subsetting data.frame in R based on names in column

Question

My data looks like this:

BLOCK1  0   1   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    513 C   A   0/1:23,12:35:99:262,0,691   19,10:-40.6,-28.8,-78.7:-11.9:6.0
2   3   

BLOCK1  0   1   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    1095    G   A   0/1:35,12:47:99:328,0,1157  30,11:-61.1,-63.4,-134.7:2.2:12.0
3   4   

BLOCK1  0   1   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    1217    G   A   0/1:22,12:34:99:314,0,730   20,10:-68.4,-54.2,-109.0:-14.2:6.0
4   5   

BLOCK1  0   1   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    1219    A   C   0/1:22,12:34:99:308,0,715   20,10:-69.9,-54.2,-107.7:-15.7:6.0
5   6   

BLOCK1  0   1   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    1721    G   C   0/1:15,6:21:99:141,0,464    7,5:-21.8,-18.5,-30.1:-3.3:4.0
6   8   

BLOCK2  0   1   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    2171    G   C   0/1:14,13:27:99:388,0,369   9,5:-35.3,-26.5,-46.7:-8.7:3.0
7   10  

BLOCK3  1   0   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    3661    G   A   0/1:148,55:203:99:1070,0,4008   107,39:-163.0,-160.9,-438.4:-2.1:33.0
8   11  

BLOCK3  1   0   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    3700    C   T   0/1:124,124:249:99:3271,0,3667  117,107:-510.2,-163.3,-565.9:-346.9:4.0
9   12  

BLOCK3  1   0   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    3754    T   C   0/1:140,107:248:99:2786,0,3946  133,101:-436.9,-85.9,-558.8:-351.0:2.0
10  

all I want is an R command that will allow me to calculate various properties of, say, columns 2 and 3 (which are columns of 0's and 1's) and do this FOR EACH block (column1). So, for example, BLOCK1 below has 4 lines in it, BLOCK2 has one line, etc. A basic question I want to know is: for each block, how many zeros are there in column 2, and how many in column 3?

Can anyone help? I tried using various forms of aggregate(), but the problem is that the FUN argument doesn't allow me to do the above. Or maybe it does, but I can't figure it out...

Answer 1

So the above solutions don't really give what I wanted, but I figured out how to do it in bash. I wanted to parse the file to keep the blocks in which, for a given block, all col2=0 and all col3=1 OR all col2=1 and all col3=0. And I also wanted a count of the number of such blocks. The following command worked:

sed ':a;N;$!ba;s/\nBLOCK/\n\nBLOCK/g;s/$/\n/' input.file |awk 'BEGIN { fg=0; num=0; block=""; type="";} { if (/^$/ && fg == 1) { print block; num++; fg=0; block=""; } else if (/^$/) {block=""; type=""; fg=0;} else if (/^BLOCK/) { block = block "\n" $; total++; } else if (fg == 0) { type=$2 " " $3; fg=1 ; block = block "\n" $; } else if (fg == 1 && type == $2 " " $3) { block = block "\n" $_ ;} else if (fg == 1 && type != $2 " " $3) { fg=2; block=""} } END {print "perfect:\t",num,"\tTotal:\t",total}' >output.file

Answer 2

You could use aggregate from base R

 aggregate(!df[,c("Col2", "Col3")], list(Col1=df[,"Col1"]), FUN=sum)
 #     Col1 Col2 Col3
 #1  BLOCK1    5    0
 #2  BLOCK2    1    0
 #3  BLOCK3    0    3

Or using data.table

  library(data.table)
  setDT(df)[, lapply(.SD, function(x) sum(!x)), by=Col1]
  #    Col1 Col2 Col3
  #1: BLOCK1    5    0
  #2: BLOCK2    1    0
  #3: BLOCK3    0    3

Update

For the combinations, perhaps you need

   as.data.frame.matrix(table(df[,1],as.character(interaction(df[,-1]))))
   #       0.1 1.0
   #BLOCK1   5   0
   #BLOCK2   1   0
   #BLOCK3   0   3

Update2

If you want to keep only cases in which the blocks have col2=0 AND col3=1 OR col2=1 AND col3=0, for ALL entries of a given block:

Changing the example dataset to show some variation (in the present dataset, the condition will select all the rows)

  df$Col3[4] <- 0
  df$Col2[8]<-0
  df$Col3[8]<-1
  df[with(df, ave(Col2==0 & Col3==1|Col2==1 & Col3==0, Col1, FUN=all)),]
  #   Col1 Col2 Col3
  #6 BLOCK2    0    1
  #7 BLOCK3    1    0
  #8 BLOCK3    0    1
  #9 BLOCK3    1    0

data

df <-    structure(list(Col1 = c("BLOCK1", "BLOCK1", "BLOCK1", "BLOCK1", 
"BLOCK1", "BLOCK2", "BLOCK3", "BLOCK3", "BLOCK3"), Col2 = c(0L, 
0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L), Col3 = c(1L, 1L, 1L, 1L, 1L, 
1L, 0L, 0L, 0L)), .Names = c("Col1", "Col2", "Col3"), class = "data.frame", row.names = c(NA, 
-9L))

Answer 3

Using dplyr:

require(dplyr)

#dummy data
d <- read.table(text="
                BLOCK1  0   1   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    513 C   A   0/1:23,12:35:99:262,0,691   19,10:-40.6,-28.8,-78.7:-11.9:6.0
2   3   

BLOCK1  0   1   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    1095    G   A   0/1:35,12:47:99:328,0,1157  30,11:-61.1,-63.4,-134.7:2.2:12.0
3   4   

BLOCK1  0   1   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    1217    G   A   0/1:22,12:34:99:314,0,730   20,10:-68.4,-54.2,-109.0:-14.2:6.0
4   5   

BLOCK1  0   1   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    1219    A   C   0/1:22,12:34:99:308,0,715   20,10:-69.9,-54.2,-107.7:-15.7:6.0
5   6   

BLOCK1  0   1   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    1721    G   C   0/1:15,6:21:99:141,0,464    7,5:-21.8,-18.5,-30.1:-3.3:4.0
6   8   

BLOCK2  0   1   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    2171    G   C   0/1:14,13:27:99:388,0,369   9,5:-35.3,-26.5,-46.7:-8.7:3.0
7   10  

BLOCK3  1   0   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    3661    G   A   0/1:148,55:203:99:1070,0,4008   107,39:-163.0,-160.9,-438.4:-2.1:33.0
8   11  

BLOCK3  1   0   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    3700    C   T   0/1:124,124:249:99:3271,0,3667  117,107:-510.2,-163.3,-565.9:-346.9:4.0
9   12  

BLOCK3  1   0   Locus_540_Transcript_32_Length_8324_genewise_newlength_8215__CDS__3870__6491    3754    T   C   0/1:140,107:248:99:2786,0,3946  133,101:-436.9,-85.9,-558.8:-351.0:2.0
10  ",fill = TRUE)

#keep only rows with BLOCK names and count zeros in column 2
d %>% filter(grepl("BLOCK",V1)) %>%
  group_by(BLOCK=V1) %>%
  summarise(ZeroCountInCol2=sum(V2==0))

# BLOCK ZeroCountInCol2
# 1 BLOCK1               5
# 2 BLOCK2               1
# 3 BLOCK3               0

Answer 4

If the name of your data.frame is dataframe :

sapply(unique(dataframe[,1]),function(block){list(nb0_col1=sum(dataframe[dataframe[,1]==block,2]==0,na.rm=T),nb0_col2=sum(dataframe[dataframe[,1]==block,3]==0,na.rm=T))})