V6

Question 1

pros of ggplot 2 compared to other packages in R

Answer 1

• is one of the most elegant and most versatile
• layered, customisable plots
• implements the grammar of graphics, a coherent system for describing and building graphs
• faster by learning one system and applying it in many places

Question 2

format needed for ggplot2

Answer 2

• must be in data.frame

- more rows than columns

Question 3

important terms :

• data
• aesthetics
• geometries
• facets
• statistics
• coordinates
• themes

Answer 3

• data: dataset being plotted
• aesthetics: scales onto which we map our data
• geometries: visual elements used for our data
• facets: plotting small multiples
• statistics: representation of our data to aid understanding
• coordinates: space on which the data will be plotted
• themes: all non-data ink.

Question 4

plot basics

Answer 4

• all ggplot2 plots with a call to ggplot(), supplying default data and aesthetic mappings, specified by ads()
• you then add layers, scales, coords and facets with +
• to save a plot to disk, use ggsave()

Question 5

layer: geoms

Answer 5

• a layer combines data, aesthetic mapping, a geom(geometric object), a stat(statistical transformation), and a position adjustment
• typically you will create layers using a geom_ function overriding the default position and stat if needed

Question 6

layer: stats

Answer 6

stat_function
statistical transformation rather than the visual appearance
e.g. -> str.identity() -> leave data as is

Question 7

layer: position adjustment:

Answer 7

resolves overlapping gems; overrides the default of the geom_ or stat_ function
e.g. position_jitter() -> jitter points to avoid overplotting

Question 8

layer: annotations

Answer 8

special types of layer: they don’t inherit global settings from the plot. they are used to add fixed reference data to plot
e.g. annotate() -> create an annotation layer

Question 9

layer: scales

Answer 9

control the details of ow data values are translated to visual properties
e.g. scale_colour_continuous() -> apply a continuous colour scale

Question 10

bar graphs - bar heights ?

Answer 10

• two different things that the heights of bars represent
• the count of cases for each group -> stat_bin()
• the value of a column in the data set -> stat_identity() leaves the y values unchanged

-> default is stat_bin

Question 11

make basic bar graph with ggplot (with value not number of cases in each group)

add or remove colour and legend + black outline

add x, y main labels

Answer 11

library(ggplot2)

# simple plot
ggplot(data=dat, aes(x=time, y=total_bill)) + geombar(stat ="identity)

# add colour and legend + black outline
ggplot(data=dat, aes(x=time, y=total_bill, fill = time)) + geombar(colour="black", stat ="identity)

# remove legend (if redundant)
\+ guides(fill=FALSE)

add x, y, main labels
+ xlab(“”)
+ ylab(“”)
+ggtitle(“”)

Question 12

code count of cases in bar graph

Answer 12

+ geom_bar(stat = “count”)

Question 13

how to code bar graph with multiple variables bar

time : xaxis
sex: color fill
total bill: y-axis

how to change colour

Answer 13

ggplot(data=tips, aes(x=time, y=total_bill, fill=sex)) + geom_bar(stat=”summary”,position = position_dodge())
-> make 2 bars right next to each other for each sex

# change colour
\+ scale_fill_manual(values=c("",""))

Question 14

code line graph

Answer 14

ggplot(data=tips, aes(x=time, y=total_bill, fill=sex)) + geom_line(stat=”summary”, size = 1.5) +
geom_point(stat=”summary”, size = 3)

Question 15

how to add different symbols into plot

Answer 15

+ scale_shape_manual(values=c(“º”, “Ω”)

Question 16

how to add error bars

Answer 16

+ geom_errorbars(aes(ymin=total_bill-sd, ymax=total_bill+sd),
width =0.1, position=position_dodge(1))

Question 17

draw a histogram with black outline and white fill

draw a density curve

Answer 17

ggplot(dat, aes(x=rating)) +
geom_histogram(binwidth=0.5, colour = “black”, fill = “white”)

ggplot(dat, aes(x=rating)) + geom_density()

Question 18

histogram add mean line

Answer 18

ggplot(dat, aes(x=rating)) +
geom_histogram(binwidth=0.5, colour = “black”, fill = “white”) + geom_vline(aes(xintercept = mean(rating, na.rm=T)),
color =”red”, livetype = “dashed”, size = 1

Question 19

overlaied histograms (two seethrough

Answer 19

ggplot(dat, aes(x=rated, fill=cond)) + geom_histogram(binwidth=.5, aplha=.5, position = “identity”)

Question 20

interleaved histograms

Answer 20

ggplot(dat, aes(x=rating, fill=cond)) + geom_histogram(binwidth=.5, position=”dodge”)

Question 21

make a basic boxplot

Answer 21

ggplot(dat, aes(x=cond, y=rating)) + geom_boxplot()

Question 22

scaterplot points and with linear regression

Answer 22

1. ggplot(dat, es(x=xvar, y=yvar)) + geom_point(shape=1)
2. ggplot(dat, aes(x=xvar, var)) + genom_point(shape = 1) + geom_smooth(method=lm) -> by default includes 95% confidence interval

Question 23

how to alter theme (the way it looks / lines)

Answer 23

theme(plot.title = element_text(lineheight=.8, face=”bold”))

Question 24

facets

Answer 24

can divide by levels
-> levels of sex

sp(the normal plotting stiff) + facet_grid(. ~sex)

Question 25

how to divide vertically or horizontally

Answer 25

sp + facet_grid(sex~day)