R for Data Science (notes) -- data transformation (used by filter)

tidy Streaming data is becoming more and more popular , I think it's inconsistent with the pipeline %>% Use , Data processing verb , Has a very important relationship .

In the least amount of time , Solve the most important 、 The most common problem , I call this efficiency ; The remaining difficulties , I call it improvement .

filter The use of Verbs

The first thing to be clear is

filter Aiming at That's ok The operation of , select Is an operation on a column

On this basis , Carry out actual combat

Use nycflights13 The data in the package
###1. Propose a line separately

Characteristics of observation data

flights
#> # A tibble: 336,776 x 19
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
#> 1 2013 1 1 517 515 2 830 819
#> 2 2013 1 1 533 529 4 850 830
#> 3 2013 1 1 542 540 2 923 850
#> 4 2013 1 1 544 545 -1 1004 1022
#> 5 2013 1 1 554 600 -6 812 837
#> 6 2013 1 1 554 558 -4 740 728
#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

#### The withdrawal month is 1, The date is 1 The data of

filter(flights, month == 1, day == 1)
#> # A tibble: 842 x 19
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
#> 1 2013 1 1 517 515 2 830 819
#> 2 2013 1 1 533 529 4 850 830
#> 3 2013 1 1 542 540 2 923 850
#> 4 2013 1 1 544 545 -1 1004 1022
#> 5 2013 1 1 554 600 -6 812 837
#> 6 2013 1 1 554 558 -4 740 728
#> # … with 836 more rows, and 11 more variables: arr_delay <dbl>, carrier <chr>,
#> # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#> # distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

Or directly assign a value to a variable

jan1 <- filter(flights, month == 1, day == 1)

Insert an operation skill , Put parentheses around the variables , You can also directly display , result

(jan1 <- filter(flights, month == 1, day == 1))

###2. Add logical operators to extract multiple lines
“&” yes “ and ”,“|” yes “ or ”,“!" Refer to “ No ”

Add the logical operator , Match the pipe , You can select multiple conditions .
for example , The month to be selected is 11 and 12 Observation options for , There will be two ways to write

filter(flights, month == 11 | month == 12)

perhaps

nov_dec <- filter(flights, month %in% c(11, 12))

%in% Is a match , It is often used in judgment

#### Reference material
https://r4ds.had.co.nz/transform.html