mgaughan/mw-lifecycle-analysis
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updating with new sampling approach for c2 and c3

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This commit is contained in:
Matthew Gaughan 2025-07-01 07:45:13 -07:00
parent edcb174d42
commit a4d8685c13
4 changed files with 18 additions and 10 deletions
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5
.sh_history
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@ -176,3 +176,8 @@ ls case1
ls
cd case2
ls
cd ..
ls
cd case3
ls
rm 062725_c3_title_cleaned.csv

6
p2_EDA/062325_EDA.R
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@ -3,10 +3,10 @@ library(tidyverse)
c1_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case1/062725_c1_cleaned_phab.csv"
c1_input_df <- read.csv(c1_count , header = TRUE)
c2_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/062725_c2_cleaned_phab.csv"
c2_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/070125_c2_title_cleaned.csv"
c2_input_df <- read.csv(c2_count , header = TRUE)
c3_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case3/062725_c3_cleaned_phab.csv"
c3_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case3/070125_c3_title_cleaned.csv"
c3_input_df <- read.csv(c3_count , header = TRUE)
library(dplyr)
@ -88,7 +88,7 @@ combined_task_df <- combined_task_df %>%
ungroup()
# 3. Plot (e.g., bar plot of mean modal verbs per group)
ggplot(combined_task_df, aes(x = source, y = whatever_subset_count, fill = AuthorWMFAffil)) +
ggplot(combined_task_df, aes(x = source, y = modal_verb_count, fill = AuthorWMFAffil)) +
geom_violin(trim = FALSE, position = position_dodge(width = 0.8), alpha = 0.6) +
stat_summary(
fun = mean,

10
p2_EDA/clean_c2c3_phab.R
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@ -65,10 +65,12 @@ write.csv(c1_sampled, "/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/ca
# C2: after 9-3-2011 before 11-27-2013
c2_desc_flags <- c2_input_df %>%
filter(comment_type == "task_description") %>%
mutate(http_flag = mapply(function(txt, ttl) http_relevant(txt) || http_relevant(ttl), comment_text, task_title)) %>%
mutate(http_flag = sapply(task_title, http_relevant)) |>
mutate(time_flag = date_created >= 1315008000 & date_created <= 1385596799) |>
select(TaskPHID, http_flag, time_flag)
#mutate(http_flag = mapply(function(txt, ttl) http_relevant(txt) || http_relevant(ttl), comment_text, task_title)) %>%
c2_flagged <- c2_input_df %>%
left_join(c2_desc_flags, by = "TaskPHID")
@ -77,11 +79,11 @@ c2_sampled <- c2_flagged |>
filter(time_flag == TRUE)
sum(c2_sampled$comment_type == "task_description")
write.csv(c2_sampled, "/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/062725_c2_cleaned.csv", row.names=FALSE)
write.csv(c2_sampled, "/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/070125_c2_title_cleaned.csv", row.names=FALSE)
# C3: after 07-01-2013 before 10-01-2015
c3_desc_flags <- c3_input_df %>%
filter(comment_type == "task_description") %>%
mutate(http_flag = mapply(function(txt, ttl) http_relevant(txt) || http_relevant(ttl), comment_text, task_title)) %>%
mutate(http_flag = sapply(task_title, http_relevant)) |>
mutate(time_flag = date_created >= 1372636800 & date_created <= 1443743999) |>
select(TaskPHID, http_flag, time_flag)
@ -93,4 +95,4 @@ c3_sampled <- c3_flagged |>
filter(time_flag == TRUE)
sum(c3_sampled$comment_type == "task_description")
write.csv(c3_sampled, "/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case3/062725_c3_cleaned.csv", row.names=FALSE)
write.csv(c3_sampled, "/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case3/070125_c3_title_cleaned.csv", row.names=FALSE)

7
p2_EDA/phab_weekly_bins.R
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@ -3,12 +3,13 @@ library(tidyverse)
c1_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case1/062725_c1_cleaned_phab.csv"
c1_input_df <- read.csv(c1_count , header = TRUE)
c2_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/062725_c2_cleaned_phab.csv"
c2_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/070125_c2_title_cleaned.csv"
c2_input_df <- read.csv(c2_count , header = TRUE)
c3_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case3/062725_c3_cleaned_phab.csv"
c3_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case3/070125_c3_title_cleaned.csv"
c3_input_df <- read.csv(c3_count , header = TRUE)
#getting the relative weeks to the publication date
relative_week <- function(date, ref_date) {
as.integer(as.numeric(difftime(date, ref_date, units = "days")) %/% 7)
@ -138,7 +139,7 @@ combined_task_df <- combined_task_df %>%
) %>%
ungroup()
library(ggdist)
ggplot(combined_df, aes(x = week_index, y = modal_verb_count, color = source, linetype=AuthorWMFAffil)) +
ggplot(combined_df, aes(x = week_index, y = modal_subset_count, color = source, linetype=AuthorWMFAffil)) +
geom_point(alpha=0.1) + # Points, with some transparency
geom_smooth(method = "loess", se = FALSE) +
theme_minimal()