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How to find the number of positive values in an R vector?
We know that positive values are greater than 0, therefore, we can use this condition with length function to find the number of positive values in a vector. For example, if we have a vector x that contains some positive and some negative values and we want to find the number of values that are positive then we can use the command length(x[x>0]).
Example1
> x1<-rnorm(50) > x1
Output
[1] 0.21314126 1.23449384 -1.02721325 -0.23168203 -1.36368881 -0.82416287 [7] 0.31224895 -0.90773340 0.10312288 -0.38914253 0.01196499 0.44875369 [13] 0.40820219 0.70172242 -0.23766272 -0.01023414 1.12403398 0.05837136 [19] -0.67403563 -0.26134292 0.31192384 -1.25116951 0.22115555 0.46544495 [25] 0.76567139 0.76948285 -1.42650924 0.24616899 0.18043015 1.04896235 [31] -1.84903391 1.06701870 -0.32702175 0.77332590 1.04121716 0.77967137 [37] -1.26687761 0.80629905 -0.52804923 1.31858926 0.96357328 0.19380413 [43] 0.54649834 0.70873506 -1.45764382 0.11327251 1.47389471 -1.24322096 [49] -0.24590239 -1.10458149
Example
> length(x1[x1>0])
Output
[1] 30
Example2
> x2<-rnorm(50) > x2
Output
[1] -0.877360446 -2.271754557 -1.786576558 -1.067972336 0.382150798 [6] 0.842677084 -0.758538398 -1.119490974 1.896355936 0.991356448 [11] 0.248436939 0.039078483 -0.255884477 -0.107051458 0.335866428 [16] 0.014851805 0.058688431 -0.174580556 1.549126253 0.344889145 [21] -0.035863719 0.620171693 -1.517864377 -1.265375065 -1.983152664 [26] -0.239450464 0.315096209 0.061669573 -0.156187228 -1.113754030 [31] 0.373451031 -0.586700604 0.006995302 -1.219234747 0.957349339 [36] -1.651528682 0.455014272 1.386421547 1.257656374 -0.341903193 [41] -0.099243682 -0.281469501 0.594459819 0.126796982 -0.974602165 [46] -1.037023537 0.489551175 1.173538172 0.531043032 0.273650579
Example
> length(x2[x2>0])
Output
[1] 26
Example3
> x3<-sample(c(-5:-1,1,5),200,replace=TRUE) > x3
Output
[1] -2 5 -5 -4 -1 -4 1 -3 1 -5 -1 -2 -3 -5 -2 1 -2 5 -4 -5 5 -4 -5 5 -3 [26] 5 -3 -5 -1 -4 -4 1 -3 1 -4 -4 -4 -2 -1 -1 -4 -1 -3 -2 -2 1 -2 1 -4 -5 [51] -2 -5 5 -1 5 1 -2 -2 5 5 5 -2 -5 -5 1 -3 -3 1 -5 -1 -1 -5 -5 -5 -4 [76] -1 -2 -3 -1 -2 -3 -2 5 -1 5 -4 -5 -3 -2 -2 -3 1 -1 -2 -5 1 -1 -5 -1 -5 [101] -5 -1 -4 -5 -5 1 -3 -4 -1 -2 1 1 5 -1 1 -2 5 -5 -3 1 1 -5 1 1 -4 [126] -5 -1 5 -1 -2 -1 5 5 5 -5 1 1 -3 -1 -2 -3 1 -3 -3 -1 -2 1 -5 -1 -1 [151] -1 -5 -2 -3 -5 -3 -5 -4 -4 1 5 -5 -4 -4 -5 -2 5 -2 -1 -1 -1 -5 -5 5 -5 [176] 5 -1 1 -2 -4 -3 -1 -4 -1 1 1 -4 5 5 -1 1 -2 -4 -5 1 -2 -1 5 5 -1
Example
> length(x3[x3>0])
Output
[1] 56
Example4
> x4<-sample(c(-10:-1,1:10),200,replace=TRUE) > x4
Output
[1] 9 5 1 5 1 2 9 2 -10 -9 10 6 -6 -2 2 3 -9 4 [19] 9 -7 2 -9 9 9 10 -6 -3 5 5 7 -4 -9 10 8 -6 -2 [37] -4 3 -10 -8 -5 10 -4 7 -1 9 -8 -2 10 -5 4 4 -2 9 [55] 4 -7 -6 8 -6 -7 7 1 -5 -3 1 -3 -2 9 8 -6 9 2 [73] -4 -7 -6 7 1 7 8 -9 5 5 -7 -6 -3 -8 -10 1 1 -3 [91] 4 6 -2 -9 5 -9 4 2 -9 3 -5 -2 -8 -1 -4 -1 7 -7 [109] -7 -3 1 -7 -10 10 -10 4 7 -9 9 4 -1 6 -2 -1 4 -2 [127] -7 -9 -1 7 -6 -8 -10 -2 7 9 5 -9 9 -10 7 -1 -4 5 [145] 9 6 -4 -6 -10 -5 2 -5 -10 7 -1 6 -5 2 6 2 6 -9 [163] -7 9 6 -3 1 4 -5 3 -10 10 1 -5 2 -2 1 2 -9 4 [181] -10 10 6 1 2 4 -1 -1 -5 -7 -5 6 -7 7 5 1 5 1 [199] 4 9
Example
> length(x4[x4>0])
Output
[1] 103
Example5
> x5<-sample(rnorm(10),50,replace=TRUE) > x5
Output
[1] 0.9014379 0.6343496 1.6775916 -1.4341079 -0.1115226 -0.2008744 [7] -0.1115226 -1.1771293 -0.2008744 0.9014379 -1.5785453 -1.4341079 [13] -1.1771293 0.9014379 -0.2008744 -0.5294469 0.6343496 -1.1771293 [19] 1.6775916 -0.2008744 1.6775916 0.6343496 0.6343496 1.6775916 [25] -1.4341079 0.2165663 1.6775916 -0.2008744 -1.5785453 0.9014379 [31] 0.9014379 0.9014379 -0.5294469 1.6775916 0.2165663 -0.5294469 [37] 0.2165663 -0.1115226 -1.4341079 -0.5294469 0.6343496 -0.1115226 [43] -1.4341079 1.6775916 -1.4341079 -0.1115226 0.2165663 -0.2008744 [49] -1.1771293 -0.2008744
Example
> length(x5[x5>0])
Output
[1] 22
Updated on: 05-Jan-2021
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