On my trip to Japan, I took this photo of the stairs leading to the “Rucker Park of Tokyo.” I crossed up some Tokyo cats, they were garbage. That one girl behind the blue pillar was practicing her hip hop power moves. She thought no one could see, but I saw.

I’ve been traveling. I’ve been starring places on google maps. I want to share my recs with friends. I also want to receive recs from friends. See this wired article that came out today!

ONE DUMB SNAG: YOU CAN NOT DO THIS SMOOTHLY USING PURE GOOGLE TOOLS

“Google Maps” (what you use on the phone) exports ‘.json’ data

yet

“Google My Maps” (what you share with friends) CANNOT import ‘.json’ data

https://productforums.google.com/forum/#!topic/maps/Ms6ouQuA4qI

For something like this, Gavin Belson would rip a new hole in some unsuspecting Hooli employee. I really hope the engineers of “Google (My) Maps” eventually roll out a backend feature that would make this post obsolete.

DUMB SNAG ELIMINATOR:  #RSTATS IS AWESOME

This is why you’re here, we’re going to fill the middle gap with a very easy #rstats script.

Step 1) Google Takeout > Google My Maps > export json

Step 2) Use R to manipulate json then export a csv spreadsheet

R:::jsonlite::fromJSON()

R:::dplyr::filter()

R:::base::write.csv()

Step 3) Google My Maps > Upload csv spreadsheet via Drag + Drop

https://support.google.com/mymaps/answer/3024925?rd=1

Step 4) Share the url link of your new map with friends

Here’s my Google My Maps of Japan

Spread the word, use this method, play a game of around the world… around the world 😉 , and share your recs.

Shoutouts to Slow Magic and O-nest in Shibuya

HERE IS THE MEAT OF THE #RSTATS CODE FOR STEP 2 (ABOVE). LOOK AT HOW SHORT AND ‘HUMAN-READABLE’ THE SYNTAX IS.

library(jsonlite)
library(dplyr)

# read in .json data
# Google -> Takeout -> Google Maps (My Places) -> Saved Places.json
# https://en.wikipedia.org/wiki/Google_Takeout

txt = '~/projects/Saved Places.json'
dat = fromJSON(txt, flatten = TRUE)

# keep the useful parts
df_feat = flatten(dat$features)
df_dat = df_feat %>% select(`properties.Location.Business Name`,
 `properties.Location.Address`,
 `properties.Location.Geo Coordinates.Latitude`,
 `properties.Location.Geo Coordinates.Longitude`
 )

# subset to specific geographies
# method 1, grep for state in address (easier)

dat_jap = df_dat %>%
 filter(grepl(pattern='Japan',x=properties.Location.Address))

# export to a csv spreadsheet
write.csv(dat_jap,file='~/projects//dat_jap.csv',row.names=FALSE)

# upload csv into Google My Maps to share