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R：根据模式删除行

[英]R: Deleting rows based on a pattern

 原文  2014-08-06 18:46:19  1   3    r/ csv/ delete-row

问题描述

我将以下数据帧另存为.csv，这是我先前代码的输出。 Value1_Cum是每个SAMPLE的值1的累积和。 也可以在这里找到： https : //dl.dropboxusercontent.com/u/16277659/SAMPLE_rs.csv 

SAMPLE;     ID;     REFERENCE_YEAR; VALUE1; VALUE1_CUM
SAMPLE1;    112;    1956;            0;     0
SAMPLE1;    112;    1957;            200;   200
SAMPLE1;    112;    1958;            NA;    NA
SAMPLE1;    112;    1959;            NA;    NA
SAMPLE1;    112;    1960;            NA;    NA
SAMPLE1;    112;    1963;            NA;    NA
SAMPLE2;    137;    1878;            0;     0
SAMPLE2;    137;    1879;          -7900;   -7900
SAMPLE2;    137;    1880;            NA;    NA
SAMPLE2;    137;    1881;            NA;    NA
SAMPLE2;    137;    1882;            NA;    NA
SAMPLE2;    137;    1890;            NA;    NA
SAMPLE2;    137;    1891;            NA;    NA
SAMPLE2;    137;    1892;         -4300;    -12200
SAMPLE2;    137;    1893;            NA;    NA
SAMPLE2;    137;    1894;            NA;    NA
SAMPLE3;    136;    1971;            0;     0
SAMPLE3;    136;    1972;            NA;    NA
SAMPLE3;    136;    1973;            NA;    NA
SAMPLE3;    136;    1974;            NA;    NA
SAMPLE3;    136;    1975;            NA;    NA
SAMPLE3;    136;    1976;            NA;    NA
SAMPLE3;    136;    1980;            NA;    NA
SAMPLE4;    0;      2000;             0;    0
SAMPLE4;    0;      2001;            NA;    NA
SAMPLE4;    0;      2002;            NA;    NA
SAMPLE4;    0;      2003;            NA;    NA
SAMPLE4;    0;      2004;            500;   500
SAMPLE4;    0;      2005;            NA;    NA
SAMPLE4;    0;      2006;            NA;    NA

为了进一步处理数据，我需要删除整个样本的所有行，其中包含0的模式，然后跟着NA（在此示例中，SAMPLE3应该被删除）。 如果值在整个SAMPLE的NA值之间（如SAMPLE2），则应保留行。 有谁知道我该怎么做？ 

SAMPLE;     ID;     REFERENCE_YEAR; VALUE1; VALUE1_CUM
SAMPLE1;    112;    1956;            0;     0
SAMPLE1;    112;    1957;            200;   200
SAMPLE1;    112;    1958;            NA;    NA
SAMPLE1;    112;    1959;            NA;    NA
SAMPLE1;    112;    1960;            NA;    NA
SAMPLE1;    112;    1963;            NA;    NA
SAMPLE2;    137;    1878;            0;     0
SAMPLE2;    137;    1879;          -7900;   -7900
SAMPLE2;    137;    1880;            NA;    NA
SAMPLE2;    137;    1881;            NA;    NA
SAMPLE2;    137;    1882;            NA;    NA
SAMPLE2;    137;    1890;            NA;    NA
SAMPLE2;    137;    1891;            NA;    NA
SAMPLE2;    137;    1892;         -4300;    -12200
SAMPLE2;    137;    1893;            NA;    NA
SAMPLE2;    137;    1894;            NA;    NA
SAMPLE4;    0;      2000;             0;    0
SAMPLE4;    0;      2001;            NA;    NA
SAMPLE4;    0;      2002;            NA;    NA
SAMPLE4;    0;      2003;            NA;    NA
SAMPLE4;    0;      2004;            500;   500
SAMPLE4;    0;      2005;            NA;    NA
SAMPLE4;    0;      2006;            NA;    NA

3 个解决方案

解决方案1
 1 已采纳  2014-08-06 20:26:18

这是一个data.table解决方案。

您的数据集： 

df <- structure(list(SAMPLE = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("SAMPLE1", "SAMPLE2", 
"SAMPLE3", "SAMPLE4"), class = "factor"), ID = c(112L, 112L, 
112L, 112L, 112L, 112L, 137L, 137L, 137L, 137L, 137L, 137L, 137L, 
137L, 137L, 137L, 136L, 136L, 136L, 136L, 136L, 136L, 136L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L), REFERENCE_YEAR = c(1956L, 1957L, 1958L, 
1959L, 1960L, 1963L, 1878L, 1879L, 1880L, 1881L, 1882L, 1890L, 
1891L, 1892L, 1893L, 1894L, 1971L, 1972L, 1973L, 1974L, 1975L, 
1976L, 1980L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L
), VALUE1 = c(0, 200, NA, NA, NA, NA, 0, -7900, NA, NA, NA, NA, 
NA, -4300, NA, NA, 0, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, 
500, NA, NA), VALUE1_CUM = structure(c(3L, 6L, 5L, 5L, 5L, 5L, 
3L, 1L, 5L, 5L, 5L, 5L, 5L, 2L, 5L, 5L, 3L, 5L, 5L, 5L, 5L, 5L, 
5L, 4L, 5L, 5L, 5L, 7L, 5L, 5L), .Label = c("   -7900", "    -12200", 
"     0", "    0", "    NA", "   200", "   500"), class = "factor")), .Names = c("SAMPLE", 
"ID", "REFERENCE_YEAR", "VALUE1", "VALUE1_CUM"), row.names = c(NA, 
-30L), class = "data.frame")

编码 

library(data.table)
as.data.table(df)[, .SD[!(VALUE1[1] == 0 & (all(is.na(VALUE1[-1]))))], by = SAMPLE]

结果 

# SAMPLE  ID REFERENCE_YEAR VALUE1 VALUE1_CUM
# 1: SAMPLE1 112           1956      0          0
# 2: SAMPLE1 112           1957    200        200
# 3: SAMPLE1 112           1958     NA         NA
# 4: SAMPLE1 112           1959     NA         NA
# 5: SAMPLE1 112           1960     NA         NA
# 6: SAMPLE1 112           1963     NA         NA
# 7: SAMPLE2 137           1878      0          0
# 8: SAMPLE2 137           1879  -7900      -7900
# 9: SAMPLE2 137           1880     NA         NA
#10: SAMPLE2 137           1881     NA         NA
#11: SAMPLE2 137           1882     NA         NA
#12: SAMPLE2 137           1890     NA         NA
#13: SAMPLE2 137           1891     NA         NA
#14: SAMPLE2 137           1892  -4300     -12200
#15: SAMPLE2 137           1893     NA         NA
#16: SAMPLE2 137           1894     NA         NA
#17: SAMPLE4   0           2000      0          0
#18: SAMPLE4   0           2001     NA         NA
#19: SAMPLE4   0           2002     NA         NA
#20: SAMPLE4   0           2003     NA         NA
#21: SAMPLE4   0           2004    500        500
#22: SAMPLE4   0           2005     NA         NA
#23: SAMPLE4   0           2006     NA         NA
#SAMPLE  ID REFERENCE_YEAR VALUE1 VALUE1_CUM

解决方案2
 0  2014-08-06 19:03:38

在观察以下输出之后，您可以简单地使用多种方法对数据集进行子集化。 我们首先在向量的1：n-1位置找到0s的位置，然后检查0位置+1是否为NA。 （我在表标题中手动添加了一些；以供阅读） 

by(data = df, INDICES = df$SAMPLE,
   FUN = function(x) any(is.na(x$VALUE1[which((x$VALUE1)[1:(length(x$VALUE1)-1)] == 0)+1])))

df$SAMPLE: SAMPLE1
[1] FALSE
---------------------------------------------------------------------------- 
df$SAMPLE: SAMPLE2
[1] FALSE
---------------------------------------------------------------------------- 
df$SAMPLE: SAMPLE3
[1] TRUE


x = "SAMPLE;     ID;      REFERENCE_YEAR;  VALUE1; VALUE1_CUM
SAMPLE1;    112;    1956;            0;     0
SAMPLE1;    112;    1957;            200;   200
SAMPLE1;    112;    1958;            NA;    NA
SAMPLE1;    112;    1959;            NA;    NA
SAMPLE1;    112;    1960;            NA;    NA
SAMPLE1;    112;    1963;            NA;    NA
SAMPLE2;    137;    1878;            0;     0
SAMPLE2;    137;    1879;          -7900;   -7900
SAMPLE2;    137;    1880;            NA;    NA
SAMPLE2;    137;    1881;            NA;    NA
SAMPLE2;    137;    1882;            NA;    NA
SAMPLE2;    137;    1890;            NA;    NA
SAMPLE2;    137;    1891;            NA;    NA
SAMPLE2;    137;    1892;         -4300;    -12200
SAMPLE2;    137;    1893;            NA;    NA
SAMPLE2;    137;    1894;            NA;    NA
SAMPLE3;    136;    1971;            0;    -500
SAMPLE3;    136;    1972;            NA;    NA
SAMPLE3;    136;    1973;            NA;    NA
SAMPLE3;    136;    1974;            NA;    NA
SAMPLE3;    136;    1975;            NA;    NA
SAMPLE3;    136;    1976;            NA;    NA
SAMPLE3;    136;    1980;            NA;    NA"

df = read.table(text = x, sep = ";", header = TRUE, colClasses= c("character", rep("numeric", 4)))

使用示例4数据的输出进行编辑 

df$SAMPLE: SAMPLE1
[1] FALSE
----------------------------------------------------------------------------------------------- 
df$SAMPLE: SAMPLE2
[1] FALSE
----------------------------------------------------------------------------------------------- 
df$SAMPLE: SAMPLE3
[1] TRUE
----------------------------------------------------------------------------------------------- 
df$SAMPLE: SAMPLE4
[1] TRUE

解决方案3
 0  2014-08-07 04:39:06

这是一个dplyr解决方案，使用dput()提供的dput() 

library(dplyr)
df %>%
    group_by(SAMPLE) %>%
    filter(! (VALUE1[[1]] == 0 & all(is.na(VALUE1[-1])) ) )

获得 

    SAMPLE  ID REFERENCE_YEAR VALUE1 VALUE1_CUM
1  SAMPLE1 112           1956      0          0
2  SAMPLE1 112           1957    200        200
3  SAMPLE1 112           1958     NA         NA
4  SAMPLE1 112           1959     NA         NA
5  SAMPLE1 112           1960     NA         NA
6  SAMPLE1 112           1963     NA         NA
7  SAMPLE2 137           1878      0          0
8  SAMPLE2 137           1879  -7900      -7900
9  SAMPLE2 137           1880     NA         NA
10 SAMPLE2 137           1881     NA         NA
11 SAMPLE2 137           1882     NA         NA
12 SAMPLE2 137           1890     NA         NA
13 SAMPLE2 137           1891     NA         NA
14 SAMPLE2 137           1892  -4300     -12200
15 SAMPLE2 137           1893     NA         NA
16 SAMPLE2 137           1894     NA         NA
17 SAMPLE4   0           2000      0          0
18 SAMPLE4   0           2001     NA         NA
19 SAMPLE4   0           2002     NA         NA
20 SAMPLE4   0           2003     NA         NA
21 SAMPLE4   0           2004    500        500
22 SAMPLE4   0           2005     NA         NA
23 SAMPLE4   0           2006     NA         NA

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