---
title: "FordGoBike Dashboard"

output: 
  flexdashboard::flex_dashboard:
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(readr)
library(tidyverse)
library(plotly)
library(DT)
library(rpivotTable)
library(lubridate)
```



```{r}
data <-read_csv("data/201904-fordgobike-tripdata.csv")

```


April 2019 
=======================================================================


Column {data-width=650}
-----------------------------------------------------------------------

### User Type

```{r}
data %>% group_by(user_type)  %>% summarise(count= n()) %>% plot_ly( x= ~user_type, y=~count, type = 'bar') %>% layout (xaxis = list (title = "User Type"), yaxis = list(title='count'))
```

Column {data-width=350}
-----------------------------------------------------------------------

### Gender Pie-Chart

```{r}
data %>% group_by(member_gender) %>% summarise(count=n()) %>% plot_ly( labels = ~member_gender, values =~count) %>% layout (xaxis = list (title = "Gender"), yaxis = list(title='count')) %>% add_pie( hole=0.2)
```



```{r}
fgb201801 <- read_csv("data/201801-fordgobike-tripdata.csv")
fgb201802 <- read_csv("data/201802-fordgobike-tripdata.csv")
fgb201803 <- read_csv("data/201803-fordgobike-tripdata.csv")
fgb201804 <- read_csv("data/201804-fordgobike-tripdata.csv")
fgb201901 <- read_csv("data/201901-fordgobike-tripdata.csv")
fgb201902 <- read_csv("data/201902-fordgobike-tripdata.csv")
fgb201903 <- read_csv("data/201903-fordgobike-tripdata.csv")
fgb201904 <- read_csv("data/201904-fordgobike-tripdata.csv")
```


```{r}
fgb201801 <- fgb201801 %>% mutate(start_station_id = as.integer(start_station_id), end_station_id = as.integer(end_station_id))
fgb201802 <- fgb201802 %>% mutate(start_station_id = as.integer(start_station_id), end_station_id = as.integer(end_station_id))
fgb201803 <- fgb201803 %>% mutate(start_station_id = as.integer(start_station_id), end_station_id = as.integer(end_station_id))
fgb201804 <- fgb201804 %>% mutate(start_station_id = as.integer(start_station_id), end_station_id = as.integer(end_station_id))
fgb201901 <- fgb201901 %>% mutate(start_station_id = as.integer(start_station_id), end_station_id = as.integer(end_station_id))
fgb201902 <- fgb201902 %>% mutate(start_station_id = as.integer(start_station_id), end_station_id = as.integer(end_station_id))
fgb201903 <- fgb201903 %>% mutate(start_station_id = as.integer(start_station_id), end_station_id = as.integer(end_station_id))
fgb201904 <- fgb201904 %>% mutate(start_station_id = as.integer(start_station_id), end_station_id = as.integer(end_station_id))


fordgobike2018 <- bind_rows(fgb201801,fgb201802,fgb201803,fgb201804)
fordgobike2019 <- bind_rows(fgb201901, fgb201902, fgb201903, fgb201904)
fordgobike <- bind_rows(fordgobike2018, fordgobike2019)
fordgobike <- fordgobike %>% mutate(age = year(now())-member_birth_year)
fordgobike <- fordgobike %>% mutate(year=year(start_time), month=month(start_time), day=day(start_time) )

```




2018 vs 2019
=====================================================================

Column {data-width=650}
---------------------------------------------------------------------

Overall Age Distribution
```{r}
fordgobike %>% filter( age < 70) %>% ggplot(aes(x=age, color = member_gender)) + geom_histogram(position = "identity") + facet_grid(member_gender~.)
```

Gender User
```{r}
fordgobike%>% ggplot(aes(x=member_gender, y=duration_sec)) + geom_bar(stat="Identity") 
```

------------------------------------------------
Comparison

```{r}
fordgobike %>% ggplot(aes(x=year)) + geom_bar()
fordgobike %>% ggplot(aes(x=month)) + geom_bar() + facet_grid(year ~ .)
fordgobike %>% ggplot(aes(x=day)) + geom_bar() + facet_grid(year ~ .)
```

Pivot Table
```{r}
data_pt <- fordgobike %>% select(member_gender,age) %>% filter( age < 70)
rpivotTable(data_pt, aggregatorName = "Count", cols = "age", rows = "member_gender", rendererName = "Heatmap")
```

Report 
========================================================================
The purpose of this dashboard is to visualize overall picture of the FordGoBike data based on gender, age and user-type. First, I created interactive basic charts with the most recent data available(April 2019). Then, 2018 first 4 months data are compared with corresponding 2019 data.