Discover and read the best of Twitter Threads about #30DayChartChallenge
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Le #30DayChartChallenge, c’est fini ! 📈📊🗺️
Retrouvez dans ce fil mes contributions pour chaque jour du défi ⬇️
Retrouvez dans ce fil mes contributions pour chaque jour du défi ⬇️
new word of the day: skijoring
I need to design a London tube map-like visualization... any leads for a- tools b-design approaches?
#TidyTuesday #30DayChartChallenge #dataviz @DataVizSociety
#TidyTuesday #30DayChartChallenge #dataviz @DataVizSociety
Already, having no better method at hand I started with pen&paper...
I only need 5 lines, and already that is difficult to layout~!
I only need 5 lines, and already that is difficult to layout~!
I posted this graph yesterday as part of the #30DayChartChallenge and a few people have asked some reasonable questions about why I chose to present this data in this way.
So I thought I'd write a thread to explain my thought process...
So I thought I'd write a thread to explain my thought process...
The first thing to say is that I don't think any dataset has some inherently 'right' type of graph. There are just different graph types which may be more or less effective at highlighting different aspects of the data.
The second is that there is no truly neutral presentation of data. Your choice of graph type, colour, scale, structure and annotations will all influence what message people take away or what patterns people see.
Feels as though I should get in on the #30DayChartChallenge action at some point.
Spent far too long failing to make a custom legend for this, so you'll just have to work it out for yourself.
Spent far too long failing to make a custom legend for this, so you'll just have to work it out for yourself.
R code here:
github.com/VictimOfMaths/…
github.com/VictimOfMaths/…
The black lines are scaled to the peak consumption level across the whole 1961-2019 period. Bars run clockwise from 1961-2019.
Five quick #rstats tips I've picked up in the plots I've made for the first category of the #30DayChartChallenge 🧵👇
1. Need to plot a million points and ggplot() is taking too long but you want to stay within the tidyverse?
👉 Use 📦{scattermore} with geom_scattermore() 🥳
1. Need to plot a million points and ggplot() is taking too long but you want to stay within the tidyverse?
👉 Use 📦{scattermore} with geom_scattermore() 🥳
2.Using several subplots together to tell a story and they each have their own colour scheme?
👉 Create a function to blend in a bit of the same colour into all the others to create a more unified look
👉 Create a function to blend in a bit of the same colour into all the others to create a more unified look
3. Struggling with squished images when using {ggimage}?
👉 Plot the data with a ratio of 1:1 and use coord_equal()
👉 If that's not an option (e.g. x is categorical), adjust the ratio of the x to y increments within coord_fixed(), e.g.:
👉 Plot the data with a ratio of 1:1 and use coord_equal()
👉 If that's not an option (e.g. x is categorical), adjust the ratio of the x to y increments within coord_fixed(), e.g.:
There are so many ways to visualize data. How to select one that is suitable for your dataset and the message you want to convey?
Here are some tools that can help you choose the right chart type – right in time for the #30DayChartChallenge 🧵[1/9]
#DataViz #DataVisualization
Here are some tools that can help you choose the right chart type – right in time for the #30DayChartChallenge 🧵[1/9]
#DataViz #DataVisualization
The Visual vocabulary from the Financial Times (@FinancialTimes) provides a great overview and some general information about each chart type. Very useful for getting inspiration. [2/9]
ft-interactive.github.io/visual-vocabul…
ft-interactive.github.io/visual-vocabul…
@FinancialTimes If you already have a specific goal or message that you want to communicate, then tools that allow you to filter charts based on their function come in handy: [3/9]
