I am analysing my data with R for the first time which is a bit challenging. I have a data frame with my data that looks like this:
head(data)
subject group age trial cond acc rt
1 S1 2 1 1 1 1 5045
2 S1 2 1 2 2 1 8034
3 S1 2 1 3 1 1 6236
4 S1 2 1 4 2 1 8087
5 S1 2 1 5 3 0 8756
6 S1 2 1 6 1 1 6619
I would like to compute a mean and standard deviation for each subject in each condition for rt and a sum for each subject in each condition for acc. All the other variables are should remain the same (group and age are subject-specific, and trial can be disregarded).
I have tried using aggregate but that seemed kind of complicated because I had to do it in several steps and re-add information...
I'd be thankful for any help =)
Edit: I realise that I wasn't being clear. I want trial to be disregarded and end up with one row per subject per condition:
head(data_new)
subject group age cond rt_mean rt_sd acc_sum
1 S1 2 1 1 7581 100 5
2 S2 2 1 2 8034 150 4
Sorry about the confusion!
The package dplyr is made for this:
library(dplyr)
d %>%
group_by(subject, cond) %>% # we group by the two values
summarise(
mean_rt = mean(rt, na.rm=T),
sd_rt = sd(rt, na.rm=T),
sum_acc = sum(acc, na.rm=T) # here we apply each function to summarise values
)
# A tibble: 3 x 5
# Groups: subject [?]
subject cond mean_rt sd_rt sum_acc
<fct> <int> <dbl> <dbl> <int>
1 S1 1 5967. 821. 3
2 S1 2 8060. 37.5 2
3 S1 3 8756 NA 0
# NA for the last sd_rt is because you can't have
# sd for a single obs.
Basically you need to group_by the columns (one or more) that you need to use as grouping, then inside summarise, you apply each function you need (mean, sd, sum, ecc) to each variable (rt, acc, ecc).
Change summarise with mutate if you want to keep all variables:
d %>%
select(-trial) %>% # use select with -var_name to eliminate columns
group_by(subject, cond) %>%
mutate(
mean_rt = mean(rt, na.rm=T),
sd_rt = sd(rt, na.rm=T),
sum_acc = sum(acc, na.rm=T)
) %>%
ungroup()
# A tibble: 6 x 9
subject group age cond acc rt mean_rt sd_rt sum_acc
<fct> <int> <int> <int> <int> <int> <dbl> <dbl> <int>
1 S1 2 1 1 1 5045 5967. 821. 3
2 S1 2 1 2 1 8034 8060. 37.5 2
3 S1 2 1 1 1 6236 5967. 821. 3
4 S1 2 1 2 1 8087 8060. 37.5 2
5 S1 2 1 3 0 8756 8756 NA 0
6 S1 2 1 1 1 6619 5967. 821. 3
Update based on op request, maybe this is what you need:
d %>%
group_by(subject, cond, group, age) %>%
summarise(
mean_rt = mean(rt, na.rm=T),
sd_rt = sd(rt, na.rm=T),
sum_acc = sum(acc, na.rm=T)
)
# A tibble: 3 x 7
# Groups: subject, cond, group [?]
subject cond group age mean_rt sd_rt sum_acc
<fct> <int> <int> <int> <dbl> <dbl> <int>
1 S1 1 2 1 5967. 821. 3
2 S1 2 2 1 8060. 37.5 2
3 S1 3 2 1 8756 NA 0
Data used:
tt <- "subject group age trial cond acc rt
S1 2 1 1 1 1 5045
S1 2 1 2 2 1 8034
S1 2 1 3 1 1 6236
S1 2 1 4 2 1 8087
S1 2 1 5 3 0 8756
S1 2 1 6 1 1 6619"
d <- read.table(text=tt, header=T)