I have the following dataset:
I have been trying to build a contingency table, by using the following code:
library(readr)
library(tidyverse)
library(magrittr)
data1 %>%
select(blockLabel, trial_resp.corr, participant) %>%
group_by(blockLabel, trial_resp.corr, participant) %$%
with(., table(blockLabel, trial_resp.corr, participant))
, , participant = pilot01
trial_resp.corr
blockLabel 0 1
auditory_only 0 12
bimodal_focus_auditory 1 71
bimodal_focus_visual 3 69
divided 74 70
visual_only 0 12
, , participant = pilot02
trial_resp.corr
blockLabel 0 1
auditory_only 0 12
bimodal_focus_auditory 1 71
bimodal_focus_visual 2 70
divided 77 67
visual_only 11 1
, , participant = pilot03
trial_resp.corr
blockLabel 0 1
auditory_only 1 11
bimodal_focus_auditory 1 71
bimodal_focus_visual 3 69
divided 75 69
visual_only 0 12
What I would like to do is to add a further column with values (under 1 and 0) converted in percentage and a final with the Total.
I do not know whether it is possible, but if not, please suggest some iterative ways (do.call(), apply(), map(), for loop) to do this if the first way is not possible.
I have used the following solution but I do not how to move on
data1%>% select(blockLabel, trial_resp.corr, participant) %>%
group_by(blockLabel, trial_resp.corr, participant) %$%
as.data.frame(with(., table(blockLabel, trial_resp.corr, participant))) %>%
mutate(freq = Freq / sum(Freq)) %>%
group_split(participant, trial_resp.corr)
[[1]]
# A tibble: 5 x 5
blockLabel trial_resp.corr participant Freq freq
<fct> <fct> <fct> <int> <dbl>
1 auditory_only 0 pilot01 0 0
2 bimodal_focus_auditory 0 pilot01 1 0.00107
3 bimodal_focus_visual 0 pilot01 3 0.00321
4 divided 0 pilot01 74 0.0791
5 visual_only 0 pilot01 0 0
[[2]]
# A tibble: 5 x 5
blockLabel trial_resp.corr participant Freq freq
<fct> <fct> <fct> <int> <dbl>
1 auditory_only 1 pilot01 12 0.0128
2 bimodal_focus_auditory 1 pilot01 71 0.0759
3 bimodal_focus_visual 1 pilot01 69 0.0737
4 divided 1 pilot01 70 0.0748
5 visual_only 1 pilot01 12 0.0128
[[3]]
# A tibble: 5 x 5
blockLabel trial_resp.corr participant Freq freq
<fct> <fct> <fct> <int> <dbl>
1 auditory_only 0 pilot02 0 0
2 bimodal_focus_auditory 0 pilot02 1 0.00107
3 bimodal_focus_visual 0 pilot02 2 0.00214
4 divided 0 pilot02 77 0.0823
5 visual_only 0 pilot02 11 0.0118
[[4]]
# A tibble: 5 x 5
blockLabel trial_resp.corr participant Freq freq
<fct> <fct> <fct> <int> <dbl>
1 auditory_only 1 pilot02 12 0.0128
2 bimodal_focus_auditory 1 pilot02 71 0.0759
3 bimodal_focus_visual 1 pilot02 70 0.0748
4 divided 1 pilot02 67 0.0716
5 visual_only 1 pilot02 1 0.00107
[[5]]
# A tibble: 5 x 5
blockLabel trial_resp.corr participant Freq freq
<fct> <fct> <fct> <int> <dbl>
1 auditory_only 0 pilot03 1 0.00107
2 bimodal_focus_auditory 0 pilot03 1 0.00107
3 bimodal_focus_visual 0 pilot03 3 0.00321
4 divided 0 pilot03 75 0.0801
5 visual_only 0 pilot03 0 0
[[6]]
# A tibble: 5 x 5
blockLabel trial_resp.corr participant Freq freq
<fct> <fct> <fct> <int> <dbl>
1 auditory_only 1 pilot03 11 0.0118
2 bimodal_focus_auditory 1 pilot03 71 0.0759
3 bimodal_focus_visual 1 pilot03 69 0.0737
4 divided 1 pilot03 69 0.0737
5 visual_only 1 pilot03 12 0.0128
Thanks
We could collapse with as.data.frame
data1 %>%
select(blockLabel, trial_resp.corr, participant) %>%
group_by(blockLabel, trial_resp.corr, participant) %$%
with(., table(blockLabel, trial_resp.corr, participant)) %>%
as.data.frame