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-rw-r--r--r.html.markdown107
1 files changed, 103 insertions, 4 deletions
diff --git a/r.html.markdown b/r.html.markdown
index 93751df5..8539b10e 100644
--- a/r.html.markdown
+++ b/r.html.markdown
@@ -3,6 +3,7 @@ language: R
contributors:
- ["e99n09", "http://github.com/e99n09"]
- ["isomorphismes", "http://twitter.com/isomorphisms"]
+ - ["kalinn", "http://github.com/kalinn"]
filename: learnr.r
---
@@ -15,7 +16,8 @@ R is a statistical computing language. It has lots of libraries for uploading an
# You can't make multi-line comments,
# but you can stack multiple comments like so.
-# in Windows or Mac, hit COMMAND-ENTER to execute a line
+# in Windows you can use CTRL-ENTER to execute a line.
+# on Mac it is COMMAND-ENTER
@@ -196,6 +198,14 @@ class(NaN) # "numeric"
# You can do arithmetic on two vectors with length greater than 1,
# so long as the larger vector's length is an integer multiple of the smaller
c(1,2,3) + c(1,2,3) # 2 4 6
+# Since a single number is a vector of length one, scalars are applied
+# elementwise to vectors
+(4 * c(1,2,3) - 2) / 2 # 1 3 5
+# Except for scalars, use caution when performing arithmetic on vectors with
+# different lengths. Although it can be done,
+c(1,2,3,1,2,3) * c(1,2) # 1 4 3 2 2 6
+# Matching lengths is better practice and easier to read
+c(1,2,3,1,2,3) * c(1,2,1,2,1,2)
# CHARACTERS
# There's no difference between strings and characters in R
@@ -234,6 +244,9 @@ class(NA) # "logical"
TRUE | FALSE # TRUE
# AND
TRUE & FALSE # FALSE
+# Applying | and & to vectors returns elementwise logic operations
+c(TRUE,FALSE,FALSE) | c(FALSE,TRUE,FALSE) # TRUE TRUE FALSE
+c(TRUE,FALSE,TRUE) & c(FALSE,TRUE,TRUE) # FALSE FALSE TRUE
# You can test if x is TRUE
isTRUE(TRUE) # TRUE
# Here we get a logical vector with many elements:
@@ -664,15 +677,101 @@ write.csv(pets, "pets2.csv") # to make a new .csv file
#########################
+# Statistical Analysis
+#########################
+
+# Linear regression!
+linearModel <- lm(price ~ time, data = list1)
+linearModel # outputs result of regression
+# =>
+# Call:
+# lm(formula = price ~ time, data = list1)
+#
+# Coefficients:
+# (Intercept) time
+# 0.1453 0.4943
+summary(linearModel) # more verbose output from the regression
+# =>
+# Call:
+# lm(formula = price ~ time, data = list1)
+#
+# Residuals:
+# Min 1Q Median 3Q Max
+# -8.3134 -3.0131 -0.3606 2.8016 10.3992
+#
+# Coefficients:
+# Estimate Std. Error t value Pr(>|t|)
+# (Intercept) 0.14527 1.50084 0.097 0.923
+# time 0.49435 0.06379 7.749 2.44e-09 ***
+# ---
+# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+#
+# Residual standard error: 4.657 on 38 degrees of freedom
+# Multiple R-squared: 0.6124, Adjusted R-squared: 0.6022
+# F-statistic: 60.05 on 1 and 38 DF, p-value: 2.44e-09
+coef(linearModel) # extract estimated parameters
+# =>
+# (Intercept) time
+# 0.1452662 0.4943490
+summary(linearModel)$coefficients # another way to extract results
+# =>
+# Estimate Std. Error t value Pr(>|t|)
+# (Intercept) 0.1452662 1.50084246 0.09678975 9.234021e-01
+# time 0.4943490 0.06379348 7.74920901 2.440008e-09
+summary(linearModel)$coefficients[,4] # the p-values
+# =>
+# (Intercept) time
+# 9.234021e-01 2.440008e-09
+
+# GENERAL LINEAR MODELS
+# Logistic regression
+set.seed(1)
+list1$success = rbinom(length(list1$time), 1, .5) # random binary
+glModel <- glm(success ~ time, data = list1,
+ family=binomial(link="logit"))
+glModel # outputs result of logistic regression
+# =>
+# Call: glm(formula = success ~ time,
+# family = binomial(link = "logit"), data = list1)
+#
+# Coefficients:
+# (Intercept) time
+# 0.17018 -0.01321
+#
+# Degrees of Freedom: 39 Total (i.e. Null); 38 Residual
+# Null Deviance: 55.35
+# Residual Deviance: 55.12 AIC: 59.12
+summary(glModel) # more verbose output from the regression
+# =>
+# Call:
+# glm(formula = success ~ time,
+# family = binomial(link = "logit"), data = list1)
+
+# Deviance Residuals:
+# Min 1Q Median 3Q Max
+# -1.245 -1.118 -1.035 1.202 1.327
+#
+# Coefficients:
+# Estimate Std. Error z value Pr(>|z|)
+# (Intercept) 0.17018 0.64621 0.263 0.792
+# time -0.01321 0.02757 -0.479 0.632
+#
+# (Dispersion parameter for binomial family taken to be 1)
+#
+# Null deviance: 55.352 on 39 degrees of freedom
+# Residual deviance: 55.121 on 38 degrees of freedom
+# AIC: 59.121
+#
+# Number of Fisher Scoring iterations: 3
+
+
+#########################
# Plots
#########################
# BUILT-IN PLOTTING FUNCTIONS
# Scatterplots!
plot(list1$time, list1$price, main = "fake data")
-# Regressions!
-linearModel <- lm(price ~ time, data = list1)
-linearModel # outputs result of regression
# Plot regression line on existing plot
abline(linearModel, col = "red")
# Get a variety of nice diagnostics