How to average variables in R based on another variable

[dyl]

I am really new to R and am hoping to find the average rate of error for each individual id I have in my data set. I have created the data set error_rates that includes the columns "sorter_id_error" and "sorter_error".

My code:

##Determine the average error_rate per volunteer id
error_data<- data.frame(sorter_id_error,sorter_error)
library(tidyverse)
library(dplyr)
error_data %>%
group_by(sorter_id_error) %>% summarise(average=mean(sorter_error))

My error:

I keep getting warnings returned and my data ends up with a bunch of NAs instead of the averages.
sorter_id_error average

1 6 NA
2 7 NA
3 17 NA
4 25 NA
5 33 NA
6 34 NA
7 41 NA
8 45 NA
9 46 NA
10 47 NA

... with 118 more rows

There were 50 or more warnings (use warnings() to see the first 50)

What I would like:
I would like to average the the sorter_error variable, so that for each averages all the values of the sorter_id_error variable by levels.

What am I doing wrong/ how can I fix my code in order to get these values?

Thanks!

[FJCC]

Perhaps you have NA values in your data. If so, these can be ignored by setting na.rm = TRUE in your call to mean().

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
d <- data.frame(id=c(1,2,3, 1, 2, 3, 1, 2, 3, 1), x=c(1,2,3,4,3,4,5,2,3,2) )
Stats <- d %>% group_by(id) %>% summarize(Avg = mean(x))
Stats
#> # A tibble: 3 x 2
#>      id   Avg
#>   <dbl> <dbl>
#> 1     1  3   
#> 2     2  2.33
#> 3     3  3.33

#with NA
d <- data.frame(id=c(1,2,3, 1, 2, 3, 1, 2, 3, 1), x=c(1,NA,3,NA,3,NA,5,2,3,2) )
Stats <- d %>% group_by(id) %>% summarize(Avg = mean(x))
Stats
#> # A tibble: 3 x 2
#>      id   Avg
#>   <dbl> <dbl>
#> 1     1    NA
#> 2     2    NA
#> 3     3    NA

#With NA and na.rm = TRUE
d <- data.frame(id=c(1,2,3, 1, 2, 3, 1, 2, 3, 1), x=c(1,NA,3,NA,3,NA,5,2,3,2) )
Stats <- d %>% group_by(id) %>% summarize(Avg = mean(x, na.rm = TRUE))
Stats
#> # A tibble: 3 x 2
#>      id   Avg
#>   <dbl> <dbl>
#> 1     1  2.67
#> 2     2  2.5 
#> 3     3  3

^{Created on 2019-07-12 by the reprex package (v0.2.1)}

[dyl]

Hmm I added the specification into my code but am still turning up this warning
accompanied by NA values:

In mean.default(sorter_error, na.rm = TRUE) :
argument is not numeric or logical: returning NA

I have also tried omitting NA values from my data using na.omit.

Here is my code with the specification:
error_data %>%
group_by(sorter_id_error) %>% summarise(average=mean(sorter_error, na.rm = TRUE))

[andresrcs]

Hi, welcome!

We don't really have enough info to help you out. Could you ask this with a minimal REPRoducible EXample (reprex)? A reprex makes it much easier for others to understand your issue and figure out how to help.

If you've never heard of a reprex before, you might want to start by reading this FAQ:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…