Usually when working with data, we want to aggregate it to extract meaning. The most common ways to aggregate are to count, sum, min or max.
# Let's count the records in our data.
rest_df_count <- rest_df %>% count()
# Now let's count the number of observations for each restaurant.
rest_df_count2 <- rest_df %>% count(NAME)
rest_df %>% count(NAME,sort=TRUE)
Many times we need to sum data columns. We also want to group things into categories.
summarise() creates a new data frame. It will have one (or more) rows for each combination of grouping variables; if there are no grouping variables, the output will have a single row summarising all observations in the input. It will contain one column for each grouping variable and one column for each of the summary statistics that you have specified.
# Let's sum the inspection scores.
rest_df %>% summarize(total_points=(sum(SCORE_INSPECTION, na.rm=TRUE)))
#Frequently we also want to group data and summarize it:
rest_df %>% group_by(NAME)%>%summarize(rest_points=(mean(SCORE_INSPECTION, na.rm=TRUE)))