1 Lattice Package in R

1.2 YAML

for html output, check link
for md output, check link

title: “Statistics Using R”
author: “Bhaskar”
date: “12/12/2020”
output:
 html_document:
  toc: true
  toc_depth: 4
  number_sections: true
  toc_float:
   collapsed: false
   smooth_scroll: false

 md_document:
  variant: markdown_github

 powerpoint_presentation:
  reference_doc: template.pptx

1.3 Lattice: Multivariate Data Visualization with R - Figures and Code

link install.packages(“mlmRev”)

1.3.1 Loading the data

data(Chem97, package = "mlmRev")
xtabs(~ score, data = Chem97)
## score
##    0    2    4    6    8   10 
## 3688 3627 4619 5739 6668 6681
str(Chem97)
## 'data.frame':    31022 obs. of  8 variables:
##  $ lea      : Factor w/ 131 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ school   : Factor w/ 2410 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ student  : Factor w/ 31022 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ score    : num  4 10 10 10 8 10 6 8 4 10 ...
##  $ gender   : Factor w/ 2 levels "M","F": 2 2 2 2 2 2 2 2 2 2 ...
##  $ age      : num  3 -3 -4 -2 -1 4 1 4 3 0 ...
##  $ gcsescore: num  6.62 7.62 7.25 7.5 6.44 ...
##  $ gcsecnt  : num  0.339 1.339 0.964 1.214 0.158 ...

1.3.2 Histogram facet plot

library("lattice")
histogram(~ gcsescore | factor(score), data = Chem97)

1.3.3 Density plot faceted

densityplot(~ gcsescore | factor(score), data = Chem97,
            plot.points = FALSE, ref = TRUE)

densityplot(~ gcsescore, groups = score, data = Chem97,
            plot.points = FALSE, ref = TRUE, 
            auto.key = list(columns = 3))

tp1 <- histogram(~ gcsescore | factor(score), data = Chem97)
plot(tp1)

class(tp1)
## [1] "trellis"
summary(tp1)
## 
## Call:
## histogram(~gcsescore | factor(score), data = Chem97)
## 
## Number of observations:
## factor(score)
##    0    2    4    6    8   10 
## 3688 3627 4619 5739 6668 6681
tp2 <- densityplot(~ gcsescore, data = Chem97, groups = score,
                   plot.points = FALSE, 
                   auto.key = list(space = "right", title = "score"))

print(tp2)

plot(tp1, split = c(1, 1, 1, 2))

## MEMSS: Data Sets from Mixed-Effects Models in S

install.packages(“MEMSS”)
link

data(Oats, package = "MEMSS") 
tp1.oats <-xyplot(yield ~ nitro | Variety + Block, data = Oats, type = 'o')

print(tp1.oats)

#Contingency Tables
xtabs(~Variety + Block, data = Oats)
##              Block
## Variety       I II III IV V VI
##   Golden Rain 4  4   4  4 4  4
##   Marvellous  4  4   4  4 4  4
##   Victory     4  4   4  4 4  4
summary(tp1.oats)
## 
## Call:
## xyplot(yield ~ nitro | Variety + Block, data = Oats, type = "o")
## 
## Number of observations:
##              Block
## Variety       I II III IV V VI
##   Golden Rain 4  4   4  4 4  4
##   Marvellous  4  4   4  4 4  4
##   Victory     4  4   4  4 4  4
print(tp1.oats[,c(3,1,4)])

# Transpose Trellis object
t(tp1.oats)

update(tp1.oats, aspect="xy", layout = c(0,18),
       between = list(x = c(0, 0, 0.5), y = 0.5))

Fine-tuning the layout: between and skip
The between argument can be a list, with components x and y (both usually 0 by default) which are numeric vectors specifying the amount of blank space between the panels (in units of character heights). x and y are repeated to account for all panels in a page, and any extra components are ignored. This is often useful in providing a visual cue separating panels into blocks.

skip argument is specified as a logical vector (default FALSE), replicated to be as long as the number of panels. For elements that are TRUE, the corresponding panel position is skipped; that is, nothing is plotted in that position. The panel that was sup- posed to be drawn there is now drawn in the next available panel position, and the positions of all the subsequent panels are bumped up accordingly.

Note :
If there is only one conditioning variable with n levels, the default value of layout is c(0,n), thus taking advantage of this automatic layout computation. When aspect = “fill” (the default in most cases), this computation is carried out with an initial aspect ratio of 1, but in the eventual display the panels are expanded to fill up all the available space.(Page no.21)

1.4 Grouped displays

A powerful construct that forces direct comparison is superposition, where data associated with dif- ferent levels of a grouping variable are rendered together within a panel, but with different graphical characteristics. For example, different curves could be drawn in different color or line type, or points could be drawn with different symbols. Superposition is usually more effective than multipanel conditioning when the number of levels of the grouping variable is small.

dotplot(variety ~ yield | site, barley, layout = c(1,6), aspect = c(0.7),
groups = year, auto.key = list(space = "right"))

1.5 Titanic Data

barchart(Class ~ Freq | Sex + Age, data = as.data.frame(Titanic),
         groups = Survived, stack = TRUE, layout = c(4, 1),
         auto.key = list(title = "Survived", columns = 2))

bc.titanic <- barchart(Class ~ Freq | Sex + Age, data = as.data.frame(Titanic),
         groups = Survived, stack = TRUE, layout = c(4, 1), 
         auto.key = list(title = "Survived", columns = 2),
         scales = list(x = "free"))
bc.titanic

update(bc.titanic, panel = function(...) {
  panel.grid(h = 0, v = -1)
  panel.barchart(...) })

update(bc.titanic, border = "transparent")

#Page no. 33

1.6 Creating a ppt and adding R plots

install.packages(c(“officer”,“rvg”))

library(tidyverse)
library(officer)
library(rvg) # rvg for micrsoft ppt

library(tidyverse)
library(officer)
library(rvg)
# Using diamonds dataset which is shipped with R
ggp <- diamonds %>%
  # Let's simplify things by only considering natural number carats
  mutate(carat = floor(carat)) %>%
  group_by(carat, cut, clarity, color) %>%
  summarise(price = mean(price)) %>%
  # Create a plot of price by carat, colour, cut, and clarity
  ggplot(aes(x = carat, y = price, fill = color)) +
  geom_bar(stat = 'identity') +
  facet_grid(cut ~ clarity) +
  # Simplify the plot layout a little
  theme_bw() +
  guides(fill = FALSE) +
  theme(panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank()); ggp

my_vec_graph <- dml(code = ggp)

1.6.1 Create a new powerpoint document

doc <- read_pptx()
doc <- add_slide(doc, ‘Title and Content’, ‘Office Theme’)

1.6.2 Add the plot

doc <- ph_with(doc, dml(code=ggp), location = ph_location_fullsize() )
print(doc, target = “my_plot.pptx”)