How to count number of Numeric values in a column

Question

I have a dataframe, and I want to produce a table of summary statistics including number of valid numeric values, mean and sd by group for each of three columns. I can't seem to find any function to count the number of numeric values in R. I can use length() which tells me how many values there are, and I can use colSums(is.na(x)) to count the number of NA values, but colSums(is.numeric(x)) doesn't work the same way.

I could use tapply with { length - number of NA values - number of blank values - number of text values } but surely there's an easier way.

My data (I want to group by Nominal, and produce summary stats on Actual, LinPred and QualPred)

structure(list(Nominal = c(1, 3, 6, 10, 30, 50, 150, 250, 1, 
3, 6, 10, 30, 50, 150, 250, 1, 3, 6, 10, 30, 50, 150, 250, 1, 
3, 6, 10, 30, 50, 150, 250, 1, 3, 6, 10, 30, 50, 150, 250, 1, 
3, 6, 10, 30, 50, 150, 250, 1, 3, 6, 10, 30, 50, 150, 250, 1, 
3, 6, 10, 30, 50, 150, 250, 1, 3, 6, 10, 30, 50, 150, 250), Actual = c(NA, 
0.422, 0.782, 1.25, 3.85, 6.94, 18.8, 31.2, 0.118, 0.361, 0.747, 
1.18, 3.58, 5.82, 16.7, 29, 0.113, 0.382, 0.692, 1.12, 3.51, 
5.43, 17.1, 28.7, 0.134, 0.402, 0.718, 1.25, 3.65, 6.52, NA, 
28.8, 0.123, 0.396, 0.664, 1.12, 3.83, 5.6, NA, 28.1, 0.112, 
0.341, 0.7, 1.08, 3.25, 5.97, NA, 27.1, 0.106, 0.35, 0.674, 1.14, 
3.28, 5.5, 17.3, 30, 0.122, 0.321, 0.673, 1.22, 3.41, 5.85, 17.6, 
28.1, 0.129, 0.351, 0.737, 1.06, 3.39, 5.53, 15.9, 28.5), LinPred = c(NA, 
3.49519490135683, 6.4706724568458, 10.3387932789814, 31.8283534019573, 
57.3678690865708, 155.393324109068, 257.881995464799, 0.982569410055046, 
2.99101676001009, 6.18138991672881, 9.76022819874748, 29.5967452353405, 
48.1108278028274, 138.036371702049, 239.698521514589, 0.941243332895477, 
3.16458628408028, 5.72680306797355, 9.26431527283265, 29.0181801551066, 
44.887393784381, 141.342457874815, 237.218956885015, 1.07941778099747, 
3.36900393602722, 6.0686652233011, 10.6136646056736, 31.1174212178803, 
55.6364968333108, NA, 245.979704049963, 0.98544222985819, 3.3177445444967, 
5.60733069952645, 9.50304445584572, 32.6552029637958, 47.7767234652982, 
NA, 239.999441704736, 0.89146667871891, 2.8478667888003, 5.91488704870955, 
9.1613151789756, 27.7001284491792, 50.9377192763467, NA, 231.456209782983, 
0.887738051402174, 3.04188235451485, 5.9023034783202, 10.0163659588551, 
28.9092709123842, 48.5084526866061, 152.684283738776, 264.805729023739, 
1.02899341554071, 2.78585700701375, 5.89347501806154, 10.7226427795477, 
30.0569707460098, 51.5984137771366, 155.332821816374, 248.031654532288, 
1.09079263735132, 3.05071081477351, 6.45849647461568, 9.31008913816238, 
29.8804015408367, 48.7733064943658, 140.324439376654, 251.563038635751
), QuadPred = c(NA, 3.46077095737974, 6.38659713413108, 10.1956079501556, 
31.4700369979564, 57.0089799611706, 157.775316006369, 268.303966059862, 
0.99289436409299, 2.96536517477853, 6.10198249392715, 9.62549220297933, 
29.2517496204359, 47.7196128593832, 139.600469198163, 248.272682787657, 
0.95232583127381, 3.13590297331348, 5.65480031033985, 9.13693141349813, 
28.6769820181676, 44.4936547741659, 143.050878627236, 245.555818447238, 
1.08417831830729, 3.33895371044810, 6.00044125019758, 10.4882228621509, 
30.8451526869812, 55.4331759085967, NA, 256.446833964951, 0.991679220421247, 
3.28844923081897, 5.54540949253351, 9.3907657095483, 32.3793538902883, 
47.5218142460371, NA, 249.828516445647, 0.899183876120787, 2.82554368740693, 
5.84875388286628, 9.05319326862309, 27.4395572248486, 50.7001828907023, 
NA, 240.411024762687, 0.884412915928806, 3.05257006009469, 5.93046554432476, 
10.0673979669, 29.0311859234644, 48.645035648271, 151.914544909710, 
261.273991566153, 1.02660962824666, 2.79491765184684, 5.92158513760114, 
10.7773327827008, 30.1813919027873, 51.7318741314584, 154.518856412401, 
245.027488125567, 1.08881969774848, 3.06145444119556, 6.48990638077339, 
9.35738460692028, 30.0044505131336, 48.9096796323938, 139.747394069421, 
248.451100154569)), .Names = c("Nominal", "Actual", "LinPred", 
"QuadPred"), row.names = c(NA, -72L), class = "data.frame")

Answer 1

Can you use something like this?

length(unique(x))

Answer 2

These are a few add-on packages that might help (see Quick-R)

Using the Hmisc package

library(Hmisc)

describe(mydata) 
# n, nmiss, unique, mean, 5,10,25,50,75,90,95th percentiles 
# 5 lowest and 5 highest scores

Using the pastecs package

library(pastecs)

stat.desc(mydata) 
# nbr.val, nbr.null, nbr.na, min max, range, sum, 
# median, mean, SE.mean, CI.mean, var, std.dev, coef.var

Using the psych package

library(psych)
describe(mydata)
# item name ,item number, nvalid, mean, sd, 
# median, mad, min, max, skew, kurtosis, se

I'd use describe.by from the psych package;

> describe.by(biastable, as.factor(Nominal))
group: 1
         var n mean   sd median trimmed  mad  min  max range  skew kurtosis   se
Nominal    1 9 1.00 0.00   1.00    1.00 0.00 1.00 1.00  0.00   NaN      NaN 0.00
Actual     2 8 0.12 0.01   0.12    0.12 0.01 0.11 0.13  0.03  0.09    -1.47 0.00
LinPred    3 8 0.99 0.08   0.98    0.99 0.10 0.89 1.09  0.20  0.04    -1.70 0.03
QuadPred   4 8 0.99 0.08   0.99    0.99 0.10 0.88 1.09  0.20 -0.04    -1.64 0.03
------------------------------------------------------------------------ 
group: 3
         var n mean   sd median trimmed  mad  min  max range skew kurtosis   se
Nominal    1 9 3.00 0.00   3.00    3.00 0.00 3.00 3.00  0.00  NaN      NaN 0.00
Actual     2 9 0.37 0.03   0.36    0.37 0.03 0.32 0.42  0.10 0.15    -1.50 0.01
LinPred    3 9 3.12 0.24   3.05    3.12 0.30 2.79 3.50  0.71 0.15    -1.52 0.08
QuadPred   4 9 3.10 0.23   3.06    3.10 0.34 2.79 3.46  0.67 0.12    -1.51 0.08
------------------------------------------------------------------------ 
group: 6
         var n mean   sd median trimmed  mad  min  max range skew kurtosis   se
Nominal    1 9 6.00 0.00   6.00    6.00 0.00 6.00 6.00  0.00  NaN      NaN 0.00
Actual     2 9 0.71 0.04   0.70    0.71 0.04 0.66 0.78  0.12 0.46    -1.30 0.01
LinPred    3 9 6.02 0.30   5.91    6.02 0.28 5.61 6.47  0.86 0.28    -1.43 0.10
QuadPred   4 9 5.99 0.31   5.93    5.99 0.25 5.55 6.49  0.94 0.26    -1.26 0.10
------------------------------------------------------------------------ 
group: 10
         var n  mean   sd median trimmed  mad   min   max range skew kurtosis   se
Nominal    1 9 10.00 0.00  10.00   10.00 0.00 10.00 10.00  0.00  NaN      NaN 0.00
Actual     2 9  1.16 0.07   1.14    1.16 0.09  1.06  1.25  0.19 0.09    -1.71 0.02
LinPred    3 9  9.85 0.60   9.76    9.85 0.74  9.16 10.72  1.56 0.24    -1.76 0.20
QuadPred   4 9  9.79 0.62   9.63    9.79 0.72  9.05 10.78  1.72 0.27    -1.65 0.21
------------------------------------------------------------------------ 
group: 30
         var n  mean   sd median trimmed  mad   min   max range skew kurtosis   se
Nominal    1 9 30.00 0.00  30.00   30.00 0.00 30.00 30.00  0.00  NaN      NaN 0.00
Actual     2 9  3.53 0.22   3.51    3.53 0.21  3.25  3.85  0.60 0.23    -1.58 0.07
LinPred    3 9 30.08 1.55  29.88   30.08 1.44 27.70 32.66  4.96 0.21    -1.27 0.52
QuadPred   4 9 29.92 1.51  30.00   29.92 1.44 27.44 32.38  4.94 0.04    -1.22 0.50
------------------------------------------------------------------------ 
group: 50
         var n  mean   sd median trimmed  mad   min   max range skew kurtosis   se
Nominal    1 9 50.00 0.00  50.00   50.00 0.00 50.00 50.00  0.00  NaN      NaN 0.00
Actual     2 9  5.91 0.51   5.82    5.91 0.43  5.43  6.94  1.51 0.90    -0.73 0.17
LinPred    3 9 50.40 3.98  48.77   50.40 3.21 44.89 57.37 12.48 0.49    -1.16 1.33
QuadPred   4 9 50.24 3.97  48.91   50.24 2.65 44.49 57.01 12.52 0.39    -1.21 1.32
------------------------------------------------------------------------ 
group: 150
         var n   mean   sd median trimmed   mad    min    max range  skew kurtosis   se
Nominal    1 9 150.00 0.00 150.00  150.00  0.00 150.00 150.00  0.00   NaN      NaN 0.00
Actual     2 6  17.23 0.97  17.20   17.23  0.67  15.90  18.80  2.90  0.25    -1.23 0.39
LinPred    3 6 147.19 8.11 147.01  147.19 11.13 138.04 155.39 17.36 -0.01    -2.22 3.31
QuadPred   4 6 147.77 7.95 147.48  147.77 10.95 139.60 157.78 18.17  0.07    -2.10 3.25
------------------------------------------------------------------------ 
group: 250
         var n   mean    sd median trimmed  mad    min    max range skew kurtosis   se
Nominal    1 9 250.00  0.00 250.00  250.00 0.00 250.00 250.00  0.00  NaN      NaN 0.00
Actual     2 9  28.83  1.18  28.70   28.83 0.89  27.10  31.20  4.10 0.59    -0.57 0.39
LinPred    3 9 246.29 10.57 245.98  246.29 9.31 231.46 264.81 33.35 0.33    -1.26 3.52
QuadPred   4 9 251.51  8.84 248.45  251.51 5.08 240.41 268.30 27.89 0.62    -1.04 2.95
>

Answer 3

Does complete.cases (or sum(complete.cases)) do what you want?

Answer 4

What are "blank values" and "text values"? If you have numeric vector then you could have NA's (is.na()), Inf's (is.infinite()), NaN's (is.nan()) and "valid" numeric values.

For "valid" numeric values (in the sense above) you could use is.finite():

is.finite(c(1,NA,Inf,NaN))
# [1]  TRUE FALSE FALSE FALSE
sum( is.finite(c(1,NA,Inf,NaN)) )
# [1] 1

So colSums(is.numeric(x)) could be done like colSums(is.finite(x)).

Answer 5

colSums(!is.na(x)) should work.