Moiya Josephs 2/3/2022
library(ggplot2)
library(curl)## Using libcurl 7.64.1 with Schannellibrary(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
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## v readr 2.1.2 v forcats 0.5.1
## v purrr 0.3.4
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## x dplyr::filter() masks stats::filter()
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## x readr::parse_date() masks curl::parse_date()The article “Joining the Avengers Is As Deadly As Jumping Off A Four-Story Building” by Walt Hickey describes how characters in the popular Marvel comic have experienced death at a rate comparable to jumping off of a four-story building. According to the article, there is a 50% death rate if you jump off a building four stories high. Hickey claims they found that if a character joins the Avengers, they have a death rate of 40%. Since these are comic book characters, and characters tend to be resurrected, the data set also included each resurrection and death for the character. It capped the number of deaths at 5. The author mentions a criterion for what does not count as a death, such as if a character fakes their demise. To be counted as a death, the death had to have been shown in the comics, or other characters had to sincerely believe that the character is dead. By these standards, Hickey compiled a CSV file that I will be using for this assignment.
The source of the article is included under the References header.
avengers_dataset_url <- "https://raw.githubusercontent.com/moiyajosephs/Data-607-Homework-1/main/avengers.csv"
avengers <- read_csv(curl(avengers_dataset_url))## Rows: 173 Columns: 21
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (18): URL, Name/Alias, Current?, Gender, Probationary Introl, Full/Reser...
## dbl (3): Appearances, Year, Years since joining
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(avengers)## # A tibble: 6 x 21
## URL `Name/Alias` Appearances `Current?` Gender `Probationary I~
## <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 http://marvel~ "Henry Jonathan~ 1269 YES MALE <NA>
## 2 http://marvel~ "Janet van Dyne" 1165 YES FEMALE <NA>
## 3 http://marvel~ "Anthony Edward~ 3068 YES MALE <NA>
## 4 http://marvel~ "Robert Bruce B~ 2089 YES MALE <NA>
## 5 http://marvel~ "Thor Odinson" 2402 YES MALE <NA>
## 6 http://marvel~ "Richard Milhou~ 612 YES MALE <NA>
## # ... with 15 more variables: Full/Reserve Avengers Intro <chr>, Year <dbl>,
## # Years since joining <dbl>, Honorary <chr>, Death1 <chr>, Return1 <chr>,
## # Death2 <chr>, Return2 <chr>, Death3 <chr>, Return3 <chr>, Death4 <chr>,
## # Return4 <chr>, Death5 <chr>, Return5 <chr>, Notes <chr>The avengers data set has 21 columns, some not necessary to view if joining the Avengers is as deadly as falling off a four story building. I selected the characters’ name, if they are a current member, the type of member they are, Honorary/Full member and if they died. I also renamed the column “Current?” to “Current Member” and “Honorary” to “Honorary Member” to make the data set more understandable.
avengers_subset <- avengers[c(2,4,7,10:11,13,15,17,19)]
names(avengers_subset)[names(avengers_subset) == 'Current?'] <- 'Current Member'
names(avengers_subset)[names(avengers_subset) == 'Honorary'] <- 'Honorary Member'
head(avengers_subset)## # A tibble: 6 x 9
## `Name/Alias` `Current Member` `Full/Reserve A~ `Honorary Membe~ Death1 Death2
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 "Henry Jonat~ YES Sep-63 Full YES <NA>
## 2 "Janet van D~ YES Sep-63 Full YES <NA>
## 3 "Anthony Edw~ YES Sep-63 Full YES <NA>
## 4 "Robert Bruc~ YES Sep-63 Full YES <NA>
## 5 "Thor Odinso~ YES Sep-63 Full YES YES
## 6 "Richard Mil~ YES Sep-63 Honorary NO <NA>
## # ... with 3 more variables: Death3 <chr>, Death4 <chr>, Death5 <chr>First I made a subset of only current members. Here I can see which characters are members of the Avengers, whether or not they are full members or honorary and if they have died at least once.
current_avengers <- subset(avengers_subset, `Current Member` == "YES")
current_avengers## # A tibble: 82 x 9
## `Name/Alias` `Current Member` `Full/Reserve A~ `Honorary Membe~ Death1 Death2
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 "Henry Jona~ YES Sep-63 Full YES <NA>
## 2 "Janet van ~ YES Sep-63 Full YES <NA>
## 3 "Anthony Ed~ YES Sep-63 Full YES <NA>
## 4 "Robert Bru~ YES Sep-63 Full YES <NA>
## 5 "Thor Odins~ YES Sep-63 Full YES YES
## 6 "Richard Mi~ YES Sep-63 Honorary NO <NA>
## 7 "Steven Rog~ YES Mar-64 Full YES <NA>
## 8 "Clinton Fr~ YES May-65 Full YES YES
## 9 "Pietro Max~ YES May-65 Full YES <NA>
## 10 "Wanda Maxi~ YES May-65 Full YES <NA>
## # ... with 72 more rows, and 3 more variables: Death3 <chr>, Death4 <chr>,
## # Death5 <chr>According to the subset and the amount of rows there are 82 current members, who are either full or honorary.
I wanted to total the amount of times an individual character has died. Since they can die and come back to life multiple times I had to combine five rows into one sum. First I changed the character value of YES to 1 or 0 is NO so that I can better aggregate the data.
current_avengers$Death1<-ifelse(!is.na(current_avengers$Death1) & current_avengers$Death1=="YES",1,0)
current_avengers$Death2<-ifelse(!is.na(current_avengers$Death2) & current_avengers$Death2=="YES",1,0)
current_avengers$Death3<-ifelse(!is.na(current_avengers$Death3) & current_avengers$Death3=="YES",1,0)
current_avengers$Death4<-ifelse(!is.na(current_avengers$Death4) & current_avengers$Death4=="YES",1,0)
current_avengers$Death5<-ifelse(!is.na(current_avengers$Death5) & current_avengers$Death5=="YES",1,0)
current_avengers## # A tibble: 82 x 9
## `Name/Alias` `Current Member` `Full/Reserve A~ `Honorary Membe~ Death1 Death2
## <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 "Henry Jona~ YES Sep-63 Full 1 0
## 2 "Janet van ~ YES Sep-63 Full 1 0
## 3 "Anthony Ed~ YES Sep-63 Full 1 0
## 4 "Robert Bru~ YES Sep-63 Full 1 0
## 5 "Thor Odins~ YES Sep-63 Full 1 1
## 6 "Richard Mi~ YES Sep-63 Honorary 0 0
## 7 "Steven Rog~ YES Mar-64 Full 1 0
## 8 "Clinton Fr~ YES May-65 Full 1 1
## 9 "Pietro Max~ YES May-65 Full 1 0
## 10 "Wanda Maxi~ YES May-65 Full 1 0
## # ... with 72 more rows, and 3 more variables: Death3 <dbl>, Death4 <dbl>,
## # Death5 <dbl>Next I used the mutate function from dyplr to add an extra row showing the total amount of times a character has died.
current_avengers <- current_avengers %>% mutate(sum = rowSums(.[5:9]))head(current_avengers)## # A tibble: 6 x 10
## `Name/Alias` `Current Member` `Full/Reserve A~ `Honorary Membe~ Death1 Death2
## <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 "Henry Jonat~ YES Sep-63 Full 1 0
## 2 "Janet van D~ YES Sep-63 Full 1 0
## 3 "Anthony Edw~ YES Sep-63 Full 1 0
## 4 "Robert Bruc~ YES Sep-63 Full 1 0
## 5 "Thor Odinso~ YES Sep-63 Full 1 1
## 6 "Richard Mil~ YES Sep-63 Honorary 0 0
## # ... with 4 more variables: Death3 <dbl>, Death4 <dbl>, Death5 <dbl>,
## # sum <dbl>To find the maximum amount of times a given character died I used the sum function on my new column, “sum”.
max(current_avengers$sum)## [1] 5There is a character that has died five times, the maximum amount a character can achieve in this data set. This is interesting but it would be nice to know which character this is.
current_avengers %>% slice_max(current_avengers$sum)## # A tibble: 1 x 10
## `Name/Alias` `Current Member` `Full/Reserve Av~ `Honorary Membe~ Death1 Death2
## <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Jocasta YES Nov-88 Full 1 1
## # ... with 4 more variables: Death3 <dbl>, Death4 <dbl>, Death5 <dbl>,
## # sum <dbl>According to the slice_max function the charter that has died the most is Jocasta. This character was also mentioned in the article as the character with the most amount of deaths.
Through data transformation I found that there were 82 current members of the Avengers. Out of this subset, Jocasta had the highest amount of deaths out of any character. To further verify this data I would use a data set or study that verifies the articles statistic of falling from a four story building is 50% death rate. Then I would use this data set and find the actual percentage from the sum of characters who were Avengers vs those who were not Avengers. To extend this article and see if this trend continues, it would be interesting to add any current members that have been added from 2015 to now.
Joining The Avengers Is As Deadly As Jumping Off A Four-Story Building