Firstly, data manipulation has been the most challenging thing to learn coming from SPSS/SAS to R. I've found, personally, that getting the data in the right shape for an analysis is usually much more difficult than the analysis itself. Secondly, a true understanding of how to deal with categorical values through the use of factors. Lastly, summary statistics and descriptives can sometimes be challenging to get in a format that is transmutable to PPT or Excel which are what (my) clients generally expect/demand for reporting.
I would focus on:
1 Data manipulation
Understanding data structures. Import/Export. Then in-depth training on the use of packages like plyer, reshape with a particular focus on how to effectively use cast with formulas and melt with ids. How to apply numerical functions within a data.frame using ddply.
2 Factoring Data
In general, an explanation of dealing with recoding with, epicalc or a user-defined function. Also an explanation of the significance of factors, levels, and labels
3 Descriptives
Take a few minutes to introduce xtabs(), table(), prop.table() using cast() from reshape to create columnar tables of data that are more reasonably exported to Excel.
Graphics are optional, if you've done a good job of the above they should be able to get the data they need to create graphs in whatever software they are most comfortable with.
4 Graphics
If you've done a good job teaching the data manipulation, getting data into the shape needed for graphing should be pretty straightforward (or at least reproducible) at this point. ggplot2 is complicated and requires a day just by itself to be played with. But it is possible to give a quick overview of it. Alternatively, base graphics are simple to understand and the help is much more clear on what things do and how the syntax works.
Note: I left out statistical analysis. However, an overview of lm() and perhaps anova(), or cor() would be helpful as a start point. But this should be explained at the same time as data.manipulation.
虽然我“写了这本书”关于从 R 到 SPSS 的迁移，但它的目标读者是程序员和据我所知更喜欢“点击”的大多数 SPSS 用户。像 Deducer（或 R Commander）这样的图形用户界面可以帮助他们有宾至如归的感觉，同时教他们 R 编程代码如何工作（如果他们想查看它）。 Deducer 的绘图生成器也做得很好，可以让您轻松创建复杂的绘图，如果您想学习 ggplot2 代码，它也会向您展示这一点。伊恩做得很好！
然而，虽然 SPSS 图形用户界面涵盖了 SPSS 功能的 98%，但 Deducer 可能只涵盖了 R 功能的 1%。这可能仍然是普通研究人员所需的 75%，但 R 的应用范围非常广泛，为了充分利用它，人们需要学习编程。我的书“R for SAS and SPSS Users”的免费版本只有 80 页，只有 80 页。涵盖了我认为最有可能让初学者感到困惑的编程领域。它位于 http://r4stats.com。
Although I "wrote the book" on R to SPSS migration, that was aimed at programmers and most SPSS users that I know prefer to "point-and-click" instead. A graphical user interface like Deducer (or R Commander) can help them feel at home while teaching them how R programming code works if they want to see it. Deducer's Plot Builder also does a nice job letting you create complex plots easily, and if you want to learn to ggplot2 code, it will show you that as well. Ian did a great job with it!
However, while the SPSS graphical user interface covers 98% of what SPSS can do, Deducer covers perhaps 1% of what R can do. That's probably still 75% of what your average researcher needs, but R is so broad that to get the most out of it people will need to learn to program. The free version of my book, "R for SAS and SPSS Users" is only 80 pages & covers the areas of programming that I think are most likely to confuse beginners. It's at http://r4stats.com.
就在最近，我有一个学生，他对统计学有一定的了解，并事先在 SPSS 中做了一些分析。然后我向他展示了如何在 R 中做同样的事情。我们浏览了代码并绘制、解释和讨论了每一行。他意识到在 R 中做这件事是多么容易和方便。因此，R 社区增长了 1。:)
Just recently I've had a student who was somewhat versed in statistics and did some analysis beforehand in SPSS. I then showed him how to do the exact same thing in R. We went through the code and plotting, explaining and debating each line. He realized how easy and convenient it is to do it in R. Thus, R community grew by 1. :)
The biggest issue that the researchers I've dealt with have is the lack of point-and-click GUI. While there are a number of efforts out there in the R community, none of them have reached the ease-of-use/power level that SPSS has.
Since coding is second nature to R users, sometimes we forget that the majority of users of statistical software can't program (and would avoid it like the plague), even though they may have a strong practical understanding of statistics.
If I had one day to bring an SPSS user into R, I'd start them on Deducer. Deducer is an R GUI project (Self promotion note: I'm the author) that should feel very familiar to a user coming from SPSS. As they find themselves needing more advanced functions, they will naturally move to the command line to fulfill their needs.
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首先，从 SPSS/SAS 到 R，数据操作一直是学习中最具挑战性的事情。我个人发现，为分析获取正确形状的数据通常比分析本身困难得多。其次，真正理解如何通过使用因素来处理分类值。最后，汇总统计数据和描述性有时很难采用可转换为 PPT 或 Excel 的格式，而这正是（我的）客户通常期望/要求的报告格式。
我将重点关注：
1 数据操作
了解数据结构。导入/导出。然后深入培训 plyer、reshape 等软件包的使用，特别关注如何有效地使用带有公式的铸造和带有ID的融化。如何使用 ddply 在 data.frame 中应用数值函数。
2 分解数据
一般来说，解释使用 Epicalc 或用户定义的函数处理重新编码。还解释了因素、级别和标签的重要性
3 描述性
花几分钟介绍 xtabs()、table()、prop.table() 使用 reshape 中的cast() 创建更合理的数据柱状表导出到 Excel。
图形是可选的，如果您很好地完成了上述工作，他们应该能够获得在他们最熟悉的任何软件中创建图形所需的数据。
4 图形
如果您已经很好地教授了数据操作，那么此时将数据转换为图形所需的形状应该非常简单（或至少是可重现的）。 ggplot2 很复杂，需要一天时间才能玩完。但可以对其进行快速概述。另外，基础图形很容易理解，并且帮助对于功能和语法如何工作更加清晰。
注：我省略了统计分析。然而，对 lm() 以及 anova() 或 cor() 的概述作为起点会有所帮助。但这应该与 data.manipulation 同时解释。
Firstly, data manipulation has been the most challenging thing to learn coming from SPSS/SAS to R. I've found, personally, that getting the data in the right shape for an analysis is usually much more difficult than the analysis itself. Secondly, a true understanding of how to deal with categorical values through the use of factors. Lastly, summary statistics and descriptives can sometimes be challenging to get in a format that is transmutable to PPT or Excel which are what (my) clients generally expect/demand for reporting.
I would focus on:
1 Data manipulation
Understanding data structures. Import/Export. Then in-depth training on the use of packages like plyer, reshape with a particular focus on how to effectively use cast with formulas and melt with ids. How to apply numerical functions within a data.frame using ddply.
2 Factoring Data
In general, an explanation of dealing with recoding with, epicalc or a user-defined function. Also an explanation of the significance of factors, levels, and labels
3 Descriptives
Take a few minutes to introduce xtabs(), table(), prop.table() using cast() from reshape to create columnar tables of data that are more reasonably exported to Excel.
Graphics are optional, if you've done a good job of the above they should be able to get the data they need to create graphs in whatever software they are most comfortable with.
4 Graphics
If you've done a good job teaching the data manipulation, getting data into the shape needed for graphing should be pretty straightforward (or at least reproducible) at this point. ggplot2 is complicated and requires a day just by itself to be played with. But it is possible to give a quick overview of it. Alternatively, base graphics are simple to understand and the help is much more clear on what things do and how the syntax works.
Note: I left out statistical analysis. However, an overview of lm() and perhaps anova(), or cor() would be helpful as a start point. But this should be explained at the same time as data.manipulation.
虽然我“写了这本书”关于从 R 到 SPSS 的迁移，但它的目标读者是程序员和据我所知更喜欢“点击”的大多数 SPSS 用户。像 Deducer（或 R Commander）这样的图形用户界面可以帮助他们有宾至如归的感觉，同时教他们 R 编程代码如何工作（如果他们想查看它）。 Deducer 的绘图生成器也做得很好，可以让您轻松创建复杂的绘图，如果您想学习 ggplot2 代码，它也会向您展示这一点。伊恩做得很好！
然而，虽然 SPSS 图形用户界面涵盖了 SPSS 功能的 98%，但 Deducer 可能只涵盖了 R 功能的 1%。这可能仍然是普通研究人员所需的 75%，但 R 的应用范围非常广泛，为了充分利用它，人们需要学习编程。我的书“R for SAS and SPSS Users”的免费版本只有 80 页，只有 80 页。涵盖了我认为最有可能让初学者感到困惑的编程领域。它位于 http://r4stats.com。
Although I "wrote the book" on R to SPSS migration, that was aimed at programmers and most SPSS users that I know prefer to "point-and-click" instead. A graphical user interface like Deducer (or R Commander) can help them feel at home while teaching them how R programming code works if they want to see it. Deducer's Plot Builder also does a nice job letting you create complex plots easily, and if you want to learn to ggplot2 code, it will show you that as well. Ian did a great job with it!
However, while the SPSS graphical user interface covers 98% of what SPSS can do, Deducer covers perhaps 1% of what R can do. That's probably still 75% of what your average researcher needs, but R is so broad that to get the most out of it people will need to learn to program. The free version of my book, "R for SAS and SPSS Users" is only 80 pages & covers the areas of programming that I think are most likely to confuse beginners. It's at http://r4stats.com.
就在最近，我有一个学生，他对统计学有一定的了解，并事先在 SPSS 中做了一些分析。然后我向他展示了如何在 R 中做同样的事情。我们浏览了代码并绘制、解释和讨论了每一行。他意识到在 R 中做这件事是多么容易和方便。因此，R 社区增长了 1。:)
Just recently I've had a student who was somewhat versed in statistics and did some analysis beforehand in SPSS. I then showed him how to do the exact same thing in R. We went through the code and plotting, explaining and debating each line. He realized how easy and convenient it is to do it in R. Thus, R community grew by 1. :)
我接触过的研究人员面临的最大问题是缺乏点击式 GUI。尽管 R 社区做出了许多努力，但没有一个达到 SPSS 的易用性/强大水平。
由于编码是 R 用户的第二天性，有时我们会忘记大多数统计软件用户不会编程（并且会像躲避瘟疫一样避免编程），即使他们可能对统计有很强的实际理解。
如果有一天我要让一名 SPSS 用户使用 R，我会先让他们使用 Deducer。 Deducer 是一个 R GUI 项目（自我推销说明：我是作者），对于来自 SPSS 的用户来说应该感到非常熟悉。当他们发现自己需要更高级的功能时，他们自然会转向命令行来满足他们的需求。
The biggest issue that the researchers I've dealt with have is the lack of point-and-click GUI. While there are a number of efforts out there in the R community, none of them have reached the ease-of-use/power level that SPSS has.
Since coding is second nature to R users, sometimes we forget that the majority of users of statistical software can't program (and would avoid it like the plague), even though they may have a strong practical understanding of statistics.
If I had one day to bring an SPSS user into R, I'd start them on Deducer. Deducer is an R GUI project (Self promotion note: I'm the author) that should feel very familiar to a user coming from SPSS. As they find themselves needing more advanced functions, they will naturally move to the command line to fulfill their needs.