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ch14 | R Documentation |
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if (interactive()) { # Chapter 14 # Summarizing Data # Starting with the Right Data ## Using factors or numeric data ## Counting unique values sapply(mtcars, function(x) length(unique(x))) ## Preparing the data cars <- mtcars[c(1,2,9,10)] cars$gear <- ordered(cars$gear) cars$am <- factor(cars$am, labels=c('auto', 'manual')) str(cars) # Describing Continuous Variables ## Talking about the center of your data mean(cars$mpg) median(cars$cyl) ## Describing the variation sd(cars$mpg) ## Checking the quantiles ### Calculating the range range(cars$mpg) ### Calculating the quantiles quantile(cars$mpg) ### Getting on speed with the quantile function quantile(cars$mpg, probs=c(0.05, 0.95)) # Describing Categories ## Counting appearances ### Creating a table amtable <- table(cars$am) amtable ### Working with tables ## Calculating proportions amtable/sum(amtable) prop.table(amtable) ## Finding the center id <- amtable == max(amtable) names(amtable)[id] # Describing Distributions ## Plotting histograms ### Making the plot hist(cars$mpg, col='grey') ### Playing with breaks hist(cars$mpg, breaks=c(5,15,25,35)) ## Using frequencies or densities ### Creating a density plot mpgdens <- density(cars$mpg) plot(mpgdens) ### Plotting densities in a histogram hist(cars$mpg, col='grey', freq=FALSE) lines(mpgdens) # Describing Multiple Variables ## Summarizing a complete dataset ### Getting the output summary(cars) ### Fixing a problem cars$cyl <- as.factor(cars$cyl) ## Plotting quantiles for subgroups boxplot(mpg ~ cyl, data=cars) ## Tracking correlations names(iris) ### Looking at relations plot(iris[-5]) ### Getting the numbers with(iris, cor(Petal.Width, Petal.Length)) ### Calculating correlations for multiple variables iris.cor <- cor(iris[-5]) str(iris.cor) iris.cor['Petal.Width', 'Petal.Length'] ### Dealing with missing values # Working with Tables ## Creating a two-way table ### Creating a table from two variables with(cars, table(am, gear)) ### Creating tables from a matrix trial <- matrix(c(34,11,9,32), ncol=2) colnames(trial) <- c('sick', 'healthy') rownames(trial) <- c('risk', 'no_risk') trial.table <- as.table(trial) trial.table ### Extracting the numbers trial.table['risk', 'sick'] ##Converting tables to a data frame trial.df <- as.data.frame(trial) str(trial.df) trial.table.df <- as.data.frame(trial.table) str(trial.table.df) ## Looking at margins and proportions ### Adding margins to the table addmargins(trial.table) addmargins(trial.table,margin=2) ### Calculating proportions prop.table(trial.table) ### Calculating proportions over columns and rows prop.table(trial.table, margin=1) }
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