R語言擴展包dplyr——數據清洗和整理

 

R語言擴展包dplyr——數據清洗和整理

標籤： 數據R語言數據清洗數據整理

2015-01-22 18:04 7357人閱讀 評論(0) 收藏 舉報

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R Programming（11） 

版權聲明：本文爲博主原創文章，未經博主容許不得轉載。

該包主要用於數據清洗和整理，coursera課程連接：Getting and Cleaning Data

也能夠載入swirl包，加載課Getting and Cleaning Data跟着學習。

以下：

 

library(swirl)
install_from_swirl("Getting and Cleaning Data")
swirl()

 

此文主要是參考R自帶的簡介：Introduce to dplyr

一、示範數據

 

> library(nycflights13)
> dim(flights)
[1] 336776     16
> head(flights, 3)
Source: local data frame [3 x 16]

  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        11      UA  N14228   1545    EWR  IAH      227
2 2013     1   1      533         4      850        20      UA  N24211   1714    LGA  IAH      227
3 2013     1   1      542         2      923        33      AA  N619AA   1141    JFK  MIA      160
Variables not shown: distance (dbl), hour (dbl), minute (dbl)

二、將過長的數據整理成友好的tbl_df數據

 

 

> flights_df <- tbl_df(flights)
> flights_df

 

 

三、篩選filter()

 

> filter(flights_df, month == 1, day == 1)
Source: local data frame [842 x 16]

   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        11      UA  N14228   1545    EWR  IAH      227
2  2013     1   1      533         4      850        20      UA  N24211   1714    LGA  IAH      227

篩選出month=1和day=1的數據

 

一樣效果的，

 

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

四、選出幾行數據slice()

 

 

slice(flights_df, 1:10)

五、排列arrange()

 

 

>arrange(flights_df, year, month, day)

將flights_df數據按照year,month,day的升序排列。

 

降序

 

>arrange(flights_df, year, desc(month), day)

R語言當中的自帶函數

 

 

flights_df[order(flights$year, flights_df$month, flights_df$day), ]
flights_df[order(desc(flights_df$arr_delay)), ]

 

 

六、選擇select()

經過列名來選擇所要的數據

select(flights_df, year, month, day)

選出三列數據
使用：符號

select(flights_df, year:day)

使用-來刪除不要的列表

 

 

select(flights_df, -(year:day))

七、變形mutate()

 

產生新的列

 

> mutate(flights_df,
+        gain = arr_delay - dep_delay,
+        speed = distance / air_time * 60)

 

 

八、彙總summarize()

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

求dep_delay的均值

 

 

九、隨機選出樣本

 

sample_n(flights_df, 10)

隨機選出10個樣本

sample_frac(flights_df, 0.01)

隨機選出1%個樣本

 

十、分組group_py()

 

by_tailnum <- group_by(flights, tailnum)
#肯定組別爲tailnum，賦值爲by_tailnum
delay <- summarise(by_tailnum,
                   count = n(),
                   dist = mean(distance, na.rm = TRUE),
                   delay = mean(arr_delay, na.rm = TRUE))
#彙總flights裏地tailnum組的分類數量，及其組別對應的distance和arr_delay的均值
delay <- filter(delay, count > 20, dist < 2000)
ggplot(delay, aes(dist, delay)) +
    geom_point(aes(size = count), alpha = 1/2) +
    geom_smooth() +
    scale_size_area()

 

 

結果都須要經過賦值存儲

 

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)

十一、引入連接符%>%

 

使用時把數據名做爲開頭，而後依次對數據進行多步操做：

 

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 > 30 | dep > 30)

前面都免去了數據名

 

 

若想要進行更多地瞭解這個包，能夠參考其自帶的說明書（60頁）：dplyr