library(tidyverse) # assume our data is stored in a folder called `data` in our R project. # within the `data` folder is our experiment folder (i.e. name of the experiment settings profile) # there can be multiple experiment folders, maybe you use the settings profiles for different conditions. # within the experiment folders, is each participant folder. # within those, is the session folder for each participant. # e.g:  # project_folder/ # └── data/ # ├── experiment_condition_1 # │ ├── Participant01 # │ │ ├──S001 # │ │ │ └── (all UXF data files) # │ │ └──S002 # │ │ └── (all UXF data files) # │ └── Participant02 # │ ├──S001 # │ │ └── (all UXF data files) # │ └──S002 # │ └── (all UXF data files) # └── experiment_condition_2 # ├── Participant03 # │ ├──S001 # │ │ └── (all UXF data files) # │ └──S002 # │ └── (all UXF data files) # └── Participant04 # ├──S001 # │ └── (all UXF data files) # └──S002 # └── (all UXF data files) # first, we load all the trial_results files. # we do this by recursively searching all files that match a pattern. # then reading all the files we find (with `map`) and binding them row-wise (`_dfr`) posture_trials <- list.files( path = "data", pattern = "trial_results.csv", full.names = TRUE, recursive = TRUE ) %>% map_dfr(read_csv) # thats all our behavioural data! # we can do any further processing or analysis on the `posture_trials` dataframe  # now if we want any movement data for each trial, # we can do this by reading each filename given in the `*_movement_filename` column. # then we can use `unnest` to give 1 row per timestep.  # (in this example, `center_eye_movement_filename`) posture_movement <- posture_trials %>% mutate(center_eye_movement = map(file.path("data", directory, center_eye_movement_filename), read_csv)) %>% unnest() # now we have 1 row per timestep. # we can now write a function which calculates (for example) path length and summarise by it. # grouping will give us 1 row per trial again calculate_path_length <- function(x, y, z) { # calculates sum of point-to-point distances. sum( (diff(x) ^ 2 + diff(y) ^ 2 + diff(z) ^ 2) ^ 0.5 ) } posture_summary <- posture_movement %>% group_by(experiment, ppid, session_num, trial_num, assessment_type) %>% summarise(path_length = calculate_path_length(pos_x, pos_y, pos_z)) # that outputs 1 row per trial, with our new movement summary summary statistic column.