Archiv der Kategorie: R code

R Studio keyboard shortcuts

When you code a lot in R it is nice to work with the keyboard. There are some commands and code you will repeat again and again and it is useful to know that there are shortcuts for certain code and commands. R studio has made a whole list of shortcuts: rstudio-IDE-cheatsheet. Here a small selection of what I have found useful:

Mac Windows
Move cursor to start of line Cmd+< home
Move cursor to end of line Cmd+> end
Change working directory Ctrl+Shift+H Ctrl+Shift+H
Select to line start/end Cmd+Shift+</> Ctrl+Shift+</>
Insert <- Option+- Alt+-
Insert %>% Cmd+Shift+M Ctrl+Shift+M
Copy line up/down Cmd+Option+▴▾ Shift+Alt+▴▾


Data wrangling with dplyr and tidyr

I’ve been introduced to two R packages lately and they have made my life so much easier. I am talking about dplyr and tidyr. They are great for manipulating data, meaning switching from wide to long data sets, summarize and group data. And they can easily be combined with plotting functions, such as ggplot.

The reason they are so useful is: more efficient coding, the syntax is easy to remember and easy to read. I will not explain each functions here, because there are already many good tutorials on the web. Here is a nice cheatsheet for starters and some more links.

Thank you Richard for introducing me to tidyr and dplyr!

Bildschirmfoto 2016-07-25 um 23.13.54


Using the by function in R

Recently, I have discovered the by function in R. With “by” you can apply any function to a data frame split by a factor. Yes, this sounds difficult, but I will show you how powerful this function is with an example.

Let’s say we have measured petal width and length of 10 individual flowers for 3 different plant species. The data frame has 3 columns (species, petal width and petal length) and 30 rows (3 species x 10 individuals). Here is the code to make this data frame:

# Create a data frame with petal length and petal width for 3 species
dat <- data.frame(species=c(rep(c(1,2,3), each=5)), petal.length=c(rnorm(5, 4.5, 1), rnorm(5, 4.5, 1), rnorm(5, 5.5, 1)), petal.width=c(rnorm(5, 2.5, 1), rnorm(5, 2.5, 1), rnorm(5, 4, 1)))

dat$species <- factor(dat$species) # make species a factor

##    species petal.length petal.width
## 1        1     4.036443   4.2636530
## 2        1     4.805463   2.9856014
## 3        1     4.416011   2.2342611
## 4        1     4.910363   2.6516114
## 5        1     4.683678   3.8766098
## 6        2     6.278742   2.3196057
## 7        2     4.537683   0.9323249
## 8        2     5.676220   2.2392741
## 9        2     3.941464   3.4618104
## 10       2     3.554382   3.3538955
## 11       3     4.834811   4.4187967
## 12       3     5.952030   4.3399565
## 13       3     6.026856   4.5964251
## 14       3     5.269738   5.8714180
## 15       3     6.897427   4.6028704

We can use the by function to calculate the mean petal length for each of the three species.

The first argument is which data frame you want to use. Next you specify by which factor you want split your data frame. And finally you say which function you want to use. In our case this is the mean.

by(dat, dat$species, function(x){
  # caculate the mean petal length for each species <- mean(x$petal.length)
## dat$species: 1
## [1] 4.570392
## -------------------------------------------------------- 
## dat$species: 2
## [1] 4.797698
## -------------------------------------------------------- 
## dat$species: 3
## [1] 5.796172

There are easier ways to calculate the mean of 3 species. But if you have understood the principle of the function, you can use it to do more complicated calculations. Here is another example, to draw a scatter plot for the petal width and petal length and drawing the regression line for each of the three species.

by(dat, dat$species, function(x){
    # caculate the mean petal length for each species <- mean(x$petal.length)

  # draw a plot for each speceis
  plot(x$petal.width, x$petal.length, xlab="petal width", ylab="petal length", ylim=c(2,8))
  abline(lm(petal.length ~ petal.width, x), lty=2)

plot of chunk unnamed-chunk-4

You might have noticed that the output looks a bit strange. This is because the output of the by function is stored in a list. List’s are complicated but also extremely powerful. For example, a data frame needs to have the same number of entries in each row or column. A list can have entries that different in length, which can be very useful at times.

But here is a little trick to get your output back into a data frame using sapply.

sp.means <- by(dat, dat$species, function(x){
# caculate the mean petal length and petal width for each species
  means <- colMeans(x[,2:3])

# use sapply to put the data back to a matrix
sp.means2 <- t(sapply(sp.means, I))
# make a data frame
new.df <-
##   petal.length petal.width
## 1     4.570392    3.202347
## 2     4.797698    2.461382
## 3     5.796172    4.765893