Rstudio calculating mean trouble

[cmjackson]

Hello,

I am trying to calucate the mean of one of my columns "response" but it seems to come up with NH when I try to calculate it. Here

below is what I have typed in so far:

library(tidyverse)
library(tidyverse)
dat <- read_csv("Learningtask.csv")
tidydat <- dat %>% select(participant, response_age_a,response_age_b, response_gender, stage, trial_type, cue, correct_response, response, correct, rt, block_s1, count_s1, count_s2)
dat %>% head ()
dat%>% glimpse()
dat_clean <-
dat %>% select(participant,
trial_type,
stage,
cue,
correct_response,
response,
correct,
rt,
block_s1,
count_s1,
count_s2)

colnames(dat_clean) <- c("participant",
"trial_type",
"stage",
"cue",
"correct_response",
"response",
"correct",
"rt",
"block_s1",
"count_s1",
"count_s2")

exclude <- c(22617, 22638, 21470, 22557, 22666, 22701, 22714, 22736, 22759, 22806,23180, 23218, 23271, 23273, 23300, 23341, 23465, 23469, 22617,22638, 21470, 22557, 22736, 22759, 23218, 23341, 23465)
dat_clean <- dat_clean %>% filter(!(participant %in% exclude))

head(dat_clean)
nc_include2 <- dat_clean %>% filter(stage =="3") %>% drop_na()

nc_includead <- nc_include2 %>% filter (trial_type == "AD")

Once I have filtered out my data I type in this sentence:

nc_includead %>% summarise(mean(response))

and it comes up with this in the output:
nc_includead %>% summarise(mean(response))

A tibble: 1 x 1

mean(response)

1 NA
Warning message:
In mean.default(response) :
argument is not numeric or logical: returning NA

I was wondering if anyone could me understand why this is happening and how to fix it.

[nirgrahamuk]

Hi!

To help us help you, could you please prepare a reproducible example (reprex) illustrating your issue? Please have a look at this guide, to see how to create one:

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.…

[cmjackson]

Hello,

I am not sure if you mean this?

head(nc_includead)
# A tibble: 6 x 11
  participant trial_type stage cue   correct_response response correct    rt block_s1
        <dbl> <chr>      <dbl> <chr> <chr>            <chr>      <dbl> <dbl>    <dbl>
1       21433 AD             3 gree… z                1              0 11767       11
2       21433 AD             3 pink… z                3              0  2427       11
3       21550 AD             3 gree… z                5              0  1851       11
4       21550 AD             3 pink… z                5              0  3329       11
5       21602 AD             3 blue… z                1              0  2283       11
6       21602 AD             3 grey… z                5              0  1378       11
# … with 2 more variables: count_s1 <dbl>, count_s2 <dbl>

[nirgrahamuk]

@cmjackson

I am not sure if you mean this?

No I didnt mean that . Please read the document.
I promise you will benefit in the long run from taking a little time with it.

If you want to skip past the opening. You can look for the text beginning:

Now you just need to put this into

[nirgrahamuk]

However, its clear now that response is a character vector rather than a numeric vector. so a conversion would be necessary

nc_include2 <- dat_clean %>% 
                    filter(stage =="3") %>% 
                    drop_na() %>%
                    mutate(response=as.numeric(response))