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@juliasilge juliasilge/january_meta.R
Last active Jan 27, 2019

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Views to answers on Stack Overflow questions
## this analysis assumes a dataframe `post_views` with columns:
## PostId
## CreationDate
## Tag
## AnswerCount
## ViewCount
library(tidyverse)
post_views %>%
distinct(PostId, .keep_all = TRUE) %>%
mutate(AnswerCount = as.factor(AnswerCount),
AnswerCount = fct_lump(AnswerCount),
AnswerCount = fct_recode(AnswerCount,
`More than 3` = "Other")) %>%
ggplot(aes(AnswerCount, ViewCount)) +
geom_boxplot() +
scale_y_log10() +
labs(x = "Answers per question", y = "Views per question",
title = "Answers and views on Stack Overflow questions",
subtitle = "There is an enormous amount of question-to-question variation")
simple_model <- post_views %>%
distinct(PostId, .keep_all = TRUE) %>%
lm(AnswerCount ~ ViewCount, data = .)
summary(simple_model)
model_with_time <- post_views %>%
distinct(PostId, .keep_all = TRUE) %>%
lm(AnswerCount ~ ViewCount + CreationDate, data = .)
summary(model_with_time)
model_no_intercept <- post_views %>%
distinct(PostId, .keep_all = TRUE) %>%
lm(AnswerCount ~ 0 + ViewCount, data = .)
summary(model_no_intercept)
log_model <- post_views %>%
distinct(PostId, .keep_all = TRUE) %>%
lm(AnswerCount ~ log10(ViewCount), data = .)
summary(log_model)
library(broom)
trained_models <- post_views %>%
replace_na(list(AnswerCount = 0)) %>%
add_count(Tag) %>%
filter(n > 1e4) %>%
nest(-Tag) %>%
mutate(Model = map(data, ~ lm(AnswerCount ~ log10(ViewCount), data = .)))
slopes <- trained_models %>%
unnest(map(Model, tidy)) %>%
filter(term == "log10(ViewCount)")
slopes
library(ggrepel)
median_slope <- slopes %>% pull(estimate) %>% median()
post_views %>%
count(Tag, sort = TRUE) %>%
inner_join(slopes) %>%
ggplot(aes(n, estimate, label = Tag)) +
geom_hline(yintercept = median_slope,
lty = 2, color = "gray70", size = 2, alpha = 0.8) +
geom_point() +
geom_text_repel(family = "IBMPlexSans-Medium") +
scale_x_log10() +
labs(x = "Number of questions",
y = "Slope (Number of answers per 10x increase in views)",
title = "Views and answers on Stack Overflow by tag",
subtitle = paste("The median increase in answers per 10x increase in views for this group of technologies is", round(median_slope, 2)))
@nfultz

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commented Jan 27, 2019

Hello, saw this linked from SO.

Since you have a large number of tags, you might consider using a mixed effect model instead and fitting a single large model. Using lme4, it would look like

model <- lmer(AnswerCount ~ (1 + log10(ViewCount) | Tag), post_views)

Stan can provide the bayesian version of that model if you wanted to take that route.

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