rmd.Rmd

---
title: "R Notebook"
output:
  word_document: default
  html_document: default
---

title: "R Notebook"
  output: officedown::rdocx_document
---




```{r, message=FALSE, error=FALSE}
library(table1)
library(readxl)
library(flextable)
library(kableExtra)
library(officedown)
library(officer)
library(lmerTest)
library(lme4)
library(rstatix)
Df <- read_excel("Eccentric_Thesis.xlsx",
                               sheet = "ESS_BFP")

```


```{r}

## Convert from character to factor data
Df$Sex <- as.factor(Df$Sex)
Df$Condition <- as.factor(Df$Condition)

## Order conditions
Df$Condition <- ordered(Df$Condition,
                          levels = c("Baseline", "Low",
                                     "Moderate","High"))
```

<!---BLOCK_LANDSCAPE_START--->

```{r, error=FALSE}
x <-table1(~  HR + RPE + VO2 + Lactate + workload  | Sex*Condition, render.continuous=c(.="Mean (SD)"),render.missing=NULL,
          data=Df,overall=FALSE)
t1flex(x)
```
<!---BLOCK_LANDSCAPE_STOP--->

```{r}
##### DESCRIPTIVES
Descriptives <- read_excel("Eccentric_Thesis.xlsx",
                        sheet = "Descriptives")

```

```{r}
x2 <-table1(~  Age + Height + Weight + VO2max + HR + Lactate +RPE | Sex, render.continuous=c(.="Mean (SD)"),render.missing=NULL,
          data=Descriptives,overall=FALSE)
t1flex(x2)
```



```{r}
###### Linear Mixed models VO2
lmModel5 = lmer(VO2 ~ Condition + Sex + (1|ID),
                data=Df, REML=FALSE)
summary(lmModel5)
# mixed model
anova(lmModel5)
#test of the random effects in the model
rand(lmModel5)


```

```{r}
# Post-hoc pairwise comparisons Holms-Bonferroni correction
pwc5 <- Df %>%
  pairwise_t_test(VO2 ~ Condition, paired = TRUE,
                  p.adjust.method	= "holm")
pwc5%>% kbl(caption = "Pairwise comparison") %>%
  kable_classic(full_width = F, html_font = "Cambria")
```


```{r}
# Effect size Cohen's D with Hedge's g correction for small sample size
Df %>% cohens_d(VO2 ~ Condition,
                paired = TRUE, hedges.correction = TRUE)%>%
  kbl(caption = "Effect Size") %>%
  kable_classic(full_width = F, html_font = "Cambria")
```


```{r}
###### Linear Mixed models HR
lmModel6 = lmer(HR ~ Condition + Sex + (1|ID),
                data=Df, REML=FALSE)
summary(lmModel6)
# mixed model
anova(lmModel6)
#test of the random effects in the model
rand(lmModel6)




```

```{r}
# Post-hoc pairwise comparisons Holms-Bonferroni correction
pwc6 <- Df %>%
  pairwise_t_test(HR  ~ Condition, paired = TRUE,
                  p.adjust.method	= "holm")
pwc6%>%
  kbl(caption = "Pairwise comparison") %>%
  kable_classic(full_width = F, html_font = "Cambria")

```

```{r}

# Effect size Cohen's D with Hedge's g correction for small sample size
Df %>% cohens_d(HR  ~ Condition,
                paired = TRUE, hedges.correction = TRUE)%>%
  kbl(caption = "Effect Size") %>%
  kable_classic(full_width = F, html_font = "Cambria")
```



```{r}
###### Linear Mixed models Lactate
lmModel7 = lmer(Lactate ~ Condition + Sex + (1|ID),
                data=Df, REML=FALSE)
summary(lmModel7)
# mixed model
anova(lmModel7)
#test of the random effects in the model
rand(lmModel7)




```


```{r}
# Post-hoc pairwise comparisons Holms-Bonferroni correction
pwc6 <- Df %>%
  pairwise_t_test(Lactate ~ Condition, paired = TRUE,
                  p.adjust.method	= "holm")
pwc6%>%
  kbl(caption = "Pairwise comparison") %>%
  kable_classic(full_width = F, html_font = "Cambria")


```


```{r}
# Effect size Cohen's D with Hedge's g correction for small sample size
Df %>% cohens_d(Re_Antegrade ~ Condition,
                paired = TRUE, hedges.correction = TRUE)%>%
  kbl(caption = "Effect Size") %>%
  kable_classic(full_width = F, html_font = "Cambria")
```



```{r}

###### Linear Mixed models RPE
lmModel8 = lmer(RPE ~ Condition + Sex + (1|ID),
                data=Df, REML=FALSE)
summary(lmModel8)
# mixed model
anova(lmModel8)
#test of the random effects in the model
rand(lmModel8)


```


```{r}
# Post-hoc pairwise comparisons Holms-Bonferroni correction
pwc8 <- Df %>%
  pairwise_t_test(RPE ~ Condition, paired = TRUE,
                  p.adjust.method	= "holm")
pwc8%>%
  kbl(caption = "Pairwise comparison") %>%
  kable_classic(full_width = F, html_font = "Cambria")


```


```{r}
# Effect size Cohen's D with Hedge's g correction for small sample size
Df %>% cohens_d(RPE ~ Condition,
                paired = TRUE, hedges.correction = TRUE)%>%
  kbl(caption = "Effect Size") %>%
  kable_classic(full_width = F, html_font = "Cambria")
```