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.
In this tutorial I show you how to do just that. You can also watch this tutorial as a video if you want to follow along while I code:
The first step is to load up some data. We’re going to use the
Orange dataset that comes built into R, which describes the growth of orange trees:
# Load the data data(Orange)
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.
head(Orange, 15) ## Tree age circumference ## 1 1 118 30 ## 2 1 484 58 ## 3 1 664 87 ## 4 1 1004 115 ## 5 1 1231 120 ## 6 1 1372 142 ## 7 1 1582 145 ## 8 2 118 33 ## 9 2 484 69 ## 10 2 664 111 ## 11 2 1004 156 ## 12 2 1231 172 ## 13 2 1372 203 ## 14 2 1582 203 ## 15 3 118 30
So in this dataset, there are five different trees, each of which have been measured at the same time points (age).
Let’s say we want to summarize this dataset to see how the different age groups compare in their growth. 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. (To run the code below, just make sure that the
'dplyr' package is installed if not already):
# install.packages("dplyr") library(dplyr) Orange_summ <- group_by(Orange, "days"=age) %>% summarize(mean_circ_mm = mean(circumference), sd_circ_mm = round(sd(circumference), 2)) Orange_summ ## # A tibble: 7 x 3 ## days mean_circ_mm sd_circ_mm ## <dbl> <dbl> <dbl> ## 1 118 31 1.41 ## 2 484 57.8 8.17 ## 3 664 93.2 17.2 ## 4 1004 134. 25.9 ## 5 1231 146. 29.2 ## 6 1372 173. 32.8 ## 7 1582 176. 33.3
Great! Now we have a summary table that we can export to Word. First, we’re going to save our table as a ‘*.csv’ file.
write.csv(Orange_summ, "Orange_summ.csv", row.names = F)
What’s important to note here is that we set
row.names to False—doing this eliminates the row numbers in our .csv file, since we don’t need them.
Next, open the .csv file. You can see below that Microsoft Excel is the default software for opening .csv files, but we don’t want that. 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.
It should look something like this.
After opening the .csv file in your text editor app, just copy and paste the text onto a blank Microsoft Word document.
In Word, highlight the text, and then go to Table » Convert » Convert Text to Table…
That will open a window where you should check that the number of columns is correct, and make sure you have chosen “Commas” in the “Separate text at” section. That’s because you saved the file as a .csv, or “comma-separated values” file.
Click “OK” and we have a table!
Next, use the “Find and Replace” function to clean up the table by going to Edit » Find » Replace. (The Mac keyboard shortcut for this is Shift + Command + H).
We want to get rid of all the double quotes in our table, so put double quotes “ in the top bar, and leave the bottom bar blank. Then click “Replace all”. Word should have found 6 replacements. This is definitely something that could have been fixed manually in this case since there are only 6 occurrences, but if your table contains a character or factor column, all the values in that column will end up having double quotes around them, so that’s where this trick comes in handy…
Now just rename the columns and reformat the table to make it nice and polished. Word has several border editing tools that allow you to change which borders are visible. I like to remove all borders first. Then, by putting your cursor in a table cell, you can go to Table Design » Border Painter, which lets you “paint” in whichever borders you do want to add.
And that’s it! You’ve just exported your first table from R into Microsoft Word.
Also be sure to check out R-bloggers for other great tutorials on learning R