diff --git a/tests/cookbook-test-suite/axes.R b/tests/cookbook-test-suite/axes.R deleted file mode 100644 index 2414364c37..0000000000 --- a/tests/cookbook-test-suite/axes.R +++ /dev/null @@ -1,180 +0,0 @@ -bp <- ggplot(PlantGrowth, aes(x=group, y=weight)) + geom_boxplot() - -bp1 <- bp + coord_flip() -save_outputs(bp1, "axes/coord flip", file_prefix="") - -# Manually set the order of a discrete-valued axis -bp2 <- bp + scale_x_discrete(limits=c("trt1","trt2","ctrl")) -save_outputs(bp2, "axes/discrete valued axes", file_prefix="") - -# Reverse the order of a discrete-valued axis -# Get the levels of the factor -flevels <- levels(PlantGrowth$group) -# "ctrl" "trt1" "trt2" -# Reverse the order -flevels <- rev(flevels) -# "trt2" "trt1" "ctrl" -bp3 <- bp + scale_x_discrete(limits=flevels) -save_outputs(bp3, "axes/reversed ordered axes - 1", file_prefix="") - -# Or it can be done in one line: -bp4 <- bp + scale_x_discrete(limits = rev(levels(PlantGrowth$group)) ) -save_outputs(bp4, "axes/reversed ordered axes - 2", file_prefix="") - -bp5 <- bp + scale_x_discrete(breaks=c("ctrl", "trt1", "trt2"), labels=c("Control", "Treat 1", "Treat 2")) -save_outputs(bp5, "axes/setting tick mark labels", file_prefix="") - -# Hide x tick marks, labels, and grid lines -bp6 <- bp + scale_x_discrete(breaks=NULL) -save_outputs(bp6, "axes/hidden tick marks labels gridline", file_prefix="") - -# Hide all tick marks and labels (on X axis), but keep the gridlines -bp7 <- bp + theme(axis.ticks = element_blank(), axis.text.x = element_blank()) -save_outputs(bp7, "axes/hidden tick marks and labels", file_prefix="") - -# Set the range of a continuous-valued axis -# These are equivalent -bp8 <- bp + ylim(0,8) -save_outputs(bp8, "axes/set range of continuous-valued axis - 1", file_prefix="") -bp9 <- bp + scale_y_continuous(limits=c(0,8)) -save_outputs(bp9, "axes/set range of continuous-valued axis - 2", file_prefix="") - -# These two do the same thing; all data points outside the graphing range are dropped, -# resulting in a misleading box plot -bp10 <- bp + ylim(5, 7.5) -save_outputs(bp10, "axes/misleading range", file_prefix="") -bp + scale_y_continuous(limits=c(5, 7.5)) - -# Using coord_cartesian "zooms" into the area -bp11 <- bp + coord_cartesian(ylim=c(5, 7.5)) -save_outputs(bp11, "axes/coord_cartesian", file_prefix="") - -# Specify tick marks directly -bp12 <- bp + coord_cartesian(ylim=c(5, 7.5)) + - scale_y_continuous(breaks=seq(0, 10, 0.25)) # Ticks from 0-10, every .25 -save_outputs(bp12, "axes/specify tick marks directly", file_prefix="") - -# Reverse order of a continuous-valued axis -bp13 <- bp + scale_y_reverse() -save_outputs(bp13, "axes/reverse y scale", file_prefix="") - -# Setting the tick marks on an axis -# This will show tick marks on every 0.25 from 1 to 10 -# The scale will show only the ones that are within range (3.50-6.25 in this case) -bp14 <- bp + scale_y_continuous(breaks=seq(1,10,1/4)) -save_outputs(bp14, "axes/manual tick marks", file_prefix="") - -# The breaks can be spaced unevenly -bp15 <- bp + scale_y_continuous(breaks=c(4, 4.25, 4.5, 5, 6,8)) -save_outputs(bp15, "axes/uneven tick marks", file_prefix="") - -# Suppress ticks and gridlines -bp16 <- bp + scale_y_continuous(breaks=NULL) -save_outputs(bp16, "axes/suppress y ticks labels and gridlines", file_prefix="") - -# Hide tick marks and labels (on Y axis), but keep the gridlines -bp17 <- bp + theme(axis.ticks = element_blank(), axis.text.y = element_blank()) -save_outputs(bp17, "axes/suppress y ticks and labels", file_prefix="") - -# Create some noisy exponentially-distributed data -xval = c(0.26932812,-0.05341404,0.36977717,0.91504712,0.46329006,0.37956526, 0.93290644,0.75558976,0.67633497,0.48655293,0.79478162,0.55109982, 0.51681398,0.81073512,0.49406579,0.93919618,0.90472008,0.98732256, 0.94379876,0.95790909,0.54614241,1.13356941,1.13299144,1.18159277, 1.16428407,1.22955005,1.21030897,1.23314811,1.53822718,1.53674330, 1.80020468,1.40774011,1.74573515,1.26651625,2.06607711,1.50237263, 1.38480531,1.83625381,2.35275649,1.99004291,2.80396442,2.20863240, 2.42998876,2.12801180,2.26290348,2.38185989,2.14936036,2.66587947, 2.64586596,2.44240603,2.39266452,3.11831215,2.70258927,2.65529134, 2.65634690,2.95984290,2.71058076,2.87919480,3.07739358,2.66841935, 3.10792706,3.17134285,3.98070271,3.55497279,3.36831009,3.31390892, 3.32753965,2.86981968,3.22741000,3.78806438,3.74434536,3.56928928, 3.83783177,3.24485807,4.05766233,4.13619455,4.26888054,3.47546258, 3.93045819,3.77620080,4.66676431,3.88059240,4.54694485,4.03915767, 4.25556093,4.39251819,4.42692029,4.23262929,4.44890758,4.84981161, 4.51104252,4.33004508,5.06350705,4.89714069,4.21599077,4.55457578, 5.04044393,4.89111297,5.03105215,4.64113164) -yval = c(1.177512e+01,7.303113e+00,6.109053e+00,2.545169e+01,3.366341e+01,1.042255e+01,2.703767e+01,1.178223e+01,4.495965e+01,1.614609e+01,4.003015e+01,1.038442e+02,4.024992e+01,4.163942e+01,9.108197e+01,3.116299e+01,2.558871e+02,7.482977e+01,2.502789e+01,5.923683e+01,3.967814e+01,9.207318e+01,1.298618e+02,1.138197e+02,1.804303e+02,3.363494e+02,3.197204e+02,4.968737e+02,1.783433e+02,4.765546e+02,4.486885e+02,6.736079e+02,4.289288e+02,3.433946e+02,5.658634e+02,4.667053e+02,5.257803e+02,3.401038e+02,6.131335e+02,5.928647e+02,7.838524e+02,7.987915e+02,3.348470e+03,1.704767e+03,1.264169e+03,2.690011e+03,2.738240e+03,1.663862e+03,5.377442e+03,3.883820e+03,6.673624e+03,1.857346e+03,6.683962e+03,1.213027e+03,1.742885e+03,2.146094e+03,4.597174e+03,4.357154e+03,8.413851e+03,8.194194e+03,7.076611e+03,1.554628e+04,6.984783e+03,1.027392e+04,1.158795e+04,9.193111e+03,3.226748e+04,3.955445e+04,2.978953e+04,1.926420e+04,7.610544e+04,2.129694e+04,1.438764e+04,7.908876e+04,2.676003e+04,1.791758e+05,3.978871e+04,9.411120e+04,4.486940e+04,1.270526e+05,1.587331e+05,1.616173e+05,3.351522e+05,3.001782e+05,2.527824e+05,2.745851e+05,3.446376e+05,1.544497e+05,1.318314e+05,8.334336e+05,2.464391e+05,8.694818e+05,2.747323e+05,6.373497e+05,2.918690e+05,9.505114e+05,7.835278e+05,3.775567e+05,1.795523e+06,1.568159e+06) - -dat <- data.frame(xval = xval, yval = yval) - -# A scatterplot with regular (linear) axis scaling -sp <- ggplot(dat, aes(xval, yval)) + geom_point() -save_outputs(sp, "axes/linear axes", file_prefix="") - -# log2 scaling of the y axis (with visually-equal spacing) -library(scales) # Need the scales package -sp1 <- sp + scale_y_continuous(trans=log2_trans()) -save_outputs(sp1, "axes/ln y axes with visual-equal spacing", file_prefix="") - -# log2 coordinate transformation (with visually-diminishing spacing) -sp2 <- sp + coord_trans(y="log2") -save_outputs(sp2, "axes/ln y axes with visually diminishing spacing", file_prefix="") - -sp3 <- sp + scale_y_continuous(trans = log2_trans(), - breaks = trans_breaks("log2", function(x) 2^x), - labels = trans_format("log2", math_format(2^.x))) -save_outputs(sp3, "axes/ln y axes with exponent tick marks", file_prefix="") - -dat10 <- data.frame(xval = xval, yval = yval) - -sp10 <- ggplot(dat10, aes(xval, yval)) + geom_point() - -# log10 -sp101 <- sp10 + scale_y_log10() -save_outputs(sp101, "axes/log_10 y axes", file_prefix="") - -# log10 with exponents on tick labels -sp102 <- sp10 + scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x), - labels = trans_format("log10", math_format(10^.x))) -save_outputs(sp102, "axes/log_10 y axes with exponent tick marks", file_prefix="") - -# Data where x ranges from 0-10, y ranges from 0-30 -set.seed(202) -dat <- data.frame(xval = runif(40,0,10), yval = runif(40,0,30)) -sp <- ggplot(dat, aes(xval, yval)) + geom_point() - -# Force equal scaling -sp4 <- sp + coord_fixed() -save_outputs(sp4, "axes/forced equal spacing", file_prefix="") - -# Equal scaling, with each 1 on the x axis the same length as y on x axis -sp5 <- sp + coord_fixed(ratio=1/3) -save_outputs(sp5, "axes/forced equal scaling", file_prefix="") - -bp10 <- bp + theme(axis.title.x = element_blank()) + # Remove x-axis label - ylab("Weight (Kg)") # Set y-axis label -save_outputs(bp10, "axes/axes labels", file_prefix="") - -# Also possible to set the axis label with the scale -# Note that vertical space is still reserved for x"s label -bp11 <- bp + scale_x_discrete(name="") + - scale_y_continuous(name="Weight (Kg)") -save_outputs(bp11, "axes/axes labels set with scale", file_prefix="") - -# Change font options: -# X-axis label: bold, red, and 20 points -# X-axis tick marks: rotate 90 degrees CCW, move to the left a bit (using vjust, -# since the labels are rotated), and 16 points -bp12 <- bp + theme(axis.title.x = element_text(face="bold", colour="#990000", size=20), - axis.text.x = element_text(angle=90, vjust=0.5, size=16)) -save_outputs(bp12, "axes/axes labels with formatting", file_prefix="") - -# Label formatters -library(scales) # Need the scales package -bp13 <- bp + scale_y_continuous(labels=percent) + - scale_x_discrete(labels=abbreviate) # In this particular case, it has no effect -save_outputs(bp13, "axes/axes labels with percent labels", file_prefix="") - -# Self-defined formatting function for times. -timeHMS_formatter <- function(x) { - h <- floor(x/60) - m <- floor(x %% 60) - s <- round(60*(x %% 1)) # Round to nearest second - lab <- sprintf("%02d:%02d:%02d", h, m, s) # Format the strings as HH:MM:SS - lab <- gsub("^00:", "", lab) # Remove leading 00: if present - lab <- gsub("^0", "", lab) # Remove leading 0 if present -} - -bp14 <- bp + scale_y_continuous(label=timeHMS_formatter) -save_outputs(bp14, "axes/axes labels with custom time labels", file_prefix="") - -# Hide all the gridlines -bp15 <- bp + theme(panel.grid.minor=element_blank(), panel.grid.major=element_blank()) -save_outputs(bp15, "axes/hidden gridlines", file_prefix="") - -# Hide just the minor gridlines -bp16 <- bp + theme(panel.grid.minor=element_blank()) -save_outputs(bp16, "axes/hidden minor gridlines", file_prefix="") - -# Hide all the horizontal gridlines -bp17 <- bp + theme(panel.grid.minor.x=element_blank(), panel.grid.major.x=element_blank()) -save_outputs(bp17, "axes/hidden horizontal gridlines", file_prefix="") - -# Hide all the vertical gridlines -bp18 <- bp + theme(panel.grid.minor.y=element_blank(), panel.grid.major.y=element_blank()) -save_outputs(bp18, "axes/hidden vertical gridlines", file_prefix="") diff --git a/tests/cookbook-test-suite/axes/axes_labels-ggplot2.png b/tests/cookbook-test-suite/axes/axes_labels-ggplot2.png deleted file mode 100644 index 0489cab1d2..0000000000 Binary files a/tests/cookbook-test-suite/axes/axes_labels-ggplot2.png and /dev/null differ diff --git a/tests/cookbook-test-suite/axes/axes_labels-plotly.png b/tests/cookbook-test-suite/axes/axes_labels-plotly.png deleted file mode 100644 index 421f6bad85..0000000000 Binary files a/tests/cookbook-test-suite/axes/axes_labels-plotly.png and /dev/null differ diff --git a/tests/cookbook-test-suite/axes/axes_labels.json b/tests/cookbook-test-suite/axes/axes_labels.json deleted file mode 100644 index 6d34337602..0000000000 --- a/tests/cookbook-test-suite/axes/axes_labels.json +++ /dev/null @@ -1,123 +0,0 @@ -{ - "data": [ - { - "y": [ - 4.17, - 5.58, - 5.18, - 6.11, - 4.5, - 4.61, - 5.17, - 4.53, - 5.33, - 5.14 - ], - "name": "ctrl", - "fillcolor": "rgb(255,255,255)", - "line": { - "color": "rgb(51,51,51)", - "width": 2, - "dash": "solid", - "shape": "linear" - }, - "xaxis": "x1", - "yaxis": "y1", - "type": "box" - }, - { - "y": [ - 4.81, - 4.17, - 4.41, - 3.59, - 5.87, - 3.83, - 6.03, - 4.89, - 4.32, - 4.69 - ], - "name": "trt1", - "fillcolor": "rgb(255,255,255)", - "line": { - "color": "rgb(51,51,51)", - "width": 2, - "dash": "solid", - "shape": "linear" - }, - "xaxis": "x1", - "yaxis": "y1", - "type": "box" - }, - { - "y": [ - 6.31, - 5.12, - 5.54, - 5.5, - 5.37, - 5.29, - 4.92, - 6.15, - 5.8, - 5.26 - ], - "name": "trt2", - "fillcolor": "rgb(255,255,255)", - "line": { - "color": "rgb(51,51,51)", - "width": 2, - "dash": "solid", - "shape": "linear" - }, - "xaxis": "x1", - "yaxis": "y1", - "type": "box" - } - ], - "layout": { - "titlefont": { - "family": "" - }, - "showlegend": false, - "xaxis": { - "title": "group", - "type": "category", - "showgrid": true, - "zeroline": false, - "showline": false, - "ticks": "outside", - "showticklabels": true, - "tickcolor": "rgb(127,127,127)", - "gridcolor": "rgb(255,255,255)" - }, - "yaxis": { - "title": "Weight (Kg)", - "type": "linear", - "showgrid": true, - "zeroline": false, - "showline": false, - "ticks": "outside", - "showticklabels": true, - "tickcolor": "rgb(127,127,127)", - "gridcolor": "rgb(255,255,255)" - }, - "legend": { - "x": 1.05, - "y": 0.5, - "font": { - "family": "" - }, - "bgcolor": "rgb(255,255,255)", - "bordercolor": "transparent", - "xanchor": "center", - "yanchor": "top" - }, - "margin": { - "r": 10 - }, - "paper_bgcolor": "rgb(255,255,255)", - "plot_bgcolor": "rgb(229,229,229)" - } -} diff --git a/tests/cookbook-test-suite/axes/axes_labels.url b/tests/cookbook-test-suite/axes/axes_labels.url deleted file mode 100644 index f58774d6e5..0000000000 --- a/tests/cookbook-test-suite/axes/axes_labels.url +++ /dev/null @@ -1 +0,0 @@ -https://plot.ly/~TestBot/617 diff --git a/tests/cookbook-test-suite/axes/axes_labels_set_with_scale-ggplot2.png b/tests/cookbook-test-suite/axes/axes_labels_set_with_scale-ggplot2.png deleted file mode 100644 index fdc498d7db..0000000000 Binary files a/tests/cookbook-test-suite/axes/axes_labels_set_with_scale-ggplot2.png and /dev/null differ diff --git a/tests/cookbook-test-suite/axes/axes_labels_set_with_scale-plotly.png b/tests/cookbook-test-suite/axes/axes_labels_set_with_scale-plotly.png deleted file mode 100644 index 3f83d0133b..0000000000 Binary files a/tests/cookbook-test-suite/axes/axes_labels_set_with_scale-plotly.png and /dev/null differ diff --git a/tests/cookbook-test-suite/axes/axes_labels_set_with_scale.json b/tests/cookbook-test-suite/axes/axes_labels_set_with_scale.json deleted file mode 100644 index e7d7b40170..0000000000 --- a/tests/cookbook-test-suite/axes/axes_labels_set_with_scale.json +++ /dev/null @@ -1,123 +0,0 @@ -{ - "data": [ - { - "y": [ - 4.17, - 5.58, - 5.18, - 6.11, - 4.5, - 4.61, - 5.17, - 4.53, - 5.33, - 5.14 - ], - "name": "ctrl", - "fillcolor": "rgb(255,255,255)", - "line": { - "color": "rgb(51,51,51)", - "width": 2, - "dash": "solid", - "shape": "linear" - }, - "xaxis": "x1", - "yaxis": "y1", - "type": "box" - }, - { - "y": [ - 4.81, - 4.17, - 4.41, - 3.59, - 5.87, - 3.83, - 6.03, - 4.89, - 4.32, - 4.69 - ], - "name": "trt1", - "fillcolor": "rgb(255,255,255)", - "line": { - "color": "rgb(51,51,51)", - "width": 2, - "dash": "solid", - "shape": "linear" - }, - "xaxis": "x1", - "yaxis": "y1", - "type": "box" - }, - { - "y": [ - 6.31, - 5.12, - 5.54, - 5.5, - 5.37, - 5.29, - 4.92, - 6.15, - 5.8, - 5.26 - ], - "name": "trt2", - "fillcolor": "rgb(255,255,255)", - "line": { - "color": "rgb(51,51,51)", - "width": 2, - "dash": "solid", - "shape": "linear" - }, - "xaxis": "x1", - "yaxis": "y1", - "type": "box" - } - ], - "layout": { - "titlefont": { - "family": "" - }, - "showlegend": false, - "xaxis": { - "title": "", - "type": "category", - "showgrid": true, - "zeroline": false, - "showline": false, - "ticks": "outside", - "showticklabels": true, - "tickcolor": "rgb(127,127,127)", - "gridcolor": "rgb(255,255,255)" - }, - "yaxis": { - "title": "Weight (Kg)", - "type": "linear", - "showgrid": true, - "zeroline": false, - "showline": false, - "ticks": "outside", - "showticklabels": true, - "tickcolor": "rgb(127,127,127)", - "gridcolor": "rgb(255,255,255)" - }, - "legend": { - "x": 1.05, - "y": 0.5, - "font": { - "family": "" - }, - "bgcolor": "rgb(255,255,255)", - "bordercolor": "transparent", - "xanchor": "center", - "yanchor": "top" - }, - "margin": { - "r": 10 - }, - "paper_bgcolor": "rgb(255,255,255)", - "plot_bgcolor": "rgb(229,229,229)" - } -} diff --git a/tests/cookbook-test-suite/axes/axes_labels_set_with_scale.url b/tests/cookbook-test-suite/axes/axes_labels_set_with_scale.url deleted file mode 100644 index 4944ba10f5..0000000000 --- a/tests/cookbook-test-suite/axes/axes_labels_set_with_scale.url +++ /dev/null @@ -1 +0,0 @@ -https://plot.ly/~TestBot/618 diff --git a/tests/cookbook-test-suite/axes/axes_labels_with_custom_time_labels-ggplot2.png b/tests/cookbook-test-suite/axes/axes_labels_with_custom_time_labels-ggplot2.png deleted file mode 100644 index 6930777217..0000000000 Binary files a/tests/cookbook-test-suite/axes/axes_labels_with_custom_time_labels-ggplot2.png and /dev/null differ diff --git a/tests/cookbook-test-suite/axes/axes_labels_with_custom_time_labels-plotly.png b/tests/cookbook-test-suite/axes/axes_labels_with_custom_time_labels-plotly.png deleted file mode 100644 index 812cab1301..0000000000 Binary files a/tests/cookbook-test-suite/axes/axes_labels_with_custom_time_labels-plotly.png and /dev/null differ diff --git a/tests/cookbook-test-suite/axes/axes_labels_with_custom_time_labels.json b/tests/cookbook-test-suite/axes/axes_labels_with_custom_time_labels.json deleted file mode 100644 index dbcc66b502..0000000000 --- a/tests/cookbook-test-suite/axes/axes_labels_with_custom_time_labels.json +++ /dev/null @@ -1,123 +0,0 @@ -{ - 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"data": [ - { - "x": [ - "Female", - "Male" - ], - "y": [ - 13.53, - 16.24 - ], - "name": "Lunch", - "marker": { - "color": "rgb(248,118,109)", - "line": { - "color": "rgb(0,0,0)", - "width": 1 - } - }, - "xaxis": "x1", - "yaxis": "y1", - "type": "bar" - }, - { - "x": [ - "Female", - "Male" - ], - "y": [ - 16.81, - 17.42 - ], - "name": "Dinner", - "marker": { - "color": "rgb(0,191,196)", - "line": { - "color": "rgb(0,0,0)", - "width": 1 - } - }, - "xaxis": "x1", - "yaxis": "y1", - "type": "bar" - } - ], - "layout": { - "titlefont": { - "family": "" - }, - "showlegend": true, - "xaxis": { - "title": "sex", - "type": "category", - "showgrid": true, - "zeroline": false, - "showline": false, - "ticks": "outside", - "showticklabels": true, - "tickcolor": "rgb(127,127,127)", - "gridcolor": "rgb(255,255,255)" - }, - "yaxis": { - "title": "total_bill", - "type": "linear", - "showgrid": true, - "zeroline": false, - "showline": false, - "ticks": "outside", - "showticklabels": true, - "tickcolor": "rgb(127,127,127)", - 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"data": [ - { - "x": [ - "Lunch", - "Dinner" - ], - "y": [ - 13.53, - 16.81 - ], - "name": "Female", - "marker": { - "color": "rgb(248,118,109)" - }, - "xaxis": "x1", - "yaxis": "y1", - "type": "bar" - }, - { - "x": [ - "Lunch", - "Dinner" - ], - "y": [ - 16.24, - 17.42 - ], - "name": "Male", - "marker": { - "color": "rgb(0,191,196)" - }, - "xaxis": "x1", - "yaxis": "y1", - "type": "bar" - } - ], - "layout": { - "titlefont": { - "family": "" - }, - "showlegend": true, - "xaxis": { - "title": "time", - "type": "category", - "showgrid": true, - "zeroline": false, - "showline": false, - "ticks": "outside", - "showticklabels": true, - "tickcolor": "rgb(127,127,127)", - "gridcolor": "rgb(255,255,255)" - }, - "yaxis": { - "title": "total_bill", - "type": "linear", - "showgrid": true, - "zeroline": false, - "showline": false, - "ticks": "outside", - "showticklabels": true, - "tickcolor": "rgb(127,127,127)", - "gridcolor": "rgb(255,255,255)" - }, - "legend": { - "x": 1.05, - "y": 0.5, - "font": { - "family": "" - }, - "bgcolor": "rgb(255,255,255)", - "bordercolor": "transparent", - "xanchor": "center", - "yanchor": "top" - }, - "annotations": [ - { - "x": 1.05, - "y": 0.52, - "xref": "paper", - "yref": "paper", - "text": "sex", - "showarrow": false, - "xanchor": "center", - "textangle": 0 - } - ], - "margin": { - "r": 10 - }, - "paper_bgcolor": "rgb(255,255,255)", - "plot_bgcolor": "rgb(229,229,229)", - "barmode": "stack" - } -} diff --git a/tests/cookbook-test-suite/bars-and-lines/multi-var-stacked-bar.url b/tests/cookbook-test-suite/bars-and-lines/multi-var-stacked-bar.url deleted file mode 100644 index 7c40438f48..0000000000 --- a/tests/cookbook-test-suite/bars-and-lines/multi-var-stacked-bar.url +++ /dev/null @@ -1 +0,0 @@ -https://plot.ly/~TestBot/640 diff --git a/tests/cookbook-test-suite/bars_and_lines.R b/tests/cookbook-test-suite/bars_and_lines.R deleted file mode 100644 index 59f4002554..0000000000 --- a/tests/cookbook-test-suite/bars_and_lines.R +++ /dev/null @@ -1,189 +0,0 @@ -df <- data.frame(time = factor(c("Lunch","Dinner"), levels=c("Lunch","Dinner")), - total_bill = c(14.89, 17.23)) -# time total_bill -# Lunch 14.89 -# Dinner 17.23 - -# Very basic bar graph -g <- ggplot(data=df, aes(x=time, y=total_bill)) + geom_bar(stat="identity") -save_outputs(g, "bars-and-lines/basic-bar", file_prefix="") - -# Map the time of day to different fill colors. These both have the same result. -g <- ggplot(data=df, aes(x=time, y=total_bill, fill=time)) + geom_bar(stat="identity") -save_outputs(g, "bars-and-lines/basic-bar-fill-colors-1", file_prefix="") -g <- ggplot(data=df, aes(x=time, y=total_bill)) + geom_bar(aes(fill=time), stat="identity") -save_outputs(g, "bars-and-lines/basic-bar-fill-colors-2", file_prefix="") - -# Add a black outline -g <- ggplot(data=df, aes(x=time, y=total_bill, fill=time)) + geom_bar(colour="black", stat="identity") -save_outputs(g, "bars-and-lines/basic-bar-fill-colors-black-outline", file_prefix="") - -# No legend, since the information is redundant -g <- ggplot(data=df, aes(x=time, y=total_bill, fill=time)) + - geom_bar(colour="black", stat="identity") + - guides(fill=FALSE) -save_outputs(g, "bars-and-lines/basic-bar-fill-colors-black-outline-no-legend", file_prefix="") - -library(reshape2) -tips -# total_bill tip sex smoker day time size -# 16.99 1.01 Female No Sun Dinner 2 -# 10.34 1.66 Male No Sun Dinner 3 -# 21.01 3.50 Male No Sun Dinner 3 -# ... <244 total rows> ... -# 22.67 2.00 Male Yes Sat Dinner 2 -# 17.82 1.75 Male No Sat Dinner 2 -# 18.78 3.00 Female No Thur Dinner 2 - -# Bar graph of counts -g <- ggplot(data=tips, aes(x=day)) + geom_bar(stat="bin") -save_outputs(g, "bars-and-lines/bar-graph-of-counts", file_prefix="") - -# Equivalent to this, since stat="bin" is the default: -g <- ggplot(data=tips, aes(x=day)) + geom_bar() -save_outputs(g, "bars-and-lines/bar-graph-of-counts-2", file_prefix="") - -# Basic line graph. These both have the same result. -g <- ggplot(data=df, aes(x=time, y=total_bill, group=1)) + geom_line() -save_outputs(g, "bars-and-lines/basic-line", file_prefix="") -g <- ggplot(data=df, aes(x=time, y=total_bill)) + geom_line(aes(group=1)) -save_outputs(g, "bars-and-lines/basic-line-2", file_prefix="") - -# Add points -g <- ggplot(data=df, aes(x=time, y=total_bill, group=1)) + geom_line() + geom_point() -save_outputs(g, "bars-and-lines/basic-line-with-points", file_prefix="") - -# Change color of both line and points -# Change line type and point type, and use thicker line and larger points -# Change points to circles with white fill -g <- ggplot(data=df, aes(x=time, y=total_bill, group=1)) + - geom_line(colour="red", linetype="dotted", size=1.5) + - geom_point(colour="red", size=4, shape=21, fill="white") -save_outputs(g, "bars-and-lines/basic-dashed-line-with-colors", file_prefix="") - -# Change the y-range to go from 0 to the maximum value in the total_bill column, -# and change axis labels -g <- ggplot(data=df, aes(x=time, y=total_bill, group=1)) + geom_line() + geom_point() + - ylim(0, max(df$total_bill)) + - xlab("Time of day") + ylab("Total bill") + - ggtitle("Average bill for 2 people") -save_outputs(g, "bars-and-lines/basic-line-fully-styled", file_prefix="") - -df1 <- data.frame(sex = factor(c("Female","Female","Male","Male")), - time = factor(c("Lunch","Dinner","Lunch","Dinner"), levels=c("Lunch","Dinner")), - total_bill = c(13.53, 16.81, 16.24, 17.42)) -# sex time total_bill -# Female Lunch 13.53 -# Female Dinner 16.81 -# Male Lunch 16.24 -# Male Dinner 17.42 - -# Stacked bar graph -- this is probably not what you want -g <- ggplot(data=df1, aes(x=time, y=total_bill, fill=sex)) + geom_bar(stat="identity") -save_outputs(g, "bars-and-lines/multi-var-stacked-bar", file_prefix="") - -# Bar graph, time on x-axis, color fill grouped by sex -- use position_dodge() -g <- ggplot(data=df1, aes(x=time, y=total_bill, fill=sex)) + geom_bar(stat="identity", position=position_dodge()) -save_outputs(g, "bars-and-lines/multi-var-grouped-bar", file_prefix="") -g <- ggplot(data=df1, aes(x=time, y=total_bill, fill=sex)) + geom_bar(stat="identity", position=position_dodge(), colour="black") -save_outputs(g, "bars-and-lines/multi-var-grouped-bar-black-outline", file_prefix="") - -# Change colors -g <- ggplot(data=df1, aes(x=time, y=total_bill, fill=sex)) + geom_bar(stat="identity", position=position_dodge(), colour="black") + - scale_fill_manual(values=c("#999999", "#E69F00")) -save_outputs(g, "bars-and-lines/multi-var-grouped-bar-colored", file_prefix="") - -# Bar graph, time on x-axis, color fill grouped by sex -- use position_dodge() -g <- ggplot(data=df1, aes(x=sex, y=total_bill, fill=time)) + geom_bar(stat="identity", position=position_dodge(), colour="black") -save_outputs(g, "bars-and-lines/multi-var-grouped-bar-reversed-vars", file_prefix="") - -# Basic line graph with points -g <- ggplot(data=df1, aes(x=time, y=total_bill, group=sex)) + geom_line() + geom_point() -save_outputs(g, "bars-and-lines/basic-line-with-points", file_prefix="") - -# Map sex to color -g <- ggplot(data=df1, aes(x=time, y=total_bill, group=sex, colour=sex)) + geom_line() + geom_point() -save_outputs(g, "bars-and-lines/basic-line-with-mapped-colors", file_prefix="") - -# Map sex to different point shape, and use larger points -g <- ggplot(data=df1, aes(x=time, y=total_bill, group=sex, shape=sex)) + geom_line() + geom_point() -save_outputs(g, "bars-and-lines/basic-line-with-symbols", file_prefix="") - -# Use thicker lines and larger points, and hollow white-filled points -g <- ggplot(data=df1, aes(x=time, y=total_bill, group=sex, shape=sex)) + - geom_line(size=1.5) + - geom_point(size=3, fill="white") + - scale_shape_manual(values=c(22,21)) -save_outputs(g, "bars-and-lines/basic-line-with-points-fully-styled", file_prefix="") - -g <- ggplot(data=df1, aes(x=sex, y=total_bill, group=time, shape=time, color=time)) + geom_line() + geom_point() -save_outputs(g, "bars-and-lines/basic-line-swapped-vars", file_prefix="") - -# A bar graph -g <- ggplot(data=df1, aes(x=time, y=total_bill, fill=sex)) + - geom_bar(colour="black", stat="identity", - position=position_dodge(), - size=.3) + # Thinner lines - scale_fill_hue(name="Sex of payer") + # Set legend title - xlab("Time of day") + ylab("Total bill") + # Set axis labels - ggtitle("Average bill for 2 people") + # Set title - theme_bw() -save_outputs(g, "bars-and-lines/finished-bar-bw-theme", file_prefix="") - -# A line graph -g <- ggplot(data=df1, aes(x=time, y=total_bill, group=sex, shape=sex, colour=sex)) + - geom_line(aes(linetype=sex), size=1) + # Set linetype by sex - geom_point(size=3, fill="white") + # Use larger points, fill with white - ylim(0, max(df1$total_bill)) + # Set y range - scale_colour_hue(name="Sex of payer", # Set legend title - l=30) + # Use darker colors (lightness=30) - scale_shape_manual(name="Sex of payer", - values=c(22,21)) + # Use points with a fill color - scale_linetype_discrete(name="Sex of payer") + - xlab("Time of day") + ylab("Total bill") + # Set axis labels - ggtitle("Average bill for 2 people") + # Set title - theme_bw() + - theme(legend.position=c(.7, .4)) # Position legend inside - # This must go after theme_bw -save_outputs(g, "bars-and-lines/finished-line", file_prefix="") - -dfn <- read.table(header=T, text=" -supp dose length - OJ 0.5 13.23 - OJ 1.0 22.70 - OJ 2.0 26.06 - VC 0.5 7.98 - VC 1.0 16.77 - VC 2.0 26.14 -") - -g <- ggplot(data=dfn, aes(x=dose, y=length, group=supp, colour=supp)) + geom_line() + geom_point() -save_outputs(g, "bars-and-lines/line-continuous-numerical-x-axis", file_prefix="") - -# Copy the data frame and convert dose to a factor -dfn2 <- dfn -dfn2$dose <- factor(dfn2$dose) -g <- ggplot(data=dfn2, aes(x=dose, y=length, group=supp, colour=supp)) + geom_line() + geom_point() -save_outputs(g, "bars-and-lines/line-continuous-categorical-x-axis", file_prefix="") - -# Use the original data frame, but put factor() directly in the plot specification - -## TODO: Uncomment when Plotly supports this -## g <- ggplot(data=dfn, aes(x=factor(dose), y=length, group=supp, colour=supp)) + geom_line() + geom_point() -## save_outputs(g, "bars-and-lines/line-continuous-categorical-x-axis-with-factor", file_prefix="") - -# Use dfn2 from above -g <- ggplot(data=dfn2, aes(x=dose, y=length, fill=supp)) + geom_bar(stat="identity", position=position_dodge()) -save_outputs(g, "bars-and-lines/bar-categorical-numerical-labels", file_prefix="") - -# Use the original data frame, but put factor() directly in the plot specification -g <- ggplot(data=dfn, aes(x=factor(dose), y=length, fill=supp)) + geom_bar(stat="identity", position=position_dodge()) -save_outputs(g, "bars-and-lines/bar-categorical-numerical-labels-with-factor", file_prefix="") - -# Add title, narrower bars, gray fill, and change axis labels -g <- ggplot(data=df, aes(x=time, y=total_bill, fill=time)) + - geom_bar(colour="black", fill="#DD8888", width=.7, stat="identity") + - guides(fill=FALSE) + - xlab("Time of day") + ylab("Total bill") + - ggtitle("Average bill for 2 people") -save_outputs(g, "bars-and-lines/basic-bar-fully-styled", file_prefix="") diff --git a/tests/cookbook-test-suite/distributions.R b/tests/cookbook-test-suite/distributions.R deleted file mode 100644 index d507f47a8a..0000000000 --- a/tests/cookbook-test-suite/distributions.R +++ /dev/null @@ -1,117 +0,0 @@ -set.seed(1234) -df <- data.frame(cond = factor( rep(c("A","B"), each=200) ), - rating = c( - c(-1.207065749, 0.277429242, 1.084441177, -2.345697703, 0.429124689, 0.506055892, -0.574739960, -0.546631856, -0.564451999, -0.890037829-0.477192700, -0.998386445, -0.776253895, 0.064458817, 0.959494059-0.110285494, -0.511009506, -0.911195417, -0.837171680, 2.415835178, 0.134088220, -0.490685897, -0.440547872, 0.459589441, -0.693720247-1.448204910, 0.574755721, -1.023655723, -0.015138300, -0.935948601, 1.102297546, -0.475593079, -0.709440038, -0.501258061, -1.629093469-1.167619262, -2.180039649, -1.340993192, -0.294293859, -0.465897540, 1.449496265, -1.068642724, -0.855364634, -0.280623002, -0.994340076-0.968514318, -1.107318193, -1.251985886, -0.523828119, -0.496849957-1.806031257, -0.582075925, -1.108889624, -1.014962009, -0.162309524, 0.563055819, 1.647817473, -0.773353424, 1.605909629, -1.157808548, 0.656588464, 2.548991071, -0.034760390, -0.669633580, -0.007604756, 1.777084448, -1.138607737, 1.367827179, 1.329564791, 0.336472797, 0.006892838, -0.455468738, -0.366523933, 0.648286568, 2.070270861-0.153398412, -1.390700947, -0.723581777, 0.258261762, -0.317059115-0.177789958, -0.169994077, -1.372301886, -0.173787170, 0.850232257, 0.697608712, 0.549997351, -0.402731975, -0.191593770, -1.194527880-0.053158819, 0.255196001, 1.705964007, 1.001513252, -0.495583443, 0.355550297, -1.134608044, 0.878203627, 0.972916753, 2.121117105, 0.414523534, -0.474718474, 0.065993494, -0.502477782, -0.825998587, 0.166989280, -0.896264626, 0.168185388, 0.354968261, -0.052105117-0.195934619, -0.649069752, -1.109767231, 0.849274203, 0.022362526, 0.831140617, -1.244287851, 0.169026414, 0.673166307, -0.026276376-0.191392169, -0.781906647, 2.058161988, 0.750501453, 1.824208302, 0.080059641, -0.631409299, -1.513288120, -0.636099831, 0.226301532, 1.013690347, 0.252750135, -1.171948313, 0.668714329, -1.650100935-0.365852248, -0.316118329, -1.948246047, 0.920057522, -0.622871595-0.334036650, 1.395147893, 0.636674411, -0.108431697, 0.513762778, 0.399271807, 1.662856447, 0.275893404, 0.506272623, 0.347551975-0.377237647, 0.097619463, 1.638744645, -0.875592474, 0.121759999, 1.362130661, -0.234621087, -1.053382808, -0.869783606, -0.390127030-0.847350073, -0.260639392, -0.414419706, -0.183050798, 0.407056098, 0.624633128, 1.678205744, -0.068693654, -0.320839913, 1.471005717, 1.704329398, 0.043244038, -0.332657319, -1.822235418, 1.411262399-0.837582434, -1.123762794, 3.043765886, 0.235021308, -0.033258610-2.732219523, -0.099790588, 0.976031735, 0.413868915, 0.912322162, 1.983732201, 1.169108514, -0.508737015, 0.704180178, -0.198416274-0.538070789, -2.855758655, -0.789646852, 0.487814635, 2.168032540, 0.500694614, 0.620210204, -0.965903210, 0.162654708, -2.078237542), - c(1.28522682, 1.49676878, 0.98551392, 1.50073352, 1.11168103, 1.56046236, 2.64246363, 1.91236284, 0.83266396, -0.31444896, 1.21805782, 0.39976476, 2.29349310, -0.80708094, 0.38424821, 1.22200837, 0.64826346, 0.19384888, 0.49527893, 1.42953610, 1.69517198, 1.46021263, 3.07348352, 1.97349757, 1.08770973, 0.14022991, 3.71914013, 1.47741550, 0.11567966, 0.98649208, 0.47560670, 0.52529578, -0.13350334, 0.91684534, 1.11916024, -0.27754212, -2.43315213, 0.54512535, 0.82951783, 1.39427377, 0.85913517, 1.21339889, -0.29777217, 1.51117526, 1.51888873, 1.05165107, 2.15727444, 1.20446847, 1.06436427, 1.06804390, 1.23693058, 1.86012391, 1.25219040, 1.46319862, -0.33637355, 0.42950248, 2.27696959, -0.42390375, 1.05806839, 1.20500281, 1.77580332, 0.45112326, 0.95862544, -0.96325507, 1.13859605, 0.13343497, 0.56135338, -0.38776528, 1.18493532, 1.46657952, 0.49538611, 2.62501106, 1.47055937, 1.74863257, 2.84940300, 0.14888639, 1.60861927, 1.78658061, 0.79382920, 1.11905236, -0.21182190, 1.27016755, 0.09902967, 1.61368286, -0.01143078, 1.11939749, -0.04652265, 0.55423682, -0.75285901, 0.92843403, 1.78544339, 0.98324752, -0.96622921, 0.17946630, 2.45604304, 2.60980539, -0.37503677, 0.43329674, 1.15362545, 1.11915622, 0.22004301, -0.15327870, 0.62057141, 1.80980821, 0.82362661, 0.15097178, 0.29562578, 2.41439150, 0.35304019, 1.56317676, 2.27171869, 1.24366490, 0.37827813, 0.75999837, 0.30772003, 2.02771712, 0.65044643, 2.34998338, 0.23838746, 0.15288275, 0.94313216, 0.82418865, 0.29554848, -0.78139681, 0.83006642, 0.08342330, 1.88261096, -0.15268545, 1.92648273, 0.15095698, 1.09247008, 1.69870272, 0.28125764, 1.35443855, 0.71202633, -0.33521293, 0.52992039, 2.41978988, 0.58586883, -0.01778246, 0.74597708, 1.13014161, 1.75532461, 1.94395988, 0.90052240, 1.96457525, 0.03574005, -1.54451340, 0.32831657, 0.28414451, -1.51603615, 1.36247176, 0.01622486, 0.57394601, -0.78710299, 1.34752420, 2.69122702, -0.07807711, 0.68744109, 2.74871306, 1.73381633, 2.71305942, 0.79476594, 0.64773995, 0.29036834, 2.23457370, -0.48583853, 1.10731422, 0.75368147, 3.05184180, 0.19196627, -0.70928817, 1.03263177, 0.76035130, -0.03912507, 0.93229109, 0.52447525, 0.12124136, 1.30083593, 0.46833769, -1.03498025, -1.85174125, 0.21941783, 2.25418687, 1.63812938, 2.01505358, 1.78250544, 1.11576402, -0.70706263, 1.00556979, 2.39722809, -2.59606353, 0.01864772, 1.90246464, 1.32874502, 1.58939440, 1.25709951, 1.33883312, 0.81464312, -0.11648914, 1.28522682, 1.49676878, 0.98551392, 1.50073352, 1.11168103, 1.56046236, 2.64246363, 1.91236284, 0.83266396, -0.31444896, 1.21805782, 0.39976476, 2.29349310, -0.80708094, 0.38424821, 1.22200837, 0.64826346, 0.19384888))) -# cond rating -# A -1.2070657 -# A 0.2774292 -# A 1.0844412 -# ... -# B 1.3388331 -# B 0.8146431 -# B -0.1164891 - - -# Basic histogram from the vector "rating". Each bin is .5 wide. -# These both do the same thing: -qplot(df$rating, binwidth=.5) -g <- ggplot(df, aes(x=rating)) + geom_histogram(binwidth=.5) -save_outputs(g, "distributions/basic-histogram", file_prefix="") - -# Draw with black outline, white fill -g <- ggplot(df, aes(x=rating)) + geom_histogram(binwidth=.5, colour="black", fill="white") -save_outputs(g, "distributions/basic-histogram-white-filling", file_prefix="") - -# Density curve -g <- ggplot(df, aes(x=rating)) + geom_density() -save_outputs(g, "distributions/basic-density-curve", file_prefix="") - -# Histogram overlaid with kernel density curve -g <- ggplot(df, aes(x=rating)) + - geom_histogram(aes(y=..density..), # Histogram with density instead of count on y-axis - binwidth=.5, - colour="black", fill="white") + - geom_density(alpha=.2, fill="#FF6666") # Overlay with transparent density plot -save_outputs(g, "distributions/basic-density-curve-with-histogram", file_prefix="") - -g <- ggplot(df, aes(x=rating)) + geom_histogram(binwidth=.5, colour="black", fill="white") + - geom_vline(aes(xintercept=mean(rating, na.rm=T)), # Ignore NA values for mean - color="red", linetype="dashed", size=1) -save_outputs(g, "distributions/histogram-with-vertical-line", file_prefix="") - -# Overlaid histograms -g <- ggplot(df, aes(x=rating, fill=cond)) + geom_histogram(binwidth=.5, alpha=.5, position="identity") -save_outputs(g, "distributions/overlaid-histograms", file_prefix="") - -# Interleaved histograms -g <- ggplot(df, aes(x=rating, fill=cond)) + geom_histogram(binwidth=.5, position="dodge") -save_outputs(g, "distributions/grouped-histograms", file_prefix="") - -# Density plots -g <- ggplot(df, aes(x=rating, colour=cond)) + geom_density() -save_outputs(g, "distributions/multiple-density-plots", file_prefix="") - -# Density plots with semi-transparent fill -g <- ggplot(df, aes(x=rating, fill=cond)) + geom_density(alpha=.3) -save_outputs(g, "distributions/filled-density-plots", file_prefix="") - -# Find the mean of each group -library(plyr) -cdf <- ddply(df, "cond", summarise, rating.mean=mean(rating)) -# cond rating.mean -# A -0.05775928 -# B 0.87324927 - - -# Overlaid histograms with means -g <- ggplot(df, aes(x=rating, fill=cond)) + - geom_histogram(binwidth=.5, alpha=.5, position="identity") + - geom_vline(data=cdf, aes(xintercept=rating.mean, colour=cond), - linetype="dashed", size=1) -## TODO: uncomment when fixed: error "rating" not found -# save_outputs(g, "distributions/overlaid-histograms-with-means", file_prefix="") - -# Density plots with means -g <- ggplot(df, aes(x=rating, colour=cond)) + geom_density() + - geom_vline(data=cdf, aes(xintercept=rating.mean, colour=cond), - linetype="dashed", size=1) -## TODO: uncomment when fixed: error "rating" not found -# save_outputs(g, "distributions/density-plot-with-means", file_prefix="") - -g <- ggplot(df, aes(x=rating)) + geom_histogram(binwidth=.5, colour="black", fill="white") + - facet_grid(cond ~ .) -save_outputs(g, "distributions/faceted-histograms", file_prefix="") - -# With mean lines, using cdf from above -g <- ggplot(df, aes(x=rating)) + geom_histogram(binwidth=.5, colour="black", fill="white") + - facet_grid(cond ~ .) + - geom_vline(data=cdf, aes(xintercept=rating.mean), - linetype="dashed", size=1, colour="red") -## TODO: uncomment when fixed: error "rating" not found -# save_outputs(g, "distributions/faceted-histograms-with-mean-lines", file_prefix="") - -# A basic box plot -g <- ggplot(df, aes(x=cond, y=rating)) + geom_boxplot() -save_outputs(g, "distributions/basic-box-plot", file_prefix="") - -# A basic box with the conditions colored -g <- ggplot(df, aes(x=cond, y=rating, fill=cond)) + geom_boxplot() -save_outputs(g, "distributions/box-plot-with-conditions-colored", file_prefix="") - -# The above adds a redundant legend. With the legend removed: -g <- ggplot(df, aes(x=cond, y=rating, fill=cond)) + geom_boxplot() + - guides(fill=FALSE) -save_outputs(g, "distributions/box-plot-with-legend-removed", file_prefix="") - -# With flipped axes -g <- ggplot(df, aes(x=cond, y=rating, fill=cond)) + geom_boxplot() + - guides(fill=FALSE) + coord_flip() -save_outputs(g, "distributions/box-plot-with-flipped-axes", file_prefix="") - -# Add a diamond at the mean, and make it larger -g <- ggplot(df, aes(x=cond, y=rating)) + geom_boxplot() + - stat_summary(fun.y=mean, geom="point", shape=5, size=4) -save_outputs(g, "distributions/box-plot-with-diamond-means", file_prefix="") - - diff --git a/tests/cookbook-test-suite/distributions/basic-box-plot-ggplot2.png b/tests/cookbook-test-suite/distributions/basic-box-plot-ggplot2.png deleted file mode 100644 index 0ca3b095fd..0000000000 Binary files a/tests/cookbook-test-suite/distributions/basic-box-plot-ggplot2.png and /dev/null differ diff --git a/tests/cookbook-test-suite/distributions/basic-box-plot-plotly.png b/tests/cookbook-test-suite/distributions/basic-box-plot-plotly.png deleted file mode 100644 index 0ed90654e8..0000000000 Binary files a/tests/cookbook-test-suite/distributions/basic-box-plot-plotly.png and /dev/null differ diff --git a/tests/cookbook-test-suite/distributions/basic-box-plot.json b/tests/cookbook-test-suite/distributions/basic-box-plot.json deleted file mode 100644 index 69a8441afb..0000000000 --- a/tests/cookbook-test-suite/distributions/basic-box-plot.json +++ /dev/null @@ -1,478 +0,0 @@ -{ - 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"family": "" - }, - "bgcolor": "rgb(255,255,255)", - "bordercolor": "transparent", - "xanchor": "center", - "yanchor": "top" - }, - "annotations": [ - { - "x": 1.05, - "y": 0.52, - "xref": "paper", - "yref": "paper", - "text": "cond", - "showarrow": false, - "xanchor": "center", - "textangle": 0 - } - ], - "margin": { - "r": 10 - }, - "paper_bgcolor": "rgb(255,255,255)", - "plot_bgcolor": "rgb(229,229,229)", - "barmode": "overlay" - } -} diff --git a/tests/cookbook-test-suite/distributions/overlaid-histograms.url b/tests/cookbook-test-suite/distributions/overlaid-histograms.url deleted file mode 100644 index b67dfc7f6b..0000000000 --- a/tests/cookbook-test-suite/distributions/overlaid-histograms.url +++ /dev/null @@ -1 +0,0 @@ -https://plot.ly/~TestBot/743 diff --git a/tests/cookbook-test-suite/facets.r b/tests/cookbook-test-suite/facets.r deleted file mode 100644 index 7c7f27b73e..0000000000 --- a/tests/cookbook-test-suite/facets.r +++ /dev/null @@ -1,74 +0,0 @@ -library(reshape2) -tips -# total_bill tip sex smoker day time size -# 16.99 1.01 Female No Sun Dinner 2 -# 10.34 1.66 Male No Sun Dinner 3 -# 21.01 3.50 Male No Sun Dinner 3 -# ... -# 22.67 2.00 Male Yes Sat Dinner 2 -# 17.82 1.75 Male No Sat Dinner 2 -# 18.78 3.00 Female No Thur Dinner 2 - -library(ggplot2) -sp <- ggplot(tips, aes(x=total_bill, y=tip/total_bill)) + geom_point(shape=1) -save_outputs(sp, 'facets/no features', file_prefix="") - -# Divide by levels of "sex", in the vertical direction -sp1 <- sp + facet_grid(sex ~ .) -save_outputs(sp1, 'facets/vertical facet_grid', file_prefix="") - -# Divide by levels of "sex", in the horizontal direction -sp2 <- sp + facet_grid(. ~ sex) -save_outputs(sp2, 'facets/horizontal facet_grid', file_prefix="") - -# Divide with "sex" vertical, "day" horizontal -sp3 <- sp + facet_grid(sex ~ day) -save_outputs(sp3, 'facets/2x4 facet_grid - sex-vertical day-horizontal', file_prefix="") - -# Divide by day, going horizontally and wrapping with 2 columns -sp4 <- sp + facet_wrap( ~ day, ncol=2) -save_outputs(sp4, 'facets/facet_wrap', file_prefix="") - -sp5 <- sp + facet_grid(sex ~ day) + - theme(strip.text.x = element_text(size=8, angle=75), - strip.text.y = element_text(size=12, face="bold"), - strip.background = element_rect(colour="red", fill="#CCCCFF")) -save_outputs(sp5, 'facets/facet label colors', file_prefix="") - -mf_labeller <- function(var, value){ - value <- as.character(value) - if (var=="sex") { - value[value=="Female"] <- "Woman" - value[value=="Male"] <- "Man" - } - return(value) -} - -sp6 <- sp + facet_grid(. ~ sex, labeller=mf_labeller) -save_outputs(sp6, 'facets/custom facet labels', file_prefix="") - -tips2 <- tips -levels(tips2$sex)[levels(tips2$sex)=="Female"] <- "Woman" -levels(tips2$sex)[levels(tips2$sex)=="Male"] <- "Man" -# total_bill tip sex smoker day time size -# 16.99 1.01 Woman No Sun Dinner 2 -# 10.34 1.66 Man No Sun Dinner 3 -# ... - -sp2 <- ggplot(tips2, aes(x=total_bill, y=tip/total_bill)) + geom_point(shape=1) -sp2 + facet_grid(. ~ sex) - -# A histogram of bill sizes -hp <- ggplot(tips, aes(x=total_bill)) + geom_histogram(binwidth=2,colour="white") - -# Histogram of total_bill, divided by sex and smoker -sp7 <- hp + facet_grid(sex ~ smoker) -save_outputs(sp7, 'facets/histogram facets', file_prefix="") - -# Same as above, with scales="free_y" -sp8 <- hp + facet_grid(sex ~ smoker, scales="free_y") -save_outputs(sp8, 'facets/histogram facets with free_y', file_prefix="") - -# With panels that have the same scaling, but different range (and therefore different physical sizes) -sp9 <- hp + facet_grid(sex ~ smoker, scales="free", space="free") -save_outputs(sp9, 'facets/histogram facets with scales=free', file_prefix="") diff --git a/tests/cookbook-test-suite/legends.R b/tests/cookbook-test-suite/legends.R deleted file mode 100644 index a998019461..0000000000 --- a/tests/cookbook-test-suite/legends.R +++ /dev/null @@ -1,132 +0,0 @@ -bp <- ggplot(data=PlantGrowth, aes(x=group, y=weight, fill=group)) + geom_boxplot() - -# Remove legend for a particular aesthetic (fill) -temp <- bp + guides(fill=FALSE) -save_outputs(temp, "legends/hidden legend - 1", file_prefix="") - -# It can also be done when specifying the scale -temp <- bp + scale_fill_discrete(guide=FALSE) -save_outputs(temp, "legends/hidden legend - 2", file_prefix="") - -# This removes all legends -temp <- bp + theme(legend.position="none") -save_outputs(temp, "legends/hidden legend - all", file_prefix="") - -bp + scale_fill_discrete(breaks=c("trt1","ctrl","trt2")) - -# These two methods are equivalent: -bp + guides(fill = guide_legend(reverse=TRUE)) -temp <- bp + scale_fill_discrete(guide = guide_legend(reverse=TRUE)) -save_outputs(temp, "legends/reversed legend", file_prefix="") - -# You can also modify the scale directly: -bp + scale_fill_discrete(breaks = rev(levels(PlantGrowth$group))) - -# Remove title for fill legend -temp <- bp + guides(fill=guide_legend(title=NULL)) -save_outputs(temp, "legends/removed legend title", file_prefix="") - -# Remove title for all legends -temp <- bp + theme(legend.title=element_blank()) -save_outputs(temp, "legends/removed legend titles", file_prefix="") - -temp <- bp + scale_fill_discrete(name="Experimental\nCondition") -save_outputs(temp, "legends/multiple line legend title", file_prefix="") - -temp <- bp + scale_fill_discrete(name="Experimental\nCondition", - breaks=c("ctrl", "trt1", "trt2"), - labels=c("Control", "Treatment 1", "Treatment 2")) -save_outputs(temp, "legends/legend title with custom labels", file_prefix="") - -# Using a manual scale instead of hue -temp <- bp + scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"), - name="Experimental\nCondition", - breaks=c("ctrl", "trt1", "trt2"), - labels=c("Control", "Treatment 1", "Treatment 2")) -save_outputs(temp, "legends/legend title with custom colors", file_prefix="") - -# A different data set -df1 <- data.frame(sex = factor(c("Female","Female","Male","Male")), - time = factor(c("Lunch","Dinner","Lunch","Dinner"), levels=c("Lunch","Dinner")), - total_bill = c(13.53, 16.81, 16.24, 17.42)) - -# A basic graph -lp <- ggplot(data=df1, aes(x=time, y=total_bill, group=sex, shape=sex)) + geom_line() + geom_point() - -# Change the legend -temp <- lp + scale_shape_discrete(name ="Payer", - breaks=c("Female", "Male"), - labels=c("Woman", "Man")) -save_outputs(temp, "legends/line chart with custom legend", file_prefix="") - -# Specify colour and shape -lp1 <- ggplot(data=df1, aes(x=time, y=total_bill, group=sex, shape=sex, colour=sex)) + geom_line() + geom_point() - -# Here"s what happens if you just specify colour -lp1 + scale_colour_discrete(name ="Payer", - breaks=c("Female", "Male"), - labels=c("Woman", "Man")) - -# Specify both colour and shape -lp1 + scale_colour_discrete(name ="Payer", - breaks=c("Female", "Male"), - labels=c("Woman", "Man")) + - scale_shape_discrete(name ="Payer", - breaks=c("Female", "Male"), - labels=c("Woman", "Man")) - -pg <- PlantGrowth # Copy data into new data frame -# Rename the column and the values in the factor -levels(pg$group)[levels(pg$group)=="ctrl"] <- "Control" -levels(pg$group)[levels(pg$group)=="trt1"] <- "Treatment 1" -levels(pg$group)[levels(pg$group)=="trt2"] <- "Treatment 2" -names(pg)[names(pg)=="group"] <- "Experimental Condition" - -# The end product -# pg -# weight Experimental Condition -# 4.17 ctrl -# 5.58 ctrl -# ... -# 5.80 trt2 -# 5.26 trt2 - -# Make the plot -ggplot(data=pg, aes(x=`Experimental Condition`, y=weight, fill=`Experimental Condition`)) + geom_boxplot() - -# Title appearance -temp <- bp + theme(legend.title = element_text(colour="blue", size=16, face="bold")) -save_outputs(temp, "legends/styled legend title", file_prefix="") - -# Label appearance -temp <- bp + theme(legend.text = element_text(colour="blue", size = 16, face = "bold")) -save_outputs(temp, "legends/styled legend labels", file_prefix="") - -bp + theme(legend.background = element_rect()) -temp <- bp + theme(legend.background = element_rect(fill="gray90", size=.5, linetype="dotted")) -save_outputs(temp, "legends/box around legend", file_prefix="") - -temp <- bp + theme(legend.position="top") -save_outputs(temp, "legends/legend on top", file_prefix="") - -# Position legend in graph, where x,y is 0,0 (bottom left) to 1,1 (top right) -temp <- bp + theme(legend.position=c(.5, .5)) -save_outputs(temp, "legends/legend in middle", file_prefix="") - -# Set the "anchoring point" of the legend (bottom-left is 0,0; top-right is 1,1) -# Put bottom-left corner of legend box in bottom-left corner of graph -temp <- bp + theme(legend.justification=c(0,0), legend.position=c(0,0)) -save_outputs(temp, "legends/legend in bottom left", file_prefix="") -# Put bottom-right corner of legend box in bottom-right corner of graph -temp <- bp + theme(legend.justification=c(1,0), legend.position=c(1,0)) -save_outputs(temp, "legends/legend in bottom right", file_prefix="") - -# No outline -ggplot(data=PlantGrowth, aes(x=group, fill=group)) + geom_bar() - -# Add outline, but slashes appear in legend -ggplot(data=PlantGrowth, aes(x=group, fill=group)) + geom_bar(colour="black") - -# A hack to hide the slashes: first graph the bars with no outline and add the legend, -# then graph the bars again with outline, but with a blank legend. -ggplot(data=PlantGrowth, aes(x=group, fill=group)) + geom_bar() + geom_bar(colour="black", show_guide=FALSE) diff --git a/tests/cookbook-test-suite/legends/box_around_legend-ggplot2.png b/tests/cookbook-test-suite/legends/box_around_legend-ggplot2.png deleted file mode 100644 index 212dbe1633..0000000000 Binary files a/tests/cookbook-test-suite/legends/box_around_legend-ggplot2.png and /dev/null differ diff --git a/tests/cookbook-test-suite/legends/box_around_legend.json b/tests/cookbook-test-suite/legends/box_around_legend.json deleted file mode 100644 index 78936bac03..0000000000 --- a/tests/cookbook-test-suite/legends/box_around_legend.json +++ /dev/null @@ -1,135 +0,0 @@ -{ - 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"title": "group", - "type": "category", - "showgrid": true, - "zeroline": false, - "showline": false, - "ticks": "outside", - "showticklabels": true, - "tickcolor": "rgb(127,127,127)", - "gridcolor": "rgb(255,255,255)" - }, - "yaxis": { - "title": "weight", - "type": "linear", - "showgrid": true, - "zeroline": false, - "showline": false, - "ticks": "outside", - "showticklabels": true, - "tickcolor": "rgb(127,127,127)", - "gridcolor": "rgb(255,255,255)" - }, - "legend": { - "x": 1.05, - "y": 0.5, - "font": { - "family": "" - }, - "bgcolor": "rgb(255,255,255)", - "bordercolor": "transparent", - "xanchor": "center", - "yanchor": "top" - }, - "annotations": [ - { - "x": 1.05, - "y": 0.52, - "xref": "paper", - "yref": "paper", - "text": "group", - "showarrow": false, - "xanchor": "center", - "textangle": 0 - } - ], - "margin": { - "r": 10 - }, - "paper_bgcolor": "rgb(255,255,255)", - "plot_bgcolor": "rgb(229,229,229)" - } -} diff --git a/tests/cookbook-test-suite/legends/styled_legend_labels.url b/tests/cookbook-test-suite/legends/styled_legend_labels.url deleted file mode 100644 index 1191dacdf0..0000000000 --- a/tests/cookbook-test-suite/legends/styled_legend_labels.url +++ /dev/null @@ -1 +0,0 @@ -https://plot.ly/~TestBot/678 diff --git a/tests/cookbook-test-suite/legends/styled_legend_title-ggplot2.png b/tests/cookbook-test-suite/legends/styled_legend_title-ggplot2.png deleted file mode 100644 index e8adb0892a..0000000000 Binary files a/tests/cookbook-test-suite/legends/styled_legend_title-ggplot2.png and /dev/null differ diff --git a/tests/cookbook-test-suite/legends/styled_legend_title.json b/tests/cookbook-test-suite/legends/styled_legend_title.json deleted file mode 100644 index 78936bac03..0000000000 --- a/tests/cookbook-test-suite/legends/styled_legend_title.json +++ /dev/null @@ -1,135 +0,0 @@ -{ - "data": [ - { - "y": [ - 4.17, - 5.58, - 5.18, - 6.11, - 4.5, - 4.61, - 5.17, - 4.53, - 5.33, - 5.14 - ], - "name": "ctrl", - "fillcolor": "rgb(248,118,109)", - "line": { - "color": "rgb(51,51,51)", - "width": 2, - "dash": "solid", - "shape": "linear" - }, - "xaxis": "x1", - "yaxis": "y1", - "type": "box" - }, - { - "y": [ - 4.81, - 4.17, - 4.41, - 3.59, - 5.87, - 3.83, - 6.03, - 4.89, - 4.32, - 4.69 - ], - "name": "trt1", - "fillcolor": "rgb(0,186,56)", - "line": { - "color": "rgb(51,51,51)", - "width": 2, - "dash": "solid", - "shape": "linear" - }, - "xaxis": "x1", - "yaxis": "y1", - "type": "box" - }, - { - "y": [ - 6.31, - 5.12, - 5.54, - 5.5, - 5.37, - 5.29, - 4.92, - 6.15, - 5.8, - 5.26 - ], - "name": "trt2", - "fillcolor": "rgb(97,156,255)", - "line": { - "color": "rgb(51,51,51)", - "width": 2, - "dash": "solid", - "shape": "linear" - }, - "xaxis": "x1", - "yaxis": "y1", - "type": "box" - } - ], - "layout": { - "titlefont": { - "family": "" - }, - "showlegend": true, - "xaxis": { - "title": "group", - "type": "category", - "showgrid": true, - "zeroline": false, - "showline": false, - "ticks": "outside", - "showticklabels": true, - "tickcolor": "rgb(127,127,127)", - "gridcolor": "rgb(255,255,255)" - }, - "yaxis": { - "title": "weight", - "type": "linear", - "showgrid": true, - "zeroline": false, - "showline": false, - "ticks": "outside", - "showticklabels": true, - "tickcolor": "rgb(127,127,127)", - "gridcolor": "rgb(255,255,255)" - }, - "legend": { - "x": 1.05, - "y": 0.5, - "font": { - "family": "" - }, - "bgcolor": "rgb(255,255,255)", - "bordercolor": "transparent", - "xanchor": "center", - "yanchor": "top" - }, - "annotations": [ - { - "x": 1.05, - "y": 0.52, - "xref": "paper", - "yref": "paper", - "text": "group", - "showarrow": false, - "xanchor": "center", - "textangle": 0 - } - ], - "margin": { - "r": 10 - }, - "paper_bgcolor": "rgb(255,255,255)", - "plot_bgcolor": "rgb(229,229,229)" - } -} diff --git a/tests/cookbook-test-suite/legends/styled_legend_title.url b/tests/cookbook-test-suite/legends/styled_legend_title.url deleted file mode 100644 index 02b7cd592c..0000000000 --- a/tests/cookbook-test-suite/legends/styled_legend_title.url +++ /dev/null @@ -1 +0,0 @@ -https://plot.ly/~TestBot/677 diff --git a/tests/cookbook-test-suite/lines.R b/tests/cookbook-test-suite/lines.R deleted file mode 100644 index 67b3a9e2d0..0000000000 --- a/tests/cookbook-test-suite/lines.R +++ /dev/null @@ -1,137 +0,0 @@ -# Some sample data -df <- read.table(header=T, text=" - cond result - control 10 -treatment 11.5 -") - -# Basic bar plot -bp <- ggplot(df, aes(x=cond, y=result)) + - geom_bar(position="dodge", stat="identity") -bp - -# Add a horizontal line -temp <- bp + geom_hline(aes(yintercept=12)) -save_outputs(temp, "lines/basic horizontal line", file_prefix="") - -# Make the line red and dashed -temp <- bp + geom_hline(aes(yintercept=12), colour="#990000", linetype="dashed") -save_outputs(temp, "lines/dashed red line", file_prefix="") - -# Draw separate hlines for each bar. First add another column to df -df$hline <- c(9,12) -# cond result hline -# control 10.0 9 -# treatment 11.5 12 - -# Need to re-specify bp, because the data has changed -bp <- ggplot(df, aes(x=cond, y=result)) + geom_bar(position=position_dodge()) - -# Draw with separate lines for each bar -bp + geom_errorbar(aes(y=hline, ymax=hline, ymin=hline), colour="#AA0000") - -# Make the lines narrower -bp + geom_errorbar(width=0.5, aes(y=hline, ymax=hline, ymin=hline), colour="#AA0000") - - -# Can get the same result, even if we get the hline values from a second data frame -# Define data frame with hline -df.hlines <- data.frame(cond=c("control","treatment"), hline=c(9,12)) -# cond hline -# control 9 -# treatment 12 - -# The bar graph are from df, but the lines are from df.hlines -bp + geom_errorbar(data=df.hlines, aes(y=hline, ymax=hline, ymin=hline), colour="#AA0000") - -df <- read.table(header=T, text=" - cond group result hline - control A 10 9 -treatment A 11.5 12 - control B 12 9 -treatment B 14 12 -") - -# Define basic bar plot -bp <- ggplot(df, aes(x=cond, y=result, fill=group)) + geom_bar(position=position_dodge()) -bp - -# The error bars get plotted over one another -- there are four but it looks like two -bp + geom_errorbar(aes(y=hline, ymax=hline, ymin=hline), linetype="dashed") - -df <- read.table(header=T, text=" - cond group result hline - control A 10 11 -treatment A 11.5 12 - control B 12 12.5 -treatment B 14 15 -") - -# Define basic bar plot -bp <- ggplot(df, aes(x=cond, y=result, fill=group)) + geom_bar(position=position_dodge()) -bp - -bp + geom_errorbar(aes(y=hline, ymax=hline, ymin=hline), linetype="dashed", position=position_dodge()) - -df <- read.table(header=T, text=" - cond xval yval - control 11.5 10.8 - control 9.3 12.9 - control 8.0 9.9 - control 11.5 10.1 - control 8.6 8.3 - control 9.9 9.5 - control 8.8 8.7 - control 11.7 10.1 - control 9.7 9.3 - control 9.8 12.0 - treatment 10.4 10.6 - treatment 12.1 8.6 - treatment 11.2 11.0 - treatment 10.0 8.8 - treatment 12.9 9.5 - treatment 9.1 10.0 - treatment 13.4 9.6 - treatment 11.6 9.8 - treatment 11.5 9.8 - treatment 12.0 10.6 -") - -library(ggplot2) - -# The basic scatterplot -sp <- ggplot(df, aes(x=xval, y=yval, colour=cond)) + geom_point() - - -# Add a horizontal line -temp <- sp + geom_hline(aes(yintercept=10)) -save_outputs(temp, "lines/hline on scatter", file_prefix="") - -# Add a red dashed vertical line -temp <- sp + geom_hline(aes(yintercept=10)) + geom_vline(aes(xintercept=11.5), colour="#BB0000", linetype="dashed") -save_outputs(temp, "lines/hline n vline on scatter", file_prefix="") - -# Add colored lines for the mean xval of each group -temp <- sp + geom_hline(aes(yintercept=10)) + - geom_line(stat="vline", xintercept="mean") -save_outputs(temp, "lines/colored lines on scatter", file_prefix="") - -# Facet, based on cond -spf <- sp + facet_grid(. ~ cond) -spf - -# Draw a horizontal line in all of the facets at the same value -temp <- spf + geom_hline(aes(yintercept=10)) -save_outputs(temp, "lines/hline on facets", file_prefix="") - 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-# summarySE provides the standard deviation, standard error of the mean, and a (default 95%) confidence interval - -## Summarizes data. -## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%). -## data: a data frame. -## measurevar: the name of a column that contains the variable to be summariezed -## groupvars: a vector containing names of columns that contain grouping variables -## na.rm: a boolean that indicates whether to ignore NA"s -## conf.interval: the percent range of the confidence interval (default is 95%) -summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE, - conf.interval=.95, .drop=TRUE) { - require(plyr) - - # New version of length which can handle NA"s: if na.rm==T, don"t count them - length2 <- function (x, na.rm=FALSE) { - if (na.rm) sum(!is.na(x)) - else length(x) - } - - # This does the summary. For each group"s data frame, return a vector with - # N, mean, and sd - datac <- ddply(data, groupvars, .drop=.drop, - .fun = function(xx, col) { - c(N = length2(xx[[col]], na.rm=na.rm), - mean = mean (xx[[col]], na.rm=na.rm), - sd = sd (xx[[col]], na.rm=na.rm) - ) - }, - measurevar - ) - - # Rename the "mean" column - datac <- rename(datac, c("mean" = measurevar)) - - datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean - - # Confidence interval multiplier for standard error - # Calculate t-statistic for confidence interval: - # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1 - ciMult <- qt(conf.interval/2 + .5, datac$N-1) - datac$ci <- datac$se * ciMult - - return(datac) -} -## Norms the data within specified groups in a data frame; it normalizes each -## subject (identified by idvar) so that they have the same mean, within each group -## specified by betweenvars. -## data: a data frame. -## idvar: the name of a column that identifies each subject (or matched subjects) -## measurevar: the name of a column that contains the variable to be summariezed -## betweenvars: a vector containing names of columns that are between-subjects variables -## na.rm: a boolean that indicates whether to ignore NA"s -normDataWithin <- function(data=NULL, idvar, measurevar, betweenvars=NULL, - na.rm=FALSE, .drop=TRUE) { - require(plyr) - - # Measure var on left, idvar + between vars on right of formula. - data.subjMean <- ddply(data, c(idvar, betweenvars), .drop=.drop, - .fun = function(xx, col, na.rm) { - c(subjMean = mean(xx[,col], na.rm=na.rm)) - }, - measurevar, - na.rm - ) - - # Put the subject means with original data - data <- merge(data, data.subjMean) - - # Get the normalized data in a new column - measureNormedVar <- paste(measurevar, "_norm", sep="") - data[,measureNormedVar] <- data[,measurevar] - data[,"subjMean"] + - mean(data[,measurevar], na.rm=na.rm) - - # Remove this subject mean column - data$subjMean <- NULL - - return(data) -} -## Summarizes data, handling within-subjects variables by removing inter-subject variability. -## It will still work if there are no within-S variables. -## Gives count, un-normed mean, normed mean (with same between-group mean), -## standard deviation, standard error of the mean, and confidence interval. -## If there are within-subject variables, calculate adjusted values using method from Morey (2008). -## data: a data frame. -## measurevar: the name of a column that contains the variable to be summariezed -## betweenvars: a vector containing names of columns that are between-subjects variables -## withinvars: a vector containing names of columns that are within-subjects variables -## idvar: the name of a column that identifies each subject (or matched subjects) -## na.rm: a boolean that indicates whether to ignore NA"s -## conf.interval: the percent range of the confidence interval (default is 95%) -summarySEwithin <- function(data=NULL, measurevar, betweenvars=NULL, withinvars=NULL, - idvar=NULL, na.rm=FALSE, conf.interval=.95, .drop=TRUE) { - - # Ensure that the betweenvars and withinvars are factors - factorvars <- vapply(data[, c(betweenvars, withinvars), drop=FALSE], - FUN=is.factor, FUN.VALUE=logical(1)) - - if (!all(factorvars)) { - nonfactorvars <- names(factorvars)[!factorvars] - message("Automatically converting the following non-factors to factors: ", - paste(nonfactorvars, collapse = ", ")) - data[nonfactorvars] <- lapply(data[nonfactorvars], factor) - } - - # Get the means from the un-normed data - datac <- summarySE(data, measurevar, groupvars=c(betweenvars, withinvars), - na.rm=na.rm, conf.interval=conf.interval, .drop=.drop) - - # Drop all the unused columns (these will be calculated with normed data) - datac$sd <- NULL - datac$se <- NULL - datac$ci <- NULL - - # Norm each subject"s data - ndata <- normDataWithin(data, idvar, measurevar, betweenvars, na.rm, .drop=.drop) - - # This is the name of the new column - measurevar_n <- paste(measurevar, "_norm", sep="") - - # Collapse the normed data - now we can treat between and within vars the same - ndatac <- summarySE(ndata, measurevar_n, groupvars=c(betweenvars, withinvars), - na.rm=na.rm, conf.interval=conf.interval, .drop=.drop) - - # Apply correction from Morey (2008) to the standard error and confidence interval - # Get the product of the number of conditions of within-S variables - nWithinGroups <- prod(vapply(ndatac[,withinvars, drop=FALSE], FUN=nlevels, - FUN.VALUE=numeric(1))) - correctionFactor <- sqrt( nWithinGroups / (nWithinGroups-1) ) - - # Apply the correction factor - ndatac$sd <- ndatac$sd * correctionFactor - ndatac$se <- ndatac$se * correctionFactor - ndatac$ci <- ndatac$ci * correctionFactor - - # Combine the un-normed means with the normed results - merge(datac, ndatac) -} - -dfc <- summarySE(df, measurevar="len", groupvars=c("supp","dose")) -# supp dose N len sd se ci -# OJ 0.5 10 13.23 4.459709 1.4102837 3.190283 -# OJ 1.0 10 22.70 3.910953 1.2367520 2.797727 -# OJ 2.0 10 26.06 2.655058 0.8396031 1.899314 -# VC 0.5 10 7.98 2.746634 0.8685620 1.964824 -# VC 1.0 10 16.77 2.515309 0.7954104 1.799343 -# VC 2.0 10 26.14 4.797731 1.5171757 3.432090 - -# Standard error of the mean -g <- ggplot(dfc, aes(x=dose, y=len, colour=supp)) + - geom_errorbar(aes(ymin=len-se, ymax=len+se), width=.1) + - geom_line() + - geom_point() - -## TODO: uncomment when fixed -# save_outputs(g, "means-and-error-bars/basic-error-bars", file_prefix="") - -# The errorbars overlapped, so use position_dodge to move them horizontally -pd <- position_dodge(.1) # move them .05 to the left and right - -g <- ggplot(dfc, aes(x=dose, y=len, colour=supp)) + - geom_errorbar(aes(ymin=len-se, ymax=len+se), width=.1, position=pd) + - geom_line(position=pd) + - geom_point(position=pd) - -## TODO: uncomment when fixed -# save_outputs(g, "means-and-error-bars/error-bars-dodged-position", file_prefix="") - -# Use 95% confidence interval instead of SEM -g <- ggplot(dfc, aes(x=dose, y=len, colour=supp)) + - geom_errorbar(aes(ymin=len-ci, ymax=len+ci), width=.1, position=pd) + - geom_line(position=pd) + - geom_point(position=pd) - -## TODO: uncomment when fixed -# save_outputs(g, "means-and-error-bars/error-bars-95-confidence", file_prefix="") - -# Black error bars - notice the mapping of "group=supp" -- without it, the error -# bars won"t be dodged! -g <- ggplot(dfc, aes(x=dose, y=len, colour=supp, group=supp)) + - geom_errorbar(aes(ymin=len-ci, ymax=len+ci), colour="black", width=.1, position=pd) + - geom_line(position=pd) + - geom_point(position=pd, size=3) -## TODO: uncomment when fixed -# save_outputs(g, "means-and-error-bars/black-error-bars", file_prefix="") - -g <- ggplot(dfc, aes(x=dose, y=len, colour=supp, group=supp)) + - geom_errorbar(aes(ymin=len-se, ymax=len+se), colour="black", width=.1, position=pd) + - geom_line(position=pd) + - geom_point(position=pd, size=3, shape=21, fill="white") + # 21 is filled circle - xlab("Dose (mg)") + - ylab("Tooth length") + - scale_colour_hue(name="Supplement type", # Legend label, use darker colors - breaks=c("OJ", "VC"), - labels=c("Orange juice", "Ascorbic acid"), - l=40) + # Use darker colors, lightness=40 - ggtitle("The Effect of Vitamin C on\nTooth Growth in Guinea Pigs") + - scale_y_continuous(limits=c(0, max(dfc$len + dfc$se)), # Set y range - breaks=0:20*4) + # Set tick every 4 - theme_bw() + - theme(legend.justification=c(1,0), legend.position=c(1,0)) # Position legend in bottom right -## TODO: uncomment when fixed -# save_outputs(g, "means-and-error-bars/fully-styled-error-bars", file_prefix="") - -# Use dose as a factor rather than numeric -dfc2 <- dfc -dfc2$dose <- factor(dfc2$dose) - -# Error bars represent standard error of the mean -g <- ggplot(dfc2, aes(x=dose, y=len, fill=supp)) + - geom_bar(position=position_dodge(), stat="identity") + - geom_errorbar(aes(ymin=len-se, ymax=len+se), - width=.2, # Width of the error bars - position=position_dodge(.9)) -save_outputs(g, "means-and-error-bars/bar-graph-with-error-bars", file_prefix="") - -# Use 95% confidence intervals instead of SEM -g <- ggplot(dfc2, aes(x=dose, y=len, fill=supp)) + - geom_bar(position=position_dodge(), stat="identity") + - geom_errorbar(aes(ymin=len-ci, ymax=len+ci), - width=.2, # Width of the error bars - position=position_dodge(.9)) -save_outputs(g, "means-and-error-bars/bar-graph-with-95-error-bars", file_prefix="") - -g <- ggplot(dfc2, aes(x=dose, y=len, fill=supp)) + - geom_bar(position=position_dodge(), stat="identity", - colour="black", # Use black outlines, - size=.3) + # Thinner lines - geom_errorbar(aes(ymin=len-se, ymax=len+se), - size=.3, # Thinner lines - width=.2, - position=position_dodge(.9)) + - xlab("Dose (mg)") + - ylab("Tooth length") + - scale_fill_hue(name="Supplement type", # Legend label, use darker colors - breaks=c("OJ", "VC"), - labels=c("Orange juice", "Ascorbic acid")) + - ggtitle("The Effect of Vitamin C on\nTooth Growth in Guinea Pigs") + - scale_y_continuous(breaks=0:20*4) + - theme_bw() -save_outputs(g, "means-and-error-bars/bar-graph-with-error-bars-fully-styled", file_prefix="") - -dfw <- read.table(header=T, text=" - subject pretest posttest - 1 59.4 64.5 - 2 46.4 52.4 - 3 46.0 49.7 - 4 49.0 48.7 - 5 32.5 37.4 - 6 45.2 49.5 - 7 60.3 59.9 - 8 54.3 54.1 - 9 45.4 49.6 - 10 38.9 48.5 - ") - -# Treat subject ID as a factor -dfw$subject <- factor(dfw$subject) - -# Convert to long format -library(reshape2) -dfw.long <- melt(dfw, - id.vars = "subject", - measure.vars = c("pretest","posttest"), - variable.name = "condition") -# subject condition value -# 1 pretest 59.4 -# 2 pretest 46.4 -# 3 pretest 46.0 -# 4 pretest 49.0 -# 5 pretest 32.5 -# 6 pretest 45.2 -# 7 pretest 60.3 -# 8 pretest 54.3 -# 9 pretest 45.4 -# 10 pretest 38.9 -# 1 posttest 64.5 -# 2 posttest 52.4 -# 3 posttest 49.7 -# 4 posttest 48.7 -# 5 posttest 37.4 -# 6 posttest 49.5 -# 7 posttest 59.9 -# 8 posttest 54.1 -# 9 posttest 49.6 -# 10 posttest 48.5 - -dfwc <- summarySEwithin(dfw.long, measurevar="value", withinvars="condition", - idvar="subject", na.rm=FALSE, conf.interval=.95) -# condition N value value_norm sd se ci -# posttest 10 51.43 51.43 2.262361 0.7154214 1.618396 -# pretest 10 47.74 47.74 2.262361 0.7154214 1.618396 - -library(ggplot2) -# Make the graph with the 95% confidence interval -g <- ggplot(dfwc, aes(x=condition, y=value, group=1)) + - geom_line() + - geom_errorbar(width=.1, aes(ymin=value-ci, ymax=value+ci)) + - geom_point(shape=21, size=3, fill="white") + - ylim(40,60) -save_outputs(g, "means-and-error-bars/within-subject-error-bars", file_prefix="") - -# Use a consistent y range -ymax <- max(dfw.long$value) -ymin <- min(dfw.long$value) - -# Plot the individuals -g <- ggplot(dfw.long, aes(x=condition, y=value, colour=subject, group=subject)) + - geom_line() + geom_point(shape=21, fill="white") + - ylim(ymin,ymax) -save_outputs(g, "means-and-error-bars/multiple-lines-1", file_prefix="") - -# Create the normed version of the data -dfwNorm.long <- normDataWithin(data=dfw.long, idvar="subject", measurevar="value") - -# Plot the normed individuals -g <- ggplot(dfwNorm.long, aes(x=condition, y=value_norm, colour=subject, group=subject)) + - geom_line() + geom_point(shape=21, fill="white") + - ylim(ymin,ymax) -save_outputs(g, "means-and-error-bars/multiple-lines-2", file_prefix="") - -# Instead of summarySEwithin, use summarySE, which treats condition as though it were a between-subjects variable -dfwc.between <- summarySE(data=dfw.long, measurevar="value", groupvars="condition", na.rm=FALSE, conf.interval=.95) -# condition N value sd se ci -# pretest 10 47.74 8.598992 2.719240 6.151348 -# posttest 10 51.43 7.253972 2.293907 5.189179 - -# Show the between-S CI"s in red, and the within-S CI"s in black -g <- ggplot(dfwc.between, aes(x=condition, y=value, group=1)) + - geom_line() + - geom_errorbar(width=.1, aes(ymin=value-ci, ymax=value+ci), colour="red") + - geom_errorbar(width=.1, aes(ymin=value-ci, ymax=value+ci), data=dfwc) + - geom_point(shape=21, size=3, fill="white") + - ylim(ymin,ymax) -save_outputs(g, "means-and-error-bars/black-and-red-error-bars", file_prefix="") - -data <- read.table(header=T, text=" - Subject RoundMono SquareMono RoundColor SquareColor - 1 41 40 41 37 - 2 57 56 56 53 - 3 52 53 53 50 - 4 49 47 47 47 - 5 47 48 48 47 - 6 37 34 35 36 - 7 47 50 47 46 - 8 41 40 38 40 - 9 48 47 49 45 - 10 37 35 36 35 - 11 32 31 31 33 - 12 47 42 42 42 -") - -# Convert it to long format -library(reshape2) -data.long <- melt(data=data, id.var="Subject", - measure.vars=c("RoundMono", "SquareMono", "RoundColor", "SquareColor"), - variable.name="Condition") -names(data.long)[names(data.long)=="value"] <- "Time" - -# Split Condition column into Shape and ColorScheme -data.long$Shape <- NA -data.long$Shape[grepl("^Round", data.long$Condition)] <- "Round" -data.long$Shape[grepl("^Square", data.long$Condition)] <- "Square" -data.long$Shape <- factor(data.long$Shape) - -data.long$ColorScheme <- NA -data.long$ColorScheme[grepl("Mono$", data.long$Condition)] <- "Monochromatic" -data.long$ColorScheme[grepl("Color$", data.long$Condition)] <- "Colored" -data.long$ColorScheme <- factor(data.long$ColorScheme, levels=c("Monochromatic","Colored")) - -# Remove the Condition column now -data.long$Condition <- NULL - -data.long -# Subject Time Shape ColorScheme -# 1 41 Round Monochromatic -# 2 57 Round Monochromatic -# 3 52 Round Monochromatic -# ... -# 1 37 Square Colored -# 2 53 Square Colored -# ... -# 11 33 Square Colored -# 12 42 Square Colored - -datac <- summarySEwithin(data.long, measurevar="Time", withinvars=c("Shape","ColorScheme"), idvar="Subject") -# Shape ColorScheme N Time Time_norm sd se ci -# Round Colored 12 43.58333 43.58333 1.212311 0.3499639 0.7702654 -# Round Monochromatic 12 44.58333 44.58333 1.331438 0.3843531 0.8459554 -# Square Colored 12 42.58333 42.58333 1.461630 0.4219364 0.9286757 -# Square Monochromatic 12 43.58333 43.58333 1.261312 0.3641095 0.8013997 - -library(ggplot2) -g <- ggplot(datac, aes(x=Shape, y=Time, fill=ColorScheme)) + - geom_bar(position=position_dodge(.9), colour="black", stat="identity") + - geom_errorbar(position=position_dodge(.9), width=.25, aes(ymin=Time-ci, ymax=Time+ci)) + - coord_cartesian(ylim=c(40,46)) + - scale_fill_manual(values=c("#CCCCCC","#FFFFFF")) + - scale_y_continuous(breaks=seq(1:100)) + - theme_bw() + - geom_hline(yintercept=38) - -## TODO: uncomment when fixed -# save_outputs(g, "means-and-error-bars/two-subject-error-bars", file_prefix="") diff --git a/tests/cookbook-test-suite/multiple_graphs_on_one_page.r b/tests/cookbook-test-suite/multiple_graphs_on_one_page.r deleted file mode 100644 index 3a0b9aa831..0000000000 --- a/tests/cookbook-test-suite/multiple_graphs_on_one_page.r +++ /dev/null @@ -1,77 +0,0 @@ -# Multiple plot function -# -# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects) -# - cols: Number of columns in layout -# - layout: A matrix specifying the layout. If present, 'cols' is ignored. -# -# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE), -# then plot 1 will go in the upper left, 2 will go in the upper right, and -# 3 will go all the way across the bottom. -# -multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) { - require(grid) - - # Make a list from the ... arguments and plotlist - plots <- c(list(...), plotlist) - - numPlots = length(plots) - - # If layout is NULL, then use 'cols' to determine layout - if (is.null(layout)) { - # Make the panel - # ncol: Number of columns of plots - # nrow: Number of rows needed, calculated from # of cols - layout <- matrix(seq(1, cols * ceiling(numPlots/cols)), - ncol = cols, nrow = ceiling(numPlots/cols)) - } - - if (numPlots==1) { - print(plots[[1]]) - - } else { - # Set up the page - grid.newpage() - pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout)))) - - # Make each plot, in the correct location - for (i in 1:numPlots) { - # Get the i,j matrix positions of the regions that contain this subplot - matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE)) - - print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row, - layout.pos.col = matchidx$col)) - } - } -} - -library(ggplot2) - -# This example uses the ChickWeight dataset, which comes with ggplot2 -# First plot -p1 <- - ggplot(ChickWeight, aes(x=Time, y=weight, colour=Diet, group=Chick)) + - geom_line() + - ggtitle("Growth curve for individual chicks") - -# Second plot -p2 <- - ggplot(ChickWeight, aes(x=Time, y=weight, colour=Diet)) + - geom_point(alpha=.3) + - geom_smooth(alpha=.2, size=1) + - ggtitle("Fitted growth curve per diet") - -# Third plot -p3 <- - ggplot(subset(ChickWeight, Time==21), aes(x=weight, colour=Diet)) + - geom_density() + - ggtitle("Final weight, by diet") - -# Fourth plot -p4 <- - ggplot(subset(ChickWeight, Time==21), aes(x=weight, fill=Diet)) + - geom_histogram(colour="black", binwidth=50) + - facet_grid(Diet ~ .) + - ggtitle("Final weight, by diet") + - theme(legend.position="none") # No legend (redundant in this graph) - -multiplot(p1, p2, p3, p4, cols=2) diff --git a/tests/cookbook-test-suite/scatterplots.R b/tests/cookbook-test-suite/scatterplots.R deleted file mode 100644 index 1f69246a2b..0000000000 --- a/tests/cookbook-test-suite/scatterplots.R +++ /dev/null @@ -1,79 +0,0 @@ -set.seed(955) -# Make some noisily increasing data -dat <- data.frame(cond = rep(c("A", "B"), each=10), - xvar = c(1.475957, -3.423712, 1.966129, 5.575364, 2.954719, 2.768286, 3.507499, 6.945000, 12.135050, 10.231673, 13.040393, 12.231689, 13.506993, 13.590874, 15.455178, 28.431185, 17.758937, 24.730797, 22.954238, 21.122766), - yvar = c(-1.315387, 3.323239, 4.452183, 4.597885, 5.697203, 5.991221, 5.764561, 10.163165, 14.805634, 11.447913, 12.163597, 10.930851, 13.491366, 11.800783, 19.246991, 13.870457, 11.031923, 22.700302, 24.877547, 22.520114)) -# cond xvar yvar -# A -4.252354091 3.473157275 -# A 1.702317971 0.005939612 -# ... -# B 17.793359218 19.718587761 -# B 19.319909163 19.647899863 - -g <- ggplot(dat, aes(x=xvar, y=yvar)) + - geom_point(shape=1) # Use hollow circles -save_outputs(g, "scatterplots/scatterplots with hollow circles", file_prefix="") - -g <- ggplot(dat, aes(x=xvar, y=yvar)) + - geom_point(shape=1) + # Use hollow circles - geom_smooth(method=lm) # Add linear regression line - # (by default includes 95% confidence region) -save_outputs(g, "scatterplots/scatterplots with shading and regression line", file_prefix="") - -g <- ggplot(dat, aes(x=xvar, y=yvar)) + - geom_point(shape=1) + # Use hollow circles - geom_smooth(method=lm, # Add linear regression line - se=FALSE) # Don"t add shaded confidence region -save_outputs(g, "scatterplots/scatterplots without shading and regression line", file_prefix="") - - -g <- ggplot(dat, aes(x=xvar, y=yvar)) + - geom_point(shape=1) + # Use hollow circles - geom_smooth() # Add a loess smoothed fit curve with confidence region -save_outputs(g, "scatterplots/scatterplots shading and smoothed fit curve", file_prefix="") - -# Set color by cond -g <- ggplot(dat, aes(x=xvar, y=yvar, color=cond)) + geom_point(shape=1) -save_outputs(g, "scatterplots/scatterplots colored by condition", file_prefix="") - -# Same, but with different colors and add regression lines -g <- ggplot(dat, aes(x=xvar, y=yvar, color=cond)) + geom_point(shape=1) + - scale_colour_hue(l=50) + # Use a slightly darker palette than normal - geom_smooth(method=lm, # Add linear regression lines - se=FALSE) # Don"t add shaded confidence region -save_outputs(g, "scatterplots/scatterplots colored by condition with regression line", file_prefix="") - -# Extend the regression lines beyond the domain of the data -g <- ggplot(dat, aes(x=xvar, y=yvar, color=cond)) + geom_point(shape=1) + - scale_colour_hue(l=50) + # Use a slightly darker palette than normal - geom_smooth(method=lm, # Add linear regression lines - se=FALSE, # Don"t add shaded confidence region - fullrange=T) # Extend regression lines -save_outputs(g, "scatterplots/scatterplots colored by condition with extended regression line", file_prefix="") - -# Set shape by cond -g <- ggplot(dat, aes(x=xvar, y=yvar, shape=cond)) + geom_point() -save_outputs(g, "scatterplots/scatterplots shaped by condition", file_prefix="") - -# Same, but with different shapes -g <- ggplot(dat, aes(x=xvar, y=yvar, shape=cond)) + geom_point() + - scale_shape_manual(values=c(1,2)) # Use a hollow circle and triangle -save_outputs(g, "scatterplots/scatterplots hollow shapes by condition", file_prefix="") - -# Round xvar and yvar to the nearest 5 -dat$xrnd <- round(dat$xvar/5)*5 -dat$yrnd <- round(dat$yvar/5)*5 - -# Make each dot partially transparent, with 1/4 opacity -# For heavy overplotting, try using smaller values -g <- ggplot(dat, aes(x=xrnd, y=yrnd)) + - geom_point(shape=19, # Use solid circles - alpha=1/4) # 1/4 opacity -save_outputs(g, "scatterplots/scatterplots with overlapped points", file_prefix="") - -# Jitter the points -# Jitter range is 1 on the x-axis, .5 on the y-axis -g <- ggplot(dat, aes(x=xrnd, y=yrnd)) + - geom_point(shape=1, # Use hollow circles - position=position_jitter(width=1,height=.5)) -save_outputs(g, "scatterplots/scatterplots with jittered points", file_prefix="") diff --git a/tests/cookbook-test-suite/scatterplots/scatterplots_colored_by_condition-ggplot2.png b/tests/cookbook-test-suite/scatterplots/scatterplots_colored_by_condition-ggplot2.png deleted file mode 100644 index 5e5cc3ba0d..0000000000 Binary files a/tests/cookbook-test-suite/scatterplots/scatterplots_colored_by_condition-ggplot2.png and /dev/null differ diff --git a/tests/cookbook-test-suite/scatterplots/scatterplots_colored_by_condition-plotly.png b/tests/cookbook-test-suite/scatterplots/scatterplots_colored_by_condition-plotly.png deleted file mode 100644 index 4ac8babb41..0000000000 Binary files a/tests/cookbook-test-suite/scatterplots/scatterplots_colored_by_condition-plotly.png and /dev/null differ diff --git a/tests/cookbook-test-suite/scatterplots/scatterplots_colored_by_condition.json b/tests/cookbook-test-suite/scatterplots/scatterplots_colored_by_condition.json deleted file mode 100644 index 77a0ac000c..0000000000 --- a/tests/cookbook-test-suite/scatterplots/scatterplots_colored_by_condition.json +++ /dev/null @@ -1,138 +0,0 @@ -{ - 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-bp1 <- bp + ggtitle("Plant growth") -save_outputs(bp1, "titles/graph with title - 1", file_prefix="") -# Equivalent to -bp2 <- bp + labs(title="Plant growth") -save_outputs(bp2, "titles/graph with title - 2", file_prefix="") - -# If the title is long, it can be split into multiple lines with \n -# As to be recognized by Plotly: use
instead of \n -bp3 <- bp + ggtitle("Plant growth with\ndifferent treatments") -save_outputs(bp3, "titles/graph with multi-line title", file_prefix="") - -# Reduce line spacing and use bold text -bp4 <- bp + ggtitle("Plant growth with\ndifferent treatments") + - theme(plot.title = element_text(lineheight=.8, face="bold")) -save_outputs(bp4, "titles/shorter and bold title", file_prefix="") diff --git a/tests/cookbook-test-suite/titles/graph_with_multi-line_title-ggplot2.png b/tests/cookbook-test-suite/titles/graph_with_multi-line_title-ggplot2.png deleted file mode 100644 index fc66b17bc7..0000000000 Binary files a/tests/cookbook-test-suite/titles/graph_with_multi-line_title-ggplot2.png and /dev/null differ diff --git a/tests/cookbook-test-suite/titles/graph_with_multi-line_title-plotly.png b/tests/cookbook-test-suite/titles/graph_with_multi-line_title-plotly.png deleted file mode 100644 index e48bfcb29f..0000000000 Binary files a/tests/cookbook-test-suite/titles/graph_with_multi-line_title-plotly.png and /dev/null differ diff --git a/tests/cookbook-test-suite/titles/graph_with_multi-line_title.json b/tests/cookbook-test-suite/titles/graph_with_multi-line_title.json deleted file mode 100644 index 6aa07018d7..0000000000 --- a/tests/cookbook-test-suite/titles/graph_with_multi-line_title.json +++ /dev/null @@ -1,124 +0,0 @@ -{ - 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