How do I compute the number of occurrences of a particular value in a row in R

I am having quite a tricky problem, which i just cannot seem to solve.

I have a large dataset (23277 rows, 151 columns). Each column has values from 0:100 (inclusive) representing probabilities assigned for events in the world.

As part of calculating the score for each individual, I need to count the occurrences of each of the values in the dataset.

I first tried apply, but I need to ignore NA's, and subset, so when i tried the following:

apply(ans.samp, 1, sum(ans.samp[ans==0]), na.rm=TRUE)

I got the error message: sum(ans.samp[ans == 0])' is not a function, character or symbol

I repeated this process with sapply, vapply, tapply and do.call to no avail.

Giving up on a vectorised solution, I wrote the following for loop.

RespCount <- function (x) { for (i in (1:nrow(x))) 
  { res <- vector(mode="numeric", length=nrow(x))
    ans.tmp <- x[i,]
    res[i] <- length(ans.tmp[ans.tmp==0])
    print(res)
  }
return(res)
}

However, after i got this working, it returns only the total sum of O in the sample.

I would appreciate some help with this, as I am under some time pressure, and I would like to be able to solve these kinds of problems in R in the future.

Sample data included for reproducibility:

structure(list(X = 1:6, X100 = c(70L, NA, 80L, 0L, 40L, NA), 
    X10 = c(30L, NA, NA, NA, NA, NA), X1 = c(50L, NA, NA, NA, 
    NA, NA), X11 = c(50L, NA, NA, NA, NA, NA), X12 = c(30L, NA, 
    NA, NA, NA, NA), X13 = c(50L, NA, NA, NA, NA, NA), X14 = c(70L, 
    NA, NA, NA, NA, NA), X15 = c(60L, NA, NA, NA, NA, NA), X158 = c(30L, 
    NA, NA, NA, NA, NA), X159 = c(50L, NA, NA, NA, NA, NA), X160 = c(80L, 
    NA, NA, NA, NA, NA), X16 = c(50L, NA, NA, NA, NA, NA), X161 = c(40L, 
    NA, NA, NA, NA, NA), X162 = c(100L, NA, NA, NA, NA, NA), 
    X163 = c(50L, NA, NA, NA, NA, NA), X164 = c(0L, NA, NA, NA, 
    NA, NA), X165 = c(0L, NA, NA, NA, NA, NA), X166 = c(20L, 
    NA, NA, NA, NA, NA), X167 = c(0L, NA, NA, NA, NA, NA), X168 = c(30L, 
    NA, NA, NA, NA, NA), X169 = c(100L, NA, NA, NA, NA, NA), 
    X170 = c(30L, NA, NA, NA, NA, NA), X17 = c(40L, NA, NA, NA, 
    NA, NA), X171 = c(50L, NA, NA, NA, NA, NA), X172 = c(20L, 
    NA, NA, NA, NA, NA), X173 = c(30L, NA, NA, NA, NA, NA), X174 = c(20L, 
    NA, NA, NA, NA, NA), X175 = c(30L, NA, NA, NA, NA, NA), X176 = c(10L, 
    NA, NA, NA, NA, NA), X177 = c(70L, NA, NA, NA, NA, NA), X178 = c(40L, 
    NA, NA, NA, NA, NA), X179 = c(70L, NA, NA, NA, NA, NA), X180 = c(0L, 
    NA, NA, NA, NA, NA), X18 = c(30L, NA, NA, NA, NA, NA), X181 = c(100L, 
    NA, NA, NA, NA, NA), X182 = c(100L, NA, NA, NA, NA, NA), 
    X183 = c(20L, NA, NA, NA, NA, NA), X184 = c(80L, NA, NA, 
    NA, NA, NA), X185 = c(90L, NA, NA, NA, NA, NA), X186 = c(0L, 
    NA, NA, NA, NA, NA), X187 = c(10L, NA, NA, NA, NA, NA), X188 = c(100L, 
    NA, NA, NA, NA, NA), X189 = c(100L, NA, NA, NA, NA, NA), 
    X190 = c(0L, NA, NA, NA, NA, NA), X19 = c(100L, NA, NA, NA, 
    NA, NA), X191 = c(0L, NA, NA, NA, NA, NA), X192 = c(90L, 
    NA, NA, NA, NA, NA), X193 = c(50L, NA, NA, NA, NA, NA), X194 = c(100L, 
    NA, NA, NA, NA, NA), X195 = c(10L, NA, NA, NA, NA, NA), X196 = c(100L, 
    NA, NA, NA, NA, NA), X197 = c(20L, NA, NA, NA, NA, NA), X198 = c(40L, 
    NA, NA, NA, NA, NA), X199 = c(20L, NA, NA, NA, NA, NA), X200 = c(0L, 
    NA, NA, NA, NA, NA), X20 = c(0L, NA, NA, NA, NA, NA), X201 = c(0L, 
    NA, NA, NA, NA, NA), X202 = c(20L, NA, NA, NA, NA, NA), X203 = c(20L, 
    NA, NA, NA, NA, NA), X204 = c(80L, NA, NA, NA, NA, NA), X205 = c(0L, 
    NA, NA, NA, NA, NA), X206 = c(80L, NA, NA, NA, NA, NA), X207 = c(0L, 
    NA, NA, NA, NA, NA), X2 = c(10L, NA, NA, NA, NA, NA), X21 = c(0L, 
    NA, NA, NA, NA, NA), X22 = c(100L, NA, NA, NA, NA, NA), X23 = c(50L, 
    NA, NA, NA, NA, NA), X24 = c(50L, NA, NA, NA, NA, NA), X25 = c(70L, 
    NA, NA, NA, NA, NA), X26 = c(60L, NA, NA, NA, NA, NA), X27 = c(40L, 
    NA, NA, NA, NA, NA), X28 = c(20L, NA, NA, NA, NA, NA), X29 = c(0L, 
    NA, NA, NA, NA, NA), X30 = c(90L, NA, NA, NA, NA, NA), X3 = c(0L, 
    NA, NA, NA, NA, NA), X31 = c(50L, NA, NA, NA, NA, NA), X32 = c(50L, 
    NA, NA, NA, NA, NA), X33 = c(0L, NA, NA, NA, NA, NA), X34 = c(50L, 
    NA, NA, NA, NA, NA), X35 = c(90L, NA, NA, NA, NA, NA), X36 = c(50L, 
    NA, NA, NA, NA, NA), X37 = c(60L, NA, NA, NA, NA, NA), X38 = c(40L, 
    NA, NA, NA, NA, NA), X39 = c(50L, NA, NA, NA, NA, NA), X40 = c(0L, 
    NA, NA, NA, NA, NA), X4 = c(50L, NA, NA, NA, NA, NA), X41 = c(90L, 
    NA, NA, NA, NA, NA), X42 = c(80L, NA, NA, NA, NA, NA), X43 = c(50L, 
    NA, NA, NA, NA, NA), X44 = c(80L, NA, NA, NA, NA, NA), X45 = c(80L, 
    NA, NA, NA, NA, NA), X46 = c(0L, NA, NA, NA, NA, NA), X47 = c(80L, 
    NA, NA, NA, NA, NA), X48 = c(20L, NA, NA, NA, NA, NA), X49 = c(100L, 
    NA, NA, NA, NA, NA), X50 = c(0开发者_Python百科L, NA, NA, NA, NA, NA), X5 = c(0L, 
    NA, NA, NA, NA, NA), X51 = c(80L, 100L, 70L, 100L, 0L, 60L
    ), X52 = c(10L, 0L, 0L, 0L, 0L, 20L), X53 = c(40L, 40L, 70L, 
    20L, 90L, 50L), X54 = c(0L, 10L, 0L, 50L, 50L, 0L), X55 = c(20L, 
    80L, 90L, 80L, 30L, 0L), X56 = c(100L, 100L, 50L, 100L, 80L, 
    100L), X57 = c(60L, 0L, 100L, 70L, 100L, 80L), X58 = c(100L, 
    100L, 100L, 50L, 100L, 100L), X59 = c(80L, 50L, 80L, 0L, 
    30L, 50L), X60 = c(70L, 50L, 60L, 50L, 100L, 100L), X6 = c(100L, 
    NA, NA, NA, NA, NA), X61 = c(50L, 50L, 50L, 30L, 70L, 50L
    ), X62 = c(20L, 50L, 40L, 40L, 50L, 100L), X63 = c(50L, 0L, 
    100L, 10L, 50L, 100L), X64 = c(60L, 30L, 0L, 50L, 50L, 50L
    ), X65 = c(50L, 50L, 70L, 80L, 50L, 50L), X66 = c(70L, 40L, 
    10L, 90L, 60L, 50L), X67 = c(30L, 50L, 50L, 0L, 50L, 60L), 
    X68 = c(30L, 0L, 0L, 40L, 70L, 80L), X69 = c(30L, NA, 70L, 
    10L, 0L, 20L), X70 = c(80L, NA, 50L, 50L, 70L, 100L), X7 = c(100L, 
    NA, NA, NA, NA, NA), X71 = c(70L, NA, 50L, 100L, 100L, 100L
    ), X72 = c(60L, NA, 70L, 50L, 80L, 50L), X73 = c(80L, NA, 
    80L, 80L, 80L, NA), X74 = c(50L, NA, 50L, 0L, 50L, NA), X75 = c(30L, 
    NA, 70L, 10L, 80L, NA), X76 = c(70L, NA, 40L, 80L, 100L, 
    NA), X77 = c(80L, NA, 50L, 100L, 40L, NA), X78 = c(80L, NA, 
    0L, 0L, 0L, NA), X79 = c(80L, NA, 50L, 50L, 50L, NA), X80 = c(40L, 
    NA, 90L, 70L, 60L, NA), X8 = c(50L, NA, NA, NA, NA, NA), 
    X81 = c(70L, NA, 60L, 40L, 80L, NA), X82 = c(80L, NA, 100L, 
    60L, 60L, NA), X83 = c(30L, NA, 100L, 30L, 0L, NA), X84 = c(80L, 
    NA, 0L, 60L, 100L, NA), X85 = c(80L, NA, 50L, 40L, 30L, NA
    ), X86 = c(50L, NA, 90L, 50L, 50L, NA), X87 = c(80L, NA, 
    50L, 70L, 20L, NA), X88 = c(40L, NA, 70L, 30L, 90L, NA), 
    X89 = c(50L, NA, 50L, 80L, 80L, NA), X90 = c(90L, NA, 100L, 
    60L, 100L, NA), X91 = c(0L, NA, 0L, 0L, 0L, NA), X9 = c(100L, 
    NA, NA, NA, NA, NA), X92 = c(50L, NA, 70L, 90L, 80L, NA), 
    X93 = c(40L, NA, 50L, 50L, 50L, NA), X94 = c(40L, NA, 0L, 
    60L, 40L, NA), X95 = c(90L, NA, 100L, 40L, 50L, NA), X96 = c(50L, 
    NA, 50L, 50L, 50L, NA), X97 = c(60L, NA, 60L, 100L, 50L, 
    NA), X98 = c(40L, NA, 40L, 0L, 0L, NA), X99 = c(30L, NA, 
    0L, 50L, 70L, NA)), .Names = c("X", "X100", "X10", "X1", 
"X11", "X12", "X13", "X14", "X15", "X158", "X159", "X160", "X16", 
"X161", "X162", "X163", "X164", "X165", "X166", "X167", "X168", 
"X169", "X170", "X17", "X171", "X172", "X173", "X174", "X175", 
"X176", "X177", "X178", "X179", "X180", "X18", "X181", "X182", 
"X183", "X184", "X185", "X186", "X187", "X188", "X189", "X190", 
"X19", "X191", "X192", "X193", "X194", "X195", "X196", "X197", 
"X198", "X199", "X200", "X20", "X201", "X202", "X203", "X204", 
"X205", "X206", "X207", "X2", "X21", "X22", "X23", "X24", "X25", 
"X26", "X27", "X28", "X29", "X30", "X3", "X31", "X32", "X33", 
"X34", "X35", "X36", "X37", "X38", "X39", "X40", "X4", "X41", 
"X42", "X43", "X44", "X45", "X46", "X47", "X48", "X49", "X50", 
"X5", "X51", "X52", "X53", "X54", "X55", "X56", "X57", "X58", 
"X59", "X60", "X6", "X61", "X62", "X63", "X64", "X65", "X66", 
"X67", "X68", "X69", "X70", "X7", "X71", "X72", "X73", "X74", 
"X75", "X76", "X77", "X78", "X79", "X80", "X8", "X81", "X82", 
"X83", "X84", "X85", "X86", "X87", "X88", "X89", "X90", "X91", 
"X9", "X92", "X93", "X94", "X95", "X96", "X97", "X98", "X99"), row.names = c(NA, 
6L), class = "data.frame")

Any insight would be greatly appreciated.

From some attempts on the small dataset above, it appears that the number is being calculated for each row, but when i return the res object, it merely gives me the final value. How can I fix this?

There're two ways to use the apply family functions. Either you do

apply(mat, 1, sum, na.rm=TRUE)

if you want to apply the function sum()to each row, passing additional parameters like na.rm=TRUE. Or you can do

apply(mat, 1, foo)

where foo() is a function of your own, defined externally, e.g.

foo <- function(x) sum(x==0, na.rm=TRUE)

Note that NA handling might also be dealt with a parameter of the function itself, with default value set to TRUE, in the above definition, as in

foo2 <- function(x, no.na=TRUE) sum(x==0, na.rm=no.na)

and you can call it as apply(mat, 1, foo2, no.na=F) although it doesn't really make sense with the sum() function (unless you want to check if there're NA values, but in this case it's better to use is.na() :-).

Finally, you can define your function directly inline as

apply(mat, 1, function(x) sum(x==0, na.rm=TRUE))

In your case, it gives me

> apply(mat, 1, function(x) sum(x==0, na.rm=TRUE))
 1  2  3  4  5  6 
22  4  9  8  7  2 

which is equivalent to apply(ex, 1, foo).

Let's call your dataset dat. You can use table() to get a table of frequencies for each value in your dataset. If you want to apply that to all data in your data frame, coerce the data to a single vector, and use table() on the resulting vector:

table(do.call('c', dat))

This gives you:

> table(do.call('c', dat))
  0   1   2   3   4   5   6  10  20  30  40  50  60  70  80  90 100 
 52   1   1   1   1   1   1  10  16  21  25  76  19  25  37  14  45 

If you want to check frequencies for individual columns, simply do:

apply(dat, 1, table)

For data in a data.frame named df,

sapply(df + 1, tabulate, 101)

produces a matrix of 101 x 151, where rows correspond to 0, 1, ..., 100 and columns to the 151 samples; a matrix might be convenient for subsequent computation, and tabulate is faster than table.

I'm trying to address the problem statement, rather than correcitng the coding problem in what appeared to be an initial partial effort. To count the number of occurrences in a row, use 'apply' with 'table'

> apply(dfrm, 1, table)
$`1`

  0   1  10  20  30  40  50  60  70  80  90 100 
 22   1   5  12  14  12  26   7  10  19   7  16 

$`2`

  0   2  10  30  40  50  80 100 
  4   1   1   1   2   6   1   3 

$`3`

  0   3  10  40  50  60  70  80  90 100 
  9   1   1   3  13   3   8   3   3   7 

$`4`

  0   4  10  20  30  40  50  60  70  80  90 100 
  8   1   3   1   3   5  11   4   3   5   2   5 

$`5`

  0   5  20  30  40  50  60  70  80  90 100 
  7   1   1   3   3  13   3   4   7   2   7 

$`6`

  0   6  20  50  60  80 100 
  2   1   2   7   2   2   7 

And notice that this result includes as a subset the x==0 case:

> sapply( apply(dfrm, 1, table), function(x) x['0'])
1.0 2.0 3.0 4.0 5.0 6.0 
 22   4   9   8   7   2