final_catwalk_analysis.rmd

---
title: "Final Catwalk Analysis"
author: "Tom"
date: "March 1, 2018"
output: html_document
---

```{r, setup, message = F, warning=F}
#====================================
library(tidyverse)
library(broom)
library(skimr)
library(purrr)
library(agricolae)
library(emmeans)
library(stringr)
library(writexl)
library(beepr)
#====================================
```


### Read data and take a look

```{r, read in data}
raw_catwalk <- read_csv("catwalk_data.csv")
```

```{r}
glimpse(raw_catwalk)
```
  
*** 
  
###

```{r}
cat_df <- raw_catwalk %>% 
  filter(gt != "1") %>% 
  mutate(age = factor(age, labels = c("4 Month", "10 Month", "17 Month")),
         gt  = factor(gt, labels = c("GCLM +/+", "GCLM -/-")))
```
### Custom function to calculate standard error (SE)
  
***
  
```{r}

se <- function(x) {
  sd(x)/sqrt(length(x))
}
```
  
***
  
### Descriptive Statistics
Summarize at multiple columns and apply multiple functions, in this case we calculate the mean and se for all of our outcome measures.

```{r}
cat_sum_df <- cat_df %>% 
  group_by(sex, age, gt) %>% 
  summarize_at(vars(run_speed:hind_stepcyc), funs(mean, se)) 
cat_sum_df

```
  
***
  
### Publication Graph 

```{r}
pd_bar <- position_dodge(width = 0.9)

ggplot(data = cat_sum_df, 
       aes(x = age, y = run_speed_mean, fill = gt,  
           group = gt)) +
  geom_errorbar(aes(ymin = run_speed_mean - run_speed_se, 
                    ymax = run_speed_mean + run_speed_se), width = 0.5, position = pd_bar) +
  geom_bar(color = "black", stat = "identity", width = 0.7, position = pd_bar) +
  
  facet_grid(~sex) +
  theme_bw() +
  scale_fill_manual(values = c("white","black")) +
  theme(legend.position = "NULL",
        legend.title = element_blank(),
        axis.title = element_text(size = 20),
        legend.background = element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        axis.text = element_text(size = 12))
```
  
***

### Prep for analysis, calculate ANOVAs and post-hocs
  
```{r}
# create dataframe for analysis
catstat_df <- raw_catwalk %>% 
  filter(gt != "1") %>% 
  mutate(age = factor(age, labels = c("YG", "AD", "OLD")),
         gt = factor(gt, labels = c("WT", "KO")),
         sex = factor(sex, labels = c("M", "F")))

catstat_df %>% 
  group_by(sex) %>% 
  count()
```
  
***
  
```{r}
# have to set to helmert contrasts fpr type 3 SS ANOVAs
options(contrasts = c("contr.helmert", "contr.poly"))
```
  
***
  
```{r}
# calculate catwalk ANOVAs and write to Excel
catstat_df %>% 
  select(run_speed:hind_stepcyc) %>% 
  map(~ car::Anova(lm(.x ~ sex * age * gt, data = catstat_df))) %>% 
  map(~ broom::tidy(.)) %>% 
  map(~ mutate(.x, p.value = round(p.value, 5)), .id = 'source') %>% 
  write_xlsx(., "catwalk_anovas.xlsx")

catstat_df %>% 
  select(run_speed:hind_stepcyc) %>% 
  map(~ car::Anova(lm(.x ~ sex * age * gt, data = catstat_df, contrasts = c("contr.helmert", "contr.poly")), type = 3)) %>% 
  map(~ broom::tidy(.)) %>% 
  map(~ mutate(.x, p.value = round(p.value, 5)), .id = 'source') %>% 
  write_xlsx(., "catwalk_anovas_type3.xlsx")


# calculate LSD post-hocs and write to excel
catstat_df %>% 
  select(run_speed:hind_stepcyc) %>% 
  map( ~ pairwise.t.test(.x,
                  catstat_df$sex:catstat_df$age:catstat_df$gt, 
                  p.adj = "none")) %>% 
  map( ~ tidy(.x)) %>% 
  map( ~ mutate(.x, p.value = round(p.value, 5))) %>%
  map( ~ filter(.x, p.value < 0.051)) %>% 
  write_xlsx(., "lsd_test.xlsx") # dont forget to rename
```

```{r}
.Last <- function() { # Beep on exiting session
  beepr::beep(4)
  Sys.sleep(1)
}
.Last
```