在R中selectdata.frame的前4行
我怎样才能selectdata.frame的前4行：
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
使用head ：
dnow <- data.frame(x=rnorm(100), y=runif(100)) head(dnow,4) ## default is 6
使用索引：
df[1:4,]
括号中的值可以被解释为逻辑，数字或字符（匹配各自的名称）：
df[row.index, column.index]
请阅读帮助（““以获得关于这个主题的更多细节，并且阅读关于R的介绍中的索引matrix 。
对于DataFrame，可以简单地input
head(data, num=10L)
以前10名为例。
对于data.frame，可以简单地input
head(data, 10)
拿到前10名
如果行数less于4行，则可以使用head函数（ head(data, 4)或head(data, n=4) ），它的作用就像一个魅力。 但是，假设我们有以下15行的数据集
>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
比方说，你想select前10行。 最简单的方法是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
但是，假设您尝试检索前19行并查看发生了什么 – 您将缺less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>
并用head（）函数，
> 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
希望这个帮助！
