Where have I been in 2018

Last year I made a post about where I have been in 2017. I was worse with R so I solved it with a combination of cleaning up the data with R, importing it into Qgis and finally edited it in HitFilms.

This time I just did it all in R. The difficult part this time was to get the Google API running for the import of Google maps. (You need to enable billing to get the whole thing running.) It was the first time I used the new ggAnimate. It is great and easy to use. Less of a hazzle then the last times I used it.

I could reuse some of last years code, so I was done quite fast. (not the greatest code tough.)


library(tidyverse)
library(jsonlite)
library(ggplot2)
library(ggmap)
library(gganimate)
library(gifski)
library(zoo)
library(lubridate)

register_google(key = “AIzaSyCTCk3yYCPEo1UKVkZm_iQk_r4wPJCHlA4”)

system.time(x <- fromJSON(“GoogleLoc.json”))

# extracting the locations dataframe
loc = x$locations

# converting time column from posix milliseconds into a readable time scale
loc$time = as.POSIXct(as.numeric(x$locations$timestampMs)/1000, origin = “1970-01-01”)

# converting longitude and latitude from E7 to GPS coordinates
loc$lat = loc$latitudeE7 / 1e7
loc$lon = loc$longitudeE7 / 1e7

# calculate the number of data points per day, month and year
loc$date <- as.Date(loc$time, ‘%Y/%m/%d’)
loc$year <- year(loc$date)
loc$month_year <- as.yearmon(loc$date)

#new dataframe with the important units
maps<- data.frame(loc$lat,loc$long,loc$date,loc$time,loc$year)

#filter out the year and convert the longitude to the proper unit.
maps1<-maps%>%filter(loc.year==2018) %>% mutate(longitude = loc.long/10^7)

#choose the 10. measurement of each day. not very elegant, but good enough.
maps2<- maps1 %>% group_by(loc.date) %>%
summarise(long=(longitude[10]),
lat=(loc.lat[10]))

#get background map. set size, zoom, kind of map)
mamap <- get_map(location=c(mean(maps2$long,na.rm=T),mean(maps2$lat,na.rm=TRUE)+3), maptype = “satellite”,zoom=5)

#put it all together.
ggmap(mamap)+
geom_point(data=maps2,aes(x=long,y=lat),size=4, col=”red”)+
geom_label(data=maps2,x=1.5,y=56,aes(label=format(as.Date(loc.date),format=”%d.%m”)),size=10,col=”black”)+
theme_void()+
#the animation part
transition_time(loc.date)+
shadow_trail(alpha=0.3,colour=”#ff695e”,size=2,max_frames = 6)

a=animate(m, renderer = ffmpeg_renderer(),duration=20)
anim_save(filename = “my2018/2018video.mp4”)

Where have I been in 2017

I created this animation in the first days of 2018. A few years ago I accepted that most of my data is stored somewhere. Instead of avoiding this like before, I started to embrace it. In Google Locations for example all the places where you have been are stored (if you didn’t disable it). It can be quite useful to remember what you did a certain day.

This data can also be downloaded and analysed. I didn’t do that, I just wanted to make a nice animation. To do thso at I imported the data it into R with the help of the json-Library. I just chose one value for each day and exported that new data. The next steps could have been done in R too, but I was less experienced with the program back then.

I imported the data to QGIS instead. With the help of the TimeManager-Plugin I exported a frame for each day. I loaded those into Hitfilms Express, which is a fantastic free video-editing software. I used gifmaker.me before to create Gifs, but they have a limit of 300 frames. I exported the video and uploaded it to Gfycat. And here it is.