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

Answer 1

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

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

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 4

Can you use something like this?

length(unique(x))

Answer 5

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