如何有条件地删除R中的行？

我有一个date.frame，看起来像：

        SNP              CLST A1 A2       FRQ IMP     POS CHR BVAL
1   rs2803291            Brahui  C  T  0.660000   0 1882185   1  878
2   rs2803291           Balochi  C  T  0.750000   0 1882185   1  878
3   rs2803291            Hazara  C  T  0.772727   0 1882185   1  878
4   rs2803291           Makrani  C  T  0.620000   0 1882185   1  878
5   rs2803291            Sindhi  C  T  0.770833   0 1882185   1  878
6   rs2803291            Pathan  C  T  0.681818   0 1882185   1  878
53  rs12060022           Brahui  T  C 0.0600000   1 3108186   1  982
54  rs12060022          Balochi  T  C 0.0416667   1 3108186   1  982
55  rs12060022           Hazara  T  C 0.0000000   1 3108186   1  982
56  rs12060022          Makrani  T  C 0.0200000   1 3108186   1  982
57  rs12060022           Sindhi  T  C 0.0625000   1 3108186   1  982
58  rs12060022           Pathan  T  C 1           1 3108186   1  982
105   rs870171           Brahui  T  G 0.2200000   0 3332664   1  976
106   rs870171          Balochi  T  G 0.3333330   0 3332664   1  976
107   rs870171           Hazara  T  G 1           0 3332664   1  976
108   rs870171          Makrani  T  G 1           0 3332664   1  976
109   rs870171           Sindhi  T  G 0.2083330   0 3332664   1  976
110   rs870171           Pathan  T  G 1           0 3332664   1  976
157  rs4282783           Brahui  G  T 1           1 4090545   1  992
158  rs4282783          Balochi  G  T 1           1 4090545   1  992
159  rs4282783           Hazara  G  T 1           1 4090545   1  992
160  rs4282783          Makrani  G  T 1           1 4090545   1  992
161  rs4282783           Sindhi  G  T 1           1 4090545   1  992
162  rs4282783           Pathan  G  T 1           1 4090545   1  992

---

我想删除所有行，其中给定SNP的每一行在

FRQ

列中的值为1。例如，每个rs4282783在FRQ列中的值为1，因此我想删除所有这些行。但我不想删除第58行，例如，它在FRQ中的值为1。有人有什么建议吗？

这里有一个使用子集和

ave

的基本R方法<代码>平均值构建组水平（SNP水平）最大值，用于通过观察对数据进行子集划分：

df[ave(df$FRQ, df$SNP, FUN=max) < 0.99999,]

        SNP    CLST A1 A2      FRQ IMP     POS CHR BVAL
1 rs2803291  Brahui  C  T 0.660000   0 1882185   1  878
2 rs2803291 Balochi  C  T 0.750000   0 1882185   1  878
3 rs2803291  Hazara  C  T 0.772727   0 1882185   1  878
4 rs2803291 Makrani  C  T 0.620000   0 1882185   1  878
5 rs2803291  Sindhi  C  T 0.770833   0 1882185   1  878
6 rs2803291  Pathan  C  T 0.681818   0 1882185   1  878

df[ave（df$FRQ，df$SNP，FUN=max）<0.99999，]
SNP CLST A1 A2 FRQ IMP POS CHR BVAL
1 rs2803291 Brahui C T 0.660000 0 1882185 1 878
2 rs2803291俾路支C T 0.750000 1882185 1 878
3 rs2803291 Hazara C T 0.772727 0 1882185 1 878
4 rs2803291 Makrani C T 0.620000 0 1882185 1 878
5 rs2803291 Sindhi C T 0.770833 0 1882185 1 878
6 rs2803291帕坦C T 0.681818 0 1882185 1 878

请注意，为了避免或减少数值不精确的问题，我使用了0.99999而不是1

数据

df <- read.table(header=T, text="SNP  CLST A1 A2   FRQ IMP  POS CHR BVAL
1   rs2803291            Brahui  C  T  0.660000   0 1882185   1  878
2   rs2803291           Balochi  C  T  0.750000   0 1882185   1  878
3   rs2803291            Hazara  C  T  0.772727   0 1882185   1  878
4   rs2803291           Makrani  C  T  0.620000   0 1882185   1  878
5   rs2803291            Sindhi  C  T  0.770833   0 1882185   1  878
6   rs2803291            Pathan  C  T  0.681818   0 1882185   1  878
53  rs12060022           Brahui  T  C 0.0600000   1 3108186   1  982
54  rs12060022          Balochi  T  C 0.0416667   1 3108186   1  982
55  rs12060022           Hazara  T  C 0.0000000   1 3108186   1  982
56  rs12060022          Makrani  T  C 0.0200000   1 3108186   1  982
57  rs12060022           Sindhi  T  C 0.0625000   1 3108186   1  982
58  rs12060022           Pathan  T  C 1           1 3108186   1  982
105   rs870171           Brahui  T  G 0.2200000   0 3332664   1  976
106   rs870171          Balochi  T  G 0.3333330   0 3332664   1  976
107   rs870171           Hazara  T  G 1           0 3332664   1  976
108   rs870171          Makrani  T  G 1           0 3332664   1  976
109   rs870171           Sindhi  T  G 0.2083330   0 3332664   1  976
110   rs870171           Pathan  T  G 1           0 3332664   1  976
157  rs4282783           Brahui  G  T 1           1 4090545   1  992
158  rs4282783          Balochi  G  T 1           1 4090545   1  992
159  rs4282783           Hazara  G  T 1           1 4090545   1  992
160  rs4282783          Makrani  G  T 1           1 4090545   1  992
161  rs4282783           Sindhi  G  T 1           1 4090545   1  992
162  rs4282783           Pathan  G  T 1           1 4090545   1  992")

---

df要删除
SNP
当
FRQ
的所有值都等于1时，您可以尝试：

library(dplyr)
df %>%
  group_by(SNP) %>%
  filter(!all(FRQ == 1))

其中：

#         SNP    CLST     A1     A2       FRQ   IMP     POS   CHR  BVAL
#       <fctr>  <fctr> <fctr> <fctr>     <dbl> <int>   <int> <int> <int>
#1   rs2803291  Brahui      C      T 0.6600000     0 1882185     1   878
#2   rs2803291 Balochi      C      T 0.7500000     0 1882185     1   878
#3   rs2803291  Hazara      C      T 0.7727270     0 1882185     1   878
#4   rs2803291 Makrani      C      T 0.6200000     0 1882185     1   878
#5   rs2803291  Sindhi      C      T 0.7708330     0 1882185     1   878
#6   rs2803291  Pathan      C      T 0.6818180     0 1882185     1   878
#7  rs12060022  Brahui      T      C 0.0600000     1 3108186     1   982
#8  rs12060022 Balochi      T      C 0.0416667     1 3108186     1   982
#9  rs12060022  Hazara      T      C 0.0000000     1 3108186     1   982
#10 rs12060022 Makrani      T      C 0.0200000     1 3108186     1   982
#11 rs12060022  Sindhi      T      C 0.0625000     1 3108186     1   982
#12 rs12060022  Pathan      T      C 1.0000000     1 3108186     1   982
#13   rs870171  Brahui      T      G 0.2200000     0 3332664     1   976
#14   rs870171 Balochi      T      G 0.3333330     0 3332664     1   976
#15   rs870171  Hazara      T      G 1.0000000     0 3332664     1   976
#16   rs870171 Makrani      T      G 1.0000000     0 3332664     1   976
#17   rs870171  Sindhi      T      G 0.2083330     0 3332664     1   976
#18   rs870171  Pathan      T      G 1.0000000     0 3332664     1   976

#SNP CLST A1 A2 FRQ IMP POS CHR BVAL
#                      
#1 rs2803291 Brahui C T 0.6600000 0 1882185 1 878
#2 rs2803291俾路支C T 0.7500000 1882185 1 878
#3 rs2803291 Hazara C T 0.7727270 0 1882185 1 878
#4 rs2803291 Makrani C T 0.6200000 0 1882185 1 878
#5 rs2803291 Sindhi C T 0.7708330 0 1882185 1 878
#6 rs2803291帕坦C T 0.6818180 0 1882185 1 878
#7 rs12060022 Brahui T C 0.06000001 3108186 1 982
#8 rs12060022俾路支T C 0.0416667 1 3108186 1 982
#9 rs12060022哈扎拉T C 0.00000001 3108186 1 982
#10 rs12060022 Makrani T C 0.0200000 1 3108186 1 982
#11 rs12060022信德电信0.0625000 1 3108186 1 982
#12 rs12060022 Pathan T C 1.0000000 1 3108186 1 982
#13 rs870171 Brahui T G 0.2200000 3332664 1 976
#14 rs870171俾路支T G 0.3333330 3332664 1 976
#15 rs870171哈扎拉T G 1.0000000 3332664 1 976
#16 rs870171 Makrani T G 1.0000000 3332664 1 976
#17 rs870171信德集团0.2083330 0 3332664 1 976
#18 rs870171帕桑T G 1.0000000 3332664 1 976
@imo的答案更为简洁，但正如我所做的那样，我将添加它。
在我看来，逻辑稍微清晰一些

# which SNPs are always 1
# For each SNP value, take the rows with that SNP, and test if all FRQ values are 1
rmSNPs <- sapply(unique(dd$SNP), function(x) all(dd$FRQ[dd$SNP == x] == 1))

# new data is old data minus row where dd$SNP is not one of those found above
newdata <- dd[dd$SNP != unique(dd$SNP)[rmSNPs], ]

#哪些SNP总是1
#对于每个SNP值，取具有该SNP的行，并测试所有FRQ值是否为1
rmSNPs这里是一个基于
数据表的方法。将“data.frame”转换为“data.table”（
setDT（df）
），按“SNP”分组，
如果
不是（
！
）
则“FRQ”中的所有元素都是1，然后获取data.table的子集

library(data.table)
setDT(df)[, if(!(all(FRQ==1))) .SD , by = SNP]

或者，如果我猜OP是指删除所有只有一个“FRQ”的SNP，那么我们可以使用
uniqueN
来查找
唯一的
元素的数量，并在
if
条件中使用，以仅保留那些具有1个以上的SNP

setDT(df)[, if(uniqueN(FRQ) > 1) .SD , by = SNP]

不错。当我们有非常相似的方法时，也许最好给另一个留下评论，这样我们就可以整合到同一个答案中+1@StevenBeaupr我认为data.table语法可以作为一个单独的答案。@StevenBeaupréI添加了另一种方法