Women’s suffrage in Switzerland at the cantonal level

There are several reasons women in Switzerland gained the right to vote only in 1971. The main reason is the direct democracy. There had to be a referendum to give women the right to vote. It’s quite likely that many countries would have introduced the women suffrage later.

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Visualizing a Whatsapp-Conversation

In the last post I showed how I imported a Whatsapp-conversation and tidied it up a bit. Now I want to analyze it. For that I will use the libraries dplyr, stringr and ggplot2.

As a first step, I format the dates properly and create some new columns. I also decide to just focus on two years, 2016 and 2017.

data=data%>%mutate(
  #convert DAte to the date format.
        Date=as.Date(Date, "%d.%m.%y"),
        year = format(Date,format="%y"),
        hour =  as.integer(substring(Time,1,2))
        #I filtered for two year, 2016/17
        )%>%filter(year=='17'|year=='16')

ggplot(data,aes(x=hour))+
  geom_histogram(fill="brown",binwidth=1,alpha=0.9)+
  labs(title="Numbers of Messages by Hour", subtitle="Total of two Years",
       y="Number Messages", x="Hour")

See more about the writing behavior of me an my friend, there is more formatting necessary. The words need to be counted too. To do so I use the stringr-library with str_count(data$Message, "\\S+")

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Importing a WhatApp-Conversation in R

I recently saw some people on Reddit analyzing their chat-conversations and I wanted to try it too. You can export a Whatsapp-Conversation by sending it as an Email to yourself. You will receive a txt-File with all the conversations. Because it isn’t formatted in a useful manner, you have to do it yourself. I will do this in this post and analyse the data in a second one. So this will be a bit more technical than usual. You can find the complete code here.

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A dot-map of Europe

This map is a more leaning on the aesthetic- then data-side. The size of the dots correlates with the size of the population in that place. The color has no meaning and is just there to look nice. For the division of places I used the NUT3 standard which is quite useful, but has its problems if you use it to compare countries. Continue reading “A dot-map of Europe”

How many pigs live in Switzerland, Austria and Germany?

31’642’218 pigs lived in Austria, Switzerland and Germany combined, if we trust the numbers of the statistical ministries. I recently saw that governments release the numbers of farm-animals in a country.

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The Trams of Zurich animated

After I saw a great post on /r/dataisbeautiful where someone mapped a place with the help of location-data of rented bicycles, I searched if there is some similar data available where I live.

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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.

 

Growth of European Countries relative to 1950


How did the populations of European countries grow after the end of the 2nd world-war? To answer this question I downloaded population-data from the United Nations. I picked 1950 as my default year and looked how it developed from there.

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