A separate ticket is required to attend this masterclass. If you would like to attend but haven't yet purchased a ticket, please contact us at info@dataharvest.eu
R is one of the most useful programming languages in data journalism. You may have heard of it, maybe even tried it a little and found the learning curve too steep. If so, this session is for you.
We are going to spend the day looking at European Environment Agency’s EPRTR (European Pollutant Release and Transfer Register) data – it’s a lot of data, and some of it is quite messy. It contains dozens, probably hundreds, of potential lines of investigation to be explored – and that’s what we’re going to do.
By the end of the day, you will know how to import data in an R environment, filter it, reshape it, and interrogate it. You will be able to make some basic graphs. Above all, you will be on the way to finding stories in the day’s chosen data, and be able to take your script away and use it again, or adapt it to other datasets. And, we hope, you will have the beginnings of a story idea.
We will assume that you are familiar with spreadsheets, but that you have no knowledge of R. You will not need to install anything – everything will be run on cloud instances of R.
If you’re already advanced with R, it is still worth coming along to use and share what you know, to support others, and to learn something new.
(If you already have a dataset you want to work with – bring that too!)
A separate ticket is required to attend this masterclass. If you would like to attend but haven't yet purchased a ticket, please contact us at info@dataharvest.eu
R is one of the most useful programming languages in data journalism. You may have heard of it, maybe even tried it a little and found the learning curve too steep. If so, this session is for you.
We are going to spend the day looking at European Environment Agency’s EPRTR (European Pollutant Release and Transfer Register) data – it’s a lot of data, and some of it is quite messy. It contains dozens, probably hundreds, of potential lines of investigation to be explored – and that’s what we’re going to do.
By the end of the day, you will know how to import data in an R environment, filter it, reshape it, and interrogate it. You will be able to make some basic graphs. Above all, you will be on the way to finding stories in the day’s chosen data, and be able to take your script away and use it again, or adapt it to other datasets.
We will assume that you are familiar with spreadsheets, but that you have no knowledge of R. You will not need to install anything – everything will be run on cloud instances of R.
If you’re already advanced with R, it is still worth coming along to use and share what you know, to support others, and to learn something new.
(If you already have a dataset you want to work with – bring that too!)
A separate ticket is required to attend this masterclass. If you would like to attend but haven't yet purchased a ticket, please contact us at info@dataharvest.eu
R is one of the most useful programming languages in data journalism. You may have heard of it, maybe even tried it a little and found the learning curve too steep. If so, this session is for you.
We are going to spend the day looking at European Environment Agency’s EPRTR (European Pollutant Release and Transfer Register) data – it’s a lot of data, and some of it is quite messy. It contains dozens, probably hundreds, of potential lines of investigation to be explored – and that’s what we’re going to do.
By the end of the day, you will know how to import data in an R environment, filter it, reshape it, and interrogate it. You will be able to make some basic graphs. Above all, you will be on the way to finding stories in the day’s chosen data, and be able to take your script away and use it again, or adapt it to other datasets.
We will assume that you are familiar with spreadsheets, but that you have no knowledge of R. You will not need to install anything – everything will be run on cloud instances of R.
If you’re already advanced with R, it is still worth coming along to use and share what you know, to support others, and to learn something new.
(If you already have a dataset you want to work with – bring that too!)
