Update dataviz-storytelling.md

Author: gndaskalova

_tutorials/dataviz-storytelling.md

@@ -978,120 +978,6 @@ ggsave(trends_diet, filename = "trends_diet.png",
        height = 5, width = 8)
 ```
 
-Knowing the sample size for each diet category is another useful bit of information, especially to support the spirit of open and transparent science. We can use `group_by()` and `tally()` to get the sample size numbers.
-
-```r
-diet_sum <- bird_models_traits %>% group_by(diet) %>%
-  tally()
-```
-
-Now that we know the numbers, we can visualise them. A barplot would be a classic way to do that, the second option present here - the area graph - is another option. Both can work well depending on the specific occasion, but the area graph does a good job at quickly communicating which categories are overrepresented and which - underrepresented.
-
-```r
-(diet_bar <- ggplot(diet_sum, aes(x = diet, y = n,
-                                   colour = diet,
-                                  fill = diet)) +
-    geom_bar(stat = "identity") +
-    scale_colour_manual(values = wes_palette("Cavalcanti1")) +
-    scale_fill_manual(values = wes_palette("Cavalcanti1")) +
-    guides(colour = FALSE))
-
-(diet_area <- ggplot(diet_sum, aes(area = n, fill = diet, label = n,
-                                 subgroup = diet)) +
-    geom_treemap() +
-    geom_treemap_subgroup_border(colour = "white", size = 1) +
-    geom_treemap_text(colour = "white", place = "center", reflow = T) +
-    scale_colour_manual(values = wes_palette("Cavalcanti1")) +
-    scale_fill_manual(values = wes_palette("Cavalcanti1")) +
-    guides(fill = FALSE))  # this removes the colour legend
-    # later on we will combine multiple plots so there is no need for the legend
-    # to be in twice
-
-# To display the legend, just remove the guides() line:
-(diet_area <- ggplot(diet_sum, aes(area = n, fill = diet, label = n,
-                                   subgroup = diet)) +
-    geom_treemap() +
-    geom_treemap_subgroup_border(colour = "white", size = 1) +
-    geom_treemap_text(colour = "white", place = "center", reflow = T) +
-    scale_colour_manual(values = wes_palette("Cavalcanti1")) +
-    scale_fill_manual(values = wes_palette("Cavalcanti1")))
-
-ggsave(diet_area, filename = "diet_area.png",
-       height = 5, width = 8)
-```
-
-<center> <img src="{{ site.baseurl }}/assets/img/tutorials/dataviz-beautification-synthesis/diet_bar.png" alt="Img" style="width: 500px;"/>  <img src="{{ site.baseurl }}/assets/img/tutorials/dataviz-beautification-synthesis/diet_area2.png" alt="Img" style="width: 500px;"/></center>
-
-__We've covered spatial representation of the data (our map), as well as the kinds of species (the diet figures), now we can cover another dimention - time! We can make a timeline of the individual studies to see what time periods are best represented.__
-
-```r
-# Timeline
-# Making the id variable a factor
-# otherwise R thinks its a number
-bird_models_traits$id <- as.factor(as.character(bird_models_traits$id))
-
-(timeline_aus <- ggplot() +
-    geom_linerange(data = bird_models_traits, aes(ymin = minyear, ymax = maxyear,
-                                                  colour = diet,
-                                                  x = id),
-                   size = 1) +
-    scale_colour_manual(values = wes_palette("Cavalcanti1")) +
-    labs(x = NULL, y = NULL) +
-    theme_bw() +
-    coord_flip())
-```
-
-Well this looks untidy! The values are not sorted properly and it looks like a mess, but that happens often when making figures, part of the figure beautification journey. We can fix the graph with the code below.
-
-<center> <img src="{{ site.baseurl }}/assets/img/tutorials/dataviz-beautification-synthesis/timeline3.png" alt="Img" style="width: 600px;"/></center>
-
-```r
-# Create a sorting variable
-bird_models_traits$sort <- bird_models_traits$diet
-bird_models_traits$sort <- factor(bird_models_traits$sort, levels = c("VertFishScav",
-                                                                      "FruiNect",
-                                                                      "Omnivore",
-                                                                      "Invertebrate",
-                                                                      "PlantSeed"),
-                          labels = c(1, 2, 3, 4, 5))
-
-bird_models_traits$sort <- paste0(bird_models_traits$sort, bird_models_traits$minyear)
-bird_models_traits$sort <- as.numeric(as.character(bird_models_traits$sort))
-```
-
-This sorting variable will help us arrange the studies first by species' diet, then by when each study started.
-
-```r
-(timeline_aus <- ggplot() +
-    geom_linerange(data = bird_models_traits, aes(ymin = minyear, ymax = maxyear,
-                                          colour = diet,
-                                          x = fct_reorder(id, desc(sort))),
-                   size = 1) +
-    scale_colour_manual(values = wes_palette("Cavalcanti1")) +
-    labs(x = NULL, y = NULL) +
-    theme_bw() +
-    coord_flip() +
-    guides(colour = F) +
-    theme(panel.grid.minor = element_blank(),
-          panel.grid.major.y = element_blank(),
-          panel.grid.major.x = element_line(),
-          axis.ticks = element_blank(),
-          legend.position = "bottom",
-          panel.border = element_blank(),
-          legend.title = element_blank(),
-          axis.title.y = element_blank(),
-          axis.text.y = element_blank(),
-          axis.ticks.y = element_blank(),
-          plot.title = element_text(size = 20, vjust = 1, hjust = 0),
-          axis.text = element_text(size = 16),
-          axis.title = element_text(size = 20)))
-
-ggsave(timeline_aus, filename = "timeline.png",
-       height = 5, width = 8)
-```
-
-<center> <img src="{{ site.baseurl }}/assets/img/tutorials/dataviz-beautification-synthesis/timeline2.png" alt="Img" style="width: 600px;"/></center>
-
 __For our final figure using our combined dataset of population trends and species' traits, we will make a figure classic - the scatterplot. Body mass can sometimes be a good predictor of how population trends and extinction risk vary, so let's find out if that's true for the temporal changes in abundance across monitored populations of Australian birds.__
 
 ```r