In [1]:
library(data.table)
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library(ggplot2)
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all_edits <- read.table("../../../results/misalignment_edit_types_tables_and_queries/attribute_aggreations_used_and_unused.tsv", header=TRUE, sep="\t")
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all_edits$human_bot_like_over_human_edit_prop = all_edits$human_bot_like_edit / all_edits$human_edit
all_edits$anon_bot_like_over_anon_edit_prop = all_edits$anon_bot_like_edit / all_edits$anon_edit
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all_edits = subset(all_edits, !(year == 2012 & month == 11) & !(year == 2017 & month == 6))
head(all_edits, n=60)
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length(all_edits$human_bot_like_over_human_edit_prop[all_edits$human_bot_like_over_human_edit_prop > .05])
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ggplot(all_edits,
aes(x=human_bot_like_over_human_edit_prop)) +
geom_histogram(bins=100);
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mean(all_edits$human_bot_like_over_human_edit_prop)
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sum(all_edits$human_bot_like_edit)/sum(all_edits$human_edit)
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sum(all_edits$human_bot_like_edit)
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summary(all_edits$human_bot_like_over_human_edit_prop)
sd(all_edits$human_bot_like_over_human_edit_prop)
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length(all_edits$anon_bot_like_over_anon_edit_prop[all_edits$anon_bot_like_over_anon_edit_prop > .05])
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ggplot(all_edits,
aes(x=anon_bot_like_over_anon_edit_prop)) +
geom_histogram(bins=100);
In [14]:
mean(all_edits$anon_bot_like_over_anon_edit_prop)
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summary(all_edits$anon_bot_like_over_anon_edit_prop)
sd(all_edits$anon_bot_like_over_anon_edit_prop)
In [16]:
sum(all_edits$anon_bot_like_edit)/sum(all_edits$anon_edit)
In [17]:
attributes(summary(all_edits$anon_bot_like_edit))
In [18]:
sum(all_edits$anon_bot_like_edit)
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