3.2 Importing the Spreadsheet into Rstudio
To get our data into R, we need to save our data in a format and at a place where RStudio can open it.
3.2.1 Saving the data in the right format
Generelly, finding the right format to import Excel can be tricky.
- If you’re using a PC or Mac, it is advised to save your Excel sheet as a comma-separated-values (.csv) file. This can be done by clicking on “save as” and then choosing (.csv) as the output format in Excel.
- With some PCs, RStudios might not be able to open such files, or the files might be distorted. In this case, you can also try to save the sheet as a normal .xslx Excel-file and try if this works.
3.2.2 Saving the data in your working directory
To function properly, you have to set a working directory for RStudio first. The working directory is a folder on your computer from which RStudio can use data, and in which output it saved.
- Therefore, create a folder on your computer and give it a meaningful name (e.g. “My Meta-Analysis”).
- Save your spreadsheet in the folder
- Set the folder as your working directory. This can be done in RStudio on the bottom-right corner of your screen. Under the tile “Files”, search for the folder on your computer, and open it.
- Once you’ve opened your folder, the file you just saved there should be in there.
- Now that you’ve opened the folder, click on the little gear wheel on top of the pane
- Then click on “Set as working directory”
Your file, and the working directory, are now where they should be!
3.2.3 Loading the data
- To import the data, simply click on the file in the bottom-right pane. Then click on Import Dataset…
- An import assistant should now pop up, which is also loading a preview of your data. This can be time-consuming sometimes, so you can skip this step if you want to, and klick straight on “Import”
As you can see, the on the top-right pane called Environment, your file is now listed as a dataset. This means that your data is now loaded and can be used by R and R code commands. Tabular datasets like the one we imported here are called data frames (
data.frame) in R lingo; so when someone is referring to a data frame, know that what she or he is talking about is dataset with columns and rows just like the Excel spreadsheet we imported.
- I also want to give my data a shorter name:
madata. To rename it, I use the following code:
madata <- Meta_Analysis_Data
This “copies” the data, and gives the copy the name
madata dataset now also appears in the Environment pane, which means that it is also loaded into R and usable via code commands.
- Now, let’s have a look at the structure of my data using the
## tibble [18 × 15] (S3: tbl_df/tbl/data.frame) ## $ Author : chr [1:18] "Call et al." "Cavanagh et al." "DanitzOrsillo" "de Vibe et al." ... ## $ TE : num [1:18] 0.709 0.355 1.791 0.182 0.422 ... ## $ seTE : num [1:18] 0.261 0.196 0.346 0.118 0.145 ... ## $ RoB : chr [1:18] "low" "low" "high" "low" ... ## $ Control : chr [1:18] "WLC" "WLC" "WLC" "no intervention" ... ## $ intervention duration: chr [1:18] "short" "short" "short" "short" ... ## $ intervention type : chr [1:18] "mindfulness" "mindfulness" "ACT" "mindfulness" ... ## $ population : chr [1:18] "undergraduate students" "students" "undergraduate students" "undergraduate students" ... ## $ type of students : chr [1:18] "psychology" "general" "general" "general" ... ## $ prevention type : chr [1:18] "selective" "universal" "universal" "universal" ... ## $ gender : chr [1:18] "female" "mixed" "mixed" "mixed" ... ## $ mode of delivery : chr [1:18] "group" "online" "group" "group" ... ## $ compensation : chr [1:18] "none" "none" "voucher/money" "voucher/money" ... ## $ instruments : chr [1:18] "DASS" "PSS" "DASS" "other" ... ## $ guidance : chr [1:18] "f2f" "self-guided" "f2f" "f2f" ...
Although this output looks kind of messy, it’s already very informative. It shows the structure of my data. In this case, i used data for which the effect sizes were already calculated. This is why the variables TE and seTE appear. I also see plenty of other variables, which correspond to the subgroups which were coded for this dataset.
Here is a (shortened) table created for my data
|Author||TE||seTE||RoB||Control||intervention duration||intervention type|
|Call et al.||0.7091362||0.2608202||low||WLC||short||mindfulness|
|Cavanagh et al.||0.3548641||0.1963624||low||WLC||short||mindfulness|
|de Vibe et al.||0.1824552||0.1177874||low||no intervention||short||mindfulness|
|Frazier et al.||0.4218509||0.1448128||low||information only||short||PCI|
|Frogeli et al.||0.6300000||0.1960000||low||no intervention||short||ACT|
|Gallego et al.||0.7248838||0.2246641||high||no intervention||long||mindfulness|
|Hazlett-Stevens & Oren||0.5286638||0.2104609||low||no intervention||long||mindfulness|
|Hintz et al.||0.2840000||0.1680000||low||information only||short||PCI|
|Kang et al.||1.2750682||0.3371997||low||no intervention||long||mindfulness|
|Kuhlmann et al.||0.1036082||0.1947275||low||no intervention||short||mindfulness|
|Lever Taylor et al.||0.3883906||0.2307689||low||WLC||long||mindfulness|
|Phang et al.||0.5407398||0.2443133||low||no intervention||short||mindfulness|
|Rasanen et al.||0.4261593||0.2579379||low||WLC||short||ACT|
|Shapiro et al.||1.4797260||0.3152817||low||WLC||long||mindfulness|
|Warnecke et al.||0.6000000||0.2490000||low||information only||long||mindfulness|