Mindfulness websearch

Load google data

library(zoo)

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

uk.90 <- read.zoo(file="~/Downloads/report-5.csv", sep=",", format="%Y-%m-%d", header=T)
de.90 <- read.zoo("~/Downloads/report-4.csv", sep=",", format = "%Y-%m-%d", header=T)
ww.M <- read.zoo("~/Downloads/report.csv", header=T, sep=",")
ww.MB <- read.zoo("~/Downloads/report-6.csv", header=T, sep=",")

Plot the data

# library(devtools)
# install_github('sinhrks/ggfortify')
library(ggfortify)
plot.ww.M <- autoplot(ww.M, colour="#0099cc") + 
  theme(legend.position="none") + 
  ggtitle("Weltweites Google Suchinteresse zum Begriff 'Mindfulness'\n2004 - 2016") + 
  ylab("Quote") + 
  xlab("Datum")

plot.ww.MB <- autoplot(ww.MB, facets = NULL) + 
  scale_color_discrete(name="Suchbegriff", labels=c("mindfulness", "burnout"))+
  ggtitle("Weltweites Google Suchinteresse zum Begriff 'Mindfulness'\n2004 - 2016") + 
  ylab("Quote") + 
  xlab("Datum")

plot.de <- autoplot(de.90, facets=NULL) + 
  scale_color_discrete(name="Suchbegriff", labels=c("mindfulness", "burnout", "achtsamkeit"))+
  ggtitle("Google Suchinteresse in den letzten 90 Tagen\n Deutschland") + 
  ylab("Quote") + 
  xlab("Datum")

plot.uk <- autoplot(uk.90, facets = NULL) + 
  scale_color_discrete(name="Suchbegriff", labels=c("mindfulness", "burnout"))+
  ggtitle("Google Suchinteresse in den letzten 90 Tagen\nUK") + 
  ylab("Quote") + 
  xlab("Datum")
plot.ww.M

## Warning: Removed 1 rows containing missing values (geom_path).

plot.ww.MB

## Warning: Removed 2 rows containing missing values (geom_path).

plot.de

## Warning: Removed 9 rows containing missing values (geom_path).

plot.uk

## Warning: Removed 6 rows containing missing values (geom_path).

#### Forecast future trend

library(forecast)

## Warning: package 'forecast' was built under R version 3.2.5

## Loading required package: timeDate

## This is forecast 7.1

ww.arima <- auto.arima(ww.M)
ww.forecast <- forecast(ww.arima, level=c(95), h=150)
autoplot(ww.forecast) + 
  ggtitle("Prognose der Entwicklung des weltweiten Suchinteresse\n2016 - 2020") + 
  ylab("Quote") + 
  xlab("Datum")

## Warning: Removed 1 rows containing missing values (geom_path).