diff options
Diffstat (limited to 'r.html.markdown')
| -rw-r--r-- | r.html.markdown | 36 |
1 files changed, 20 insertions, 16 deletions
diff --git a/r.html.markdown b/r.html.markdown index e19eaeb8..2cf63288 100644 --- a/r.html.markdown +++ b/r.html.markdown @@ -2,21 +2,23 @@ language: R author: e99n09 author_url: http://github.com/e99n09 - +filename: learnr.r --- R is a statistical computing language. It has lots of good built-in functions for uploading and cleaning data sets, running common statistical tests, and making graphs. You can also easily compile it within a LaTeX document. -```r +```python # Comments start with hashtags. # You can't make a multi-line comment per se, # but you can stack multiple comments like so. -################################################################################### +# Hit COMMAND-ENTER to execute a line + +######################### # The absolute basics -################################################################################### +######################### # NUMBERS @@ -122,9 +124,9 @@ myFunc <- function(x) { # Called like any other R function: myFunc(5) # => [1] 19 -################################################################################### +######################### # Fun with data: vectors, matrices, data frames, and arrays -################################################################################### +######################### # ONE-DIMENSIONAL @@ -133,7 +135,7 @@ vec <- c(8, 9, 10, 11) vec # => [1] 8 9 10 11 # The class of a vector is the class of its components class(vec) # => [1] "numeric" -# If you vectorize items of different classes, weird coersions happen +# If you vectorize items of different classes, weird coercions happen c(TRUE, 4) # => [1] 1 4 c("dog", TRUE, 4) # => [1] "dog" "TRUE" "4" @@ -258,7 +260,7 @@ array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2)) # LISTS (MULTI-DIMENSIONAL, POSSIBLY RAGGED, OF DIFFERENT TYPES) # Finally, R has lists (of vectors) -list1 <- list(time = 1:40, price = c(rnorm(40,.5*list1$time,4))) # generate random +list1 <- list(time = 1:40, price = c(rnorm(40,.5*list1$time,4))) # random list1 # You can get items in the list like so @@ -266,9 +268,9 @@ list1$time # You can subset list items like vectors list1$price[4] -################################################################################### +######################### # The apply() family of functions -################################################################################### +######################### # Remember mat? mat @@ -289,15 +291,17 @@ apply(mat, MAR = 2, myFunc) # [3,] 11 23 # Other functions: ?lapply, ?sapply +# Don't feel too intimidated; everyone agrees they are rather confusing + # The plyr package aims to replace (and improve upon!) the *apply() family. install.packages("plyr") require(plyr) ?plyr -################################################################################### +######################### # Loading data -################################################################################### +######################### # "pets.csv" is a file on the internet pets <- read.csv("http://learnxinyminutes.com/docs/pets.csv") @@ -306,14 +310,14 @@ head(pets, 2) # first two rows tail(pets, 1) # last row # To save a data frame or matrix as a .csv file -write.csv(pets, "pets2.csv") # to make a new .csv file in the working directory +write.csv(pets, "pets2.csv") # to make a new .csv file # set working directory with setwd(), look it up with getwd() # Try ?read.csv and ?write.csv for more information -################################################################################### -# Plots and tests -################################################################################### +######################### +# Plots +######################### # Scatterplots! plot(list1$time, list1$price, main = "fake data") |