Before we continue, we have to get something out of the way: there are good reasons *not*

to learn R! I do not want to discourage you from learning R --- personally, I think R is

one of the greatest tool a researcher or data analyst can have --- but I also understand

that it's not for everyone and it is important to consider the cost of learning R and the

possible alternatives.

```{r, echo=FALSE, fig.cap="Regardless of what R enthusiasts like me will tell you, R is not the solution to every problem.", fig.alt="A picture of the Evergreen container ship stuck sideways in the Suez Canal"}

First of all, R can be challenging to learn. Learning any programming language requires

continuous practice, and depending on your previous experience and your ability to *"think

like a computer"*, it will take months or years before you can work efficiently in R.

Getting there can sometimes be frustrating. Even if you already know another programming

language, R may be difficult to learn, because it can be a little weird.[^why-not-1] This

investment of time and cognitive energy may never pay off if you only do a small analysis

every once in a while or if your current workflow is already very efficient.

[^why-not-1]: For examples of R's weirdness, I recommend John Cook's talk [*The R Language

The Good The Bad & The Ugly*](https://youtu.be/6S9r_YbqHy8) and Roger Peng's talk

[*Teaching R to New Users: From tapply to Tidyverse*](https://youtu.be/5033jBHFiHE)*.*

There are free alternatives to R that do not require coding. Some of them specialize in

particular parts of the data analysis workflow, for example,

- [OpenRefine](https://openrefine.org/) and

[DataWrangler](http://vis.stanford.edu/wrangler/) for data wrangling, and

- [RAWgraphs](https://rawgraphs.io/), [Flourish](https://flourish.studio/),

[DataWrapper](https://www.datawrapper.de/), and [Tableau

Public](https://public.tableau.com/) for data visualization.

Others offer comprehensive functionality that rivals that of expensive software like SPSS.

[Jamovi](https://www.jamovi.org/) and [JASP](https://jasp-stats.org/), for example, are

graphical user interfaces that are built on top of R and allow you to utilize much of R's

power without writing any code.

```{r, echo=FALSE, fig.cap="There are plenty of fish in the open source sea and Jamovi is a particularly tasty one.", fig.alt="A screenshot of Jamovi's user interface"}

There are many well-rounded commercial software packages for data analysis, such as SPSS,

SAS, Stata, MPlus, and MATLAB. Chances are, you are already familiar with at least one of

them. It's worth considering that some commercial tools are the gold standard for certain

types of analyses and the exact same methods may not be available in R.[^why-not-2]

Moreover, commercial software often does a better job than R at guiding you through the

jungle of statistical tests and models, they provide customer support and there is

normally a single, well-documented way to solve a certain type of problem. In contrast, R

often offers a multitude of ways to reach the same goal. While this allows you to choose

the tools that suit your needs, this choice can be difficult. Consequently, R requires a

more thorough understanding of what your are trying to achieve.

[^why-not-2]: For example, handling missing data with Maximum Likelihood in MPlus is not

possible in the equivalent R package, `{MClust}`.

```{r, echo=FALSE, fig.cap="Welcome to R! Here's how you quit...", fig.alt="A gif of the R console cursor blinking expectantly"}

Point-and-click graphical user interface such as SPSS or Jamovi only work well if you stay

within the limits that their developers had in mind. Programming gives you much more

versatility and flexibility,[^why-not-3] which makes it a useful skill to have under your

belt, even if you end up not using it much. However, R is by no means the only programming

language you could choose to learn. For example,

[^why-not-3]: In computer science this is referred to as the [*expressive

power*](https://en.wikipedia.org/wiki/Expressive_power_(computer_science)) of a

programming language

- [Python](https://www.python.org/) is worth a look if you want to learn a language that

is more universally useful than R while still being excellent for data analysis,

- [Julia](https://julialang.org/) is a great choice If you need a language that is as

focused on interactive scientific computing as R, but more modern and a lot faster

- [Javascript](https://developer.mozilla.org/en-US/docs/Web/JavaScript), the language of

the web, is great if you are interested in building interactive data visualizations,

but it is also shaping up to become a serious contender in the data analysis

space.[^why-not-4]

[^why-not-4]: For example, see <https://js4ds.org/>