### Imports
library(tidyverse)
### Constants
### Definitions
ratings <- "<cfp ratings file>"
### Read in data
bsides_nash_2018 <- readr::read_csv(ratings)
### Clean dataframe
### Save data
# average of average of two scores
bsides_nash_2018 %>%
mutate(title = as.integer(as.factor(title))) %>%
# mutate(title = stringr::str_wrap(stringr::str_sub(title, 1, 30), 14)) %>% # replace above line with this one to get titles
group_by(title) %>%
select(title, content_score, applicability_score) %>%
gather("type", "score", -title) %>%
summarize(score.ave = round(mean(score), 1)) %>%
ungroup() %>%
ggplot(aes(x=0, y=0, label=score.ave)) +
geom_text() +
viridis::scale_fill_viridis(option="D") +
facet_wrap(~title) +
theme(
strip.text = element_text(size=6),
panel.grid.minor = element_blank(),
axis.ticks = element_blank(),
legend.position='none',
axis.text = element_blank(),
axis.title = element_blank()
)
### median of average of two scores
bsides_nash_2018 %>%
mutate(title = as.integer(as.factor(title))) %>%
# mutate(title = stringr::str_wrap(stringr::str_sub(title, 1, 30), 14)) %>% # replace above line with this one to get titles
group_by(title) %>%
select(title, content_score, applicability_score) %>%
gather("type", "score", -title) %>%
summarize(score.ave = round(median(score), 1)) %>%
ungroup() %>%
ggplot(aes(x=0, y=0, label=score.ave)) +
geom_text() +
viridis::scale_fill_viridis(option="D") +
facet_wrap(~title) +
theme(
strip.text = element_text(size=6),
panel.grid.minor = element_blank(),
axis.ticks = element_blank(),
legend.position='none',
axis.text = element_blank(),
axis.title = element_blank()
)
### Load data (run this if an Rda is already created)
# Distribution of scores and median compared to overall median
bsides_nash_2018 %>%
mutate(title = as.integer(as.factor(title))) %>%
# mutate(title = stringr::str_wrap(stringr::str_sub(title, 1, 30), 14)) %>% # replace above line with this one to get titles
group_by(title) %>%
mutate(content_score.title.median = median(content_score)) %>%
mutate(applicability_score.title.median = median(applicability_score)) %>%
ungroup() %>%
mutate(content_score.overall.median = median(content_score)) %>%
mutate(applicability_score.overall.median = median(applicability_score)) %>%
ggplot(aes(x=content_score, applicability_score)) +
geom_jitter(alpha=0.5) +
geom_point(aes(x=content_score.title.median, y=applicability_score.title.median, group=title), color="red", alpha=0.1) +
geom_point(aes(x=content_score.overall.median, y=applicability_score.overall.median, group=title), color="green", alpha=0.1) +
scale_x_continuous(expand=c(0,0), limits=c(1,5), breaks=0:5) +
scale_y_continuous(expand=c(0,0), limits=c(1,5), breaks=0:5) +
facet_wrap(~title) +
theme(
strip.text = element_text(size=6),
panel.grid.minor = element_blank(),
axis.ticks = element_blank(),
legend.position='none',
axis.text = element_blank()
)
uuid::UUIDgenerate()
glimpse(bsides_nash_2018)