Great work on your first mini-data analysis deliverable. Please see detailed feedback below.
You need to include a description of your project in your README.md so that someone who is not familiar with your repo can easily orient oneself in it (make sure to do this as we will be evaluating after you submit your third deliverable). Please also add a more detailed breakdown/summary of all the files in your repository.
Thanks for knitting your .Rmd file and uploading the knitted .md file to your repo.
Quick note that you can use suppressMessages()around your library() call to avoid showing the messages when loading libraries.
Ex 1.1: It would have been helpful to provide a short description/summary of each of these datasets rather than just list them.
Ex 1.2: Great use of head(), class(), and dim() functions to explore the four datasets, glimpse() is another great function to know. You needed to briefly verbally summarize the characteristics of these datasets at a high level.
Ex 1.3: Excellent justification of the two selected datasets.
Ex 1.4: You needed to come up with one research question for each of the two datasets selected in 1.3.
This ("I am interested in potentially investigate the spread of tree species among different regions and roads of Vancouver, and possibly form a comprehensive graph. Another possible direction is the relationship between heights, diameters and date planted of individual trees and how trees are spread within different regions") needed to be rephrased as questions.
And you needed to include another question about the apt_buildings dataset.
Ex 2.1: Great exploration of the vancouver_trees dataset.
Good use of both dplyr and ggplot2 to answer a series of interesting questions. You do not need to create a separate df in order to apply ggplot2 functions to a subset of the data. Instead, you should be using a piping operator to layer dplyr and ggplot functions on top of one another : e.g. vancouver_trees %>% filter(diameter < 50) %>% ggplot(aes(x = diameter)) + geom_density(aes(fill = curb),alpha = 0.3) - this will avoid creating unnecessary files in your environment.
In your knitted .md file, none of the visualizations were rendered. That is because you needed to separate codes for each visualization in separate code chunks. Please always double-check the knitted file to make sure everything renders correctly.
Ex 2.2: Great summaries of what you are trying to accomplish with each exercise, good discussion of the results.
Ex 3: Overall, excellent research questions that you should be able to answer in the next two deliverables.
Please let me know if you have any questions. Grades will be posted later today on Canvas.
Hi Christina,
Great work on your first mini-data analysis deliverable. Please see detailed feedback below.
suppressMessages()around yourlibrary()call to avoid showing the messages when loading libraries.head(),class(), anddim()functions to explore the four datasets,glimpse()is another great function to know. You needed to briefly verbally summarize the characteristics of these datasets at a high level.vancouver_trees %>% filter(diameter < 50) %>% ggplot(aes(x = diameter)) + geom_density(aes(fill = curb),alpha = 0.3)- this will avoid creating unnecessary files in your environment.Please let me know if you have any questions. Grades will be posted later today on Canvas.
Best, Albina