---
title: "LinkedIn Data Dashboard"
output: 
  flexdashboard::flex_dashboard:
   orientation: rows
   vertical_layout: fill
   #runtime: shiny
   source_code: embed
---

```{r setup, include=FALSE}
#Sys.getlocale("LC_CTYPE")
# Import all needed libraries or install if not found
if(!require(rmarkdown)) install.packages("rmarkdown")
if(!require(shiny)) install.packages("shiny")

if(!require(flexdashboard)) install.packages("flexdashboard")
if(!require(ggplot2)) install.packages("ggplot2")
if(!require(readxl)) install.packages("readxl")
if(!require(tidyverse)) install.packages("tidyverse")
if(!require(plotly)) install.packages("plotly")
if(!require(ggmap)) install.packages("ggmap")
if(!require(data.table)) install.packages("data.table")
if(!require(readr)) install.packages("readr")
if(!require(lubridate)) install.packages("lubridate")
if(!require(zoo)) install.packages("zoo")
if(!require(ggplot2)) install.packages("ggplot2")
if(!require(hrbrthemes)) install.packages("hrbrthemes")
if(!require(forcats)) install.packages("forcats")
if(!require(forcats)) install.packages("forcats")

### --- Read Data from LinkedIn Files --- ###
# Profile
Profile = read.csv("./Input Data/Profile.csv")
First_Name = Profile$First.Name
Last_Name = Profile$Last.Name
Position = Profile$Headline

# Registration
Registration = read.csv("./Input Data/Registration.csv")
Registration$Registered.At = mdy(substr(Registration$Registered.At,1,8))

# Connections
Connections = read.csv("./Input Data/Connections.csv", skip = 3)
Connections = Connections %>%
  mutate(`Connected.On` = dmy(Connected.On)
         , `Month_Connected` = floor_date(`Connected.On`, unit = "months")
         )

# Reactions
Reactions = read.csv("./Input Data/Reactions.csv")
Reactions$Date = as.yearmon(Reactions$Date)

# Invitations
Invitations = read.csv("./Input Data/Invitations.csv")

# Positions
Positions = read.csv("./Input Data/Positions.csv")
Number_Of_Positions = nrow(Positions)
Companies_Worked_For = n_distinct(Positions$Company.Name)

Started_Work = dmy(paste0("01 ",Positions[Number_Of_Positions,5]))

# Estimate Years of experience
Years_Of_Experience = round(as.numeric((Sys.Date() - as.Date(Started_Work))/365),digits = 0)

# Endorsements
Endorsements = read_tsv("./Input Data/Endorsement Received Info.csv")
Endorsements = Endorsements %>%
  mutate(`Endorsement Date` = ymd(substr(`Endorsement Date`,1,10))
         , `Endorsement_Month` = floor_date(`Endorsement Date`, unit = "months")
         , `Endorsement_Year` = year(`Endorsement Date`)
         )

```


Main Info {data-icon="fa-globe"}
=============================
Row {data-width=150}
--------------------------------------
### Profile
```{r}
# Current Working Position on LinkedIn
valueBox(value = paste0(First_Name," ",Last_Name),icon = "far fa-address-card",caption = Position,color = "Green")
```

### Dashboard Generation Date
```{r}
# Date of Registration
dashborad_date = as.Date(Sys.Date())
valueBox(value = dashborad_date,icon = "fas fa-calendar-alt",caption = "Dashboard Generation Date",color = "#add8e6")
```

### Registration Date
```{r}
# Date of Registration
registration = Registration$Registered.At
valueBox(value = registration,icon = "fas fa-calendar-alt",caption = "LinkedIn Registration Date",color = "purple")
```

### Total LinkedIn Connections
```{r}
# Total Connections on LinkedIn
connections_number = Connections %>% nrow()
valueBox(value = connections_number,icon = "fa-user-plus",caption = "Total LinkedIn Connections",color = "green")
```

### Invitations Received
```{r}
# Number of invitations received
invitations_received_number = Invitations %>% filter(Direction == "INCOMING") %>% nrow()
valueBox(value = invitations_received_number,icon = "fas fa-check",caption = "Total Invitations Received Last 6 Months",color = "orange")
```

### Invitations Sent
```{r}
# Number of invitations sent
invitations_sent_number = Invitations %>% filter(Direction == "OUTGOING") %>% nrow()
valueBox(value = invitations_sent_number,icon = "fas fa-angle-double-right",caption = "Total Invitations Sent Last 6 Months",color = "blue")
```


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

### Network Growth
```{r}
# Visualize number of connections by month
conn=Connections %>% group_by(Month_Connected) %>%
  summarise(count=n())

ggplot(conn, aes(x=Month_Connected, y=count)) +
  geom_line(color="#69b3a2"
            , size=1
            , alpha=0.9
            #, linetype=2
            ) +
  #geom_label(aes(label = count)) +
  theme_ipsum() +
  ggtitle("Number of Connections per Month")

```

### Type of Connections
```{r}
# Visualize top 5 connections by type
conn_typ=Connections %>% group_by(Position) %>%
  summarise(count=n()) %>%
  filter(Position != "") %>%
  arrange(count) %>%
  top_n(5)

conn_typ %>%
  mutate(Position = fct_reorder(Position, count)) %>%
  ggplot( aes(x=Position, y=count)) +
    geom_bar(stat="identity", fill="#69b3a2", alpha=.6, width=.4) +
    coord_flip() +
    geom_label(aes(label = count)) +
    xlab("") +
    theme_ipsum() +
  ggtitle("Top 5 LinkedIn Connections")


```

Work Experience and Endorsements {data-icon="fab fa-black-tie"}
=============================
Row {data-width=150}
--------------------------------------

### Positions
```{r}
# Number of different positions held
valueBox(value = Number_Of_Positions,icon = "far fa-address-card",caption = "Number of Positions Held",color = "Purple")
```


### Companies
```{r}
# Number of different companies worked for
valueBox(value = Companies_Worked_For,icon = "fas fa-address-card",caption = "Number of Companies Worked for",color = "Blue")
```

### Year of Experience
```{r}
# Total Years of experience
valueBox(value = Years_Of_Experience,icon = "fas fa-award",caption = "Years of Experience",color = "Green")
```

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

### Endorsements by Year
```{r}
# Visualize number of endorsements by year
endorsements_count = Endorsements %>% group_by(Endorsement_Year) %>%
  summarise(count=n())

ggplot(endorsements_count, aes(x=Endorsement_Year, y=count)) +
 geom_bar(stat="identity"
          , fill="#69b3a2"
          , alpha=.6
          , width=.4
          ) +
  geom_label(aes(label = count)) +
  theme_ipsum() +
  ggtitle("Number of Endorsements per Year")

```

### Type of Endorsements
```{r}
# Visualize top 5 type of endorsements by times endorsed
endorsements_typ=Endorsements %>% group_by(`Skill Name`) %>%
  summarise(count=n()) %>%
  arrange(count) %>%
  top_n(5)

endorsements_typ %>%
  mutate(`Skill Name` = fct_reorder(`Skill Name`, count)) %>%
  ggplot( aes(x=`Skill Name`, y=count)) +
    geom_bar(stat="identity", fill="#69b3a2", alpha=.6, width=.4) +
    coord_flip() +
    xlab("") +
    geom_label(aes(label = count)) +
    theme_ipsum() +
  ggtitle("Top 5 LinkedIn Skills Endorsed")


```