我有2个相等列大小为5的数据帧 . 前4列名称相同,最后一列不同 . 我在最后一列中报告值(T),表明各个数据帧中的每个平均值和西格玛都有异常值 .
My first data frame - df1
TimeStamp <- c("2015-04-01 11:40:13", "2015-04-03 02:54:45")
ID <- c("DL1X8", "DL202")
Avg <- c(38.1517, 0.7131)
Sig <- c(11.45880000, 0.01257816)
Outlier_Avg <- c("T","T")
df1 <- data.frame(TimeStamp, ID, Avg, Sig,Outlier_Avg)
+---------------------+-------+---------+-------------+-------------+
| TimeStamp | ID | Avg | Sig | Outlier_Avg |
+---------------------+-------+---------+-------------+-------------+
| 2015-04-01 11:40:13 | DL1X8 | 38.1517 | 11.45880000 | T |
| 2015-04-03 02:54:45 | DL202 | 0.7131 | 0.01257816 | T |
+---------------------+-------+---------+-------------+-------------+
My Second data frame - df2
TimeStamp <- c("2015-04-01 11:40:13", "2015-04-04 02:57:45", "2015-04-06 09:54:45")
ID <- c("DL1X8", "DP308","DM3X8")
Avg <- c(38.1517, 24.7131, 0.0234)
Sig <- c(11.4588, 6.0175,0.0665)
Outlier_Sig <- c("T","T","T")
df2 <- data.frame(TimeStamp, ID, Avg, Sig,Outlier_Sig)
+---------------------+-------+---------+---------+-------------+
| TimeStamp | ID | Avg | Sig | Outlier_Sig |
+---------------------+-------+---------+---------+-------------+
| 2015-04-01 11:40:13 | DL1X8 | 38.1517 | 11.4588 | T |
| 2015-04-04 02:57:45 | DP308 | 24.7131 | 6.0175 | T |
| 2015-04-06 09:54:45 | DM3X8 | 0.0234 | 0.0665 | T |
+---------------------+-------+---------+---------+-------------+
Desired Output:
我试图得到一个看起来像这样的df3
+---------------------+-------+---------+-------------+-------------+-------------+
| TimeStamp | ID | Avg | Sig | Outlier_Avg | Outlier_Sig |
+---------------------+-------+---------+-------------+-------------+-------------+
| 2015-04-01 11:40:13 | DL1X8 | 38.1517 | 11.45880000 | T | T |
| 2015-04-03 02:54:45 | DL202 | 0.7131 | 0.01257816 | T | N/A |
| 2015-04-04 02:57:45 | DP308 | 24.7131 | 6.0175 | N/A | T |
| 2015-04-06 09:54:45 | DM3X8 | 0.0234 | 0.0665 | N/A | T |
+---------------------+-------+---------+-------------+-------------+-------------+
我尝试使用merge(df1,df2) . 它仅返回匹配的行,因此只返回1行 . 我需要返回所有行并放入N / A,如上所示 . 你能帮我这个吗?
1 回答
使用
all
参数:这是使用
all.x = TRUE
和all.y = TRUE
的简写,它们是单独的参数,可以让您控制合并的data.frame中包含x(在您的情况下为df1
)和y(在您的情况下为df2
)中的哪些观察 . 例如,见: