74 lines
2.3 KiB
R
74 lines
2.3 KiB
R
library(tidyverse)
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neurobiber_description_pca_csv <-"~/p2/quest/090425_description_PCA_df.csv"
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neurobiber_description_pca_df <- read.csv(neurobiber_description_pca_csv , header = TRUE)
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neurobiber_subcomment_pca_csv <-"~/p2/quest/090425_subcomment_PCA_df.csv"
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neurobiber_subcomment_pca_df <- read.csv(neurobiber_subcomment_pca_csv , header = TRUE)
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# look at correlation between PC1, PC2, and different outcome variables
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library(dplyr)
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description_anova_results <- neurobiber_description_pca_df %>%
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group_by(source) %>%
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group_map(~ summary(aov(PC2 ~ phase, data = .x)), .keep = TRUE)
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description_anova_results
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discussion_anova_results <- neurobiber_subcomment_pca_df %>%
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group_by(source) %>%
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group_map(~ summary(aov(PC2 ~ phase, data = .x)), .keep = TRUE)
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discussion_anova_results
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# look at the representative comments for PC1 and PC2
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top5 <- neurobiber_subcomment_pca_df %>%
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filter(source=="c2") |>
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arrange(desc(PC2)) %>%
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slice(15:30) %>%
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pull(text)
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bottom5 <- neurobiber_subcomment_pca_df %>%
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filter(source=="c2") |>
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arrange(PC2) %>%
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slice(15:30) %>%
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pull(text)
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cat("Top 15:30 comment_text by score:\n")
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print(top5)
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cat("\nBottom 15:30 comment_text by score:\n")
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print(bottom5)
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aggregated_neurobiber_description_pca_df <- neurobiber_description_pca_df |>
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group_by(AuthorWMFAffil, week_index, source, priority) %>%
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summarise(mean_PC1 = median(PC1),
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mean_PC2 = median(PC2),
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mean_PC3 = median(PC3),
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mean_PC4 = median(PC4),
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mean_PC5 = median(PC5))
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library(scales)
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library(ggplot2)
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affiliationColors <-
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setNames( c('#5da2d8', '#c7756a')
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,c("False", "True"))
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long_df <- aggregated_neurobiber_description_pca_df %>%
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tidyr::pivot_longer(
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cols = starts_with("mean_PC"),
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names_to = "PC",
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values_to = "PC_value"
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)
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ggplot(long_df, aes(x = week_index, y = PC_value, color = AuthorWMFAffil, group = AuthorWMFAffil)) +
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geom_line(size = 1) +
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facet_grid(PC ~ source, scales = "free_y") +
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scale_color_manual(values = affiliationColors, name = "WMF Affiliation") +
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scale_x_continuous(breaks = pretty_breaks()) +
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scale_y_continuous(limits = c(-10, 10)) +
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labs(x = "Week Index", y = "Mean PC Value",
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title = "Weekly Median PC Values by Source and PC, Colored by WMF Affiliation") +
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theme_minimal(base_size = 14) +
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theme(legend.position = "top") |