選擇R中

Question

第4行的data.frame的如何選擇第4行的data.frame的：

   Weight Response 
1 Control  59  0.0 
2 Treatment  90  0.8 
3 Treatment  47  0.1 
4 Treamment 106  0.1 
5 Control  85  0.7 
6 Treatment  73  0.6 
7 Control  61  0.2 

Answer 1

使用head：

dnow <- data.frame(x=rnorm(100), y=runif(100)) 
head(dnow,4) ## default is 6 

Answer 2

使用索引：

df[1:4,] 

凡在括號內的值可以解釋爲邏輯，數字或字符（與各個名稱匹配）：

df[row.index, column.index] 

閱讀幫助（`[``for more關於這個問題的細節，以及在引言閱讀index matrices到R.

Answer 3

對於以數據幀一個可以簡單地輸入

head(data, num=10L) 

拿到第10例。

對於data.frame一個可以簡單地輸入

head(data, 10) 

獲得第一10.

Answer 4

如果有小於4行，則可以使用head函數（head(data, 4)或head(data, n=4)）和它的作用就像一種魅力。但是，假設我們有以下15行數據集：

>data <- data <- read.csv("./data.csv", sep = ";", header=TRUE) 

>data 
LungCap Age Height Smoke Gender Caesarean 
1 6.475 6 62.1 no male  no 
2 10.125 18 74.7 yes female  no 
3 9.550 16 69.7 no female  yes 
4 11.125 14 71.0 no male  no 
5 4.800 5 56.9 no male  no 
6 6.225 11 58.7 no female  no 
7 4.950 8 63.3 no male  yes 
8 7.325 11 70.4 no male   no 
9 8.875 15 70.5 no male  no 
10 6.800 11 59.2 no male  no 
11 6.900 12 59.3 no male  no 
12 6.100 13 59.4 no male  no 
13 6.110 14 59.5 no male  no 
14 6.120 15 59.6 no male  no 
15 6.130 16 59.7 no male  no 

比方說，您要選擇前10行。最簡單的方法是data[1:10, ]。

> data[1:10,] 
    LungCap Age Height Smoke Gender Caesarean 
1 6.475 6 62.1 no male  no 
2 10.125 18 74.7 yes female  no 
3 9.550 16 69.7 no female  yes 
4 11.125 14 71.0 no male  no 
5 4.800 5 56.9 no male  no 
6 6.225 11 58.7 no female  no 
7 4.950 8 63.3 no male  yes 
8 7.325 11 70.4 no male   no 
9 8.875 15 70.5 no male  no 
10 6.800 11 59.2 no male  no 

然而，假設您嘗試檢索前19行，看看會發生什麼 - 你會遺漏值

> data[1:19,] 
    LungCap Age Height Smoke Gender Caesarean 
1  6.475 6 62.1 no male  no 
2  10.125 18 74.7 yes female  no 
3  9.550 16 69.7 no female  yes 
4  11.125 14 71.0 no male  no 
5  4.800 5 56.9 no male  no 
6  6.225 11 58.7 no female  no 
7  4.950 8 63.3 no male  yes 
8  7.325 11 70.4 no male   no 
9  8.875 15 70.5 no male  no 
10  6.800 11 59.2 no male  no 
11  6.900 12 59.3 no male  no 
12  6.100 13 59.4 no male  no 
13  6.110 14 59.5 no male  no 
14  6.120 15 59.6 no male  no 
15  6.130 16 59.7 no male  no 
NA  NA NA  NA <NA> <NA>  <NA> 
NA.1  NA NA  NA <NA> <NA>  <NA> 
NA.2  NA NA  NA <NA> <NA>  <NA> 
NA.3  NA NA  NA <NA> <NA>  <NA> 

，並用頭（）函數，

> head(data, 19) # or head(data, n=19) 
    LungCap Age Height Smoke Gender Caesarean 
1 6.475 6 62.1 no male  no 
2 10.125 18 74.7 yes female  no 
3 9.550 16 69.7 no female  yes 
4 11.125 14 71.0 no male  no 
5 4.800 5 56.9 no male  no 
6 6.225 11 58.7 no female  no 
7 4.950 8 63.3 no male  yes 
8 7.325 11 70.4 no male   no 
9 8.875 15 70.5 no male  no 
10 6.800 11 59.2 no male  no 
11 6.900 12 59.3 no male  no 
12 6.100 13 59.4 no male  no 
13 6.110 14 59.5 no male  no 
14 6.120 15 59.6 no male  no 
15 6.130 16 59.7 no male  no 

希望得到這個幫助！

Answer 5

如果有人願意dplyr的解決方案，這是非常直觀的：

dt <- dt %>% 
    slice(1:4)