Often you may want to calculate the mean by group in R. There are three methods you can use to do so:

**Method 1: Use base R.**

aggregate(df$col_to_aggregate, list(df$col_to_group_by), FUN=mean)

**Method 2: Use the dplyr() package.**

**library(dplyr)
df %>%
group_by(col_to_group_by) %>%
summarise_at(vars(col_to_aggregate), list(name = mean))
**

**Method 3: Use the data.table package.**

**library(data.table)
dt[ ,list(mean=mean(col_to_aggregate)), by=col_to_group_by]
**

The following examples show how to use each of these methods in practice.

**Method 1: Calculate Mean by Group Using Base R**

The following code shows how to use the **aggregate() **function from base R to calculate the mean points scored by team in the following data frame:

#create data frame df <- data.frame(team=c('a', 'a', 'b', 'b', 'b', 'c', 'c'), pts=c(5, 8, 14, 18, 5, 7, 7), rebs=c(8, 8, 9, 3, 8, 7, 4)) #view data frame df team pts rebs 1 a 5 8 2 a 8 8 3 b 14 9 4 b 18 3 5 b 5 8 6 c 7 7 7 c 7 4 #find mean points scored by team aggregate(df$pts, list(df$team), FUN=mean) Group.1 x 1 a 6.50000 2 b 12.33333 3 c 7.00000

**Method 2: Calculate Mean by Group Using dplyr**

The following code shows how to use the **group_by****()** and **summarise_at()** functions from the **dplyr** package to calculate the mean points scored by team in the following data frame:

library(dplyr)#create data frame df <- data.frame(team=c('a', 'a', 'b', 'b', 'b', 'c', 'c'), pts=c(5, 8, 14, 18, 5, 7, 7), rebs=c(8, 8, 9, 3, 8, 7, 4)) #find mean points scored by teamdf %>% group_by(team) %>% summarise_at(vars(pts), list(name = mean)) # A tibble: 3 x 2 team name <fct> <dbl> 1 a 6.5 2 b 12.3 3 c 7

**Method 3: Calculate Mean by Group Using data.table**

The following code shows how to calculate the mean points scored by team in the following data frame:

library(data.table)#create data frame df <- data.frame(team=c('a', 'a', 'b', 'b', 'b', 'c', 'c'), pts=c(5, 8, 14, 18, 5, 7, 7), rebs=c(8, 8, 9, 3, 8, 7, 4)) #convert data frame to data table setDT(df) #find mean points scored by teamdf[ ,list(mean=mean(pts)), by=team] team mean 1: a 6.50000 2: b 12.33333 3: c 7.00000

Notice that all three methods return identical results.

**Related:** A Complete Guide to the mean Function in R

**Additional Resources**

How to Calculate the Sum by Group in R

How to Calculate Quantiles by Group in R

I found method 3 incredibly helpful. Thank you very much. My econometrics assignment just turned out well.

I have the following types of data. Some times there is also another data having missing values. I tried the above three methods to calculate the mean of each groups. But not working. Can you help me please? Thank you.

Treat AFI121

CON 50.64880952

CON 53.25595238

CON 58.12903226

CON 49.26785714

CON 49.37125749

CON 39.02380952

CON 52.13690476

CON 48.61309524

ANT 56.97959184

ANT 49.20238095

ANT 51.70238095

ANT 51.7797619

ANT 53.82142857

ANT 53.60714286

ANT 50.25

ANT 48.17032967

D400 52.99378882

D400 53.59868421

D400 53.61904762

D400 53.2797619

D400 48.6547619

D400 54.35119048

D400 54.92261905

D400 50.32738095

D800 54.76190476

D800 54.64880952

D800 53.89880952

D800 51.61585366

D800 55.15483871

D800 50.7202381

D800 56.05031447

D800 46.86904762

D1200 49.69642857

D1200 55.60509554

D1200 53.27380952

D1200 46.8452381

D1200 50.89285714

D1200 52.26190476

D1200 51.46428571

D1200 52.66666667

How can also test the mean separation values by using Tukey, LSD AND Duncan multiple range test methods for the above one way anova? Many thanks

email; sefibahir2009@gmail.com

I have the following types of data. Some times there is also another data having missing values. I tried the above three methods to calculate the mean of each groups. But not working. Can you help me please? Thank you.

Treat AFI121

CON 50.64880952

CON 53.25595238

CON 58.12903226

CON 49.26785714

CON 49.37125749

CON 39.02380952

CON 52.13690476

CON 48.61309524

ANT 56.97959184

ANT 49.20238095

ANT 51.70238095

ANT 51.7797619

ANT 53.82142857

ANT 53.60714286

ANT 50.25

ANT 48.17032967

D400 52.99378882

D400 53.59868421

D400 53.61904762

D400 53.2797619

D400 48.6547619

D400 54.35119048

D400 54.92261905

D400 50.32738095

D800 54.76190476

D800 54.64880952

D800 53.89880952

D800 51.61585366

D800 55.15483871

D800 50.7202381

D800 56.05031447

D800 46.86904762

D1200 49.69642857

D1200 55.60509554

D1200 53.27380952

D1200 46.8452381

D1200 50.89285714

D1200 52.26190476

D1200 51.46428571

D1200 52.66666667

How can also test the mean separation values by using Tukey, LSD AND Duncan multiple range test methods for the above one way anova? Many thanks email; sefibahir2009@gmail.com

January 27, 2023 at 11:18 am

I have the following types of data (5 treatments each with 8 replications, total 5*8=40 observations. i.e CRD design and wanted to do One way ANOVA). Some times there is also a similar data but having missing values. Can you help me please to calculate the following five questions?. I tried so many times but R software is not friendly but I want to learn more.

Things that I need to calculate;

1. ANOVA analysis (one way anova)

2. Summary or descriptive statistics

3. Mean separation using Tukey, LSD and Duncan multiple range test methods for the above one way anova?

4. Incase if the data will have a missing value, how to calculate the above things by avoiding the missing value?

5. Assigning letters for the significant differences between or among treatment groups?

6. Is that also possible to do the above statsiscial analysis if there are more than 1 or 2 dependent variables (factors)? what is the syntax to do the analysis?

Treat AFI121

CON 50.64880952

CON 53.25595238

CON 58.12903226

CON 49.26785714

CON 49.37125749

CON 39.02380952

CON 52.13690476

CON 48.61309524

ANT 56.97959184

ANT 49.20238095

ANT 51.70238095

ANT 51.7797619

ANT 53.82142857

ANT 53.60714286

ANT 50.25

ANT 48.17032967

D400 52.99378882

D400 53.59868421

D400 53.61904762

D400 53.2797619

D400 48.6547619

D400 54.35119048

D400 54.92261905

D400 50.32738095

D800 54.76190476

D800 54.64880952

D800 53.89880952

D800 51.61585366

D800 55.15483871

D800 50.7202381

D800 56.05031447

D800 46.86904762

D1200 49.69642857

D1200 55.60509554

D1200 53.27380952

D1200 46.8452381

D1200 50.89285714

D1200 52.26190476

D1200 51.46428571

D1200 52.66666667

Many thanks email; sefibahir2009@gmail.com