Merged changes for r

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Adam 2013-07-01 19:22:35 -07:00
commit 555a67424d

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@ -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