---
title: "Import exercise diaries"
author: "Johannes Zauner"
format:
html:
self-contained: true
code-tools: true
---
## Preface
This document imports the `exercisediary` and shows descriptive statistics for the site.
## Setup
```{r}
#| message: false
library(tidyverse)
library(LightLogR)
library(glue)
library(readxl)
library(gt)
library(gtsummary)
remote <-
"https://raw.githubusercontent.com/MeLiDosProject/Data_Metadata_Conventions/main/scripts/"
c("labeling",
"radio_factors",
"time_summaries",
"prepare_codebook",
"filefinder",
"general_parameters",
"coltype_checker",
"diarydate",
"tables"
) |> walk(\(x) source(paste0(remote, x, ".R")))
```
## Preparation
```{r}
#collect codebook
codebook <- prepare_codebook("MeLiDosEveningDiaries_DataDictionary_2024-10-16.csv", form.filter = "exercise_diary")
#collect files
files <- filefinder("exercisediary", continuous = TRUE, individual = TRUE)
#import files
data <- read_csv(files, show_col_types = FALSE) |>
rename_with(\(x) str_remove(x, "_v2$")) |>
drop_na(redcap_repeat_instance)
#check column types
coltype_check <- coltype_checker(codebook, data)
coltype_check$details |> gt()
#collect relevant columns: POSIXct, Date & numeric
relevant_columns <-
coltype_check$details |>
pull(col)
#add specific character columns
relevant_columns <- c("record_id", relevant_columns)
#select relevant columns
data <- data |> select(any_of(relevant_columns))
#label variables
data <-
data |>
add_radio_factors(codebook,
var_col = `Variable / Field Name`,
type_col = `Field Type`,
levels_col = `Choices, Calculations, OR Slider Labels`
) |>
add_col_labels(codebook, var_col = `Variable / Field Name`, label_col = `Field Label`) |>
relocate(record_id, any_of(codebook$`Variable / Field Name`)) |>
select(-uuid_3, -serializedresult_3, -supplementaldata_3, -status_3, -scheduledate_3)
attr(data$record_id, "label") <- "Record ID"
```
## Translate comments into english
Native language is translated into English with AI and later checked by a site researcher.
```{r}
#| label: translate into english with AI
# library(ellmer)
#
# #Providing the relevant codebook portions
# codebook_red <-
# codebook|>
# pmap(~ paste(paste(names(codebook), c(...), sep = ": "), collapse = ", ")) |>
# list_c() |>
# paste0(collapse = "newline: ")
#
# chat <- chat_openai(paste0("Clean the dataset according to the instructions in the output structure. Here is the codebook: "))
#
# #Providing the input
# data_red <-
# data|>
# select(record_id, type)
# data_red <-
# data_red |>
# pmap(~ paste(paste(names(data_red), c(...), sep = ": "), collapse = ", "))
#
# #creating an output structure
# type_data <- type_object(
# record_id = type_string("use the record_id information"),
# type = type_string("copy the type information here", required = FALSE),
# type_english = type_string("translation of the type into english", required = FALSE)
# )
#
# data_llm <-
# parallel_chat_structured(
# chat,
# data_red,
# type = type_data
# )
#
# #Ensure that no NA is caught as string
# data_llm <-
# data_llm |>
# mutate(type = case_when(type == "NA" ~ NA, .default = type),
# type_english = case_when(type_english == "NA" ~ NA, .default = type_english))
#
# #check that input and output are identical
# stopifnot("Input must by identical to output check" =
# all(data_llm$type == data$type, na.rm = TRUE))
# stopifnot("Input must by identical to output check" =
# all(data_llm$record_id == data$record_id, na.rm = TRUE))
#
# data_llm <-
# data_llm |>
# distinct(record_id, type, .keep_all = TRUE)
#
# path <- "../data/AI_translations/"
# if(!dir.exists(path)) dir.create(path, recursive = TRUE)
# write_csv(data_llm, "../data/AI_translations/exercisediary.csv")
```
```{r}
data_llm <-
read_csv("../data/AI_translations/exercisediary.csv")
#add output to original
data <- data |> left_join(data_llm, by = c("record_id", "type"))
```
## Set relevant dates
```{r}
#if data was collected between 14:00 and 24:00, it is assigned to the same day.
#if collected between 00:00 and 13:59, it is assigned to the previous day.
data <- data |> diarydate(startdate_3)
attr(data$Date, "label") <- "Date"
```
## Adjust minute column names
```{r}
data <-
data |>
mutate(across(c(commute, sedentary), \(x) as.difftime(x, unit = "mins")))
data <-
data |>
add_col_labels(codebook, var_col = `Variable / Field Name`, label_col = `Field Label`)
```
## Summarize results
```{r}
table_exercisediary(data)
gtsave(table_exercisediary(data) |> as_gt(), filename = "../output/tables/table_exercisediary.png", vwidth = 800)
```
### Export
```{r}
data <- data |> rename(Id = record_id)
exercisediary <- data
path <- "../data/imported/continuous/"
if(!dir.exists(path)) dir.create(path, recursive = TRUE)
save(exercisediary, file = "../data/imported/continuous/exercisediary.RData")
```