In jrcalabrese/faceup: faceup

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

#devtools::install_github("jrcalabrese/faceup", force = TRUE)
library(faceup)
library(dplyr)
library(tidyr)

Preparation

# Generate fake OpenFace .csv files
make_fake2(path = "~/Desktop/for_practice/raw_data", 
          n = 100, 
          loop = 25, 
          dyad = c("Mother", "Daughter"),
          seed = 12345)

# Load those big files, subset only relevant columns, and print output somewhere else
make_small(input_dir = "~/Desktop/for_practice/raw_data", 
           output_dir = "~/Desktop/for_practice/small_data")

# Load all all OpenFace files as a list
mylist <- load_list(path = "~/Desktop/for_practice/small_data")

# Stack the list of dataframes into a single long dataframe
dat <- long_stack(mylist)

Management

# Print statistics based on raw confidence and success
print_stats(
  dat = dat,
  id_num = "clipID",
  output_dir = "~/Desktop/for_practice/processed_output"
)

# Filter rows based on your exclusion criteria
dat <- dat %>% 
  filter_rows(conf_removal = 0.50, 
              succ_removal = 0.50, 
              conf_thres = 0.40, 
              succ_thres = TRUE, 
              output_dir = "~/Desktop/for_practice/processed_output")

# Remove any orphans
# We only want whole dyads, no half-dyads allowed
dat <- dat %>%
  remove_orphans(
    id_num = clipID,
    output_dir = "~/Desktop/for_practice/processed_output"
  )

# Average rows together by smushing
dat <- dat %>%
  smush_rows(smush = 30,
             id_num = clipID)

pivot_dyad(
  dat = dat,
  id_num = "clipID",
  output_dir = "~/Desktop/for_practice/processed_output")

jrcalabrese/faceup documentation built on May 16, 2022, 3:14 p.m.