) {
-  ggplot() +
-    geom_sf(data=subset(df, !is.na(R002)), aes(fill=factor(R003)), color=NA, alpha=.8) +
-    scale_fill_brewer(palette = "RdBu") +
-    labs(title='Average household income per capita', fill="Income decile") +
-    theme_void()
-}
-```
 
 
 ## Map population by race
 
 Here, we map the spatial distribution of the black population.
 
-```{r, message = FALSE, eval=FALSE}
+```{r, message = FALSE, eval=!is.null(df)}
 df$prop_black <- df$P003 / df$P001
   
 ggplot() +
@@ -103,14 +85,5 @@ ggplot() +
   theme_void()
 
 ```
-```{r, message = FALSE, echo=FALSE}
-if (!is.null(df)) {
-  df$prop_black <- df$P003 / df$P001
-  
-  ggplot() +
-    geom_sf(data=subset(df, P001 >0), aes(fill=prop_black), color=NA, alpha=.8) +
-    scale_fill_distiller(palette = "RdPu", direction = 1, labels = percent)+
-    labs(title='Proportion of black population', fill="Black population") +
-    theme_void()
-}
-```
+
+