X-Men Comics

Data manipulation, Lollipop graph, Grouping using floor division

Published

June 29, 2020

Notable topics: Data manipulation, Lollipop graph, Grouping using floor division

Recorded on: 2020-06-29

Timestamps by: Eric Fletcher

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Screencast

Timestamps

separate
tidyr

Using separate to separate the name from secrete identity in the character column

summarizeacross
dplyr

Using summarize and across to find the frequency of the action variables and find out how many issues each action was used for each character

geom_colfct_reorder
ggplot2forcats

Create a geom_col chart to visualize which character speaks in the most issues

geom_pointgeom_textgeom_text_repelsummarize
ggplot2ggrepeldplyr

Create a geom_point chart to visualize each character’s average lines per issue in which the character is depicted

geom_pointgeom_textgeom_text_repelsummarize
ggplot2ggrepeldplyr

Create a geom_point chart to visualize each character’s average thoughts per issue in which the character is depicted

geom_pointgeom_textgeom_text_repelsummarize
ggplot2ggrepeldplyr

Create a geom_point chart to visualize character’s speech versus thought ratio per issue in which the character is depicted

geom_pointpivot_longerfct_reorder
ggplot2tidyrforcats

Create a geom_point to visualize character’s number of lines while in costume versus not in costume

geom_pointgeom_textgeom_text_repelsummarize
ggplot2ggrepeldplyr

Create a geom_point chart to visualize the lines in costume versus lines out of costume ratio

geom_pointfct_reordergeom_errorbarh
ggplot2forcats

Create a lollipop graph using geom_point and geom_errorbarh to visualize the lines in costume versus lines out of costume ratio and their distance from 1.0 (1 to 1)

summarizegroup_byarrange
dplyr

Use summarize to find the frequency of each location and the total number of unique issues where the location is used

summarizefct_lump
dplyrforcats

Use summarize and fct_lump to count how many issues each author has written while lumping together all authors except the most frequent

summarizefct_lump
dplyrforcats

Use summarize and fct_lump to see if the authors rates of passing the Bechdel test differ from one another

geom_linesummarize
dplyr

Create a geom_line chart to visualize if the rates of passing the Bechdel test changed over time and floor division %/% to generate 20 observations per group

geom_colsummarizefct_lumpfacet_wrap
ggplotdplyrforcats

Create a geom_col to visualize the amount of lines each character has per issue over time giving context to Bechdel test passing rates

Summary of screencast