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ggplot2 is - like lattice based on the grid graphics system (Paul Murrell) graphics and parts of graphics are objects and they are manipulable

begin with an empty object to see the structure: ]]> ]]> what we see are empty place holders when we use str() to explore the structure of the object we see that it is a list with length 9 ]]> ]]>

Now we fill this structure - first the three main steps: the first argument to ggplot is data then specify what graphics shapes you are going to use to view the data (e.g. geom_line() or geom_point()). specify what features (or aesthetics) will be used (e.g. what variables will determine x- and y-locations) with the aes() function if these aesthetics are intented to be used in all layers it is more convenient to specify them in the ggplot object

first we create a little sample data frame\small ]]> ]]> ]]> ]]> ]]> then we create a ggplot object containing the data and some standard aesthetics (here we define the x and the y positions) add one or more geoms, we begin with geom_point ]]> ]]> ]]> ggp1.pdf

ggplot() creates an object - every "+" adds something to this object (change the object) the default method of ggplot() is print(), which creates the plot it is better to store the object - so you can change it (e.g. you can change the data frame)

so we add another layer, which adds a label to the points (use geom_text) ]]> ]]> aes(label=l) maps the l variable to the label aesthetic, and hjust and vjust define where our labels are placed ggp2.pdf

imagine you have worked a little time on a plot - and then you detect a mistake in your data, so the real data frame looks different so you can replace the old, wrong data by the new data (using %+%) ]]> ]]> ]]> ggp3.pdf

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by using the line geom you can join the points (we use the new data) ]]> ]]> ggp4.pdf

you can also join the points in the order of the data fram by using the path geom instead\footnotesize ]]> ]]> ggp5.pdf

Adding extra lines: there are three geoms: abline, vline, hline abline adds one or more lines with specified slope and intercept to the plot\footnotesize ]]> ]]> ]]> ]]> ]]> ]]> attachment:ggp6.png

adding lines referring to the data frame ]]> ggp7.pdf

the same works for the hline and the vline geom which add horizonal and vertical line(s) argument: yintercept, xintercept respectively setting and mapping are possible ]]> ]]> ggp8.pdf

some other layers for 1 continuous variable: geom_boxplot() geom_histogram() geom_density() some other layers for 1 discrete variable: geom_bar() some other layers for 2 or more continuous variables: geom_smooth() geom_density2d() geom_contour() geom_quantile()

use our data frame or load it: load("20150310data.rdata") create a new variable EC1 containing the first 2 letters of the Event.Code column, use the function str_sub() from the stringr package (type ?str_sub to get help)

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Create the five plots and save them into a file. create a plot using ggplot, map the variable EC1 to x and use geom_bar() now to the plot again, but add another aesthetic: fill (colour of the filling); map fill to Stim.Type add the position argument to geom_bar(), set it to "fill" now add facet_wrap(~testid) to show the same graph per time make a graph facetted per child showing stacked hit/incorrect bars with time on the x axis

create a plot using ggplot, map the variable EC1 to x and use geom_bar() ]]> ]]> ]]> attachment:plot1.png now to the plot again, but add another aesthetic: fill (colour of the filling); map fill to Stim.Type ]]> ]]> ]]> attachment:plot1.png add the position argument to geom_bar(), set it to "fill" ]]> ]]> ]]> attachment:plot3.png now add facet_wrap(~testid) to show the same graph per time ]]> ]]> ]]> attachment:plot4.png now add facet_wrap(~testid) to show the same graph per time ]]> ]]> ]]> attachment:plot4a.png make a graph facetted per child showing stacked hit/incorrect bars with time on the x axis ]]> ]]> attachment:plot5.png

What if we want to change the colours? this leads to another important type of component not yet mentioned if you map a variable to a aesthetic is these done in a default way, in this case some reddish colour is mapped to hit while light blue is mapped to incorrect; in addition a discrete range of colours is automatically used these rules of mapping are called scales different type of scales exists for the axes, colours, shapes etc, some of them exists in discrete and continuous versions, some in just one of them (in general one can say, everytime there can be a legend there is a scale) the name convention: scale_aesthetic_specification. for example scale_x_discrete for customizing a discrete x axis (e.g. in barplots)

to change our discrete colour scale for the filling we type \footnotesize ]]> attachment:ggp10.png There are other ways to customize a discrete colour/fill scales scale_colour_grey() scale_colour_hue() scale_colour_brewer() ]]> ]]> ]]>

Not only colour scales are modifiable here scale_aesthetic_specification becomes scale_x_continuous() or scale_y_continuous() to transform a axis we can use the trans arg we now create a scatter plot with Trial on the x-axis and TTime on the y-axis the respective geom is geom_point() if we look at the distribution of the y values we see that they are right skewed ]]> ]]> attachment:ggp11.png to face the skewness we can transform the y-axis maybe with a square root function ]]> ]]> attachment:ggp12.png still a little skewed... ... so maybe we should try (/usr/share/texlive/texmf-dist/tex/latex/base/article.cls Document Class: article 2018/09/03 v1.4i Standard LaTeX document class (/usr/share/texlive/texmf-dist/tex/latex/base/size12.clo)) (/usr/share/texlive/texmf-dist/tex/latex/amsfonts/amssymb.sty (/usr/share/texlive/texmf-dist/tex/latex/amsfonts/amsfonts.sty)) (/usr/share/texlive/texmf-dist/tex/latex/amsmath/amsmath.sty For additional information on amsmath, use the `?' option. (/usr/share/texlive/texmf-dist/tex/latex/amsmath/amstext.sty (/usr/share/texlive/texmf-dist/tex/latex/amsmath/amsgen.sty)) (/usr/share/texlive/texmf-dist/tex/latex/amsmath/amsbsy.sty) (/usr/share/texlive/texmf-dist/tex/latex/amsmath/amsopn.sty)) (/usr/share/texlive/texmf-dist/tex/latex/amscls/amsthm.sty) (/usr/share/texlive/texmf-dist/tex/latex/base/inputenc.sty) No file latex_6f039602e893e250af4d36788a338962115664ab_p.aux. ! Missing $ inserted. $ l.10 x^ {1/3} (/usr/share/texlive/texmf-dist/tex/latex/amsfonts/umsa.fd) (/usr/share/texlive/texmf-dist/tex/latex/amsfonts/umsb.fd) ! Missing $ inserted. $ l.11 \end{document} [1] (./latex_6f039602e893e250af4d36788a338962115664ab_p.aux) ) (see the transcript file for additional information) Output written on latex_6f039602e893e250af4d36788a338962115664ab_p.dvi (1 page, 296 bytes). Transcript written on latex_6f039602e893e250af4d36788a338962115664ab_p.log. ]]>we first have to write a new transformation\small ]]> ]]> ]]> attachment:ggp13.png For standard transformation there is a short cut: ]]> scale_x_log10() scale_x_reverse() scale_x_sqrt() Other transformation available: \small

colour scales have also a continuous version (we have seen it in the first bar plot) scale_colour_gradient() scale_colour_gradient2() scale_colour_gradientn() scale_linetype_continuous() and scale_linetype_discrete() scale_shape_continuous() and scale_shape_discrete() scale_size_continuous() and scale_size_discrete() scale_x_date() scale_discrete()

* Create a scatter plot with Trial on the x-axis and TTime on the y-axis. Map colour to to age column. Looking at the pattern in the graph, is there relation between age and reaction time? * Make a plot which has a facet for each child containing the histogram of TTime, map fill to ..count.. (fill=..count.. inside aes()) * then add scale_fill_gradient() and set its arguments low and high to, say green and red resp. (or make your own choice) * do the same but now do the facetting by testid (and second per Stim.Type level). What can you conclude from these graphs?

Create a scatter plot with Trial on the x-axis and TTime on the y-axis. Map colour to to age column. Looking at the pattern in the graph, is there relation between age and reaction time? ]]> ]]> attachment:ggp14.png Make a plot which has a facet for each child containing the histogram of TTime, map fill to ..count.. (fill=..count.. inside aes()) ]]> attachment:ggp15.png then add scale_fill_gradient() and set its arguments low and high to, say green and red resp. (or make your own choice) ]]> attachment:ggp15.png do the same but now do the facetting by testid (and second per Stim.Type level). What can you conclude from these graphs? ]]> attachment:ggp16.png do the same but now do the facetting by testid (and second per Stim.Type level). What can you conclude from these graphs? ]]> attachment:ggp16.png

stringr - easy string manipulation lubridate - easy date time manipulation reshape2, tidyr - data manipulation, melting devtools, testthat - package developement etc