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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")
937
Use the COUNTIF function to count how many times a particular value appears in a range of cells.
Use the COUNT function to get the number of entries in a number field that is in a range or array of numbers. For example, you can enter the following formula to count the numbers in the range A1:A20: =COUNT(A1:A20). In this example, if five of the cells in the range contain numbers, the result is 5.
To find the first numeric cell which includes times, dates and numbers, you can use this formula: Select a cell which you place the finding result, type this formula =INDEX(A1:A10,MATCH(TRUE,INDEX(ISNUMBER(A1:A10),0),0)), and press Enter key. Now it returns the actual value of the first numeric in the specified list.
The simplest way to do this is with the COUNTIF function , which takes two arguments , range and criteria : = COUNTIF ( range , criteria ) All test scores are in... In this example, the goal is to count the number of cells in a range that contain negative numbers.
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
>
colSums(!is.na(x)) should work.
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