---
title: "Ncube fitness training dashboard"
output: 
  flexdashboard::flex_dashboard:
    vertical_layout: fill
    orientation: rows
    source_code: embed
---

<!--- .css scripts can be added directly into html markdown docs --->
<style type="text/css">

.navbar-inverse .navbar-nav>.active>a, .navbar-inverse .navbar-nav>.active>a:hover, .navbar-inverse .navbar-nav>.active>a:focus {
color:gold!important;
background-color: #303030;
border-color: #002a4c;
font-weight: 900;
}

.chart-title {
    border-bottom: 1px solid #d7d7d7;
    color: orange;
    font-size: 20px;
    font-weight: 700;
    padding: 7px 10px 4px;
}

.heading-level1 {  /* chart_title  */
   color: #00467f;
    font-size: 18px;
    font-weight: 500;
}

h4, .h4 {
   color: #002a4c;
    font-size: 15px;
    font-weight: 550;
}

a:visited {
   color: rgb(50%, 0%, 50%);
}

</style>


<!--- the example above changes the header bars colors --->


```{r}
pacman::p_load("tidyverse",
               "tidymodels",
               "magrittr",
               "here", 
               "scales", 
               "glmnet", 
               "stacks", 
               "janitor", 
               "finetune", 
               "vip",
               "data.table",
               "DT",
               "alluvial",
               "extrafont",
               "gt",
               "gtsummary",
               "flexdashboard")

```

Overview
=======================================================================

Row
-----------------------------------------------------------------------

```{r}
df<-readr::read_csv("fitness_class_2212.csv")   #readin
df<-df|> 
  mutate_at(c(5,6,7),as.factor) |>   # change these columns to factors
  mutate(attended2=ifelse(attended==1,"yes","no"),
         day_of_week = 
           fct_collapse(day_of_week,Wed=c("Wed","Wednesday"),
                                  Fri=c("Fri.","Fri"), # collapse redundant levels
                                  Mon=c("Mon","Monday")),
         category=fct_collapse(category,unknown=c("-")), # add category(unknown)
         days_before=readr::parse_number(days_before)) # leave only numbers in the dataset
```

### Total number of members recorded {.value-box}

```{r}
wardTotal <- nrow(df)
valueBox(wardTotal, "Total number of members recorded", icon="fa-desktop", color = ggthemes::tableau_color_pal()(1))
```

### maximum months as member {.value-box}

```{r}
bcTotal <- max(df$months_as_member, na.rm = TRUE)
valueBox(bcTotal, "maximum number of months",  icon="fa-desktop", color = "orange")
```

### attendance proportion {.value-box}

```{r}
perCU <- round(mean(df$attended)*100,2)
valueBox(paste(perCU, "%"), "Proportion of members that attended", icon="fa-desktop", color = tvthemes::avatar_pal()(1))
```

### mean weight of attendees

```{r}
cu_rolling_avg <- mean(df$weight, na.rm = TRUE)          #7 day average CU Boulder
gauge(value = cu_rolling_avg,
      min = min(df$weight,na.rm = T), 
      max = max(df$weight,na.rm = T), 
      sectors = gaugeSectors(success=c(55,85),
                             warning=c(86,100),
                             danger=c(101,200)))                  
```

column {data-width=400}
-----------------------------------------------------------------------

### Proportion of attendees

```{r,fig.height=6,fig.width=10}
loadfonts(quiet=TRUE)

iv_rates <- df |>
  mutate(attended=ifelse(attended2==1,"yes","no"),
         attended=as.factor(attended2)) |> 
  group_by(attended) |>
  summarize(count = n()) |> 
  mutate(prop = count/sum(count)) |>
  ungroup() 

plot<-iv_rates |>
  ggplot(aes(x=attended, y=prop, fill=attended)) + 
  geom_col(color="black",width = 0.5)+ 
  theme(legend.position="bottom") + 
  geom_label(aes(label=scales::percent(prop)), color="white") + 
  labs(
    title = "attendance ratio",
    subtitle = "Fitness analysis",
    y = "proportion(%)", 
    x = "attendance",
    fill="attendance",
    caption="B.Ncube::Data enthusiast") + 
  scale_y_continuous(labels = scales::percent)+ 
  tvthemes::scale_fill_kimPossible()+
  tvthemes::theme_theLastAirbender(title.font="Slayer",
                                   text.font = "Slayer")+
  theme(legend.position = 'right')
plot
```

### attendence status vs numeric variables

```{r,fig.height=6,fig.width=10}
subset <- df |>
  dplyr::mutate(days_before=as.numeric(days_before),
                attended = ifelse(attended==1,"yes","no")) |> 
  dplyr::select(days_before,months_as_member,weight,attended)


# Bring in external file for visualisations
source('functions/visualisations.R')

# Use plot function
plot <- histoplotter(subset, attended, 
                     chart_x_axis_lbl = "attendence status", 
                     chart_y_axis_lbl = 'Measures',
                     boxplot_color = 'navy', 
                     boxplot_fill = '#89CFF0', 
                     box_fill_transparency = 0.2) 

# Add extras to plot
plot + 
  tvthemes::theme_theLastAirbender() +
  tvthemes::scale_color_attackOnTitan()+
  theme(legend.position = 'top') 
 
```

Histograms
===

Row
---
### histogram of months as a member

```{r,fig.height=6,fig.width=10}
library(statip)


min_val <- min(df$months_as_member)
max_val <- max(df$months_as_member)
mean_val <- mean(df$months_as_member)
med_val <- median(df$months_as_member)
mod_val <- mfv(df$months_as_member)


id_02_hist <- df |> 
  ggplot() +
  geom_histogram(aes(x = months_as_member), 
                 fill = "firebrick", alpha = 0.66) +
  labs(title = "months_as_member distribution") +
  theme(plot.title = element_text(hjust = 0.5),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank())+
  ggthemes::scale_fill_tableau()+
  tvthemes::theme_theLastAirbender(title.font="Slayer",text.font = "Slayer")+
  geom_vline(xintercept = min_val, color = 'gray33', linetype = "dashed", size = 1.3)+
  geom_vline(xintercept = mean_val, color = 'cyan', linetype = "dashed", size = 1.3)+
  geom_vline(xintercept = med_val, color = 'red', linetype = "dashed", size = 1.3 )+
  geom_vline(xintercept = mod_val, color = 'yellow', linetype = "dashed", size = 1.3 )+
  geom_vline(xintercept = max_val, color = 'gray33', linetype = "dashed", size = 1.3 )

id_02_hist

```

### log transformed data
```{r,fig.height=6,fig.width=10}

id_02_log_hist <- df |> 
  ggplot() +
  geom_histogram(aes(x = months_as_member), 
                 fill = "firebrick", alpha = 0.66) +
  labs(title = "months_as_member log distribution") +
  scale_x_log10() +
  theme(plot.title = element_text(hjust = 0.5),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank())+
  ggthemes::scale_fill_tableau()+
  tvthemes::theme_theLastAirbender(title.font="Slayer",text.font = "Slayer")

id_02_log_hist
 
```

Counts
===

Row
---
### category ,attendance and time

```{r,fig.height=6,fig.width=10}
df|>
  dplyr::mutate(attended = ifelse(attended==1,"yes","no"))|>
  dplyr::group_by(attended,category,time) |> 
  dplyr::summarize(count=n()) |> 
   dplyr::mutate(prop=count/sum(count)) |> 
  ggplot2::ggplot(aes(x = category, y = count,fill=attended))+
  ggplot2::geom_bar(stat = "identity")+
  tvthemes::scale_fill_avatar()+
  ggplot2::facet_wrap(~time,scales="free")+
  tvthemes::theme_theLastAirbender(title.font="Slayer",
                                   text.font = "Slayer")+
  ggplot2::theme(legend.position = 'right',
        axis.text.x = element_text(face="bold", 
                                   color="brown",
                                   size=8, 
                                   angle=45))
```

Chi-squared tests
===

Row
---
### relationship between time,category and attendance

````{r,fig.height=6,fig.width=10}
df|>
  mutate(weight = replace_na(weight, mean(weight, na.rm = T)),
         attended = ifelse(attended==1,"yes","no"))|> 
  select(attended,time,category) |> 
  tbl_summary(
    by = attended,
    statistic = list(
      all_continuous() ~ "{mean} ({sd})",
      all_categorical() ~ "{n} / {N} ({p}%)"), 
    label = time ~ "time of the day")|> 
  add_p(test = all_continuous() ~ "t.test",
        pvalue_fun = function(x) style_pvalue(x, digits = 2))|> 
   modify_header(statistic ~ "**Test Statistic**")|>
  bold_labels()|> 
  modify_fmt_fun(statistic ~ style_sigfig) 

```

### relationship between day and attendance

````{r,fig.height=6,fig.width=10}
df|>
  mutate(weight = replace_na(weight, mean(weight, na.rm = T)),
         attended = ifelse(attended==1,"yes","no"))|> 
  select(attended,day_of_week) |> 
  tbl_summary(
    by = attended,
    statistic = list(
      all_continuous() ~ "{mean} ({sd})",
      all_categorical() ~ "{n} / {N} ({p}%)"), 
    label = day_of_week ~ "time of week")|> 
  add_p(test = all_continuous() ~ "t.test",
        pvalue_fun = function(x) style_pvalue(x, digits = 2))|> 
   modify_header(statistic ~ "**Test Statistic**")|>
  bold_labels()|> 
  modify_fmt_fun(statistic ~ style_sigfig) 

```
t-tests
===

Row
---
### relationship between variables

````{r,fig.height=6,fig.width=10}
df|>
  mutate(weight = replace_na(weight, mean(weight, na.rm = T)),
         attended = ifelse(attended==1,"yes","no"))|> 
  select(attended,weight,months_as_member) |> 
  tbl_summary(
    by = attended,
    statistic = list(
      all_continuous() ~ "{mean} ({sd})",
      all_categorical() ~ "{n} / {N} ({p}%)"), 
    label = months_as_member ~ "number of months as a member")|> 
  add_p(test = all_continuous() ~ "t.test",
        pvalue_fun = function(x) style_pvalue(x, digits = 2))|> 
   modify_header(statistic ~ "**Test Statistic**")|>
  bold_labels()|> 
  modify_fmt_fun(statistic ~ style_sigfig) 

```
Data
==============================================================

### fitness training dataset

```{r}
datatable(df)
```