---
title: "K&I webstats"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: scroll
    source_code: embed
    
---

```{r setup, include=FALSE, cache=TRUE}
library(flexdashboard); require(lubridate)
```

About this site
===================================================================
`r paste("Date run:", lubridate::today())`

This dashboard presents summary statistics for 12 K&I websites which account for the majority of web hits for K&I products.
The sites are:

- [Fingertips](http://fingertips.phe.phe.org.uk)
- [PHOF](http://www.phoutcomes.info)
- [NOO](http://www.noo.org.uk)
- [CHimat](http://www.chimat.org.uk)
- [Local health](http://www.localhealth.org.uk)
- [Mental health intelligence network ](http://www.yhpho.org.uk/default.aspx?RID=198138)
- [YHPHO](http://www.yhpho.org.uk)
- [Healthier lives](http://healthierlives.phe.org.uk)
- [End of life intelligence network](http://www.endoflifecare-intelligence.org.uk/home)
- [IHAL](https://www.improvinghealthandlives.org.uk)
- [SHAPE](https://shape.phe.org.uk)
- [Tobacco profiles](http://www.tobaccoprofiles.info)

(Note: PHOF, Healthier lives and Tobacco profiles are built on the Fingertips platform)

The dashboard shows:

*In the summary statistics tab *

1. Hits for yesterday, 30 days ago, 365 days ago, and % change from this day last year for each site
2. Hits for the last month and the last year for each site 
3. Global hits for all sites combined

*In the heat maps tab* 

1. Daily hits since April 2013 represented as a 'calendar' heatmap



Summary statistics
===================================================================

```{r}

  library(dplyr)         # data manipulation
  library(data.table)    # fast file download and combination
  library(lubridate)    # date manipulation
  library(tidyr)      # reshaping
  require(knitr)         # table and html
  library(RGA)          # access to Google analystics API
  library(gridExtra)    # chart layout
  library(ggplot2)      # charting
  library(viridis)
  library(ggfortify)     # charting extras
  library(ggTimeSeries)   # charting extras
```

```{r}
## authorise access to google API
ga_token <- authorize(client.id = "934830359575-tbflfk33ce99kgatrte3ecpt4gj5kdhf.apps.googleusercontent.com", client.secret = "l5BP84XI1I5d0qrC3f21s9Gp", cache = TRUE)

## extract profile ids, website urls and site names

lookup <- list_profiles()
lookup1 <- as.data.frame(lookup[c(3,23,42,45,46,63, 112,122,137,133,149,150),c(8,5,1)])

## extract data and create data table
ids <- as.character(lookup1[,"id"])
df <- data.frame()

for(i in 1:length(ids)) {
  id<-ids[i]
  first <- firstdate(id)
  ga <- get_ga(id, start.date = first, end.date = "today",
            metrics = "ga:users,
            ga:sessions,
            ga:avgTimeonPage,
            ga:pageviews",
            dimension = "ga:date" )
  ga <- cbind(id, ga)
  df <- rbind(df, ga)
}

df <- as.data.table(df) ## convert to data.table format

df$id <- as.character(df$id) ## convert id to character 
lookup1 <- as.data.table(lookup1) ## convert lookup to data.table format for linkage

## set keys
setkey(df, id) 

setkey(lookup1, id)

## add names and urls to data frame
df1 <- df[lookup1]

## convert and shorten date
df1$date <-ymd(substring(df1$date, 1, 10))

df1 <- df1 %>% unite("webname", name:websiteUrl, sep = "_")

df1 <- df1 %>% mutate(dwell.time = pageviews * avgTimeonPage / 86400)
```


```{r}
## Extract specific date values

df2 <- df1 %>% group_by(webname) %>% 
  filter(date == lubridate::today() - 1 | date == lubridate::today() - 30|date == lubridate::today() - 365 ) %>% 
  select(webname, date, pageviews) %>%
  spread(date, pageviews) 

names(df2)[2:4] <- c("Last year", "30 days ago", "Yesterday" )

df3 <- df2 %>% 
  mutate(`Annual % change`= paste(round(100 *(Yesterday - `Last year`)/`Last year`,2), "%")) 


## and sum over time
df4 <- df1 %>% group_by(webname) %>% 
  filter(date >=lubridate::today() - 30) %>% 
  summarise(monthtotal = sum(pageviews))

  
  df5 <- df1 %>% group_by(webname) %>% 
    filter(date >=lubridate::today() - 365) %>% 
    summarise(yeartotal = sum(pageviews))
  
## and for total hits
totpv <- df1 %>% group_by(date) %>% summarise(pageviews = sum(pageviews)) 

# totpv1 <- totpv %>%  
#   filter(date == lubridate::today() - 1 | date == lubridate::today() - 30|date == lubridate::today() - 364 ) %>% 
#   select(date, pageviews) %>%
#   spread(date, pageviews) 
# 
# names(totpv1)[1:3] <- c("this day last year", "30 days ago", "yesterday" )
# 
# totpv2 <- totpv1 %>% 
#   mutate(`Annual % change `= paste(round(100 *(yesterday - `this day last year`)/`this day last year`,2), "%")) 
# 
# totpv3 <- totpv %>%  
#   filter(date >=lubridate::today() - 30) %>% 
#   summarise(monthtotal = sum(pageviews))
# 
#   
# totpv4 <- totpv %>%  
#     filter(date >=lubridate::today() - 365) %>% 
#     summarise(yeartotal = sum(pageviews))
# 
# totsum <- cbind(totpv2,totpv3, totpv4 )


dfsum <- merge(df3, df4, by = 'webname')
dfsum <- merge(dfsum, df5, by = 'webname')
dfsum <- dfsum %>% arrange(desc(yeartotal)) 
# dfsum <- rbind(dfsum, totsum)
rownames(dfsum) <- c("NOO", "Fingertips", "CHimat", "Localhealth", "PHOF", "IHAL", "YHPHO", "Mental health", "End of Life", "Healthier Lives", "SHAPE", "Tobacco")
dfsum <- dfsum %>% select(-webname)
```

Row
-------------------------------------------------------------------
### Total pageviews yesterday

```{r}
yesterday <- colSums(dfsum[, -4])[[3]]
valueBox(yesterday, "Yesterday hits", icon = "fa-thumbs-up")
```

### Total pageviews in last month
```{r}
lastmonth <- colSums(dfsum[, -4])[[4]]
valueBox(lastmonth, "Last month hits ", icon = "fa-thumbs-up")
```

### Total pageviews in last year
```{r}
lastyear <- colSums(dfsum[, -4])[[5]]
prevyear <- totpv %>% 
    filter(date <=lubridate::today() - 365 & date > lubridate::today() - 730) %>% 
    summarise(prevyeartotal = sum(pageviews))
valueBox(lastyear, "Last year hits", icon = "fa-line-chart", 
         color = ifelse(lastyear < prevyear, "warning", "primary"))
```

### Summary table
```{r}
DT::datatable(dfsum) 
df1 <- select(df1, date, users, sessions, pageviews, avgTimeonPage, dwell.time, webname) ## select relevant fields
```

Heatmaps
====================================================================
Row {.tabset}
-----------------------------------------------------------------------

### Total

```{r}
totpv <- df1 %>% group_by(date) %>% summarise(Pageviews = sum(pageviews)) 
gtot <- ggplot_calendar_heatmap(filter(totpv,date >= "2013-04-01"), "date", "Pageviews")
gtot <-gtot + scale_fill_viridis(option = "D") + facet_wrap(~Year, ncol = 1)

gtot + ggtitle("Total pageviews of K&I web products")



```

### Chimat

```{r}
gch <- ggplot_calendar_heatmap(filter(df1,date >= "2013-04-01" & webname == "www.chimat.org.uk_http://www.chimat.org.uk"), "date", "pageviews")
gch <-gch + scale_fill_viridis(option = "D") + facet_wrap(~Year, ncol = 1)

gch + ggtitle("Total pageviews: Chimat")
```

### PHOF

```{r}
gch <- ggplot_calendar_heatmap(filter(df1,date >= "2013-04-01" & webname == "PHOF_http://www.phoutcomes.info"), "date", "pageviews")
gch <-gch + scale_fill_viridis(option = "D") + facet_wrap(~Year, ncol = 1)

gch + ggtitle("Total pageviews: PHOF")
```

### Fingertips

```{r}
gft <- ggplot_calendar_heatmap(filter(df1,date >= "2013-04-01" & webname == "Fingertips site data_http://phe.org.uk"), "date", "pageviews")
gft <-gft + scale_fill_viridis(option = "D") + facet_wrap(~Year, ncol = 1)

gft <- gft + ggtitle("Total pageviews: Fingertips")


gft
```

### NOO

```{r}
gnoo <- ggplot_calendar_heatmap(filter(df1,date >= "2013-04-01" & webname == "www.noo.org.uk/_http://www.noo.org.uk/"), "date", "pageviews")
gnoo <-gnoo + scale_fill_viridis(option = "D") + facet_wrap(~Year, ncol = 1)

gnoo + ggtitle("Total pageviews: NOO")

if(!require(plotly))install.packages("plotly")
library(plotly)

```

### Local health

```{r}

gnoo <- ggplot_calendar_heatmap(filter(df1,date >= "2013-04-01" & webname == "www.localhealth.org.uk_http://www.localhealth.org.uk"), "date", "pageviews")
gnoo <-gnoo + scale_fill_viridis(option = "D") + facet_wrap(~Year, ncol = 1)

gnoo + ggtitle("Total pageviews: Local health")

if(!require(plotly))install.packages("plotly")
library(plotly)

```