diff options
| -rw-r--r-- | r.html.markdown | 71 |
1 files changed, 46 insertions, 25 deletions
diff --git a/r.html.markdown b/r.html.markdown index 38317776..2cf63288 100644 --- a/r.html.markdown +++ b/r.html.markdown @@ -5,7 +5,7 @@ author_url: http://github.com/e99n09 filename: learnr.r --- -R is a statistical computing language. +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. ```python @@ -14,36 +14,30 @@ R is a statistical computing language. # You can't make a multi-line comment per se, # but you can stack multiple comments like so. -# Protip: hit COMMAND-ENTER to execute a line +# Hit COMMAND-ENTER to execute a line ######################### # The absolute basics ######################### -# NUMERICS +# NUMBERS -# We've got numbers! Behold the "numeric" class +# We've got doubles! Behold the "numeric" class 5 # => [1] 5 class(5) # => [1] "numeric" +# We've also got integers! They look suspiciously similar, +# but indeed are different +5L # => [1] 5 +class(5L) # => [1] "integer" # Try ?class for more information on the class() function # In fact, you can look up the documentation on just about anything with ? -# Numerics are like doubles. There's no such thing as integers -5 == 5.0 # => [1] TRUE -# Because R doesn't distinguish between integers and doubles, -# R shows the "integer" form instead of the equivalent "double" form -# whenever it's convenient: -5.0 # => [1] 5 - # All the normal operations! 10 + 66 # => [1] 76 53.2 - 4 # => [1] 49.2 -3.37 * 5.4 # => [1] 18.198 2 * 2.0 # => [1] 4 -3 / 4 # => [1] 0.75 -2.0 / 2 # => [1] 1 +3L / 4 # => [1] 0.75 3 %% 2 # => [1] 1 -4 %% 2 # => [1] 0 # Finally, we've got not-a-numbers! They're numerics too class(NaN) # => [1] "numeric" @@ -107,6 +101,17 @@ while (a > 4) { # Operations on entire vectors (i.e. a whole row, a whole column) # or apply()-type functions (we'll discuss later) are preferred +# IF/ELSE + +# Again, pretty standard +if (4 > 3) { + print("Huzzah! It worked!") +} else { + print("Noooo! This is blatantly illogical!") +} +# => +# [1] "Huzzah! It worked!" + # FUNCTIONS # Defined like so: @@ -126,8 +131,8 @@ myFunc(5) # => [1] 19 # ONE-DIMENSIONAL # You can vectorize anything, so long as all components have the same type -vec <- c(4, 5, 6, 7) -vec # => [1] 4 5 6 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 coercions happen @@ -135,15 +140,27 @@ c(TRUE, 4) # => [1] 1 4 c("dog", TRUE, 4) # => [1] "dog" "TRUE" "4" # We ask for specific components like so (R starts counting from 1) -vec[1] # => [1] 4 -# We can also search for the indices of specific components -which(vec %% 2 == 0) +vec[1] # => [1] 8 +# We can also search for the indices of specific components, +which(vec %% 2 == 0) # => [1] 1 3 +# or grab just the first or last entry in the vector +head(vec, 1) # => [1] 8 +tail(vec, 1) # => [1] 11 # If an index "goes over" you'll get NA: vec[6] # => [1] NA +# You can find the length of your vector with length() +length(vec) # => [1] 4 # You can perform operations on entire vectors or subsets of vectors vec * 4 # => [1] 16 20 24 28 vec[2:3] * 5 # => [1] 25 30 +# and there are many built-in functions to summarize vectors +mean(vec) # => [1] 9.5 +var(vec) # => [1] 1.666667 +sd(vec) # => [1] 1.290994 +max(vec) # => [1] 11 +min(vec) # => [1] 8 +sum(vec) # => [1] 38 # TWO-DIMENSIONAL (ALL ONE CLASS) @@ -273,6 +290,7 @@ apply(mat, MAR = 2, myFunc) # [2,] 7 19 # [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. @@ -303,13 +321,13 @@ write.csv(pets, "pets2.csv") # to make a new .csv file # Scatterplots! plot(list1$time, list1$price, main = "fake data") -# Fit a linear model -myLm <- lm(price ~ time, data = list1) -myLm # outputs result of regression +# Regressions! +linearModel <- lm(price ~ time, data = list1) +linearModel # outputs result of regression # Plot regression line on existing plot -abline(myLm, col = "red") +abline(linearModel, col = "red") # Get a variety of nice diagnostics -plot(myLm) +plot(linearModel) # Histograms! hist(rpois(n = 10000, lambda = 5), col = "thistle") @@ -325,4 +343,7 @@ require(ggplot2) ``` +## How do I get R? +* Get R and the R GUI from [http://www.r-project.org/](http://www.r-project.org/) +* [RStudio](http://www.rstudio.com/ide/) is another GUI |