data-raw/lecture_learning/lecture_learning-dataprep.R

In OpenIntroStat/openintro: Datasets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

# packages used

library(haven) # this package has read_spss for loading in SPSS file
library(dplyr)
library(tidyr)


# get initial data

raw_data <- read_spss(here::here("data-raw/lecture_learning/memory.sav"))

# remove all attributes imported from spss using zap_ functions

raw_data <- raw_data |>
  zap_label() |>
  zap_labels() |>
  zap_formats() |>
  zap_widths()


# change column names from original to more meaningful and standardized names

colnames(raw_data) <- c(
  "Student", "Gender", "Age", "MWlive", "MWvideo",
  "MEMlive", "MEMvideo", "INTlive", "INTvideo", "MOTlive",
  "MOTvideo", "MOTcompare"
)

# create tidy datasets out of the raw_data data by making type of instruction
# its own Method column and creates response values column; must do this
# for each of the four measurements: mindwandering, memory, interest,
# and motivation

MW <- raw_data |>
  select(Student, Gender, MWlive, MWvideo) |>
  rename(Live = MWlive) |>
  rename(Video = MWvideo) |>
  pivot_longer(cols = c(Live, Video), names_to = "Method", values_to = "Mindwander")

MEM <- raw_data |>
  select(Student, MEMlive, MEMvideo) |>
  rename(Live = MEMlive) |>
  rename(Video = MEMvideo) |>
  pivot_longer(cols = c(Live, Video), names_to = "Method", values_to = "Memory")

INT <- raw_data |>
  select(Student, INTlive, INTvideo) |>
  rename(Live = INTlive) |>
  rename(Video = INTvideo) |>
  pivot_longer(cols = c(Live, Video), names_to = "Method", values_to = "Interest")

MOT <- raw_data |>
  select(Student, MOTlive, MOTvideo, MOTcompare) |>
  rename(Live = MOTlive) |>
  rename(Video = MOTvideo) |>
  rename(Motivation_both = MOTcompare) |>
  pivot_longer(cols = c(Live, Video), names_to = "Method", values_to = "Motivation_single")

# make one big tidy dataset called lecture_learning by joining together in

lecture_learning <- MW |>
  full_join(MEM) |>
  full_join(INT) |>
  full_join(MOT)

# change coded values in lecture_learning

lecture_learning <- lecture_learning |>
  mutate(Gender = ifelse(Gender == "0", "Male", "Female"))

lecture_learning <- lecture_learning |>
  mutate(Interest = case_when(
    Interest == 1 ~ "least interest",
    Interest == 2 ~ "little interest",
    Interest == 3 ~ "neutral",
    Interest == 4 ~ "more interest",
    Interest == 5 ~ "greatest interest"
  ))

lecture_learning <- lecture_learning |>
  mutate(Motivation_single = case_when(
    Motivation_single == 1 ~ "very unmotivated",
    Motivation_single == 2 ~ "somewhat unmotivated",
    Motivation_single == 3 ~ "neutral",
    Motivation_single == 4 ~ "somewhat motivated",
    Motivation_single == 5 ~ "very motivated"
  ))

lecture_learning <- lecture_learning |>
  mutate(Motivation_both = case_when(
    Motivation_both == 1 ~ "Video",
    Motivation_both == 2 ~ "Live",
    Motivation_both == 3 ~ "Equally Motivated"
  )) |>
  janitor::clean_names()

# save

usethis::use_data(lecture_learning, overwrite = TRUE)