inst/doc/logicalQueries.R

In loon: Interactive Statistical Data Visualization

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
    echo = TRUE, 
    tidy.opts = list(width.cutoff = 65),
    tidy = FALSE)

set.seed(12314159)

imageDirectory <- "./images/logic"
dataDirectory <- "data"

## ----library_loon, eval = FALSE, echo = TRUE, fig.align="center", fig.width = 6, fig.height = 4, out.width = "75%", warning=FALSE, message=FALSE----
#  library(loon)

## ----new variates-------------------------------------------------------------
data(mtcars, package = "datasets")

mtcars$country <- c("Japan", "Japan", "Japan", "USA", "USA", "USA", "USA", 
                    "Germany", "Germany", "Germany", "Germany", "Germany", 
                    "Germany", "Germany", "USA", "USA", "USA", "Italy", 
                    "Japan", "Japan", "Japan", "USA", "USA", "USA", "USA", 
                    "Italy", "Germany", "UK", "USA", "Italy", "italy", "Sweden")
mtcars$continent <- c("Asia", "Asia", "Asia", "North America", "North America", 
                      "North America", "North America", "Europe", "Europe", 
                      "Europe", "Europe", "Europe", "Europe", "Europe",  
                      "North America", "North America", "North America", 
                      "Europe", "Asia", "Asia", "Asia", "North America", 
                      "North America", "North America", "North America", 
                      "Europe", "Europe", "Europe", "North America", 
                      "Europe", "Europe", "Europe" )
mtcars$company <- c("Mazda", "Mazda", "Nissan", "AMC", "AMC", "Chrysler", 
                    "Chrysler", "Mercedes", "Mercedes", "Mercedes", "Mercedes",
                    "Mercedes", "Mercedes", "Mercedes", "GM", "Ford", 
                    "Chrysler", "Fiat", "Honda", "Toyota", "Toyota", "Chrysler", 
                    "AMC", "GM", "GM", "Fiat", "Porsche", "Lotus", "Ford", 
                    "Ferrari", "Maserati", "Volvo")

mtcars$Engine <- factor(c("V-shaped", "Straight")[mtcars$vs +1], 
                        levels = c("V-shaped", "Straight"))
mtcars$Transmission <- factor(c("automatic", "manual")[mtcars$am +1], 
                              levels = c("automatic", "manual"))

mtcars$vs <- NULL  # These are redundant now
mtcars$am <- NULL  # 

## ----define variable types----------------------------------------------------
varTypes <- split(names(mtcars), 
                  sapply(mtcars, 
                         FUN = function(x){
                             if(is.factor(x)|is.character(x)){ 
                                 "categorical"
                                  } else {"numeric"} } ))

## ----histograms of categorical variates, eval = FALSE-------------------------
#  for (varName in varTypes$categorical) {
#      assign(paste0("h_", varName),
#             l_hist(mtcars[ , varName], showFactors = TRUE,
#                    xlabel = varName, title = varName,
#                    linkingGroup = "Motor Trend"))
#  }

## ---- eval = FALSE------------------------------------------------------------
#  p <- with(mtcars, l_plot(disp, cyl,
#                           xlabel = "engine displacement", ylabel = "number of cylinders",
#                           title = "1974 Motor Trend cars",
#                           linkingGroup = "Motor Trend",
#                           size = 10, showScales = TRUE,
#                           itemLabel = rownames(mtcars), showItemLabels = TRUE
#                           ))

## ---- eval = FALSE------------------------------------------------------------
#  data <- data.frame(A = sample(c(rnorm(10), NA), 10, replace = FALSE),
#                     B = sample(c(rnorm(10), NA), 10, replace = FALSE),
#                     C = sample(c("firebrick", "steelblue", NA), 10, replace = TRUE),
#                     D = sample(c(1:10, NA), 10, replace = FALSE))
#  p_test <- l_plot(x = data$A, y = data$B, color = data$C, linkingGroup = "test missing")
#  h_test <- l_hist(x = data$D, color = data$C, linkingGroup = "test missing")

## ---- eval = FALSE------------------------------------------------------------
#  p_test["selected"] <- (data$A > 0)

## ---- eval = FALSE------------------------------------------------------------
#  LogVal <- data$A > data$B
#  p["selected"] <- logVal[1 + as.numeric(p_test["linkingKey"])]