# mean

0th

Percentile

##### Arithmetic Mean

Generic function for the (trimmed) arithmetic mean.

Keywords
univar
##### Usage
mean(x, ...)
"mean"(x, trim = 0, na.rm = FALSE, ...)
##### Arguments
x
An R object. Currently there are methods for numeric/logical vectors and date, date-time and time interval objects. Complex vectors are allowed for trim = 0, only.
trim
the fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed. Values of trim outside that range are taken as the nearest endpoint.
na.rm
a logical value indicating whether NA values should be stripped before the computation proceeds.
...
further arguments passed to or from other methods.
##### Value

If trim is zero (the default), the arithmetic mean of the values in x is computed, as a numeric or complex vector of length one. If x is not logical (coerced to numeric), numeric (including integer) or complex, NA_real_ is returned, with a warning.If trim is non-zero, a symmetrically trimmed mean is computed with a fraction of trim observations deleted from each end before the mean is computed.

##### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

weighted.mean, mean.POSIXct, colMeans for row and column means.

• mean
• mean.default
##### Examples
library(base) x <- c(0:10, 50) xm <- mean(x) c(xm, mean(x, trim = 0.10)) 
Documentation reproduced from package base, version 3.2.4, License: Part of R 3.2.4

### Community examples

wallnerp17 at May 5, 2020 base v3.6.2

wallnerp17 at May 5, 2020 base v3.6.2

06adsilva@gmail.com at Apr 6, 2020 base v3.6.2

dr.rakhi.issrani00@gmail.com at Jan 26, 2020 base v3.6.2

data <- c(1,2,3,4,5,6,7) a <- mean(data) print(a)

dr.rakhi.issrani00@gmail.com at Jan 26, 2020 base v3.6.2

sld1@williams.edu at Jan 12, 2020 base v3.6.2

sld1@williams.edu at Jan 12, 2020 base v3.6.2

sld1@williams.edu at Jan 12, 2020 base v3.6.2

sld1@williams.edu at Jan 12, 2020 base v3.6.2

sld1@williams.edu at Jan 8, 2020 base v3.6.2

sld1@williams.edu at Jan 8, 2020 base v3.6.2

sld1@williams.edu at Jan 8, 2020 base v3.6.2

muskaanjain246@gmail.com at Sep 22, 2019 base v3.6.1

num<- c(5,2,3,6.4,na) xy<- mean(num) print(xy)

muskaanjain246@gmail.com at Sep 22, 2019 base v3.6.1

naman.upadhyay@duke.edu at Jul 31, 2019 base v3.6.1

habk18@yahoo.com at May 31, 2019 base v3.6.0

habk18@yahoo.com at May 31, 2019 base v3.6.0

austineokenu@gmail.com at May 26, 2019 base v3.6.0

kaungnyeinaye80@gmail.com at Apr 29, 2019 base v3.6.0

mridul.khanna.2017@sse.ac.in at Apr 29, 2019 base v3.6.0

brezeseid@gmail.com at Apr 21, 2019 base v3.5.3

ph<- c(1,1,2,3,5,8,21) mean(ph)

acbellaver@hotmail.com at Apr 8, 2019 base v3.5.3

ahm168 at Mar 20, 2019 base v3.5.3

**DATA ** total_data <- c(4, 5, 2, 7,10, 8, 8, 5, 6, 1) n : 10 **NORMAL MEAN** mean_total_data <- mean(total_data) **TRIMMED MEAN** trimmed_mean_total_data <- mean(total_data, trim = 0.1)  With "trim = 0.1", so from data population, n=10, will be sorted and trimmed ( 0.1 * 10 = 1) (10% from n population), for each initial and end of data, into n=8 from (4, 5, 2, 7,10, 8, 8, 5, 6, 1) sorted into (1, 2, 4, 5, 5, 6, 7, 8, 8, 10) then trimmed 0.1 into (2, 2, 4, 5, 5, 6, 7, 8, 8)

yemikhafid@yahoo.com at Mar 12, 2019 base v3.5.3

Age_Children mean(x, …) mean(x, trim = 0, na.rm = FALSE, …) c(12, 10, 9, 9, 7, 8, 5) value <- mean(Age_Children) print(value)

yemikhafid@yahoo.com at Mar 12, 2019 base v3.5.3

germancb79@gmail.com at Feb 14, 2019 base v3.5.2

germancb79@gmail.com at Feb 14, 2019 base v3.5.2

germancb79@gmail.com at Feb 14, 2019 base v3.5.2

germancb79@gmail.com at Feb 14, 2019 base v3.5.2

germancb79@gmail.com at Feb 14, 2019 base v3.5.2

germancb79@gmail.com at Feb 14, 2019 base v3.5.2

germancb79@gmail.com at Feb 14, 2019 base v3.5.2

germancb79@gmail.com at Feb 14, 2019 base v3.5.2

salma.abdeljalil@gmail.com at Jan 10, 2019 base v3.5.2

mean(c(2,6,9,9,9,8)

salma.abdeljalil@gmail.com at Jan 10, 2019 base v3.5.2

salma.abdeljalil@gmail.com at Jan 10, 2019 base v3.5.2

Eve.lyttle@gmail.com at Jan 2, 2019 base v3.5.2

Eve.lyttle@gmail.com at Jan 2, 2019 base v3.5.2

Eve.lyttle@gmail.com at Jan 2, 2019 base v3.5.2

Eve.lyttle@gmail.com at Jan 2, 2019 base v3.5.2

Eve.lyttle@gmail.com at Jan 2, 2019 base v3.5.2

sinyoffers@gmail.com at Sep 14, 2018 base v3.5.1

values <- c(3, 5, 7, 4, 9, 2, 8, 6, 3, 6, 7) mean(values, trim = .2) mean(values, trim = .3) mean(values, trim = .5)

sinyoffers@gmail.com at Sep 14, 2018 base v3.5.1

sinyoffers@gmail.com at Sep 14, 2018 base v3.5.1

sinyoffers@gmail.com at Sep 14, 2018 base v3.5.1

lucianaioja11@gmail.com at Sep 13, 2018 base v3.5.1

lucianaioja11@gmail.com at Sep 13, 2018 base v3.5.1

lucianaioja11@gmail.com at Sep 13, 2018 base v3.5.1

neralla.ravi@gmail.com at Sep 8, 2018 base v3.5.1

neralla.ravi@gmail.com at Sep 8, 2018 base v3.5.1

neralla.ravi@gmail.com at Sep 8, 2018 base v3.5.1

neralla.ravi@gmail.com at Sep 8, 2018 base v3.5.1

neralla.ravi@gmail.com at Sep 8, 2018 base v3.5.1

function (x, ...) NULL

neralla.ravi@gmail.com at Sep 8, 2018 base v3.5.1

neralla.ravi@gmail.com at Sep 8, 2018 base v3.5.1

ch.asdeshwal@gmail.com at Sep 1, 2018 base v3.5.1

x <- c(11, 12, 16, NA) mean(x, TRUE)

ch.asdeshwal@gmail.com at Sep 1, 2018 base v3.5.1

conquistadordeabuga@gmail.com at Aug 25, 2018 base v3.5.1

mean(c(1:9,NA), trim=0.1, na.rm = TRUE)

u15023045@tuks.co.za at Aug 22, 2018 base v3.5.1

wzhang001@hust.edu.cn at Aug 17, 2018 base v3.5.1

wzhang001@hust.edu.cn at Aug 17, 2018 base v3.5.1

wzhang001@hust.edu.cn at Aug 16, 2018 base v3.5.1

huadan.song@gmail.com at Aug 9, 2018 base v3.5.1

edgk_307@yahoo.co.in at Jul 21, 2018 base v3.5.1

a<-c(1,2,3:10) b<-mean(a) print(b)

edgk_307@yahoo.co.in at Jul 21, 2018 base v3.5.1

rashidamuckadam@gmail.com at Jul 21, 2018 base v3.5.1

data <- ( 1,2,3,4,5,8) y<-mean(data) print(y)

rashidamuckadam@gmail.com at Jul 21, 2018 base v3.5.1

norbert.nsereko@gmail.com at Jul 18, 2018 base v3.5.1

norbert.nsereko@gmail.com at Jul 18, 2018 base v3.5.1

chauhanlucky080@gmail.com at Jul 10, 2018 base v3.5.1

joakinmtz@gmail.com at Jun 25, 2018 base v3.5.0

#Nuevo ejemplo > datos<-(2,4,8,9,9,10) > media<-mean(datos) > media

joakinmtz@gmail.com at Jun 25, 2018 base v3.5.0

iglesiaschina@gmail.com at Jun 5, 2018 base v3.5.0

data <- c(1, 2, 4, 5, 6) a <- sd (data) print (a)

iglesiaschina@gmail.com at Jun 5, 2018 base v3.5.0

sarahnjeru90@gmail.com at Jun 4, 2018 base v3.5.0

Sreya at May 14, 2018 base v3.5.0

pfisterer.thomas@freenet.de at May 2, 2018 base v3.5.0

data <- c(1:10,NA, 12:21) print(mean(data, na.rm=TRUE))

pfisterer.thomas@freenet.de at May 2, 2018 base v3.5.0

derya.sahin@sesdata.com at Apr 25, 2018 base v3.5.0

derya.sahin@sesdata.com at Apr 25, 2018 base v3.5.0

dileepmungara@gmail.com at Mar 29, 2018 base v3.4.3

zhaozhouxia@gmail.com at Mar 23, 2018 base v3.4.3

data <- c(1, 2, 3, 4, 5, 6) b <- mean(data) print(b)

zhaozhouxia@gmail.com at Mar 23, 2018 base v3.4.3

zhaozhouxia@gmail.com at Mar 23, 2018 base v3.4.3

geetanjali.devdikar@gmail.com at Mar 16, 2018 base v3.4.3

geetanjali.devdikar@gmail.com at Mar 16, 2018 base v3.4.3

geetanjali.devdikar@gmail.com at Mar 16, 2018 base v3.4.3

geetanjali.devdikar@gmail.com at Mar 16, 2018 base v3.4.3

geetanjali.devdikar@gmail.com at Mar 16, 2018 base v3.4.3

geetanjali.devdikar@gmail.com at Mar 16, 2018 base v3.4.3

geetanjali.devdikar@gmail.com at Mar 16, 2018 base v3.4.3

geetanjali.devdikar@gmail.com at Mar 16, 2018 base v3.4.3

vanderleymatias@gmail.com at Mar 10, 2018 base v3.4.3

vanderleymatias@gmail.com at Mar 10, 2018 base v3.4.3

vanderleymatias@gmail.com at Mar 10, 2018 base v3.4.3

sarah.resch@barclays.com at Mar 4, 2018 base v3.4.3

sarah.resch@barclays.com at Mar 4, 2018 base v3.4.3

AnamariaSuciu at Mar 4, 2018 base v3.4.3

#new example data <- c(4.5,6.7,3.6,4.4) mean <- mean(data) mean

AnamariaSuciu at Mar 4, 2018 base v3.4.3

50141201@mylife.unisa.ac.za at Feb 26, 2018 base v3.4.3

doungkamol130@gmail.com at Feb 13, 2018 base v3.4.3

suryajit.maddy@gmail.com at Feb 9, 2018 base v3.4.3

a<-c(1,24,5,6,7,8,8,9,7,5,4,3,3,32,3,55,6,NA) mean(a,na.rm=TRUE)

ajs17g@my.fsu.edu at Feb 1, 2018 base v3.4.3

st059410@student.spbu.ru at Jan 30, 2018 base v3.4.3

tylercook@ufl.edu at Jan 29, 2018 base v3.4.3

x<-c(1,2,3,4) y<-mean(x) print(y)

tylercook@ufl.edu at Jan 29, 2018 base v3.4.3

kalest at Jan 18, 2018 base v3.4.3

> Data <- c(2,9,2,-2,4) > a <- mean(Data) > a

kalest at Jan 18, 2018 base v3.4.3

makhethapalesa9@gmail.com at Dec 11, 2017 base v3.4.3

scores <- c(56,77,68,94,88,63) AvgScores <- mean(scores) print (AvgScore)

makhethapalesa9@gmail.com at Dec 11, 2017 base v3.4.3

Scores <- c(88, 76,45,65, 56,89,94,34) AvgScore <- mean(Scores) print (AvgScore)

makhethapalesa9@gmail.com at Dec 11, 2017 base v3.4.3

danielzmbr84@gmail.com at Dec 8, 2017 base v3.4.3

danielzmbr84@gmail.com at Dec 8, 2017 base v3.4.3

danielzmbr84@gmail.com at Dec 8, 2017 base v3.4.3

malochka_a at Dec 4, 2017 base v3.4.3

atauskey@gmail.com at Dec 2, 2017 base v3.4.3

r data <- c(1,2,3,4,5,6,6,7,x,9) mean <- mean(data, na.rm = TRUE) print(mean) 

inesteix@aol.com at Nov 7, 2017 base v3.4.1

inesteix@aol.com at Nov 7, 2017 base v3.4.1

devin.clementi@anu.edu.au at Jul 20, 2017 base v3.4.1

nlujambio@outlook.com at May 11, 2017 base v3.4.0

require(base) + mean(c(6 , 7, 7, 7, 8, 8, 8, 9)) + mean(c(1, 2, NA), na.rm = TRUE) + print

jorge.rdz3288@gmail.com at May 8, 2017 base v3.4.0

#data data <- c(14, 16, 19, 20, 21, 23) a <- mean(data) print(a)

callegari1978@gmail.com at Feb 16, 2017 base v3.3.2

## Mean of a matrix r values <- c(1:9) mat <- matrix(as.vector(values), nrow=3, ncol=3) av<-mean(mat) print(av) 

richie@datacamp.com at Jan 17, 2017 base v3.3.2

![taylor-swift-meanness](http://i.giphy.com/118BgDvP27VbSo.gif) The mean of random numbers should be close to (but not exactly) what you ask for: {r} x <- rnorm(1000, mean = 5) mean(x)  If there are any missing values, the mean is also missing. {r} mean(c(1, 2, NA))  You can exclude missing values by setting na.rm = TRUE. {r} mean(c(1, 2, NA), na.rm = TRUE)  The trim argument removes the lowest and highest values. This makes your estimate of the population mean more robust when you have extreme outliers. The following dataset has 10% taken from a wide distribution that will generate many outliers. The mean you expect to get is (close to) 0. {r} x <- c(rnorm(900), rnorm(100, sd = 1000)) mean(x) mean(x, trim = 0.1) # trim is equivalent to filtering by quantile mean(x[x > quantile(x, 0.1) & x < quantile(x, 0.9)]) # same # median is another robust estimator of location median(x)  If x has length 0, the result is [NaN](https://www.rdocumentation.org/packages/base/topics/is.finite). {r} mean(numeric())  Unlike sum(), you can't spread the values across multiple inputs. {r} mean(4, 6, 7, 3, 5) # Probably not what you wanted mean(c(4, 6, 7, 3, 5)) 

Anahí at Nov 13, 2016 base v3.3.1

**Mean** r fit = (1,3,5,7) mean_fit = mean(fit) print(mean_fit) 

Anahí at Nov 13, 2016 base v3.3.1

**Sd** r data <- c(2,4,6,3) var <- sd(var) print(data) 

filipsch at Oct 19, 2016 base v3.3.1

Depending on the value of na.rm, you can get different results if you're dealing with missing values: r mean(c(1:10, NA, 12:20)) mean(c(1:10, NA, 12:20), na.rm = TRUE) 

jake@datacamp.com at Oct 11, 2016 base v3.3.1

r data <- c(12, 14, 15, 20, 23, 30) a <- mean(data) print(a)