Data Vizualization 2: HW2

library(data.table)
library(ggplot2)
library(gganimate)

df <- data.table(readRDS('flats.rds'))
# colors that I used "#006D77", "#66B7B0" ,"#EDF6F9"

Reproduce the below plots:

Task 1

Task 2

Task 3

## `geom_smooth()` using formula 'y ~ x'

Task 4

Task 5

Task 6

Task 7

Bonus exercises

Geocode the (center of the) 23 districts in Budapest (like we did in the class this week), and show the distribution of comfort level for each district on a map. Yeah, using pie charts via scatterpie! You can use get_stamenmap from the ggmap package to grab the background raster image:

Now use the location data from above, but instead of points, place small pie-charts (!) on the map to show the distribution of comfort level for each district. You might need to pivot your long table into a wide one using dcast. Don’t forget to order the labels of Comfort_lev first:

Note, that the echo = FALSE parameter was added to the above code chunks to prevent printing of the R code that generated the plot … but you should not do that, as we want to see how you solved the exercise.