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Scales of Aggression: Data for Dyadic-Level Aggression
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This R markdown provides the data preparation for the part our project analyzing the fractal structure of body movement during conflict, as part of a larger project investigating the fractal structure of conflict from international to interpersonal levels (Blau & Paxton, in press, Complexity).

To run this from scratch, you will need the following files:

  • ./data/raw_movement_data/prepped_data-DCC.csv: File with raw movement data derived from head-mounted accelerometers. Data were originally collected as part of Paxton and Dale (2017, Frontiers in Psychology). Data are freely available in the OSF repository for the original project (https://osf.io/x9ay6/) and linked in the OSF repository for the current project (https://osf.io/8qcya/).
  • ./scripts/soa-required_packages.r: Installs required libraries, if they are not already installed. NOTE: This should be run before running this script.

The code will output time series of movement events taken from the continuous time series in two ways:

  • threshold: count an event as occurring if the change in Euclidean acceleration from sample to sample exceeds the 90th percentile in change-in-acceleration for that participant in that conversation
  • derivative: count an event as occurring if we identify a switch point in Euclidean jerk (the derivative of acceleration)

Although both analyses provide consistent patterns of results, we ultimately chose to use the threshold data because it is more reflective of perceptible changes in movement by their partners.

Data are saved in the ./data/movement_data/ directory, which is created along the way. Output data are then used for the DFA analyses and in figure generation by the soa-code_data_figures.Rmd file.

Code written by: A. Paxton (University of Connecticut)

Date last modified: 04 November 2020

Preliminaries

# clear things out
rm(list=ls())

# load in the required packages
source('./scripts/soa-libraries_and_functions.r')

# load in the data
movement_data = read.table('./data/raw_movement_data/prepped_data-DCC.csv',
                           sep=',', header=TRUE)
cutoff_data = read.table('./data/raw_movement_data/DCC-cutoff_jounce.csv',
                         sep=',', header=TRUE)

Data preparation

First, we'll convert our raw x,y,z coordinates to Euclidean acceleration.

# get Euclidean acceleration
movement_data = movement_data %>% ungroup() %>%
  group_by(dyad,partic,conv.num,conv.type,cond) %>%
  mutate(euclid_accel = c(NA,euclidean(x,y,z))) %>%
  select(-x, -y, -z) %>%
  dplyr::filter(!is.na(euclid_accel)) %>%
  mutate(euclid_accel = scale(euclid_accel))

Then, we'll trim the data to exclude the calibration and instruction times.

# prepare to identify starting cutoff points
cutoff_points = cutoff_data %>% 
  rename(cutoff.t = t)

# implement cutoff based on movement 
movement_data = movement_data %>%  ungroup() %>%
  merge(., cutoff_points, 
        by = c('dyad','conv.num','conv.type','cond')) %>%
  group_by(dyad, conv.num, conv.type, cond) %>%
  dplyr::filter(t > unique(cutoff.t)) %>%
  select(-one_of('cutoff.t','cutoff'))

Generating event time series

Here, let's generate the two candidate time series.

Using 90th percentile movement threshold

# identify events using thresholded Euclidean acceleration
threshold_timeseries = movement_data %>% ungroup() %>%
  group_by(partic, dyad, conv.num, conv.type, cond) %>%
  mutate(threshold = quantile(euclid_accel, .9)) %>%
  mutate(over_threshold = (euclid_accel > threshold)*1) %>%
  mutate(switch_point = over_threshold - lag(over_threshold, 
                                             default = first(over_threshold))) %>%
  dplyr::filter(switch_point==1)

# figure out how many events we've identified per participant
threshold_events = threshold_timeseries %>% ungroup() %>%
  group_by(partic, dyad, conv.num, conv.type, cond) %>%
  summarise(num_events = sum(over_threshold),
            threshold = unique(threshold))
## `summarise()` regrouping output by 'partic', 'dyad', 'conv.num', 'conv.type' (override with `.groups` argument)

# print the minimum number of identified events per participant
min(threshold_events$num_events)
## [1] 1123

# wipe out the unnecessary variables
threshold_timeseries = threshold_timeseries %>% ungroup() %>%
  select(-over_threshold, -switch_point)

Using first derivative of Euclidean acceleration

# identify events using the first derivative of the Euclidean acceleration
derivative_timeseries = movement_data %>% ungroup() %>%
  group_by(partic, dyad, conv.num, conv.type, cond) %>%
  mutate(jerk = c(0,diff(euclid_accel) / diff(t))) %>%
  mutate(over_threshold = (jerk > 0)*1) %>%
  mutate(switch_point = over_threshold - lag(over_threshold, 
                                             default = first(over_threshold))) %>%
  dplyr::filter(switch_point==1)

# figure out how many events we've identified per participant
derivative_events = derivative_timeseries %>% ungroup() %>%
  group_by(partic, dyad, conv.num, conv.type, cond) %>%
  summarise(num_events = sum(over_threshold))
## `summarise()` regrouping output by 'partic', 'dyad', 'conv.num', 'conv.type' (override with `.groups` argument)

# print the minimum number of events identified per participants
min(derivative_events$num_events)
## [1] 10086

# wipe out the unnecessary variables
derivative_timeseries = derivative_timeseries %>% ungroup() %>%
  select(-switch_point, -over_threshold)

Export time series

Finally, we'll go ahead and save each individual's resulting event series from each of the two techniques.

# create the export directory if we don't have it yet
output_directory = file.path('./data/movement_data/')
dir.create(output_directory, 
           showWarnings = FALSE)
# thanks to user Parfait (https://stackoverflow.com/a/50954201)
threshold_group_dfs = by(threshold_timeseries, 
                         threshold_timeseries[,c("partic", "dyad", "conv.type")], 
                         function(sub){
                           
                           # construct the file name
                           file_name <- paste("partic",
                                              max(as.character(sub$partic)), 
                                              "dyad",
                                              max(as.character(sub$dyad)),
                                              "type",
                                              max(as.character(sub$conv.type)), sep="_")
                           
                           # write each dataframe to a separate CSV
                           write.csv(sub, 
                                     paste0(output_directory, 
                                            "threshold-", file_name, ".csv"), 
                                     row.names = FALSE)
                           
                           # return each separate dataframe
                           return(sub)
                         })

Export derivative-based event series

# thanks to user Parfait (https://stackoverflow.com/a/50954201)
derivative_group_dfs = by(derivative_timeseries, 
                          derivative_timeseries[,c("partic", "dyad", "conv.type")], 
                          function(sub){
                            
                            # construct the file name
                            file_name <- paste("partic",
                                               max(as.character(sub$partic)), 
                                               "dyad",
                                               max(as.character(sub$dyad)),
                                               "type",
                                               max(as.character(sub$conv.type)), sep="_")
                            
                            # write each dataframe to a separate CSV
                            write.csv(sub, 
                                      paste0(output_directory,
                                             "derivative-", file_name, ".csv"), 
                                      row.names = FALSE)
                            
                            # return each separate dataframe
                            return(sub)
                          })