# 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

Live Demo

> 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

Live Demo

> 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

Live Demo

> 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

Live Demo

> 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

Live Demo

> 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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