ggplot

> head(BOD)

  Time demand
1    1    8.3
2    2   10.3
3    3   19.0
4    4   16.0
5    5   15.6
6    7   19.8

> summary(BOD)

      Time           demand     
 Min.   :1.000   Min.   : 8.30  
 1st Qu.:2.250   1st Qu.:11.62  
 Median :3.500   Median :15.80  
 Mean   :3.667   Mean   :14.83  
 3rd Qu.:4.750   3rd Qu.:18.25  
 Max.   :7.000   Max.   :19.80

ggplot

>  p <- ggplot() +
+   #single layer - points
+   layer(data=BOD, #data.frame
+     mapping=aes(y=demand,x=Time),
+     stat="identity", #use original data
+     geom="point", #plot data as points
+     position="identity",
+     params = list(na.rm = TRUE),
+     show.legend = FALSE
+   )+ #layer of lines
+   layer( data=BOD, #data.frame
+     mapping=aes(y=demand,x=Time),
+     stat="identity", #use original data
+     geom="line", #plot data as a line
+     position="identity",
+     params = list(na.rm = TRUE),
+     show.legend = FALSE
+   ) +
+   coord_cartesian() + #cartesian coordinates
+   scale_x_continuous() + #continuous x axis
+   scale_y_continuous()  #continuous y axis
> p #print the plot

ggplot

> ggplot(data=BOD, map=aes(y=demand,x=Time)) + geom_point()+geom_line()

Overview

data

> p<-ggplot(data=BOD)

layers (geoms)

> p<-p + geom_point(aes(y=demand, x=Time))
> p

Overview

data

> p<-ggplot(data=BOD)

layers (geoms)

> p<-p + geom_point(aes(y=demand, x=Time))

scales

> p <- p + scale_x_sqrt(name="Time")
> p

Layers

layers of data driven objects
- geometric objects to represent data
- statistical methods to summarize the data
- mapping of aethetics
- position control

`geom_` and `stat_`

coupled together
engage either
stat_identity

`geom_`

data - obvious
mapping - aesthetics

If omitted, inherited from ggplot()

stat - the stat_ function
position - overlapping geoms

`geom_`

> ggplot(data=BOD, aes(y=demand, x=Time)) + geom_point()
> #OR
> ggplot(data=BOD) + geom_point(aes(y=demand, x=Time))

Optional mapping

alpha - transparency
colour - colour of the geometric features
fill - colour of the geometric features
linetype - fill colour of geometric features
size - size of geometric features such as points or text
shape - shape of geometric features such as points
weight - weightings of values

`geom_point`

> head(CO2)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8
4   Qn1 Quebec nonchilled  350   37.2
5   Qn1 Quebec nonchilled  500   35.3
6   Qn1 Quebec nonchilled  675   39.2

> summary(CO2)

     Plant             Type         Treatment       conc          uptake     
 Qn1    : 7   Quebec     :42   nonchilled:42   Min.   :  95   Min.   : 7.70  
 Qn2    : 7   Mississippi:42   chilled   :42   1st Qu.: 175   1st Qu.:17.90  
 Qn3    : 7                                    Median : 350   Median :28.30  
 Qc1    : 7                                    Mean   : 435   Mean   :27.21  
 Qc3    : 7                                    3rd Qu.: 675   3rd Qu.:37.12  
 Qc2    : 7                                    Max.   :1000   Max.   :45.50  
 (Other):42

`geom_point`

> ggplot(CO2)+geom_point(aes(x=conc,y=uptake), colour="red")

`geom_point`

> ggplot(CO2)+geom_point(aes(x=conc,y=uptake, colour=Type))

`geom_point`

> ggplot(CO2)+geom_point(aes(x=conc,y=uptake),
+  stat="summary",fun.y=mean)

Example data sets

> head(diamonds)

# A tibble: 6 x 10
  carat       cut color clarity depth table price     x     y     z
  <dbl>     <ord> <ord>   <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1  0.23     Ideal     E     SI2  61.5    55   326  3.95  3.98  2.43
2  0.21   Premium     E     SI1  59.8    61   326  3.89  3.84  2.31
3  0.23      Good     E     VS1  56.9    65   327  4.05  4.07  2.31
4  0.29   Premium     I     VS2  62.4    58   334  4.20  4.23  2.63
5  0.31      Good     J     SI2  63.3    58   335  4.34  4.35  2.75
6  0.24 Very Good     J    VVS2  62.8    57   336  3.94  3.96  2.48

> summary(diamonds)

     carat               cut        color        clarity          depth           table      
 Min.   :0.2000   Fair     : 1610   D: 6775   SI1    :13065   Min.   :43.00   Min.   :43.00  
 1st Qu.:0.4000   Good     : 4906   E: 9797   VS2    :12258   1st Qu.:61.00   1st Qu.:56.00  
 Median :0.7000   Very Good:12082   F: 9542   SI2    : 9194   Median :61.80   Median :57.00  
 Mean   :0.7979   Premium  :13791   G:11292   VS1    : 8171   Mean   :61.75   Mean   :57.46  
 3rd Qu.:1.0400   Ideal    :21551   H: 8304   VVS2   : 5066   3rd Qu.:62.50   3rd Qu.:59.00  
 Max.   :5.0100                     I: 5422   VVS1   : 3655   Max.   :79.00   Max.   :95.00  
                                    J: 2808   (Other): 2531                                  
     price             x                y                z         
 Min.   :  326   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
 1st Qu.:  950   1st Qu.: 4.710   1st Qu.: 4.720   1st Qu.: 2.910  
 Median : 2401   Median : 5.700   Median : 5.710   Median : 3.530  
 Mean   : 3933   Mean   : 5.731   Mean   : 5.735   Mean   : 3.539  
 3rd Qu.: 5324   3rd Qu.: 6.540   3rd Qu.: 6.540   3rd Qu.: 4.040  
 Max.   :18823   Max.   :10.740   Max.   :58.900   Max.   :31.800

Primary geometric objects

`geom_bar`

Feature	geom	stat	position
Histogram	`_bar`	`_bin`	stack

> ggplot(diamonds) + geom_bar(aes(x = carat))

plot of chunk geomBar1

`geom_bar`

Feature	geom	stat	position
Barchart	`_bar`	`_bin`	stack

> ggplot(diamonds) + geom_bar(aes(x = cut))

plot of chunk geomBar2

`geom_bar`

Feature	geom	stat	position
barchart	`_bar`	`_bin`	stack

> ggplot(diamonds) + geom_bar(aes(x = cut, fill = clarity))

plot of chunk geomBar3

`geom_bar`

Feature	geom	stat	position
barchart	`_bar`	`_bin`	stack

> ggplot(diamonds) + geom_bar(aes(x = cut, fill = clarity))

plot of chunk geomBar4

`geom_bar`

Feature	geom	stat	position
barchart	`_bar`	`_bin`	dodge

> ggplot(diamonds) + geom_bar(aes(x = cut, fill = clarity),
+   position='dodge')

plot of chunk geomBar5

`geom_boxplot`

Feature	geom	stat	position
boxplot	`_boxplot`	`_boxplot`	dodge

> ggplot(diamonds) + geom_boxplot(aes(x = "carat", y = carat))

plot of chunk geomBox1

`geom_boxplot`

Feature	geom	stat	position
boxplot	`_boxplot`	`_boxplot`	dodge

> ggplot(diamonds) + geom_boxplot(aes(x = cut, y = carat))

plot of chunk geomBox2

`geom_line`

Feature	geom	stat	position
line	`_line`	`_identity`	identity

> head(CO2, 3)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8

> ggplot(CO2) + geom_line(aes(x = conc, y = uptake))

plot of chunk geomLine1

`geom_line`

Feature	geom	stat	position
line	`_line`	`_identity`	identity

> head(CO2, 3)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8

> ggplot(CO2) + geom_line(aes(x = conc, y = uptake, group=Plant))

plot of chunk geomLine2

`geom_line`

Feature	geom	stat	position
line	`_line`	`_identity`	identity

> head(CO2, 3)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8

> ggplot(CO2) + geom_line(aes(x = conc, y = uptake, color=Plant))

plot of chunk geomLine3

`geom_line`

Feature	geom	stat	position
line	`_line`	`_summary`	identity

> head(CO2, 3)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8

> ggplot(CO2) + geom_line(aes(x = conc, y = uptake),
+     stat = "summary", fun.y = mean, color='blue')

plot of chunk geomLine4

`geom_point`

Feature	geom	stat	position
point	`_point`	`_identity`	identity

> head(CO2, 3)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8

> ggplot(CO2) + geom_point(aes(x = conc, y = uptake))

plot of chunk geomPoint1

`geom_point`

Feature	geom	stat	position
point	`_point`	`_identity`	identity

> head(CO2, 3)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8

> ggplot(CO2) + geom_point(aes(x = conc, y = uptake, fill=Treatment),
+   shape=21)

plot of chunk geomPoint2

`geom_smooth`

Feature	geom	stat	position
smoother	`_smooth`	`_smooth`	identity

> head(CO2, 3)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8

> ggplot(CO2) + geom_smooth(aes(x = conc, y = uptake), method='lm')

plot of chunk geomSmooth1

`geom_smooth`

Feature	geom	stat	position
smoother	`_smooth`	`_smooth`	identity

> head(CO2, 3)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8

> ggplot(CO2) + geom_smooth(aes(x = conc, y = uptake, fill=Treatment))

plot of chunk geomSmooth2

`geom_polygon`

Feature	geom	stat	position
polygon	`_polygon`	`_identity`	identity

> library(maps)
> library(mapdata)
> aus <- map_data("worldHires", region="Australia")
> head(aus,3)

      long       lat group order    region              subregion
1 142.1461 -10.74943     1     1 Australia Prince of Wales Island
2 142.1430 -10.74525     1     2 Australia Prince of Wales Island
3 142.1406 -10.74113     1     3 Australia Prince of Wales Island

> ggplot(aus, aes(x=long, y=lat, group=group)) +
+     geom_polygon()

plot of chunk geomPolygon

`geom_tile`

Feature	geom	stat	position
tile	`_tile`	`_identity`	identity

> head(faithfuld,3)

# A tibble: 3 x 3
  eruptions waiting     density
      <dbl>   <dbl>       <dbl>
1  1.600000      43 0.003216159
2  1.647297      43 0.003835375
3  1.694595      43 0.004435548

> ggplot(faithfuld, aes(waiting, eruptions)) +
+       geom_tile(aes(fill = density))

plot of chunk geomTile

`geom_raster`

Feature	geom	stat	position
raster	`_raster`	`_identity`	identity

> head(faithfuld,3)

# A tibble: 3 x 3
  eruptions waiting     density
      <dbl>   <dbl>       <dbl>
1  1.600000      43 0.003216159
2  1.647297      43 0.003835375
3  1.694595      43 0.004435548

> ggplot(faithfuld, aes(waiting, eruptions)) +
+       geom_raster(aes(fill = density))

plot of chunk geomRaster

Secondary geometric objects

Example data set

> head(warpbreaks)

  breaks wool tension
1     26    A       L
2     30    A       L
3     54    A       L
4     25    A       L
5     70    A       L
6     52    A       L

> summary(warpbreaks)

     breaks      wool   tension
 Min.   :10.00   A:27   L:18   
 1st Qu.:18.25   B:27   M:18   
 Median :26.00          H:18   
 Mean   :28.15                 
 3rd Qu.:34.00                 
 Max.   :70.00

`geom_errorbar`

Feature	geom	stat	position
errorbar	`_identity`	`_identity`	identity

> library(dplyr)
> library(gmodels)
> warpbreaks.sum <- warpbreaks %>% group_by(wool) %>%
+     summarise(Mean=mean(breaks), Lower=ci(breaks)[2], Upper=ci(breaks)[3])
> warpbreaks.sum

# A tibble: 2 x 4
    wool     Mean    Lower    Upper
  <fctr>    <dbl>    <dbl>    <dbl>
1      A 31.03704 24.76642 37.30765
2      B 25.25926 21.57994 28.93858

`geom_errorbar`

Feature	geom	stat	position
errorbar	`_identity`	`_identity`	identity

> ggplot(warpbreaks.sum) +
+     geom_errorbar(aes(x = wool, ymin = Lower, ymax = Upper))

plot of chunk geomErrorbar1a

`geom_errorbar`

Feature	geom	stat	position
errorbar	`_identity`	`_summary`	identity

> head(warpbreaks,3)

  breaks wool tension
1     26    A       L
2     30    A       L
3     54    A       L

> ggplot(warpbreaks) + geom_errorbar(aes(x = wool, y = breaks),
+     stat = "summary", fun.data = "mean_cl_boot")

plot of chunk geomErrorbar2

Coordinate systems

> head(CO2,3)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8

> ggplot(CO2)+geom_point(aes(x=conc,y=uptake))+
+   coord_cartesian() #default

Coordinate systems

> head(CO2,3)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8

> ggplot(CO2)+geom_point(aes(x=conc,y=uptake))+
+   coord_polar()

Coordinate systems

> head(CO2,3)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4
3   Qn1 Quebec nonchilled  250   34.8

> ggplot(CO2)+geom_point(aes(x=conc,y=uptake))+
+  coord_flip()

Coordinate systems

> #Orthographic coordinates
> library(maps)
> library(mapdata)
> aus <- map_data("worldHires", region="Australia")
> ggplot(aus, aes(x=long, y=lat, group=group)) +
+   coord_map("ortho", orientation=c(-20,125,23.5))+
+   geom_polygon()

`scale_x_` and `scale_y_`

Axis titles

> head(CO2,2)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4

> ggplot(CO2, aes(y=uptake,x=conc)) + geom_point()+
+   scale_x_continuous(name="CO2 conc")

plot of chunk scalex

`scale_x_` and `scale_y_`

Axis titles with math

> head(CO2,2)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4

> ggplot(CO2, aes(y=uptake,x=conc)) + geom_point()+
+   scale_x_continuous(name=expression(Ambient~CO[2]~concentration~(mg/l)))

plot of chunk scalex2

`scale_x_` and `scale_y_`

Axis more padding

> ggplot(CO2, aes(y=uptake,x=conc)) + geom_point()+
+   scale_x_continuous(name="CO2 conc", expand=c(0,200))

plot of chunk scalex3

`scale_x_` and `scale_y_`

Axis on a log scale

> ggplot(CO2, aes(y=uptake,x=conc)) + geom_point()+
+   scale_x_log10(name="CO2 conc",
+     breaks=as.vector(c(1,2,5,10) %o% 10^(-1:2)))

plot of chunk scalex4

`scale_x_` and `scale_y_`

Axis representing categorical data

> ggplot(CO2, aes(y=uptake,x=Treatment)) + geom_point()+
+   scale_x_discrete(name="Treatment")

plot of chunk scalex5

Other scales

size of points (thickness of lines)
shape of points
linetype of lines
color of lines or points
fill of shapes

`scale_size`

Size according to continuous variable

> state=data.frame(state.x77,state.region, state.division,state.center) %>%
+     select(Illiteracy,state.region,x,y)
> head(state,2)

        Illiteracy state.region         x       y
Alabama        2.1        South  -86.7509 32.5901
Alaska         1.5         West -127.2500 49.2500

> ggplot(state, aes(y=y,x=x)) + geom_point(aes(size=Illiteracy))

plot of chunk scaleSize1

`scale_size`

Discrete sizes ranging in size from 2 to 4

> head(state,2)

        Illiteracy state.region         x       y
Alabama        2.1        South  -86.7509 32.5901
Alaska         1.5         West -127.2500 49.2500

> ggplot(state, aes(y=y,x=x)) + geom_point(aes(size=state.region))+
+   scale_size_discrete(name="Region", range=c(2,10))

plot of chunk scaleSize2

`scale_size`

Manual sizes (2 and 4)

> head(state,2)

        Illiteracy state.region         x       y
Alabama        2.1        South  -86.7509 32.5901
Alaska         1.5         West -127.2500 49.2500

> ggplot(state, aes(y=y,x=x)) + geom_point(aes(size=state.region))+
+     scale_size_manual(name="Region", values=c(2,5,6,10))

plot of chunk scaleSize3

`scale_shape`

> head(CO2,2)

  Plant   Type  Treatment conc uptake
1   Qn1 Quebec nonchilled   95   16.0
2   Qn1 Quebec nonchilled  175   30.4

> ggplot(CO2, aes(y=uptake,x=conc)) + geom_point(aes(shape=Treatment))

plot of chunk scaleShape1

`scale_shape`

> CO2 = CO2 %>% mutate(Comb=interaction(Type, Treatment))
> ggplot(CO2, aes(y=uptake,x=conc)) + geom_point(aes(shape=Comb))+
+   scale_shape_discrete(name="Type",
+    breaks=c("Quebec.nonchilled","Quebec.chilled",
+             "Mississippi.nonchilled","Mississippi.chilled"),
+    labels=c("Quebec non-chilled","Quebec chilled",
+             "Miss. non-chilled","Miss. chilled"))

plot of chunk scaleShape2

`scale_linetype`

> head(CO2,2)

  Plant   Type  Treatment conc uptake              Comb
1   Qn1 Quebec nonchilled   95   16.0 Quebec.nonchilled
2   Qn1 Quebec nonchilled  175   30.4 Quebec.nonchilled

> ggplot(CO2, aes(y=uptake,x=conc)) + geom_smooth(aes(linetype=Comb))+
+   scale_linetype_discrete(name="Type")

plot of chunk scaleLinetype

`scale_linetype`

> head(CO2,2)

  Plant   Type  Treatment conc uptake              Comb
1   Qn1 Quebec nonchilled   95   16.0 Quebec.nonchilled
2   Qn1 Quebec nonchilled  175   30.4 Quebec.nonchilled

> ggplot(CO2, aes(y=uptake,x=conc)) + geom_smooth(aes(linetype=Treatment))+
+   scale_linetype_manual(name="Treatment", values=c("dashed","dotted"))

plot of chunk scaleLinetype1

`scale_fill` and `scale_color`

> head(faithfuld,2)

# A tibble: 2 x 3
  eruptions waiting     density
      <dbl>   <dbl>       <dbl>
1  1.600000      43 0.003216159
2  1.647297      43 0.003835375

> ggplot(faithfuld, aes(waiting, eruptions)) +
+     geom_raster(aes(fill = density)) +
+     scale_fill_continuous(low='red',high='blue')

plot of chunk scaleFill1

Facets

Panels - matrices of plots

facet_wrap
facet_grid

Facets

> ggplot(CO2)+geom_point(aes(x=conc,y=uptake, colour=Type))+ 
+ facet_wrap(~Plant)

Facets

> ggplot(CO2)+geom_line(aes(x=conc,y=uptake, colour=Type))+ 
+ facet_wrap(~Plant, scales='free_y')

Facets

> ggplot(CO2)+geom_point(aes(x=conc,y=uptake, colour=Type))+ 
+ facet_grid(Type~Treatment)

`theme_classic`

> ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
+     geom_point() + theme_classic()

`theme_bw`

> ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
+     geom_point() + theme_bw()

`theme_grey`

> ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
+     geom_point() + theme_grey()

`theme_minimal`

> ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
+     geom_point() + theme_minimal()

`theme_linedraw`

> ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
+     geom_point() + theme_linedraw()

`theme_light`

> ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
+     geom_point() + theme_light()

others

> png('images/xkcd.png', width=500, height=500, res=200)
> library(xkcd)  
> library(sysfonts)
> #library(extrafont)
> #download.file("http://simonsoftware.se/other/xkcd.ttf", dest="xkcd.ttf")
> ##font_import(".")
> #loadfonts()
> xrange <- range(CO2$conc)
> yrange <- range(CO2$uptake)
> ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth(position='jitter', size=1.5) +
+     #geom_point() +
+         theme_minimal()+theme(text=element_text(size=16, family='xkcd'))+
+         xkcdaxis(xrange, yrange)
> 
> dev.off()

Practice

> head(state)

           Illiteracy state.region         x       y
Alabama           2.1        South  -86.7509 32.5901
Alaska            1.5         West -127.2500 49.2500
Arizona           1.8         West -111.6250 34.2192
Arkansas          1.9        South  -92.2992 34.7336
California        1.1         West -119.7730 36.5341
Colorado          0.7         West -105.5130 38.6777

Calculate the mean and 95% confidence interval of Illiteracy per state.region and plot them. and plot them

Practice

> head(state)

           Illiteracy state.region         x       y
Alabama           2.1        South  -86.7509 32.5901
Alaska            1.5         West -127.2500 49.2500
Arizona           1.8         West -111.6250 34.2192
Arkansas          1.9        South  -92.2992 34.7336
California        1.1         West -119.7730 36.5341
Colorado          0.7         West -105.5130 38.6777

> library(gmodels)
> state.sum = state %>% group_by(state.region) %>%
+     summarise(Mean=mean(Illiteracy), Lower=ci(Illiteracy)[2],
+                                    Upper=ci(Illiteracy)[3])
> state.sum

# A tibble: 4 x 4
   state.region     Mean     Lower     Upper
         <fctr>    <dbl>     <dbl>     <dbl>
1     Northeast 1.000000 0.7860119 1.2139881
2         South 1.737500 1.4431367 2.0318633
3 North Central 0.700000 0.6101452 0.7898548
4          West 1.023077 0.6553719 1.3907819

> ggplot(state.sum, aes(y=Mean, x=state.region)) + geom_point() +
+     geom_errorbar(aes(ymin=Lower, ymax=Upper), width=0.1)

plot of chunk statesum

Practice

> ggplot(state.sum, aes(y=Mean, x=state.region)) + geom_point() +
+     geom_errorbar(aes(ymin=Lower, ymax=Upper), width=0.1) +
+         scale_x_discrete('Region') +
+         scale_y_continuous('Illiteracy rate (%)')+
+             theme_classic() +
+                 theme(axis.line.y=element_line(),axis.line.x=element_line())

Practice

Overlay illiteracy data onto map of US

> library(mapdata)
> US <- map_data("worldHires", region="USA")
> ggplot(US) +
+     geom_polygon(aes(x=long, y=lat, group=group)) +
+         geom_point(data=state,aes(y=y,x=x, size=Illiteracy),color='red')

Practice

Overlay illiteracy data onto map of US

> library(mapdata) 
> US <- map_data("worldHires", region="USA")
> ggplot(US) +
+     geom_polygon(aes(x=long, y=lat, group=group)) +
+         geom_point(data=state,aes(y=y,x=x, size=Illiteracy),color='red')+
+             coord_map(xlim=c(-150,-50),ylim=c(20,60)) + theme_minimal()

Practice

> MACNALLY <- read.csv('../data/macnally.csv',
+   header=T, row.names=1, strip.white=TRUE)
> head(MACNALLY)

                HABITAT GST EYR
Reedy Lake        Mixed 3.4 0.0
Pearcedale  Gipps.Manna 3.4 9.2
Warneet     Gipps.Manna 8.4 3.8
Cranbourne  Gipps.Manna 3.0 5.0
Lysterfield       Mixed 5.6 5.6
Red Hill          Mixed 8.1 4.1

Calculate the mean and standard error of GST and plot them

Practice

Calculate the mean and standard error of GST and plot mean and confidence bars

> library(gmodels)
> ci(MACNALLY$GST)

  Estimate   CI lower   CI upper Std. Error 
  4.878378   4.035292   5.721465   0.415704

> MACNALLY.agg = MACNALLY %>% group_by(HABITAT) %>%
+   summarize(Mean=mean(GST), Lower=ci(GST)[2], Upper=ci(GST)[3])
> ggplot(MACNALLY.agg, aes(y=Mean, x=HABITAT)) +
+     geom_errorbar(aes(ymin=Lower, ymax=Upper), width=0.1)+
+     geom_point() + theme_classic()

plot of chunk ggMacnally1

Practice

You can also use ggplot’s summary

> library(tidyverse)
> MACNALLY.melt = MACNALLY %>% gather(key=variable, value=value,-HABITAT)
> ggplot(MACNALLY.melt, aes(y=value, x=HABITAT)) +
+    stat_summary(fun.y='mean', geom='point')+
+    stat_summary(fun.data='mean_cl_normal', geom='errorbar', width=0.1)+
+        facet_grid(~variable)

plot of chunk ggMacnally3

> #and bootstrapped means..
> ggplot(MACNALLY.melt, aes(y=value, x=HABITAT)) +
+    stat_summary(fun.y='mean', geom='point')+
+    stat_summary(fun.data='mean_cl_boot', geom='errorbar', width=0.1)+
+        facet_grid(~variable)

plot of chunk ggMacnally3

Workshop 5.2: The Grammar of Graphics

Murray Logan

16 Jul 2017

Graphics in R

Options

Packages

Graphics infrustructure

ggplot

ggplot

ggplot

ggplot

Overview

Overview

Layers

Layers

geom_ and stat_

geom_

geom_

Optional mapping

geom_point

geom_point

geom_point

geom_point

Example data sets

Primary geometric objects

geom_bar

geom_bar

geom_bar

geom_bar

geom_bar

geom_boxplot

geom_boxplot

geom_line

geom_line

geom_line

geom_line

geom_point

geom_point

geom_smooth

geom_smooth

geom_polygon

geom_tile

geom_raster

Secondary geometric objects

Example data set

geom_errorbar

geom_errorbar

geom_errorbar

Coordinate systems

Coordinate systems

Coordinate systems

Coordinate systems

Coordinate systems

Scales

scale_x_ and scale_y_

scale_x_ and scale_y_

scale_x_ and scale_y_

scale_x_ and scale_y_

scale_x_ and scale_y_

Other scales

scale_size

scale_size

scale_size

scale_shape

scale_shape

scale_linetype

scale_linetype

scale_fill and scale_color

Facets

Facets

Facets

Facets

Facets

Themes

theme_classic

theme_bw

theme_grey

theme_minimal

theme_linedraw

theme_light

`geom_` and `stat_`

`geom_`

`geom_`

`geom_point`

`geom_point`

`geom_point`

`geom_point`

`geom_bar`

`geom_bar`

`geom_bar`

`geom_bar`

`geom_bar`

`geom_boxplot`

`geom_boxplot`

`geom_line`

`geom_line`

`geom_line`

`geom_line`

`geom_point`

`geom_point`

`geom_smooth`

`geom_smooth`

`geom_polygon`

`geom_tile`

`geom_raster`

`geom_errorbar`

`geom_errorbar`

`geom_errorbar`

`scale_x_` and `scale_y_`

`scale_x_` and `scale_y_`

`scale_x_` and `scale_y_`

`scale_x_` and `scale_y_`

`scale_x_` and `scale_y_`

`scale_size`

`scale_size`

`scale_size`

`scale_shape`

`scale_shape`

`scale_linetype`

`scale_linetype`

`scale_fill` and `scale_color`

`theme_classic`

`theme_bw`

`theme_grey`

`theme_minimal`

`theme_linedraw`

`theme_light`