magrittr pipes look like this: %>%. They are a way of calling functions that easily pass the result of one function into another. It makes your code cleaner, and it kind of feels like you’re reading a sentence that explains what you are doing to the data.
Let’s look at example over in this blog.
# mutate() adds new variables that are functions of existing variables
# I'm going to add a new variable which is the year.
# Notice how I am using a %>% to use the mutate function on the rest_df.
rest_df <- rest_df %>% mutate('SEATTLE'= if_else(CITY=='Seattle', 'Seattle', 'Not Seattle'))
# select() picks variables based on their names.
less_columns_df <- rest_df %>% select('NAME', 'SEAT_CAP', 'VIOLATIONTYPE')
# filter() picks cases based on their values.
biz_df <- rest_df %>% dplyr::filter(str_detect(NAME, 'BIZ'))
bizzaro_df <- rest_df %>% dplyr::filter(str_detect(NAME, 'BIZZARRO'))
# you can stack the two operations together; like nesting a function
less_col_biz <- rest_df %>% select('NAME', 'VIOLATIONTYPE', 'VIOLATIONDESCR','SCORE_INSPECTION" %>% dplyr::filter(str_detect(NAME, 'BIZZARRO'))
# arrange() changes the ordering of the rows.
# the default is ascending order, if you want descending use: desc
ordered_df <- less_col_biz%>% arrange(desc(SCORE_INSPECTION))In class exercise 1:
Now it’s your turn to try, try to filter a restaurant you know, and select three columns you are interested in, then order descending by the inspection score.