Many scientists are based out of corporations or universities who contract with Microsoft, so Excel would be the default solution for working with spreadsheets.
Also, when it comes to “office” applications, there is no real substitute for Excel. Word processing, presentations, email, notes; there are many open and closed source alternatives that will do the same if not better than MS Office applications. Excel, however, is the exception.
LibreOffice Calc, G-Sheets, Apple’s Numbers, or the myriad of competitor office solutions have never matched Excel for in-depth analyses or overall function. For just basic features, one could limp by with most alternatives, but doing real analytical work within spreadsheets requires Excel.
“Real analytical work” shouldn’t be done in spreadsheets at all. You should use a database. Basic spreadsheet features are all you should ever use spreadsheet software to do anyway.
While you will commonly hear that you shouldn’t use Excel as a database, it happens all the time.
Excel is generally more accessible than something like Access or other proprietary database applications, and given that a lot of initial data originally lives in a spreadsheet, it’s the simplest solution that doesn’t require something like SQL coding knowledge to access.
Basic spreadsheet features are all you should ever use spreadsheet software to do anyway.
It depends on what you mean when you say “basic”. A spreadsheet with filters or maybe some pivot tables? A spreadsheet connecting to 12 others with refreshes created using VBA code so that end users just need to click a button and see their data? A spreadsheet that connects to a database, runs several queries, and spits out data in an easy to read form? There are folks who consider pivot tables and the use of any code to be “advanced” use of Excel. There are also folks who have made full-on applications with Excel and consider those to be made with only “intermediate” grade knowledge.
I’ve found that the more you know about an application like Excel, the more you realize what you don’t know.
Excel does 1000 different things, and for 998 of them, there’s at least one better option.
The two things Excel does best: 1) be accessible to everyone from the greenest high schooler to the most senior IT admin. 2) do those 1000 different things at least somewhat competently.
Exactly. Like personally I’d rather do libreoffice for data entry, spit out a csv, and slap that into an R based analyzer, that’s because I have an irrational hate for excel’s graphs compared to ggplot2. I do use excel a lot though in my job because fuck it it just works for basically everything
“Real analytical work” (I will take that to mean work people actually care about and may even pay good money for), is done with whatever does the job, on the given timeframe, and the analyst, researcher, or team are comfortable with. That may well be Excel. Or not. Depending on the task and people. But your audience will always care more for the appropriateness of your analytical approach for the given audience and task, and of course your results, rather than the tools you used to get there. Of course spreadsheets have limitations and one will do well to know them.
I have already seen data having to be thrown away because the researcher copied and pasted it incorrectly from multiple spreadsheets and no one could tell what the correct data was anymore. No one should be doing this if they are responsibly doing “real analytical work”.
As a user you don’t always have access to the database. It’s much easier to work out of Excel than to find the right person to ask in the corporate hierarchy just for them to say no.
I’m as much of an R fangirl as the next lady, but still scientists come from any number of technical skill sets. Hardcore analytics is probably gonna flounder in excel, but if you can’t convince IT to let you have something better you can throw together some chi square test or an anova to get an analysis of your data. And often that will be enough.
No one does real analytical work with excel… If one is using excel, they are doing basic analytical work that can be done pretty much by every spreadsheet software.
It is just habit. People are used to excel, and are not competent enough to use more advanced tools to do real analytical work. And that’s fine. If one is good in a lab doesn’t necessarily need be good in data science
In college a professor gave us some homework to be done in excel, and as the nerd that I am, I asked if Livre Office was ok because I use Linux and have no access to Excel. The professor was like, well in that case everyone do the homework on R or python. My classmates were really mad at me for that.
By experience, being a scientist doesn’t mean one is the smartest guy in the room. Just that one has passion and luck and luxury to pursue that passion.
Many use alternatives to excel (R, python, Matlab, libreoffice).
For others installing a software is challenging enough that they use whatever provided by IT.
The remaining don’t give a sh*it, they are too busy in exploiting or in being exploited. No time to think about what is better
I’ve had the same copy of excel since high school, and it’s done a damn fine job processing experimental date through undergrad, my PhD, and 6 years as a working researcher.
It’s also the software pretty much everyone has, so you can easily share data with collaborators and other researchers. And it has a ton of functionality so you can process and analyze data easily, and create the visuals for papers very easily.
You are completely right, and the Open Science movement is catching on. The idea is to give everyone access to the (anonymised) data and use only tools that are freely accessible, even to scientists from developing countries without Microsoft licenses, so that they too can rerun your analyses and verify your results. You shouldn’t be getting downvoted.
In science, it is important to have verifiable and replicable results. This means everything you use - from ingredients to software - should be transparent. We can’t examine Excel’s source code, so we don’t know if it is working as it claims to be. Most scientific disciplines are moving towards open source, open access etc., and you can’t use Excel in fields like physics or mathematical biology. But molecular biology is a bit of a holdout.
Why are scientists using a paid service such as Excel anyway? Shouldn’t they be using something like Libre Open Office?
Many scientists are based out of corporations or universities who contract with Microsoft, so Excel would be the default solution for working with spreadsheets.
Also, when it comes to “office” applications, there is no real substitute for Excel. Word processing, presentations, email, notes; there are many open and closed source alternatives that will do the same if not better than MS Office applications. Excel, however, is the exception.
LibreOffice Calc, G-Sheets, Apple’s Numbers, or the myriad of competitor office solutions have never matched Excel for in-depth analyses or overall function. For just basic features, one could limp by with most alternatives, but doing real analytical work within spreadsheets requires Excel.
“Real analytical work” shouldn’t be done in spreadsheets at all. You should use a database. Basic spreadsheet features are all you should ever use spreadsheet software to do anyway.
While you will commonly hear that you shouldn’t use Excel as a database, it happens all the time.
Excel is generally more accessible than something like Access or other proprietary database applications, and given that a lot of initial data originally lives in a spreadsheet, it’s the simplest solution that doesn’t require something like SQL coding knowledge to access.
It depends on what you mean when you say “basic”. A spreadsheet with filters or maybe some pivot tables? A spreadsheet connecting to 12 others with refreshes created using VBA code so that end users just need to click a button and see their data? A spreadsheet that connects to a database, runs several queries, and spits out data in an easy to read form? There are folks who consider pivot tables and the use of any code to be “advanced” use of Excel. There are also folks who have made full-on applications with Excel and consider those to be made with only “intermediate” grade knowledge.
I’ve found that the more you know about an application like Excel, the more you realize what you don’t know.
Excel does 1000 different things, and for 998 of them, there’s at least one better option.
The two things Excel does best: 1) be accessible to everyone from the greenest high schooler to the most senior IT admin. 2) do those 1000 different things at least somewhat competently.
Exactly. Like personally I’d rather do libreoffice for data entry, spit out a csv, and slap that into an R based analyzer, that’s because I have an irrational hate for excel’s graphs compared to ggplot2. I do use excel a lot though in my job because fuck it it just works for basically everything
“Real analytical work” (I will take that to mean work people actually care about and may even pay good money for), is done with whatever does the job, on the given timeframe, and the analyst, researcher, or team are comfortable with. That may well be Excel. Or not. Depending on the task and people. But your audience will always care more for the appropriateness of your analytical approach for the given audience and task, and of course your results, rather than the tools you used to get there. Of course spreadsheets have limitations and one will do well to know them.
I have already seen data having to be thrown away because the researcher copied and pasted it incorrectly from multiple spreadsheets and no one could tell what the correct data was anymore. No one should be doing this if they are responsibly doing “real analytical work”.
As a user you don’t always have access to the database. It’s much easier to work out of Excel than to find the right person to ask in the corporate hierarchy just for them to say no.
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I’m as much of an R fangirl as the next lady, but still scientists come from any number of technical skill sets. Hardcore analytics is probably gonna flounder in excel, but if you can’t convince IT to let you have something better you can throw together some chi square test or an anova to get an analysis of your data. And often that will be enough.
Removed by mod
No one does real analytical work with excel… If one is using excel, they are doing basic analytical work that can be done pretty much by every spreadsheet software.
It is just habit. People are used to excel, and are not competent enough to use more advanced tools to do real analytical work. And that’s fine. If one is good in a lab doesn’t necessarily need be good in data science
Gnumeric is superior for numerical evaluation.
Also any analysis on scale will use some proper programming language often in C or Fortran since Excel is simply far too slow.
In college a professor gave us some homework to be done in excel, and as the nerd that I am, I asked if Livre Office was ok because I use Linux and have no access to Excel. The professor was like, well in that case everyone do the homework on R or python. My classmates were really mad at me for that.
By experience, being a scientist doesn’t mean one is the smartest guy in the room. Just that one has passion and luck and luxury to pursue that passion.
Many use alternatives to excel (R, python, Matlab, libreoffice).
For others installing a software is challenging enough that they use whatever provided by IT.
The remaining don’t give a sh*it, they are too busy in exploiting or in being exploited. No time to think about what is better
I’ve had the same copy of excel since high school, and it’s done a damn fine job processing experimental date through undergrad, my PhD, and 6 years as a working researcher.
It’s also the software pretty much everyone has, so you can easily share data with collaborators and other researchers. And it has a ton of functionality so you can process and analyze data easily, and create the visuals for papers very easily.
You are completely right, and the Open Science movement is catching on. The idea is to give everyone access to the (anonymised) data and use only tools that are freely accessible, even to scientists from developing countries without Microsoft licenses, so that they too can rerun your analyses and verify your results. You shouldn’t be getting downvoted.
Why should they do that?
In science, it is important to have verifiable and replicable results. This means everything you use - from ingredients to software - should be transparent. We can’t examine Excel’s source code, so we don’t know if it is working as it claims to be. Most scientific disciplines are moving towards open source, open access etc., and you can’t use Excel in fields like physics or mathematical biology. But molecular biology is a bit of a holdout.