Error in calculating standard error: (list) object cannot be coerced to type 'double'

Question

I have calculated the mean of the observations for each treatment using

Data %>% group_by(Treatment, Rep) %>% summarise(Mean = mean(Nitrogen, na.rm = TRUE)) 

I have got a single value for each treatment. Now I want to calculate the standard deviation and standard error of the mean. For which I have used,

Data %>% group_by(Treatment, Rep) %>% summarise_each(funs = mean, sd, se=sd(.)/sqrt(n()), na.rm = TRUE) 

But it gives an error. I am not sure what is my mistake. Thank you!

Answer 1

summarise_each is getting deprecated and funs is replaced by list

library(dplyr)
Data %>%
   group_by(Treatment) %>%
   summarise_at(vars(-group_cols()), list(mean = ~mean(., na.rm = TRUE), 
                       sd = ~sd(., na.rm = TRUE), 
                       se=~ sd(., na.rm = TRUE)/sqrt(n())))

If we are not sure about the column types, check

str(Data)

and apply the functions only on the numeric columns. Without changing much in the previous code, replace the summarise_at to summarise_if for numeric columns

 Data %>%
   group_by(Treatment, Rep) %>%
   summarise_if(is.numeric, list(mean = ~mean(., na.rm = TRUE), 
                       sd = ~sd(., na.rm = TRUE), 
                       se=~ sd(., na.rm = TRUE)/sqrt(n())))

If some columns have class factor and needs to be used for the mean/sd , then first convert those column/columns to numeric with as.numeric(as.character(Data[[yourcolumn]]))

It can be reproduced with iris data

data(iris)
iris %>% 
  group_by(Species) %>%
  summarise_at(vars(-group_cols()), list(mean = ~mean(., na.rm = TRUE), 
                        sd = ~sd(., na.rm = TRUE), 
                        se=~ sd(., na.rm = TRUE)/sqrt(n())))
# A tibble: 3 x 13
#  Species Sepal.Length_me… Sepal.Width_mean Petal.Length_me… Petal.Width_mean Sepal.Length_sd Sepal.Width_sd Petal.Length_sd
#  <fct>              <dbl>            <dbl>            <dbl>            <dbl>           <dbl>          <dbl>           <dbl>
#1 setosa              5.01             3.43             1.46            0.246           0.352          0.379           0.174
#2 versic…             5.94             2.77             4.26            1.33            0.516          0.314           0.470
#3 virgin…             6.59             2.97             5.55            2.03            0.636          0.322           0.552
# … with 5 more variables: Petal.Width_sd <dbl>, Sepal.Length_se <dbl>, Sepal.Width_se <dbl>, Petal.Length_se <dbl>,
#   Petal.Width_se <dbl>

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In the OP's post, some of the functions have anonymous function and na.rm = TRUE seems to be from the mean (not clear).