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法律研究如何在 R 中將資料框轉換為時間序列物件後，提取包含名稱的列？
在 R 中訪問資料框的列，我們只需要使用 $ 符號，但如果資料框被轉換為時間序列物件，則所有列都將表現為時間序列，因此，我們不能簡單地使用 $ 符號。為此，我們需要使用單方括號並將其中的適當列傳遞進去。請檢視以下示例以瞭解其工作原理。
示例 1
考慮以下資料框
> set.seed(147) > x1<-rpois(20,5) > x2<-rpois(20,8) > x3<-rpois(20,3) > df1<-data.frame(x1,x2,x3) > df1
輸出
x1 x2 x3 1 5 11 4 2 5 5 3 3 4 6 2 4 10 8 1 5 4 6 3 6 4 9 3 7 9 7 4 8 4 4 1 9 3 8 6 10 5 9 2 11 4 13 2 12 4 6 3 13 6 5 2 14 8 10 3 15 1 10 3 16 5 9 3 17 7 8 6 18 7 5 0 19 4 8 2 20 5 5 7
將 df1 轉換為時間序列物件
示例
> df1_time_series<-ts(df1) > df1_time_series Time Series: Start = 1 End = 20 Frequency = 1
輸出
x1 x2 x3 1 5 11 4 2 5 5 3 3 4 6 2 4 10 8 1 5 4 6 3 6 4 9 3 7 9 7 4 8 4 4 1 9 3 8 6 10 5 9 2 11 4 13 2 12 4 6 3 13 6 5 2 14 8 10 3 15 1 10 3 16 5 9 3 17 7 8 6 18 7 5 0 19 4 8 2 20 5 5 7
提取上述時間序列的列
示例
> df1_time_series[,"x1"] Time Series: Start = 1 End = 20 Frequency = 1
輸出
[1] 5 5 4 10 4 4 9 4 3 5 4 4 6 8 1 5 7 7 4 5
示例
> df1_time_series[,"x2"] Time Series: Start = 1 End = 20 Frequency = 1
輸出
[1] 11 5 6 8 6 9 7 4 8 9 13 6 5 10 10 9 8 5 8 5
示例
> df1_time_series[,"x3"] Time Series: Start = 1 End = 20 Frequency = 1
輸出
[1] 4 3 2 1 3 3 4 1 6 2 2 3 2 3 3 3 6 0 2 7
示例 2
> y1<-rnorm(20,1,0.25) > y2<-rnorm(20,1,0.078) > y3<-rnorm(20,1,0.045) > y4<-rnorm(20,1,0.65) > df2<-data.frame(y1,y2,y3,y4) > df2
輸出
y1 y2 y3 y4 1 0.4610082 1.1123116 0.9937312 1.60152771 2 1.2245278 1.1441032 0.9955816 1.01301470 3 0.9281928 0.9471151 1.0130205 1.73380614 4 0.6132334 0.9914514 1.0478584 1.12878115 5 0.8047991 0.9364563 1.0559170 0.11453683 6 1.3873896 0.9890774 0.8793818 1.08303443 7 0.8734964 0.9923517 1.0456627 1.40754764 8 0.5829787 1.1520386 1.0679080 -0.06112731 9 0.7886331 1.2120417 1.0131238 1.12503045 10 1.4817215 1.1045179 0.9894544 1.00392323 11 1.1166086 0.9957914 0.9241877 0.37224585 12 1.0734553 1.0714675 1.0013594 0.46353553 13 1.0378841 0.9814108 1.0169206 1.57986107 14 0.5939274 0.9737219 1.0043724 0.17741973 15 1.1111737 0.9444893 1.0601156 0.96969383 16 1.2379935 0.9730605 1.0632339 0.39235006 17 1.2920541 0.8550713 0.9872660 0.42308594 18 0.7378359 1.0077608 1.0571702 1.34754960 19 0.7497949 0.9085073 1.0041391 1.04504683 20 1.0315004 1.1117264 0.9580732 1.13297488
示例
> df2_ts<-ts(df2) > df2_ts Time Series: Start = 1 End = 20 Frequency = 1
輸出
y1 y2 y3 y4 1 0.4610082 1.1123116 0.9937312 1.60152771 2 1.2245278 1.1441032 0.9955816 1.01301470 3 0.9281928 0.9471151 1.0130205 1.73380614 4 0.6132334 0.9914514 1.0478584 1.12878115 5 0.8047991 0.9364563 1.0559170 0.11453683 6 1.3873896 0.9890774 0.8793818 1.08303443 7 0.8734964 0.9923517 1.0456627 1.40754764 8 0.5829787 1.1520386 1.0679080 -0.06112731 9 0.7886331 1.2120417 1.0131238 1.12503045 10 1.4817215 1.1045179 0.9894544 1.00392323 11 1.1166086 0.9957914 0.9241877 0.37224585 12 1.0734553 1.0714675 1.0013594 0.46353553 13 1.0378841 0.9814108 1.0169206 1.57986107 14 0.5939274 0.9737219 1.0043724 0.17741973 15 1.1111737 0.9444893 1.0601156 0.96969383 16 1.2379935 0.9730605 1.0632339 0.39235006 17 1.2920541 0.8550713 0.9872660 0.42308594 18 0.7378359 1.0077608 1.0571702 1.34754960 19 0.7497949 0.9085073 1.0041391 1.04504683 20 1.0315004 1.1117264 0.9580732 1.13297488
示例
> df2_ts[,"y1"] Time Series: Start = 1 End = 20 Frequency = 1
輸出
[1] 0.4610082 1.2245278 0.9281928 0.6132334 0.8047991 1.3873896 0.8734964 [8] 0.5829787 0.7886331 1.4817215 1.1166086 1.0734553 1.0378841 0.5939274 [15] 1.1111737 1.2379935 1.2920541 0.7378359 0.7497949 1.0315004
示例
> df2_ts[,"y2"] Time Series: Start = 1 End = 20 Frequency = 1
輸出
[1] 1.1123116 1.1441032 0.9471151 0.9914514 0.9364563 0.9890774 0.9923517 [8] 1.1520386 1.2120417 1.1045179 0.9957914 1.0714675 0.9814108 0.9737219 [15] 0.9444893 0.9730605 0.8550713 1.0077608 0.9085073 1.1117264
示例
> df2_ts[,"y3"] Time Series: Start = 1 End = 20 Frequency = 1
輸出
[1] 0.9937312 0.9955816 1.0130205 1.0478584 1.0559170 0.8793818 1.0456627 [8] 1.0679080 1.0131238 0.9894544 0.9241877 1.0013594 1.0169206 1.0043724 [15] 1.0601156 1.0632339 0.9872660 1.0571702 1.0041391 0.9580732
示例
> df2_ts[,"y4"] Time Series: Start = 1 End = 20 Frequency = 1
輸出
[1] 1.60152771 1.01301470 1.73380614 1.12878115 0.11453683 1.08303443 [7] 1.40754764 -0.06112731 1.12503045 1.00392323 0.37224585 0.46353553 [13] 1.57986107 0.17741973 0.96969383 0.39235006 0.42308594 1.34754960 [19] 1.04504683 1.13297488
更新於: 2020-11-23
378 次檢視
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