Is there a tidyverse equivalent to SELECT...COUNT(*)...GROUP BY...? - r

Question

I want to understand how to do accomplish "group by" and "count" functionality in the tidyverse. I looked at quite a few posts without finding quite what I wanted; if there is an answer to this already posted, I would appreciate the link.
For example, I am looking for outliers in data; I want to know which places received the most "bad" measures:
place = rep(c('AL','AK','AZ','AR','CA','CO','CT','DE','FL','GA','HI'), times=4)
measure = rep(c('meas1','meas2','meas3','meas4'), each=11)
set.seed(200)
rating = sample(c('good','bad'), size = 44, prob=c(2,1), replace=T)
df = data.frame(place, measure, rating)
> df
place measure rating
1 AL meas1 good
2 AK meas1 good
3 AZ meas1 good
4 AR meas1 bad
5 CA meas1 bad
6 CO meas1 bad
7 CT meas1 bad
8 DE meas1 good
9 FL meas1 good
10 GA meas1 good
....(etc).....
I want to understand how to do this using the tidyverse. This approach using sqldf gives me what I want, i.e. tells me which places had the most "bad" ratings, and ranks the places by their "bad-ness"
library(sqldf)
sqldf("SELECT place, rating, COUNT(*) AS Count FROM df GROUP BY place, rating ORDER BY rating, count DESC").
place rating Count
1 CA bad 3
2 AK bad 2
3 AR bad 1
4 CO bad 1
5 CT bad 1
6 DE bad 1
7 FL bad 1
8 GA bad 1
9 AL good 4
10 AZ good 4
11 HI good 4
....(etc)....
Is there a way to do get similar results in the tidyverse?

Answer 1

For an introduction to these basic operations in the tidyverse, I'd suggest reading Wickham and Grolemund's excellent R for Data Science in the first instance: http://r4ds.had.co.nz/
You can use dplyr and magrittr packages to do the following in an easy to follow way:
# Install the tidyverse
library(tidyverse)
# Create data
place = rep(c('AL','AK','AZ','AR','CA','CO','CT','DE','FL','GA','HI'), times=4)
measure = rep(c('meas1','meas2','meas3','meas4'), each=11)
set.seed(200)
rating = sample(c('good','bad'), size = 44, prob=c(2,1), replace=T)
df = data.frame(place, measure, rating)
# Do some analysis
df %>%
group_by(place) %>%
summarise(mean_score = mean(rating == "good"), n = n()) %>%
arrange(desc(mean_score))
Here, we "group by" restaurant name "then" "summarise" each grouping by the mean number of 'good' ratings it received (creating a new variable), "then" "arrange" the output in descending order by this 'mean_score'.
We also create the new 'n' variable in the summarise function which counts the number of ratings that each mean is based on (i.e. so that if we see that one restaurant only had 2 ratings we would know that the mean may not be representative: see http://www.evanmiller.org/how-not-to-sort-by-average-rating.html for a comprehensive example of this).