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MWF_Gait_Descriptives.Rmd
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MWF_Gait_Descriptives.Rmd
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---
title: "MWF_Gait_Thickness_Descriptives"
author: "Narlon Cassio"
date: "21/04/2021"
output:
pdf_document:
toc: yes
number_sections: yes
toc_depth: 5
word_document:
toc: yes
toc_depth: 5
geometry: "left = 1cm, right = 1cm, top = 1cm, bottom = 2.5cm"
header-includes:
- \usepackage{caption}
- \usepackage{pdflscape}
- \newcommand{\blandscape}{\begin{landscape}}
- \newcommand{\elandscape}{\end{landscape}}
editor_options:
chunk_output_type: console
---
\newpage
```{r setup, include = FALSE}
knitr::opts_chunk$set(
message = FALSE,
fig.height = 4, fig.width = 5, fig.align="center"
)
```
\small
# Loading packages
```{r, }
#------------------------------------------------------------------#
# Loading packages ####
#------------------------------------------------------------------#
library(tidyverse)
library(tableone)
library(gvlma)
library(openxlsx)
library(broom)
library(psych)
library(lsmeans)
library(knitr)
library(rstatix)
library(performance)
```
# Loading data
```{r, message = FALSE}
#------------------------------------------------------------------#
# Loading data ####
#------------------------------------------------------------------#
# Outcome and descriptive data
all_data_clean <- read.xlsx("all_data_clean.xlsx") %>%
filter(id != "RVCI_081") %>% # Removing 2 RVCI subject without MWF data
filter(id != "RVCI_038")
```
# Data management
## Checking data
## Demographics
```{r, }
# Checking variables
## Continous demographics data
kable(digits = 3, describe(all_data_clean[c("age",
"moca",
"mmse",
"height",
"weight",
"bmi")]))
## Histograms
all_data_clean %>%
select("id", "age", "moca", "mmse", "height", "weight", "bmi") %>%
pivot_longer(values_to = "data", names_to = "measures", col = 2:7) %>%
ggplot(aes(data, fill = measures)) +
geom_histogram(colour = "black", binwidth = function(x) 2 * IQR(x) / (length(x)^(1/3))) +
facet_wrap(~measures, scales = "free")+
theme_minimal() +
theme(legend.position = "none")
## Normality tests
kable(digits = 2, caption = "Normality test", all_data_clean %>%
shapiro_test(age, moca, mmse, height, weight, bmi))
## Converting Fazekas scores to character
all_data_clean <- all_data_clean %>%
mutate(fazekas_score = as.character(fazekas_score))
## Categorical demographics data
kable(print(printToggle = FALSE, CreateTableOne(data = all_data_clean, c("sex", "fazekas_score", "education_r"))))
kable(print(printToggle = FALSE, CreateTableOne(data = all_data_clean,
c("fci_1_arthritis",
"fci_2_osteoporosis",
"fci_3_asthma",
"fci_4_copd_ards_or_emphysema",
"fci_5_angina",
"fci_6_congestive_heart_failure_or_heart_disease",
"fci_7_heart_attack_myocardial_infarct",
"fci_8_neurological_disease",
"fci_9_stroke_or_tia",
"fci_10_peripheral_vascular_disease",
"fci_11_diabetes_type_i_and_ii",
"fci_12_upper_gastrointestinal_disease",
"fci_13_depression",
"fci_14_anxiety_or_panic_disorders",
"fci_15_visual_impairment",
"fci_16_hearing_impairment",
"fci_17_degenerative_disc_disease",
"fci_18_obesity_and_or_body_mass_index_30",
"fci_19_thyroid_disease",
"fci_20_cancer",
"fci_21_hypertension",
"fci_total"))))
## Total Functional commorbidity index
kable(digits = 2, describe(all_data_clean[c("fci_total")], fast = TRUE, IQR = TRUE))
```
### Gait data
```{r, }
# Checking variables
# Distance, ambulation time, velocity, step count, cadence, double support time
kable(digits = 3, describe(all_data_clean[c("velocity_ST",
"step_length_avg_ST",
"cycle_time_avg_ST",
"stride_time_sd_avg_ST",
"cycle_time_cov_ST",
"leg_length")]))
## Histograms
all_data_clean %>%
select("id", "velocity_ST", "step_length_avg_ST", "cycle_time_avg_ST", "stride_time_sd_avg_ST", "cycle_time_cov_ST", "leg_length") %>%
pivot_longer(values_to = "data", names_to = "measures", col = 2:7) %>%
ggplot(aes(data, fill = measures)) +
geom_histogram(colour = "black", binwidth = function(x) 2 * IQR(x) / (length(x)^(1/3))) +
facet_wrap(~measures, scales = "free")+
theme_minimal() +
theme(legend.position = "none")
## Normality tests
kable(digits = 3, caption = "Normality test", all_data_clean %>%
shapiro_test(velocity_ST,
step_length_avg_ST,
cycle_time_avg_ST,
stride_time_sd_avg_ST,
cycle_time_cov_ST,
leg_length))
# Transforming non-normal variables of interest
## Gait variability data
all_data_clean <- all_data_clean %>%
mutate(cycle_time_cov_ST_lg = log1p(cycle_time_cov_ST))
## Checking distributions
hist(all_data_clean$cycle_time_cov_ST_lg)
## Normality tests
kable(digits = 2, caption = "Normality test", all_data_clean %>%
shapiro_test(cycle_time_cov_ST_lg))
```
### Myelin data
```{r, fig.height=10, fig.width=7}
# Checking variables
# Means for MWF ROI and Whole-brain
kable(digits = 3, describe(all_data_clean[c("ALL_mean",
"Ant_CR_mean",
"Ant_IC_mean",
"BCC_mean",
"Cingulum_mean",
"Ext_Cap_mean",
"Fornix_mean",
"GCC_mean",
"Post_CR_mean",
"Post_IC_mean",
"Post_TR_mean",
"Retro_IC_mean",
"Sag_Strat_mean",
"SCC_mean",
"SLF_mean",
"Sup_CR_mean",
"MWF_TOT_WM_mean",
"Frontal_WM_all_mean",
"Parietal_WM_all_mean",
"eicv_cm3",
"wmh_cm3")]))
## Histograms
all_data_clean %>%
select("id", "ALL_mean",
"Ant_CR_mean",
"Ant_IC_mean",
"BCC_mean",
"Cingulum_mean",
"Ext_Cap_mean",
"Fornix_mean",
"GCC_mean",
"Post_CR_mean",
"Post_IC_mean",
"Post_TR_mean",
"Retro_IC_mean",
"Sag_Strat_mean",
"SCC_mean",
"SLF_mean",
"Sup_CR_mean",
"MWF_TOT_WM_mean",
"Frontal_WM_all_mean",
"Parietal_WM_all_mean",
"eicv_cm3",
"wmh_cm3") %>%
pivot_longer(values_to = "data", names_to = "measures", col = 2:22) %>%
ggplot(aes(data, fill = measures)) +
geom_histogram(colour = "black", binwidth = function(x) 2 * IQR(x) / (length(x)^(1/3))) +
facet_wrap(~measures, ncol = 3, scales = "free") +
theme_minimal() +
theme(legend.position = "none")
## Normality tests
kable(digits = 2, caption = "Normality test", all_data_clean %>%
shapiro_test(ALL_mean,
Ant_CR_mean,
Ant_IC_mean,
BCC_mean,
Cingulum_mean,
Ext_Cap_mean,
Fornix_mean,
GCC_mean,
Post_CR_mean,
Post_IC_mean,
Post_TR_mean,
Retro_IC_mean,
Sag_Strat_mean,
SCC_mean,
SLF_mean,
Sup_CR_mean,
MWF_TOT_WM_mean,
Frontal_WM_all_mean,
Parietal_WM_all_mean,
eicv_cm3,
wmh_cm3))
```
```{r}
# Transforming non-normal variables
## MWF data
all_data_clean <- all_data_clean %>%
mutate(Ant_IC_mean = ifelse(Ant_IC_mean >= 0.21, NA, Ant_IC_mean), # Removing outlier in Anterior Internal capsule
BCC_mean_lg = log1p(BCC_mean),
Ext_Cap_mean_lg = log1p(Ext_Cap_mean))
## WMH data
all_data_clean <- all_data_clean %>%
mutate(wmh_cm3_lg = log1p(wmh_cm3))
## Checking distributions
all_data_clean %>%
select(id, Ant_IC_mean, BCC_mean_lg, Ext_Cap_mean_lg, wmh_cm3_lg) %>%
pivot_longer(values_to = "data", names_to = "measures", col = 2:5) %>%
ggplot(aes(data, fill = measures)) +
geom_histogram(colour = "black", binwidth = function(x) 2 * IQR(x) / (length(x)^(1/3))) +
facet_wrap(~measures, scales = "free") +
theme_minimal() +
theme(legend.position = "none")
## Normality tests
kable(digits = 2, caption = "Normality test", all_data_clean %>%
shapiro_test(Ant_IC_mean,
BCC_mean_lg,
Ext_Cap_mean_lg,
wmh_cm3_lg))
```
```{r, }
# Saving final dataset
write.xlsx(all_data_clean, "all_data_clean_final.xlsx")
```