10.9 Meeting Videos

10.9.1 Cohort 5

Meeting chat log
00:13:43    Njoki Njuki Lucy:   Is it best to visualize the variation in a categorical variable with only two levels using a bar chart? If not, what's the chart to use if I may ask?
00:16:00    Ryan Metcalf:   Great question Njoki, Categorical, by definition is a set that a variable can have. Say, Male / Female / Other. This example indicates a variable can have three states. It depends on your data set.
00:16:51    Eileen: bar or pie chart?
00:16:51    Ryan Metcalf:   There are other forms of presentation other than a bar chart. I.E “quantifying” each category.
00:18:37    Eileen: box chart
00:18:46    Njoki Njuki Lucy:   thank you so much  everyone :)
00:24:31    lucus w:    This website is excellent in determining geom to use: www.data-to-viz.com
00:25:22    Njoki Njuki Lucy:   awesome, thanks
00:25:44    Eileen: Box charts are great for showing outliers
00:26:31    Federica Gazzelloni:    other interesting resources:
00:26:34    Federica Gazzelloni:    https://www.r-graph-gallery.com/ggplot2-package.html
00:26:51    Federica Gazzelloni:    http://www.cookbook-r.com/Graphs/
00:34:19    Amitrajit:  what is the difference in putting aes()  inside geom_count() rather than main ggplot() call?
00:35:38    Ryan Metcalf:   Like maybe Supply vs Demand curves?
00:41:16    Federica Gazzelloni:    what about the factor() that we add to a variable when we apply a color?
00:42:33    Susie Neilson:  I do aes your way Jon!
00:43:07    Federica Gazzelloni:    and grouping inside the aes
00:49:27    Amitrajit:  thanks!
00:49:32    Federica Gazzelloni:    thanks
00:49:35    Njoki Njuki Lucy:   thank you, bye
00:49:45    Eileen: Thank you!

10.9.2 Cohort 6

Meeting chat log
00:06:21    Matthew Efoli:  good evening Daniel and Esmeralda
00:07:39    Matthew Efoli:  hello everyone
00:08:08    Daniel Adereti: Hello Matthew!
00:08:44    Daniel Adereti: I guess we can start? so we can finish the 2 chapters as Exploratory Data Analysis is quite long and involved
00:09:04    Freya Watkins:  Sounds good! Hi all :)
00:10:55    Freya Watkins:  yes can see
00:23:14    Daniel Adereti: na > Not available
00:23:32    Maria Eleni Soilemezidi:    rm = remove
00:25:49    Esmeralda Cruz: yes
00:26:29    Esmeralda Cruz: to remove the outliers maybe?
00:29:20    Adeyemi Olusola:    No
00:29:22    Freya Watkins:  we can't see it no
00:29:27    Maria Eleni Soilemezidi:    no we can't see it!
00:29:38    Maria Eleni Soilemezidi:    thank you! Yes
00:32:57    Daniel Adereti: Cedric's article is a nice one! Helpful to understand descriptive use case of different plot ideas
00:43:19    Daniel Adereti: we can do the exercises
00:43:27    Esmeralda Cruz: ok
00:43:28    Maria Eleni Soilemezidi:    yes, sure!
00:45:20    Adeyemi Olusola:    we can try reorder
00:45:28    Adeyemi Olusola:    from the previous example
00:51:44    Maria Eleni Soilemezidi:    that's a good idea
00:52:28    Daniel Adereti: Thanks!
00:52:42    Daniel Adereti: cut_in_color_graph <- diamonds %>% 
  group_by(color, cut) %>% 
  summarise(n = n()) %>% 
  mutate(proportion_cut_in_color = n/sum(n)) %>%
  ggplot(aes(x = color, y = cut))+
  geom_tile(aes(fill = proportion_cut_in_color))+
  labs(fill = "proportion\ncut in color")
00:53:32    Esmeralda Cruz: 😮
00:53:47    Adeyemi Olusola:    smiles
00:54:13    Adeyemi Olusola:    but lets try reorder...I think we should be able to pull something from it, though not sure about the heatmap thingy
00:54:26    Adeyemi Olusola:    on our own though*
01:05:38    Maria Eleni Soilemezidi:    no worries! Thank you for the presentation, Matthew! :)
01:05:39    Freya Watkins:  Thanks Matthew!
01:06:44    Maria Eleni Soilemezidi:    bye everyone, see you next week!

10.9.3 Cohort 7

10.9.4 Cohort 8

Meeting chat log
00:42:29    Ahmed:  https://www.causact.com/index.html#welcome
00:42:54    Abdou:  Thanks!