在 R 中选择 data.frame 的前 4 行

Question

如何选择data.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

Answer 1

使用索引：

df[1:4,]

括号中的值可以解释为逻辑、数字或字符（匹配各自的名称）：

df[row.index, column.index]

阅读 help(`[`) 了解有关此主题的更多详细信息，并阅读 R 简介中的 index matrices。

Answer 2

使用head:

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

Answer 3

对于在 DataFrame 中可以简单地键入

head(data, num=10L)

例如获得前 10 个。

对于 data.frame，可以简单地键入

head(data, 10)

获得前 10 个。

Answer 4

如果您的行数少于 4 行，您可以使用 head 函数（head(data, 4) 或 head(data, n=4)），它就像一个魅力。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

假设您要选择前 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 行并查看会发生什么 - 您将缺少值

> 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>

并且使用 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

希望对您有所帮助！

Answer 5

如果有人对dplyr 解决方案感兴趣，它非常直观：

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