Reviews Ideas

libraries of interest

library(tidyverse)

Create example data set Publication url date type_of_study design_rating positive_or_negative

reviews <- data.frame("title_key" = 1:10, "year" = c(2015,2009,2011,2015,2019,2008,2002,2001,1999,2018), "design_quality_rating" = c(2,2,3,1,5,2,4,1,3,4), "positive" = c(0,1,1,0,1,0,0,1,1,0))

create histogram

ggplot(reviews) + aes(design_quality_rating) + geom_histogram(binwidth = 1, color="darkblue", fill="lightblue") + labs(title = "Quality of Evidence Distribution", subtitle = "As of May 2019", x = "Quality Rating: 1 Very Poor -- 5 Very Good", y = "Count")

NA

create tally

tally(group_by(reviews, design_quality_rating))

New Review from June 2019 adds to evidence

reviews_june_2019 <- data.frame("title_key" = 11:20, "year" = c(2012,2001,2012,2000,2015,2002,2002,1997,1999,2019), "design_quality_rating" = c(4,5,3,3,5,1,2,1,3,3), "positive" = c(1,1,0,0,1,1,0,1,1,0))

ggplot(reviews_june_2019) + aes(design_quality_rating) + geom_histogram(binwidth = 1, color="darkblue", fill="lightblue") + labs(title = "Quality of Evidence Distribution", subtitle = "As of May 2019", x = "Quality Rating: 1 Very Poor -- 5 Very Good", y = "Count")


tally(group_by(reviews_june_2019, design_quality_rating))

merge both reviews

reviews_all <- full_join(reviews_june_2019, reviews)

Joining, by = c("title_key", "year", "design_quality_rating", "positive")

ggplot(reviews_all) + aes(design_quality_rating) + geom_histogram(binwidth = 1, color="darkblue", fill="lightblue") + labs(title = "Quality of Evidence Distribution", subtitle = "As of May 2019", x = "Quality Rating: 1 Very Poor -- 5 Very Good", y = "Count")


tally(group_by(reviews_all, design_quality_rating))

NA

Making tables easily when data updates, can collapse rows as desired

fancy_table_of_findings <- table(reviews_all$design_quality_rating, reviews_all$positive)

colnames(fancy_table_of_findings) <- c("Negative", "Positive")
rownames(fancy_table_of_findings) <- c("Very Poor", "Poor", "Acceptable", "Good", "Very Good" )

print(fancy_table_of_findings)

            
             Negative Positive
  Very Poor         1        3
  Poor              3        1
  Acceptable        3        3
  Good              2        1
  Very Good         0        3

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