R Language Expansion pack dplyr--data cleaning and Sorting _ data

This package is mainly used for data cleaning and finishing, Coursera course Links: getting and cleaning

You can also load the swirl package, load class getting and cleaning data to follow.

As follows:

Library (Swirl)
install_from_swirl ("Getting and cleaning Data")
swirl ()

This article is mainly refer to the introduction of R: Introduce to Dplyr

1. Model data

> Library (NYCFLIGHTS13)
> Dim (flights)
[1] 336776
> Head (Flights, 3)
source:local Data frame [3 x] year

  month day Dep_time dep_delay arr_time Arr_delay carrier Tailnum flight Origin dest Air_time
  1 2013     1   1      517         2      830      UA N14228 1545 EWR  iah      227
2 2013     1   1         4      850      UA  N24211   1714    LGA  Iah
3 2013     1   1      542         2      923      AA  n619aa   1141    JFK  MIA      160
Variables not shown:distance (dbl), hour (dbl), Minute (dbl)

2, will be too long data to organize into friendly TBL_DF data

> FLIGHTS_DF <-tbl_df (flights)
> FLIGHTS_DF

3, filtering filter ()

> Filter (FLIGHTS_DF, month = 1, day = = 1)
source:local data frame [842 x] year

   month Day Dep_time Y arr_time Arr_delay carrier Tailnum flight Origin dest Air_time
1  2013     1   1      517         2      830      UA  N14228   1545    EWR  iah
2  2013     1   1         4      850      UA  N24211   1714    LGA  iah      227

Filter out month=1 and Day=1 data

The same effect,

Flights_df[flights_df$month = = 1 & flights_df$day = 1,]

4, select a few rows of data slice ()

Slice (FLIGHTS_DF, 1:10)

5, arrange Arrange ()

>arrange (FLIGHTS_DF, year, month, day)

Arranges FLIGHTS_DF data in ascending order of Year,month,day.

Descending

>arrange (FLIGHTS_DF, year, DESC (month), day)

Self-band functions in R language

Flights_df[order (Flights$year, Flights_df$month, Flights_df$day),]
Flights_df[order (DESC (flights_df$arr_ delay)),]

6. Choose Select ()

Select the data you want by the column name

Select (FLIGHTS_DF, year, month, day)

Select three columns of data
Using: Symbol

Select (FLIGHTS_DF, Year:day)

Use-To remove a list of not

Select (FLIGHTS_DF,-(Year:day))

7. Deformation mutate ()

Create a new column

> Mutate (FLIGHTS_DF,
+        gain = Arr_delay-dep_delay,
+        speed = Distance/air_time * 60)

8, summary summarize ()

<pre name= "code" class= "HTML" >> summarise (flights,
+           delay = mean (dep_delay, na.rm = TRUE)

To find the mean value of Dep_delay

9, randomly selected samples

Sample_n (FLIGHTS_DF, 10)

Randomly selected 10 samples

Sample_frac (FLIGHTS_DF, 0.01)

Randomly selected 1% samples

10. Group Group_py ()

By_tailnum <-group_by (flights, tailnum)
#确定组别为tailnum, assigned to By_tailnum delay <-summarise
(By_tailnum,
                   count = N (),
                   dist = mean (distance, na.rm = true),
                   delay = mean (arr_delay, na.rm = TRUE)
#汇总flights里地tail The number of categories in the NUM group, and their corresponding distance and arr_delay mean
delay <-filter (delay, Count >, dist <)
Ggplot (Delay, AES (Dist, delay)) +
    Geom_point (aes (size = count), alpha = 1/2) +
    Geom_smooth () +
    Scale_size_area ()

Results need to be stored by assigning

A1 <-group_by (flights, year, month, day) A2 <-Select (A1, Arr_delay, Dep_delay) A3 <-summarise
(a2,
  arr = Mean (Arr_delay, na.rm = True),
  dep = mean (Dep_delay, na.rm = true)
A4 <-filter (A3, arr > 30 | DEP > 30)

11, introduce the link character%>%

Use the data name as the starting point, and then do a multi-step operation on the data:

Flights%>%
    group_by (year, month, day)%>%
    Select (Arr_delay, Dep_delay)%>% summarise
    (arr
        = Mean (Arr_delay, na.rm = True),
        dep = mean (Dep_delay, na.rm = True)
    %>%
    filter (arr > | dep > 30)

The data name is removed from the front.

For more information on this package, refer to its own instructions (60 pages): Dplyr