Laboratory 4
DATA ANALYSIS AND VISUALIZATION IN R, WINTER 2025 EDITION
Basics of ggplot2
The ggplot2 package allows the creation of elegant
graphics and plots. It is particularly useful in the analysis of
multidimensional and/or multiserial data and as an aid for data
mining.
Two basic features of the package:
- the data we want to present on the plot must be of type
data.frameortibble, - graphics consist of layers that we add with the
+operator to the base object.
The basic function with which we need to start creating each graphic
is ggplot(). Two most common arguments of this function
are:
- a set of data in the form of data frame,
- the
aes()(aesthetics) function which is used for the features mapping.
However, just calling the ggplot() function, even with a
dataset and a mapping specified, will not produce any meaningful
effect.
library (ggplot2)
df1 <- data.frame (a = 1:10, b = (1:10) + runif (10,-2,2), c = rnorm (10, mean=1:10))
df1## a b c
## 1 1 -0.537723668 2.534975
## 2 2 0.007775793 3.343147
## 3 3 4.733468513 1.905554
## 4 4 3.684972042 2.584301
## 5 5 5.754120714 3.507788
## 6 6 4.306885478 6.228185
## 7 7 5.813534214 6.057211
## 8 8 6.033869652 6.415369
## 9 9 9.634629773 9.457097
## 10 10 11.214659069 9.123157
Only after adding the appropriate layer (in this case we want to draw
a scatter plot using geom_point() geometry) can we obtain a
graph.
The ggplot2 package is quite flexible. The same graph can be obtained using different operations.
ggplot (df1, aes (x = a, y = b)) + geom_point ()
ggplot (df1) + geom_point (aes (x = a, y = b))
ggplot () + geom_point (data = df1, aes (x = a, y = b))
ggplot (df1, aes (x = a)) + geom_point (aes(y = b))In addition, you can (and it is even quite recommended for the purity of the code) place the base layer in a variable and then add subsequent layers to it.
There is also an option to “replace” the data frame “on the fly”
using the %+% operator.
Visual properties
Graphical parameters of layers
Each type of layer (called geometry) has variety of
parameters which allows you to customize its look. The most popular are
size, shape, color and
fill.
g <- ggplot(df1, aes(x = a))
g +
geom_point (aes (y = b), size = 4, shape = 22, color = "red") +
geom_point (aes (y = c), size = 3, shape = 21, fill = "blue")
A layer with lines instead of points is obtained by using
geom_line() geometry which is controlled by the parameters
linewidth and linetype.
g +
geom_point (aes (y = b), size = 4, shape = 22, color = "red", fill = "blue") +
geom_line (aes (y = c), linewidth = 1.2, linetype = 3, color = "darkgreen")
The ggplot2 package also allows you to fit a linear
model to a series by using the geom_smooth() layer.
g +
geom_smooth (aes (y = b), method = "lm", linewidth = 1.2, color = "red") +
geom_point (aes (y = b), shape = 21, size = 3, fill = "black")
Using the alpha parameter one can adjust the
transparency of some layers. This is especially useful when dealing with
a large amount of data points which overlap.
df.norm <- data.frame (x = rnorm (1e4, 0, 1), y = rnorm (1e4, 0, 1))
g.norm <- ggplot (df.norm, aes (x = x, y = y))
# No transparency
g.norm + geom_point (shape = 21)
df.norm2 <- data.frame (x = rnorm (1e4,2,1), y = rnorm( 1e4,-2,1))
df.norm3 <- data.frame (x = rnorm (1e4,-2,1), y = rnorm (1e4,-2,1))
# No transparency
g.norm +
geom_point (shape=21, fill="blue", colour="blue") +
geom_point (data = df.norm2, shape=21, fill = "green", colour = "green") +
geom_point (data = df.norm3, shape=21, fill = "red", colour = "red")
# 90% of transparency
g.norm +
geom_point (shape=21, fill = "blue", colour = "blue", alpha = 0.1) +
geom_point (data = df.norm2, shape = 21, fill = "green", colour = "green", alpha = 0.1) +
geom_point (data = df.norm3, shape = 21,fill = "red", colour = "red", alpha = 0.1)
Themes
In addition to the graphic parameters relating to specific layers, it
is also possible to modify the properties of the entire plot using the
theme() function.
g +
geom_smooth (aes (y = b), method = "lm", linewidth = 1.2, color = "red") +
geom_point (aes (y = b), shape = 21, size = 3, fill = "black") +
theme (axis.title = element_text (size = 14),
axis.text = element_text (size = 14))
There are many predefined themes, e.g. theme_bw(),
theme_minimal() or theme_classic(), which can
be used as a first choice for plotting neat figures.
g +
geom_smooth (aes (y = b), method = "lm", linewidth = 1.2, color = "red") +
geom_point (aes (y = b), shape = 21, size = 3, fill = "black") +
theme_bw ()
g +
geom_smooth (aes (y = b), method = "lm", linewidth = 1.2, color = "red") +
geom_point (aes (y = b), shape = 21, size = 3, fill = "black") +
theme_minimal ()
g +
geom_smooth (aes (y = b), method = "lm", linewidth = 1.2, color = "red") +
geom_point (aes (y = b), shape = 21, size = 3, fill = "black") +
theme_classic ()
The predefined styles can by further modified by combining them with
the theme() function.
my_theme <- theme_dark() + theme (axis.title = element_text (size = 14),
axis.text = element_text (size = 14))
g +
geom_smooth (aes (y = b), method = "lm", linewidth = 1.2, color = "red") +
geom_point (aes(y = b), shape = 21, size = 3, fill = "black") +
my_theme
To gain access to all the data from which the plot is built, call the
ggplot_build() function, passing the plot saved to the
variable as an argument. The result of the function is a list containing
data frames for all layers of the plot and all information about its
graphical configuration.
p <- g +
geom_smooth (aes (y = b), method = "lm", linewidth = 1.2, color = "red") +
geom_point (aes (y = b), shape = 21, size = 3, fill = "black")
ggplot_build(p)## $data
## $data[[1]]
## x y ymin ymax se flipped_aes PANEL
## 1 1.000000 0.03007699 -1.94364548 2.003799 0.8559059 FALSE 1
## 2 1.113924 0.15753375 -1.78081786 2.095885 0.8405673 FALSE 1
## 3 1.227848 0.28499052 -1.61826500 2.188246 0.8253478 FALSE 1
## 4 1.341772 0.41244728 -1.45600239 2.280897 0.8102543 FALSE 1
## 5 1.455696 0.53990405 -1.29404657 2.373855 0.7952937 FALSE 1
## 6 1.569620 0.66736081 -1.13241516 2.467137 0.7804739 FALSE 1
## 7 1.683544 0.79481757 -0.97112702 2.560762 0.7658029 FALSE 1
## 8 1.797468 0.92227434 -0.81020224 2.654751 0.7512894 FALSE 1
## 9 1.911392 1.04973110 -0.64966229 2.749124 0.7369429 FALSE 1
## 10 2.025316 1.17718786 -0.48953009 2.843906 0.7227732 FALSE 1
## 11 2.139241 1.30464463 -0.32983010 2.939119 0.7087909 FALSE 1
## 12 2.253165 1.43210139 -0.17058840 3.034791 0.6950073 FALSE 1
## 13 2.367089 1.55955816 -0.01183279 3.130949 0.6814346 FALSE 1
## 14 2.481013 1.68701492 0.14640708 3.227623 0.6680855 FALSE 1
## 15 2.594937 1.81447168 0.30409969 3.324844 0.6549737 FALSE 1
## 16 2.708861 1.94192845 0.46121152 3.422645 0.6421137 FALSE 1
## 17 2.822785 2.06938521 0.61770696 3.521063 0.6295211 FALSE 1
## 18 2.936709 2.19684197 0.77354830 3.620136 0.6172121 FALSE 1
## 19 3.050633 2.32429874 0.92869562 3.719902 0.6052041 FALSE 1
## 20 3.164557 2.45175550 1.08310679 3.820404 0.5935153 FALSE 1
## 21 3.278481 2.57921227 1.23673748 3.921687 0.5821650 FALSE 1
## 22 3.392405 2.70666903 1.38954115 4.023797 0.5711733 FALSE 1
## 23 3.506329 2.83412579 1.54146915 4.126782 0.5605613 FALSE 1
## 24 3.620253 2.96158256 1.69247084 4.230694 0.5503510 FALSE 1
## 25 3.734177 3.08903932 1.84249371 4.335585 0.5405652 FALSE 1
## 26 3.848101 3.21649608 1.99148367 4.441508 0.5312273 FALSE 1
## 27 3.962025 3.34395285 2.13938534 4.548520 0.5223614 FALSE 1
## 28 4.075949 3.47140961 2.28614238 4.656677 0.5139918 FALSE 1
## 29 4.189873 3.59886638 2.43169802 4.766035 0.5061432 FALSE 1
## 30 4.303797 3.72632314 2.57599555 4.876651 0.4988402 FALSE 1
## 31 4.417722 3.85377990 2.71897896 4.988581 0.4921071 FALSE 1
## 32 4.531646 3.98123667 2.86059361 5.101880 0.4859675 FALSE 1
## 33 4.645570 4.10869343 3.00078705 5.216600 0.4804442 FALSE 1
## 34 4.759494 4.23615019 3.13950974 5.332791 0.4755588 FALSE 1
## 35 4.873418 4.36360696 3.27671595 5.450498 0.4713309 FALSE 1
## 36 4.987342 4.49106372 3.41236456 5.569763 0.4677785 FALSE 1
## 37 5.101266 4.61852048 3.54641986 5.690621 0.4649170 FALSE 1
## 38 5.215190 4.74597725 3.67885231 5.813102 0.4627593 FALSE 1
## 39 5.329114 4.87343401 3.80963913 5.937229 0.4613153 FALSE 1
## 40 5.443038 5.00089078 3.93876483 6.063017 0.4605915 FALSE 1
## 41 5.556962 5.12834754 4.06622160 6.190473 0.4605915 FALSE 1
## 42 5.670886 5.25580430 4.19200942 6.319599 0.4613153 FALSE 1
## 43 5.784810 5.38326107 4.31613613 6.450386 0.4627593 FALSE 1
## 44 5.898734 5.51071783 4.43861721 6.582818 0.4649170 FALSE 1
## 45 6.012658 5.63817459 4.55947543 6.716874 0.4677785 FALSE 1
## 46 6.126582 5.76563136 4.67874035 6.852522 0.4713309 FALSE 1
## 47 6.240506 5.89308812 4.79644766 6.989729 0.4755588 FALSE 1
## 48 6.354430 6.02054489 4.91263850 7.128451 0.4804442 FALSE 1
## 49 6.468354 6.14800165 5.02735860 7.268645 0.4859675 FALSE 1
## 50 6.582278 6.27545841 5.14065747 7.410259 0.4921071 FALSE 1
## 51 6.696203 6.40291518 5.25258759 7.553243 0.4988402 FALSE 1
## 52 6.810127 6.53037194 5.36320359 7.697540 0.5061432 FALSE 1
## 53 6.924051 6.65782870 5.47256147 7.843096 0.5139918 FALSE 1
## 54 7.037975 6.78528547 5.58071796 7.989853 0.5223614 FALSE 1
## 55 7.151899 6.91274223 5.68772982 8.137755 0.5312273 FALSE 1
## 56 7.265823 7.04019900 5.79365338 8.286745 0.5405652 FALSE 1
## 57 7.379747 7.16765576 5.89854404 8.436767 0.5503510 FALSE 1
## 58 7.493671 7.29511252 6.00245589 8.587769 0.5605613 FALSE 1
## 59 7.607595 7.42256929 6.10544141 8.739697 0.5711733 FALSE 1
## 60 7.721519 7.55002605 6.20755126 8.892501 0.5821650 FALSE 1
## 61 7.835443 7.67748281 6.30883410 9.046132 0.5935153 FALSE 1
## 62 7.949367 7.80493958 6.40933646 9.200543 0.6052041 FALSE 1
## 63 8.063291 7.93239634 6.50910267 9.355690 0.6172121 FALSE 1
## 64 8.177215 8.05985311 6.60817486 9.511531 0.6295211 FALSE 1
## 65 8.291139 8.18730987 6.70659294 9.668027 0.6421137 FALSE 1
## 66 8.405063 8.31476663 6.80439464 9.825139 0.6549737 FALSE 1
## 67 8.518987 8.44222340 6.90161556 9.982831 0.6680855 FALSE 1
## 68 8.632911 8.56968016 6.99828921 10.141071 0.6814346 FALSE 1
## 69 8.746835 8.69713692 7.09444714 10.299827 0.6950073 FALSE 1
## 70 8.860759 8.82459369 7.19011896 10.459068 0.7087909 FALSE 1
## 71 8.974684 8.95205045 7.28533250 10.618768 0.7227732 FALSE 1
## 72 9.088608 9.07950722 7.38011383 10.778901 0.7369429 FALSE 1
## 73 9.202532 9.20696398 7.47448741 10.939441 0.7512894 FALSE 1
## 74 9.316456 9.33442074 7.56847615 11.100365 0.7658029 FALSE 1
## 75 9.430380 9.46187751 7.66210153 11.261653 0.7804739 FALSE 1
## 76 9.544304 9.58933427 7.75538366 11.423285 0.7952937 FALSE 1
## 77 9.658228 9.71679103 7.84834136 11.585241 0.8102543 FALSE 1
## 78 9.772152 9.84424780 7.94099228 11.747503 0.8253478 FALSE 1
## 79 9.886076 9.97170456 8.03335295 11.910056 0.8405673 FALSE 1
## 80 10.000000 10.09916133 8.12543885 12.072884 0.8559059 FALSE 1
## group colour fill linewidth linetype weight alpha
## 1 -1 red grey60 1.2 1 1 0.4
## 2 -1 red grey60 1.2 1 1 0.4
## 3 -1 red grey60 1.2 1 1 0.4
## 4 -1 red grey60 1.2 1 1 0.4
## 5 -1 red grey60 1.2 1 1 0.4
## 6 -1 red grey60 1.2 1 1 0.4
## 7 -1 red grey60 1.2 1 1 0.4
## 8 -1 red grey60 1.2 1 1 0.4
## 9 -1 red grey60 1.2 1 1 0.4
## 10 -1 red grey60 1.2 1 1 0.4
## 11 -1 red grey60 1.2 1 1 0.4
## 12 -1 red grey60 1.2 1 1 0.4
## 13 -1 red grey60 1.2 1 1 0.4
## 14 -1 red grey60 1.2 1 1 0.4
## 15 -1 red grey60 1.2 1 1 0.4
## 16 -1 red grey60 1.2 1 1 0.4
## 17 -1 red grey60 1.2 1 1 0.4
## 18 -1 red grey60 1.2 1 1 0.4
## 19 -1 red grey60 1.2 1 1 0.4
## 20 -1 red grey60 1.2 1 1 0.4
## 21 -1 red grey60 1.2 1 1 0.4
## 22 -1 red grey60 1.2 1 1 0.4
## 23 -1 red grey60 1.2 1 1 0.4
## 24 -1 red grey60 1.2 1 1 0.4
## 25 -1 red grey60 1.2 1 1 0.4
## 26 -1 red grey60 1.2 1 1 0.4
## 27 -1 red grey60 1.2 1 1 0.4
## 28 -1 red grey60 1.2 1 1 0.4
## 29 -1 red grey60 1.2 1 1 0.4
## 30 -1 red grey60 1.2 1 1 0.4
## 31 -1 red grey60 1.2 1 1 0.4
## 32 -1 red grey60 1.2 1 1 0.4
## 33 -1 red grey60 1.2 1 1 0.4
## 34 -1 red grey60 1.2 1 1 0.4
## 35 -1 red grey60 1.2 1 1 0.4
## 36 -1 red grey60 1.2 1 1 0.4
## 37 -1 red grey60 1.2 1 1 0.4
## 38 -1 red grey60 1.2 1 1 0.4
## 39 -1 red grey60 1.2 1 1 0.4
## 40 -1 red grey60 1.2 1 1 0.4
## 41 -1 red grey60 1.2 1 1 0.4
## 42 -1 red grey60 1.2 1 1 0.4
## 43 -1 red grey60 1.2 1 1 0.4
## 44 -1 red grey60 1.2 1 1 0.4
## 45 -1 red grey60 1.2 1 1 0.4
## 46 -1 red grey60 1.2 1 1 0.4
## 47 -1 red grey60 1.2 1 1 0.4
## 48 -1 red grey60 1.2 1 1 0.4
## 49 -1 red grey60 1.2 1 1 0.4
## 50 -1 red grey60 1.2 1 1 0.4
## 51 -1 red grey60 1.2 1 1 0.4
## 52 -1 red grey60 1.2 1 1 0.4
## 53 -1 red grey60 1.2 1 1 0.4
## 54 -1 red grey60 1.2 1 1 0.4
## 55 -1 red grey60 1.2 1 1 0.4
## 56 -1 red grey60 1.2 1 1 0.4
## 57 -1 red grey60 1.2 1 1 0.4
## 58 -1 red grey60 1.2 1 1 0.4
## 59 -1 red grey60 1.2 1 1 0.4
## 60 -1 red grey60 1.2 1 1 0.4
## 61 -1 red grey60 1.2 1 1 0.4
## 62 -1 red grey60 1.2 1 1 0.4
## 63 -1 red grey60 1.2 1 1 0.4
## 64 -1 red grey60 1.2 1 1 0.4
## 65 -1 red grey60 1.2 1 1 0.4
## 66 -1 red grey60 1.2 1 1 0.4
## 67 -1 red grey60 1.2 1 1 0.4
## 68 -1 red grey60 1.2 1 1 0.4
## 69 -1 red grey60 1.2 1 1 0.4
## 70 -1 red grey60 1.2 1 1 0.4
## 71 -1 red grey60 1.2 1 1 0.4
## 72 -1 red grey60 1.2 1 1 0.4
## 73 -1 red grey60 1.2 1 1 0.4
## 74 -1 red grey60 1.2 1 1 0.4
## 75 -1 red grey60 1.2 1 1 0.4
## 76 -1 red grey60 1.2 1 1 0.4
## 77 -1 red grey60 1.2 1 1 0.4
## 78 -1 red grey60 1.2 1 1 0.4
## 79 -1 red grey60 1.2 1 1 0.4
## 80 -1 red grey60 1.2 1 1 0.4
##
## $data[[2]]
## y x PANEL group shape colour size fill alpha stroke
## 1 -0.537723668 1 1 -1 21 black 3 black NA 0.5
## 2 0.007775793 2 1 -1 21 black 3 black NA 0.5
## 3 4.733468513 3 1 -1 21 black 3 black NA 0.5
## 4 3.684972042 4 1 -1 21 black 3 black NA 0.5
## 5 5.754120714 5 1 -1 21 black 3 black NA 0.5
## 6 4.306885478 6 1 -1 21 black 3 black NA 0.5
## 7 5.813534214 7 1 -1 21 black 3 black NA 0.5
## 8 6.033869652 8 1 -1 21 black 3 black NA 0.5
## 9 9.634629773 9 1 -1 21 black 3 black NA 0.5
## 10 11.214659069 10 1 -1 21 black 3 black NA 0.5
##
##
## $layout
## <ggproto object: Class Layout, gg>
## coord: <ggproto object: Class CoordCartesian, Coord, gg>
## aspect: function
## backtransform_range: function
## clip: on
## default: TRUE
## distance: function
## expand: TRUE
## is_free: function
## is_linear: function
## labels: function
## limits: list
## modify_scales: function
## range: function
## render_axis_h: function
## render_axis_v: function
## render_bg: function
## render_fg: function
## setup_data: function
## setup_layout: function
## setup_panel_guides: function
## setup_panel_params: function
## setup_params: function
## train_panel_guides: function
## transform: function
## super: <ggproto object: Class CoordCartesian, Coord, gg>
## coord_params: list
## facet: <ggproto object: Class FacetNull, Facet, gg>
## compute_layout: function
## draw_back: function
## draw_front: function
## draw_labels: function
## draw_panels: function
## finish_data: function
## init_scales: function
## map_data: function
## params: list
## setup_data: function
## setup_params: function
## shrink: TRUE
## train_scales: function
## vars: function
## super: <ggproto object: Class FacetNull, Facet, gg>
## facet_params: list
## finish_data: function
## get_scales: function
## layout: data.frame
## map_position: function
## panel_params: list
## panel_scales_x: list
## panel_scales_y: list
## render: function
## render_labels: function
## reset_scales: function
## resolve_label: function
## setup: function
## setup_panel_guides: function
## setup_panel_params: function
## train_position: function
## super: <ggproto object: Class Layout, gg>
##
## $plot
##
## attr(,"class")
## [1] "ggplot_built"Grouping
Although the ggplot2 package can be used to plot data
stored in wide tables, it works much better with long tables (tidy
data). Placing the series one under the other instead of one next
to the other and adding a character or factor
column with the series label allows automatic series recognition and
legend generation, called grouping. To do this, proper mapping
should be done inside the aes() function.
library(dplyr)
library (tidyr)
# Pivot df1 from wide table to long table and add a new column
df1 %>%
pivot_longer (cols = 2:3, names_to = "ser") %>%
mutate (n = sample (20)) -> df2
df2## # A tibble: 20 × 4
## a ser value n
## <int> <chr> <dbl> <int>
## 1 1 b -0.538 8
## 2 1 c 2.53 10
## 3 2 b 0.00778 18
## 4 2 c 3.34 11
## 5 3 b 4.73 17
## 6 3 c 1.91 3
## 7 4 b 3.68 5
## 8 4 c 2.58 16
## 9 5 b 5.75 12
## 10 5 c 3.51 1
## 11 6 b 4.31 14
## 12 6 c 6.23 2
## 13 7 b 5.81 7
## 14 7 c 6.06 6
## 15 8 b 6.03 19
## 16 8 c 6.42 13
## 17 9 b 9.63 9
## 18 9 c 9.46 15
## 19 10 b 11.2 20
## 20 10 c 9.12 4
Size mapping is also beneficial, for example, if we want to visualize the number of observations entering a given point.
To perform grouping in geom_smooth geometry, assign a
column name to the group parameter inside the
aes() function.
# Data generation
df3 <- data.frame (x = c (0:10, 5:15, 10:20),
y = c (0:10 + runif(11,-2,2), 5:15 + runif(11,-3,3), 10:20 + runif(11,-2,2)),
ser = rep (c ("R1","R2","R3"), each = 11))
g3 <- ggplot (df3, aes (x = x, y = y))
p <- geom_point (aes (fill = ser), size = 3, shape = 21)
# Without linear fit
g3 + p
Manual selection of colors
The colors for individual series are selected automatically, but there is nothing stopping you from choosing them yourself.
g3 + p +
geom_smooth (aes (color = ser), method = "lm") +
scale_fill_manual (values = c ("red", "orange", "gold")) +
scale_color_manual (values = c ("red", "orange", "gold"))
Histograms
Another important type of plot is the histogram. The layer
geom_histogram() displays the counts (or density) with
bars, while geom_freqpoly() shows the empirical
distributions with lines. Let’s start with generating some random series
from normal distributions.
# Data generation
sizes <- c( 1e2, 1e3, 1e4)
sigmas <- c (0.5, 1)
mu <- 0
x1 <- rnorm (sizes[1], mu, sigmas[1])
x2 <- rnorm (sizes[2], mu, sigmas[1])
x3 <- rnorm (sizes[3], mu, sigmas[1])
x4 <- rnorm (sizes[1], mu, sigmas[2])
x5 <- rnorm (sizes[2], mu, sigmas[2])
x6 <- rnorm (sizes[3], mu, sigmas[2])
df.norm <- data.frame (x = c(x1, x2, x3, x4, x5, x6),
size = rep (sizes, sizes) %>% as.factor(),
sigma = rep (sigmas, each = sum (sizes)) %>% as.factor())
df.norm %>% filter (size == as.character (sizes[3])) %>% ggplot () -> gh
gh + geom_histogram (aes (x = x, fill = sigma, color = sigma))
Using the position parameter you can control the
positioning of several histograms relative to each other. The default
value (stack) stacks one bar on top of the other,
fill normalizes the sum to one, and dodge
displays the bars side by side.
The identity option obscures one series from another, so
using transparency is useful.
To show the probability density instead of the number of counts, use
the after_stat (density) function. If we also need to plot
a certain function (e.g. probability density of the normal
distribution), for the entire x-axis range, we use the
stat_function() function. We provide the name of the
function (fun=...) and its parameters using a list
(args = list(...)).
gh +
geom_histogram (aes (x = x, y = after_stat (density), fill = sigma, colour = sigma),
position="identity", alpha = 0.3) +
stat_function (fun = dnorm, args = list (mean = 0, sd = 0.5))
Faceting
In the case of a larger number of series that can be grouped
according to one or more features, it is convenient to use the faceting
mechanism using the facet_grid() or
facet_wrap() functions.
ggplot (df.norm) +
geom_histogram (aes (x = x, y = after_stat (density), fill = size, colour = size), alpha = 0.6) +
facet_grid (size ~ sigma)
Saving plots
Plots made using the ggplot2 package are saved to the
file in a different way than in the case of the basic
graphics package. The ggsave (filename)
function is used for this purpose, the basic argument of which is the
string storing the file name, and the additional parameters include:
plot- specifies which plot should be saved (defaultplot = last_plot()),device- specifies the format in which the file is to be saved (e.g. “png”, “eps”, “pdf”),width,height,units,dpi- image parameters such as height, width, unit in which the height and width are expressed, and resolution.