We’re going to open the file in TextEdit or a similar text editor by right-clicking on our file and choosing the appropriate app.Īfter opening the.
You can see below that Microsoft Excel is the default software for opening. What’s important to note here is that we set row.names to False-doing this eliminates the row numbers in our. write.csv(Orange_summ, "Orange_summ.csv", row.names = F) First, we’re going to save our table as a ‘*.csv’ file. Great! Now we have a summary table that we can export to Word.
If you’re interested in learning more about how to summarize data like this, check out our full online course, “The Basics of R (for ecologists)” here. Summarize(mean_circ_mm = mean(circumference), sd_circ_mm = round(sd(circumference), 2)) (To run the code below, just make sure that the 'dplyr' package is installed if not already): # install.packages("dplyr") In the script below I’ve organized the data so that now we have a table called Orange_summ, which shows the mean and standard deviation of the tree circumferences for each age group. Let’s say we want to summarize this dataset to see how the different age groups compare in their growth. So in this dataset, there are five different trees, each of which have been measured at the same time points (age). If we view the data, we can see the following columns: “Tree”, which contains an identifier for each tree that was measured “age”, which contains the age (in days) of the tree at the time of measurement and “circumference”, which is the circumference of the tree trunk, measured in millimeters.
We’re going to use the Orange dataset that comes built into R, which describes the growth of orange trees: # Load the data You can also watch this tutorial as a video if you want to follow along while I code: In this tutorial I show you how to do just that. As scientists, we often have data or results in R that we want to export to Microsoft Word for the reports or publications that we’re writing.
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