Calculate Mean Across Rows with Na Values in R

R: How to calculate mean for each row with missing values using dplyr

df %>% 
  mutate(means=rowMeans(., na.rm=TRUE))

The . is a "pronoun" that references the data frame df that was piped into mutate.

  A B  C    means
1 3 0  9 4.000000
2 4 6 NA 5.000000
3 5 8  1 4.666667

You can also select only specific columns to include, using all the usual methods (column names, indices, grep, etc.).

df %>% 
  mutate(means=rowMeans(.[ , c("A","C")], na.rm=TRUE))

  A B  C means
1 3 0  9     6
2 4 6 NA     4
3 5 8  1     3

Calculating mean with NA value present in a data.frame using R

You need to use na.rm = TRUE:

df2<-df1%>%
  group_by(st, date)%>%
  summarise(ph=mean(ph, na.rm = TRUE))

df2
# A tibble: 3 x 3
# Groups:   st [3]
     st date          ph
  <int> <chr>      <dbl>
1     1 01/02/2004     5
2     2 01/02/2004     8
3    16 01/02/2004     6

How to calculate means when you have missing values?

To match mean with excel you can repeat the time value df number of times.

mean(rep(df$time, df$df))
#[1] 17.85714

Average across Columns in R, excluding NAs

You want rowMeans() but importantly note it has a na.rm argument that you want to set to TRUE. E.g.:

> mat <- matrix(c(23,2,NA,NA,2,9,23,2,9), ncol = 3)
> mat
     [,1] [,2] [,3]
[1,]   23   NA   23
[2,]    2    2    2
[3,]   NA    9    9
> rowMeans(mat)
[1] NA  2 NA
> rowMeans(mat, na.rm = TRUE)
[1] 23  2  9

To match your example:

> dat <- data.frame(Trait = c("DF","DG","DH"), mat)
> names(dat) <- c("Trait", paste0("Col", 1:3))
> dat
  Trait Col1 Col2 Col3
1    DF   23   NA   23
2    DG    2    2    2
3    DH   NA    9    9
> dat <- transform(dat, Col4 = rowMeans(dat[,-1], na.rm = TRUE))
> dat
  Trait Col1 Col2 Col3 Col4
1    DF   23   NA   23   23
2    DG    2    2    2    2
3    DH   NA    9    9    9

How to calculate mean value for each column ignoring NA

For a data.table dt, that looks like this:

dt
   Var1 Var2 Var3 Var4 Var12
1:    1   NA    2    3     4
2:    5    6    2    3     3
3:   NA    7    8   NA     4

You can simply use lapply():

dt[, lapply(.SD, mean, na.rm = TRUE)]

The result is:

   Var1 Var2 Var3 Var4    Var12
1:    3  6.5    4    3 3.666667

Calculate mean of each row in a large list of dataframes in R

We may bind the list elements to a single data and then use a group by mean operation

library(dplyr)
bind_rows(lst1) %>%
    group_by(id) %>%
    summarise(value_mean = mean(value, na.rm = TRUE), .groups = 'drop')

-output

# A tibble: 3 x 2
  id    value_mean
  <chr>      <dbl>
1 id1         0.25
2 id2         0.25
3 id3         0.5 

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If the datasets have a the same dimension and the 'id' are in same order, extract the 'value' column, use Reduce to do elementwise + and divide by the length of list

Reduce(`+`, lapply(lst1, `[[`, "value"))/length(lst1)
[1] 0.25 0.25 0.50

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Or a more efficient approach is with dapply/t_list from collapse

library(collapse)
dapply(t_list(dapply(lst1, `[[`, "value")), fmean)
  V1   V2   V3 
0.25 0.25 0.50 

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