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1-preparation.Rmd
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1-preparation.Rmd
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---
title: "1-preparation"
author: "bernard-liew"
date: "2020-06-11"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
# Load library
```{r message=FALSE, warning=FALSE}
# Helper packages
library (tidyverse)
library (tidyselect)
library (arsenal)
library (janitor)
library (magrittr)
# Import
library(readxl)
library (xlsx)
# Statistics
library (mice)
library (VIM)
library (corrr)
```
# Import data
```{r}
rm (list = ls())
dat <- read_excel("data/stops_dat.xlsx",
sheet = "data")
dat <- dat[c(1:300),]
cnames <- names (dat)
cnames_mod <- str_replace_all (tolower(cnames),
pattern = " ",
repl = "_")
names (dat) <- cnames_mod
df_orig <- dat
```
# Tidy data
## Remove dummary variables
```{r}
dummy_var <- grep ("dummy|0,|v_", cnames_mod, value = TRUE)
dat_sub <- dat[, !names (dat) %in% dummy_var]
(ncol (dat_sub) + length (dummy_var)) == length (names (dat))
df_orig_sub <- dat_sub
```
## Group variables
```{r}
cnames_mod <- names (dat_sub)
# Time related variables
base_var <- grep ("baseline", cnames_mod, value = TRUE)
wk5_var <- grep ("5week|5_week", cnames_mod, value = TRUE)
wk10_var <- grep ("10week|10_week", cnames_mod, value = TRUE)
wk26_var <- grep ("26week|26_week", cnames_mod, value = TRUE)
wk52_var <- grep ("52week|52_week", cnames_mod, value = TRUE)
# Baseline variables
id_var<- tidyselect::vars_select (cnames_mod,
c("stops_id":
"baseline_date"))
orebro_var <- grep ("orebro", cnames_mod, value = TRUE)
fab_var <- grep ("fab", cnames_mod, value = TRUE)
work_var <- grep ("work|industry|occupation", cnames_mod, value = TRUE)
symptom_var <- grep ("symptom|next", cnames_mod, value = TRUE)
symptomLoc_var <- grep ("location", cnames_mod, value = TRUE)
episode_var <- grep ("episode", cnames_mod, value = TRUE)
sfbs_var <- grep ("sfbs", cnames_mod, value = TRUE)
other_var <- grep ("other_rx", cnames_mod, value = TRUE) # remove
treatment_var <- tidyselect::vars_select (cnames_mod,
c("treatment:_number_of_practitioners_seen_(all)":
"10:_rest_of_life"))
meds_var <- tidyselect::vars_select (cnames_mod, c("paracetamol":
"medication:_number_of_med_types_(all)")) # remove
misc_var <- tidyselect::vars_select (cnames_mod,
c("weight_(kg)":
"sa1_(code)"))
ses_var <- grep ("ses:", cnames_mod, value = TRUE)
personal_var <- tidyselect::vars_select (cnames_mod,
c("highest_level_of_education":
"number_of_smokes...316"))
symptHx_var <- tidyselect::vars_select (cnames_mod,
c("have_you_taken_any_tablets_for_your_back_today?":
"if_you_have_leg_and_back_symptoms,_are_they_related?...338"))
aggr_var <- grep ("agg|difficult|sorer", cnames_mod, value = TRUE)
ease_var <- grep ("ease|comfortable", cnames_mod, value = TRUE)
obj_curve_var <- tidyselect::vars_select (cnames_mod,
c("assessment:_obs:_standing-increased_lordosis":
"list_corresponding_to_worst_side"))
obj_rom_var <- tidyselect::vars_select (cnames_mod,
c("amt:_flexion_range_(cm)...462":
"range_of_side_with_most_limited_lf"))
obj_core_var <- tidyselect::vars_select (cnames_mod,
c("core_stability_in_standing":
"ta_low_tone"))
obj_sit_var <- grep ("sitting:", cnames_mod, value = TRUE)
obj_neuro_var <- tidyselect::vars_select (cnames_mod,
c("positive_slr":
"clinical_radiculopathy_with_no_mechanical_cause_on_imaging"))
aggr2_var <- tidyselect::vars_select (cnames_mod,
c("oswestry:_sitting_(p3)":
"repeated_movements:_reducible?"))
obj_core2_var <- grep ("multifidus", cnames_mod, value = TRUE)
obj_disc_var <- tidyselect::vars_select (cnames_mod,
c("regular_compression":
"degree_of_nerve_contact"))
redflag_var <- tidyselect::vars_select (cnames_mod,
c("constant_symptoms":
"general_health_state"))
euroqol_var <- grep ("euroqol", cnames_mod, value = TRUE)
```
## Rename variables
### Baseline outcome variables
```{r}
dat_base_var <- dat_sub[, names (dat_sub) %in% base_var]
dat_base_var <- dat_base_var[, !names (dat_base_var) %in% c ("baseline_date",
"baseline_time_(0_weeks)",
"baseline_pain_(leg)...9")]
dat_base_var $working_at_baseline <- NULL
new.name1 <- paste0("base_", c ("osw",
"lbp",
"lp"))
new.name2 <- paste0 ("base_osw_q", c(1:10))
names(dat_base_var) <- c(new.name1, new.name2)
```
### Week 5 outcome variables
```{r}
dat_wk5_var <- dat_sub [, names (dat_sub) %in% wk5_var]
dat_wk5_var <- dat_wk5_var [, !names (dat_wk5_var) %in% c ("5_week_outcome_date",
"5_weeks_(days_since_om_completed)",
"5_weeks_(weeks_since_om_completed)")]
new.name1 <- paste0("wk5_", c ("osw",
"lbp",
"lp",
"oreb"))
new.name2 <- paste0 ("wk5_oreb_q", c(4:24))
names(dat_wk5_var) <- c(new.name1, new.name2)
```
### Week 10 outcome variables
```{r}
dat_wk10_var <- dat_sub [, names (dat_sub) %in% wk10_var]
dat_wk10_var <- dat_wk10_var [, !names (dat_wk10_var) %in% c ("10_week_outcome_date",
"10_weeks_(days_since_om_completed)",
"10_weeks_(weeks_since_om_completed)")]
new.name1 <- paste0("wk10_", c ("osw",
"lbp",
"lp",
"oreb"))
new.name2 <- paste0 ("wk10_oreb_q", c(4:24))
names(dat_wk10_var) <- c(new.name1, new.name2)
```
### Week 26 outcome variables
```{r}
dat_wk26_var <- dat_sub [, names (dat_sub) %in% wk26_var]
dat_wk26_var <- dat_wk26_var [, !names (dat_wk26_var) %in% c ("26_week_outcome_date",
"26_weeks_(days_since_om_completed)",
"26_weeks_(weeks_since_om_completed)")]
new.name1 <- paste0("wk26_", c ("osw",
"lbp",
"lp",
"oreb"))
new.name2 <- paste0 ("wk26_oreb_q", c(4:24))
names(dat_wk26_var) <- c(new.name1, new.name2)
```
### Week 52 outcome variables
```{r}
dat_wk52_var <- dat_sub [, names (dat_sub) %in% wk52_var]
dat_wk52_var <- dat_wk52_var [, !names (dat_wk52_var) %in% c ("52_week_outcome_date",
"52_weeks_(days_since_om_completed)",
"52_weeks_(weeks_since_om_completed)")]
new.name1 <- paste0("wk52_", c ("osw",
"lbp",
"lp",
"oreb"))
new.name2 <- paste0 ("wk52_oreb_q", c(4:24))
names(dat_wk52_var) <- c(new.name1, new.name2)
```
### Baseline id variables
```{r}
dat_id_var <- dat_sub [, names (dat_sub) %in% id_var]
dat_id_var <- dat_id_var [, !names (dat_id_var) %in% c ("baseline_date")]
names(dat_id_var) <- c("id", "rx_grp", "rx_subgrp")
```
### Baseline orebro
```{r}
orebro_var_sub <- orebro_var[!grepl("5w|10w|26w|52w|...37", orebro_var)]
dat_orebro_var <- dat_sub [, names (dat_sub) %in% orebro_var_sub ]
new.name1 <- c("base_oreb", paste0 ("base_oreb_q", c(4:24)))
names(dat_orebro_var) <- new.name1
```
### Baseline fab
```{r}
dat_fab_var <- dat_sub [, names (dat_sub) %in% fab_var]
names(dat_fab_var) <- "base_fab"
```
### Baseline work
```{r}
work_var2 <- tidyselect::vars_select (work_var,
-c("19:_reason_pre-injury_work_status...64":
"working_other_than_nd_due_to_current__lbp",
"pre-inj_workers'_perceived_chance_of_working_at_6/12...72",
"workers'_perception_of_heavy_or_monotonous_work...75" ,
"pre-injury_workers'_sick_leave_duration_due_to_pain...78",
"pre-injury_house-keepers'_or_workers'_sick_leave_duration_due_to_pain...81",
"pre-injury_house-keepers'_or_workers'_sick_leave_duration_due_to_pain...80",
"ses:_index_of_education_and_occupation" ,
"occupation" ))
dat_work_var <- dat_sub [, names (dat_sub) %in% work_var2]
new.name1 <- c("work_status", "work_duty", "reas_not_work", "pre_work_status",
"pre_work_duty", "reas_pre_work_status", "pre_perc_work6mth",
"work", "prec_work", "work_statifac", "pre_sickleave_pain",
"pain_interfer", "industry", "hx_heavywork")
new.name2 <- paste0 ("base_", new.name1)
names(dat_work_var) <- new.name2
```
### Baseline symptoms
```{r}
symptom_var2 <- tidyselect::vars_select (symptom_var ,
-c("duration_of_leg_symptoms...100":
"duration_of_symptoms:_longest_lx_or_leg" ,
"if_you_have_leg_and_back_symptoms,_are_they_related?...337",
"first_time_symptoms_started...422",
"time_of_day_symptoms_came_on...428",
"how_next_day...434" ,
"symptoms_following_day_much_worse_(p25)",
"constant_symptoms",
"night_symptoms" ))
dat_symptom_var <- dat_sub [, names (dat_sub) %in% symptom_var2 ]
new.name1 <- c("lbp_symptom", "leg_symptom", "lbp_duration", "leg_duration",
"freq_symptom", "back_leg_relation", "onset_symptom",
"time_symptom", "next_day_symptom", "pain_side")
new.name2 <- paste0 ("base_", new.name1)
names(dat_symptom_var) <- new.name2
```
### Baseline symptom location
```{r}
symptomLoc_var2 <- tidyselect::vars_select (symptomLoc_var ,
-c("pain_location:_body_chart_-_psych...96",
"pain_location:_body_chart_-_psych...97",
"duration_of_leg_symptoms+pain_location_(body_chart)"))
dat_symptomLoc_var <- dat_sub [, names (dat_sub) %in% symptomLoc_var2]
new.name1 <- c("loc_antthigh_pain", "loc_belowknee_pain", "loc_leg_pain", "loc_body_ratio",
"loc_mostpain")
new.name2 <- paste0 ("base_", new.name1)
names(dat_symptomLoc_var) <- new.name2
```
### Baseline episode
```{r}
episode_var2 <- tidyselect::vars_select (episode_var ,
-c( "previous_lbp_episodes:_<=4,_>4,_n_appl" ,
"not_the_first_ep_and_previous_lbp_episodes:_<=4,_>4,_n_appl"))
dat_episode_var <- dat_sub [, names (dat_sub) %in% episode_var2]
new.name1 <- c("first_episode", "first_episode_period")
new.name2 <- paste0 ("base_", new.name1)
names(dat_episode_var ) <- new.name2
```
### Baseline sfbs
```{r}
dat_sfbs_var <- dat_sub [, names (dat_sub) %in% sfbs_var]
new.name1 <- c("freq_1", "freq_2", "freq_3", "freq_4", "freq_5",
"sciatica_freq_lbp", "sciatica_freq_nolbp", "bother_1",
"bother_2", "bother_3", "bother_4", "bother_5",
"sciatica_bother_lbp", "sciatica_bother_nolbp")
new.name2 <- paste0 ("base_sfbs_", new.name1)
names(dat_sfbs_var) <- new.name2
```
### Baseline other
```{r}
dat_other_var <- dat_sub [, names (dat_sub) %in% other_var]
```
### Baseline treatment
```{r}
treatment_var2 <- tidyselect::vars_select (treatment_var,
c( "total_number_of_treatments_across_all_types...228",
"8:_satisfaction_with_healthcare" ,
"9:_satisfaction_results",
"10:_rest_of_life" ))
dat_treatment_var <- dat_sub [, names (dat_sub) %in% treatment_var2]
new.name1 <- c("total_rx_no", "statisfac_healthcare", "statisfac_result", "rest_of_life")
new.name2 <- paste0 ("base_", new.name1)
names(dat_treatment_var) <- new.name2
```
### Baseline medicine
```{r}
treatment_var2 <- tidyselect::vars_select (treatment_var,
c( "total_number_of_treatments_across_all_types...228",
"8:_satisfaction_with_healthcare" ,
"9:_satisfaction_results",
"10:_rest_of_life" ))
dat_meds_var <- dat_sub [, names (dat_sub) %in% meds_var]
```
### Baseline demographics
```{r}
misc_var2 <- tidyselect::vars_select (misc_var,
c( "weight_(kg)",
"height_(cm)"))
dat_misc_var <- dat_sub [, names (dat_sub) %in% misc_var2]
new.name1 <- c("ht", "wt")
new.name2 <- paste0 ("base_", new.name1)
names(dat_misc_var) <- new.name2
```
### Baseline ses
```{r}
dat_ses_var <- dat_sub [, names (dat_sub) %in% ses_var]
new.name1 <- c("ses_income", "ses_economic", "ses_occ", "ses_resource", "ses_advantage")
new.name2 <- paste0 ("base_", new.name1)
names(dat_ses_var) <- new.name2
```
### Baseline symptom history variables
```{r}
symptHx_var2 <- tidyselect::vars_select (symptHx_var,
c("currently_having_a_flare_up?",
"p&n/n_below__waist_in_past_3_days"))
dat_symptHx_var <- dat_sub [, names (dat_sub) %in% symptHx_var2]
new.name1 <- c("current_flare", "pnn_below_waist")
new.name2 <- paste0 ("base_", new.name1)
names(dat_symptHx_var) <- new.name2
```
### Baseline aggravating variables
```{r}
aggr_var2 <- tidyselect::vars_select (aggr_var,
-c("done_anything_to_agg?" ,
"agg:_lying_on_back...360",
"agg:_lying_on_stomach...362",
"what_is_most_difficult_about_getting_into_a_car?...370",
"what_is_most_difficult_about__sit_to_stand?",
"what_is_most_difficult_about_getting_into_a_car?...369",
"what_is_most_difficult_about_sit_to_stand?",
"sorer_to_lie_on_one_side_than_the_other?"))
dat_aggr_var <- dat_sub [, names (dat_sub) %in% aggr_var2]
new.name1 <- c( "walk", "stand", "sitfirm", "sitsoft", "lie_back", "lie_stom", "car", "sts",
"cough", "bend", "lift")
new.name2 <- paste0 ("base_agg_", new.name1)
names(dat_aggr_var) <- new.name2
```
### Baseline easing variables
```{r}
ease_var2 <- tidyselect::vars_select (ease_var,
-c("most_comfortable_side_to_lie_on?...386" ,
"most_comfortable_side_to_lie_on?...387" ,
"ease:_lying_sore_side...416",
"ease:_lying_good_side...418",
"assessment:_obs:_standing-increased_lordosis",
"observation:_standing-increased_lordosis",
"decreased_lordosis...446",
"decreased_lordosis...447",
"movement_eases_sympt",
"night_pain+_movement_eases"))
dat_ease_var <- dat_sub [, names (dat_sub) %in% ease_var2]
new.name1 <- c("stand", "sitfirm", "sitsoft", "walk", "lie_back", "lie_back_bent", "lie_sore", "lie_good", "lie_stom")
new.name2 <- paste0 ("base_ease_", new.name1)
names(dat_ease_var) <- new.name2
```
### Baseline lumbar curve variables
```{r}
obj_curve_var2 <- tidyselect::vars_select (obj_curve_var,
c("observation:_standing-increased_lordosis",
"decreased_lordosis...447" ,
"presence_of_a_list...452",
"list_direction...457" ,
"list_corresponding_to_worst_side" ))
dat_obj_curve_var <- dat_sub [, names (dat_sub) %in% obj_curve_var2]
new.name1 <- c("inc_lord", "dec_lord", "list", "list_dir", "list_dir_side")
new.name2 <- paste0 ("base_", new.name1)
names(dat_obj_curve_var) <- new.name2
```
### Baseline rom variables
```{r}
obj_rom_var2 <- tidyselect::vars_select (obj_rom_var,
c("amt:_flexion_range_(cm)...462",
"amt:_extension_range_(cm)" ,
"amt:_llf_range_(cm)",
"amt:_rlf_range_(cm)"))
dat_obj_rom_var <- dat_sub [, names (dat_sub) %in% obj_rom_var2]
new.name1 <- c("flex", "ext", "llf", "rlf")
new.name2 <- paste0 ("base_rom_", new.name1)
names(dat_obj_rom_var) <- new.name2
```
### Baseline core variables
```{r}
obj_core_var2 <- tidyselect::vars_select (obj_core_var,
c("core_stability_in_standing" ,
"ta_quality_(lower_threshold)" ,
"ta_hypervig.",
"ta_low_tone"))
dat_obj_core_var <- dat_sub [, names (dat_sub) %in% obj_core_var2]
new.name1 <- c("core", "ta_qual", "ta_hyper", "ta_tone")
new.name2 <- paste0 ("base_", new.name1)
names(dat_obj_core_var) <- new.name2
```
### Baseline sitting variables
```{r}
obj_sit_var2 <- tidyselect::vars_select (obj_sit_var,
c("sitting:_degree_of_flexed_posture...495" ,
"sitting:_pain_response_to_correction...500"))
dat_obj_sit_var <- dat_sub [, names (dat_sub) %in% obj_sit_var2]
new.name1 <- c("sit_flex", "sit_pain_corr")
new.name2 <- paste0 ("base_", new.name1)
names(dat_obj_sit_var) <- new.name2
```
### Baseline neuro variables
```{r}
obj_neuro_var2 <- tidyselect::vars_select (obj_neuro_var,
c("positive_slr",
"positive_pkb",
"comparable_sensory_deficit...508",
"comparable_myo_deficit...509",
"comparable_knee_jerk_deficit",
"comparable_ankle_jerk_reflex",
"total_comparable_neuro_signs...513",
"clinical_radiculo-_pathy...515",
"clinical_radiculopathy_with_no_mechanical_cause_on_imaging" ))
dat_obj_neuro_var <- dat_sub [, names (dat_sub) %in% obj_neuro_var2]
new.name1 <- c("slr", "pkb", "sens_deficit", "myo_deficit", "knee_jerk_deficit", "ank_jerk_deficit", "total_neuro", "radiculo", "radiculo_image")
new.name2 <- paste0 ("base_", new.name1)
names(dat_obj_neuro_var) <- new.name2
```
### Baseline aggravating2 variables
```{r}
aggr2_var2 <- tidyselect::vars_select (aggr2_var,
c("history_of_heavy_lifting_job_(p25)",
"discogenic_pain" ,
"discogenic_factors",
"repeated_movements:_reducible?"))
dat_aggr2_var <- dat_sub [, names (dat_sub) %in% aggr2_var2]
new.name1 <- c("hx_lift", "disc_pain", "disc_fac", "rep_movt_reduce")
new.name2 <- paste0 ("base_", new.name1)
names(dat_aggr2_var) <- new.name2
```
### Baseline multifidus variables
```{r}
obj_core2_var2 <- tidyselect::vars_select (obj_core2_var,
-c("multifidus_substitution"))
dat_obj_core2_var <- dat_sub [, names (dat_sub) %in% obj_core2_var2 ]
new.name1 <- c("mf_tone", "mf_qual", "mf_hyper")
new.name2 <- paste0 ("base_", new.name1)
names(dat_obj_core2_var) <- new.name2
```
### Baseline disc variables
```{r}
obj_disc_var2 <- tidyselect::vars_select (obj_disc_var,
c("regular_compression",
"palpation:_comparable_level_of_pain_and/or_stiffness"))
dat_obj_disc_var <- dat_sub [, names (dat_sub) %in% obj_disc_var2]
new.name1 <- c("reg_compress", "comparable_stiff")
new.name2 <- paste0 ("base_", new.name1)
names(dat_obj_disc_var) <- new.name2
```
### Baseline redflag variables
```{r}
redflag_var2 <- tidyselect::vars_select (redflag_var,
c("general_health_state"))
dat_redflag_var <- dat_sub [, names (dat_sub) %in% redflag_var2]
new.name1 <- c("gen_health")
new.name2 <- paste0 ("base_", new.name1)
names(dat_redflag_var) <- new.name2
```
### Baseline euroqol_var variables
```{r}
euroqol_var2 <- tidyselect::vars_select (euroqol_var,
c("21:_euroqol:_mobility",
"euroqol:_person_appll_care",
"euroqol:_usual_activities",
"euroqol:_pain/discomfort",
"euroqol:_anxiety/depression",
"euroqol_utility_score_(uk_tto)"))
dat_euroqol_var <- dat_sub [, names (dat_sub) %in% euroqol_var2]
new.name1 <- c("mobility", "person_care", "activities", "pain", "anx_dep", "utility")
new.name2 <- paste0 ("base_euroq_", new.name1)
names(dat_euroqol_var) <- new.name2
```
# Create new dataframe
```{r}
df <- bind_cols(dat_id_var,
dat_misc_var,
dat_base_var,
dat_orebro_var,
dat_wk5_var,
dat_wk10_var,
dat_wk26_var,
dat_wk52_var,
dat_aggr_var,
dat_aggr2_var,
dat_ease_var,
dat_obj_core2_var,
dat_obj_core_var,
dat_obj_curve_var,
dat_obj_disc_var,
dat_obj_neuro_var,
dat_obj_rom_var,
dat_obj_sit_var,
dat_redflag_var,
dat_ses_var,
dat_sfbs_var,
dat_symptHx_var,
dat_symptom_var,
dat_symptomLoc_var,
dat_treatment_var,
dat_episode_var,
dat_fab_var,
dat_euroqol_var)
```
# Check class
```{r}
df_chr2num <- df %>%
dplyr::select_if(is.character) %>%
dplyr::select (matches ("wk|oreb|rom")) %>%
mutate_all(as.numeric)
df[names (df_chr2num)] <- df_chr2num
df_class <- map_chr(df, class)
df_num2chr <- df %>%
dplyr::select_if(is.numeric) %>%
dplyr::select (-matches ("id|ht|wt|time|osw|lbp|lp|osw|oreb|rom|gen|ses|sfbs|total_rx|fab|euroq|ratio|duration")) %>%
mutate_all(as.character)
df[names (df_num2chr)] <- df_num2chr
df_chr <- df %>%
dplyr::select_if(is.character)
df_num <- df %>%
dplyr::select_if(is.numeric)
```
```{r}
tab1 <- tableby (~., data = df, digits = 2, digits.p = 2)
summary (tab1)
```
# Create factor levels
```{r}
df$rx_grp <- tolower(df$rx_grp )
df$rx_subgrp <- tolower(df$rx_subgrp)
df$base_leg_duration <- as.numeric(df$base_leg_duration)
df$base_loc_body_ratio <- as.numeric (df$base_loc_body_ratio)
df$base_pain_side <- tolower(df$base_pain_side)
df$base_core <- ifelse (df$base_core == "nil", NA, df$base_core)
df$base_mf_tone <- ifelse (df$base_mf_tone == "nil", NA, df$base_mf_tone)
df$base_leg_symptom <- ifelse (df$base_leg_symptom == "nil", "no", df$base_leg_symptom)
df$base_list_dir_side <- as.character(df$base_list_dir_side)
df_chr <- df %>%
dplyr::select_if(is.character) %>%
mutate_all(as.factor)
levels (df_chr$rx_grp) <- c ("advice", "individualisedphysio")
levels (df_chr$rx_subgrp) <- c ("dhr", "nrdp", "rdp", "mfp", "mtg" )
levels (df_chr$base_agg_walk) <-
levels (df_chr$base_agg_stand) <-
levels (df_chr$base_agg_sitfirm) <-
levels (df_chr$base_agg_sitsoft) <-
c("0", "1", "2", "3", "4") # 0 good, 4 bad
levels (df_chr$base_agg_lie_back) <-
levels (df_chr$base_agg_lie_stom) <-
levels (df_chr$base_agg_car) <-
levels (df_chr$base_agg_sts) <-
levels (df_chr$base_agg_cough) <-
levels (df_chr$base_agg_bend) <-
levels (df_chr$base_agg_lift) <-
c("0", "1", "2", "3", "4", "5") # 0 good, 5 bad
levels (df_chr$base_ease_stand) <-
levels (df_chr$base_ease_sitfirm) <-
levels (df_chr$base_ease_sitsoft) <-
levels (df_chr$base_ease_walk) <-
levels (df_chr$base_ease_lie_back) <-
levels (df_chr$base_ease_lie_back_bent) <-
levels (df_chr$base_ease_lie_sore) <-
levels (df_chr$base_ease_lie_good) <-
levels (df_chr$base_ease_lie_stom) <-
c("0", "1") # 0 good, 1 bad
levels (df_chr$base_hx_lift) <-
levels (df_chr$base_disc_pain) <-
levels (df_chr$base_rep_movt_reduce) <-
c("0", "1") # 0 yes, 1 no
levels (df_chr$base_disc_fac) <-
as.character (c(0:9))
levels (df_chr$base_core) <-
c("minimal", "fair", "good" )
levels (df_chr$base_ta_qual) <-
levels (df_chr$base_ta_hyper) <- # 0 yes, 1 no
levels (df_chr$base_ta_tone) <- # 0 yes, 1 no
levels (df_chr$base_mf_hyper) <- # 0 yes, 1 no
levels (df_chr$base_mf_qual) <- # 0 yes, 1 no
c("0", "1")
levels (df_chr$base_mf_tone) <- c("> 30% MVC", "< 30% MVC")
levels (df_chr$base_inc_lord) <- c("0", "1", "2") # 0 nil, 1 mild, 2 mod
levels (df_chr$base_dec_lord) <- c("0", "1", "2", "3") # 0 nil, 1 mild, 2 mod, 3 marked
levels (df_chr$base_list) <- c("0", "1", "2") # 0 nil, 1 mild, 2 mod
levels (df_chr$base_list_dir) <- c("0", "1", "2") # 0 na, 1 left, 2 right
levels (df_chr$base_reg_compress) <- c("0", "1") # 0 yes, 1 no
levels (df_chr$base_slr) <-
levels (df_chr$base_pkb) <-
levels (df_chr$base_sens_deficit) <-
levels (df_chr$base_myo_deficit)<-
levels (df_chr$base_knee_jerk_deficit) <-
levels (df_chr$base_ank_jerk_deficit) <-
levels (df_chr$base_total_neuro) <-
levels (df_chr$base_radiculo) <-
levels (df_chr$base_radiculo_image) <-
c("0", "1") # 0 yes, 1 no
levels (df_chr$base_sit_flex) <- c("0", "1", "2", "3") # 0 nil, 1 mild, 2 mod, 3 mark
levels (df_chr$base_sit_pain_corr) <- c("0", "1", "2") # 0 improve, 1 same, 2 worse
levels (df_chr$base_current_flare) <-
levels (df_chr$base_pnn_below_waist)
levels (df_chr$base_list_dir_side) <- c("0", "1") # 0 yes, 1 no
levels (df_chr$base_back_leg_relation) <- c("0", "1", "2") # 0 yes, 1 no
levels (df_chr$base_lbp_symptom) <- c("no", "yes")
levels (df_chr$base_leg_symptom) <- c("no", " ant thigh", "below knee", "both") # 0 deep, 1 superficial, 2 na # need to check
levels (df_chr$base_freq_symptom) <- c("0", "1", "2") # 0 <50%, 1 >50%, 2 constant
levels (df_chr$base_back_leg_relation) <- c("0", "1", "2") # 0 yes, 1 no, 2 na
levels (df_chr$base_onset_symptom) <-
levels (df_chr$base_time_symptom) <-
levels (df_chr$base_next_day_symptom) <- c("n_appl", "0", "1", "2", "3")
levels (df_chr$base_pain_side) <- c("n_appl", "left", "right")
levels (df_chr$base_loc_antthigh_pain) <-
levels (df_chr$base_loc_belowknee_pain) <-
levels (df_chr$base_loc_leg_pain) <-
c("0", "1") # 0 yes, 1 no
levels (df_chr$base_loc_mostpain) <-
c("0", "1", "2", "3", "4", "5") # 0 back, 1 buttock, 2 groin, 3 thigh, 4 leg, 5 foot
levels (df_chr$base_statisfac_healthcare) <-
levels (df_chr$base_statisfac_result) <-
levels (df_chr$base_rest_of_life) <- c("0", "1", "2", "3", "4") # 0 dissatistied 4 very satisfied
levels (df_chr$base_first_episode) <- c("0", "1") # 0 yes, 1 no
# 0 <1mth , 1 2-6mth, 2 6-12mth, 3 1-2yr, 4 >3yr, 5 na
levels (df_chr$base_first_episode_period) <- c("0", "1", "2", "3", "4", "5")
```
```{r}
df2 <- df %>%
dplyr::select_if(is.numeric) %>%
bind_cols(df_chr)
```
```{r}
var_keep1 <- !grepl ("osw_q|ses|sfbs|euroq|statisfac|hyper|total_rx|rest|first|fab", names (df2))
df3 <- df2 [, var_keep1 ]
```
# Save cleaned data
```{r}
saveRDS (df3, "output/df_clean.RDS")
xlsx::write.xlsx2 (df3, "output/data_clean.xlsx")
```
# Load cleaned data
```{r}
rm (list = ls())
df <- readRDS("output/df_clean.RDS")
```
# Select important variables
```{r}
df_sub <- df[, grepl ("id|rx_grp|rx_sub|oreb_q|osw|lbp|_lp", names (df))]