ggpipe: Create a new ggplot

In statisticiansix/ggpipe: Enables ggplot2 to be used with pipes

Description

"ggpipe()" creates a ggplot object in the column plot within the piped data frame. Unlike ggplot it requires a set of default data defined under data_column.

Usage

ggpipe(.data, data_column = data, ...)

Arguments

.data	Data frame with nested data column
data_column	Column where the nested data is located and the default data to use within ggplot
...	Other arguments passed onto ggplot

Examples

# Let us first recreate the examples within ?ggplot
=====
Generate some sample data, then compute mean and standard deviation
in each group
df <- data.frame(
  gp = factor(rep(letters[1:3], each = 10)),
  y = rnorm(30)
)

ds <- df %>%
  group_by(gp)%>%
  summarise(mean=mean(y),
            sd=sd(y))

data <- bind_cols(df %>% nest('df'=everything()),
                  ds %>% nest('ds'=everything()))

# The summary data frame ds is used to plot larger red points on top
# of the raw data. Note that we don't need to supply data or mapping
# in each layer because the defaults from ggplot() are used.
example1 <- data %>%
  ggpipe(df,aes(gp,y)) %>%
  gglayer(geom_point) %>%
  gglayer(geom_point,data=ds,aes(y=mean),colour='red',size=3)

example1$plot

# Same plot as above, declaring only the data frame in ggplot().
# Note how the x and y aesthetics must now be declared in
# each geom_point() layer.
example2 <- data %>%
  ggpipe(df) %>%
  gglayer(geom_point,aes(gp,y)) %>%
  gglayer(geom_point,data=ds,aes(gp,mean),colour='red',size=3)

example2$plot

# Within ggpipe we cannot fully specify the plot in each layer, we
# currently need to define a default data set within the plot initiation.
# Apart from that we can recreate the final example from ?ggplot

example3 <- data %>%
  ggpipe(df) %>%
  gglayer(geom_point,aes(gp,y)) %>%
  gglayer(geom_point,data=ds,aes(gp,mean),colour='red',size=3)%>%
  gglayer(geom_errorbar, data = ds,
          aes(gp, mean, ymin = mean - sd, ymax = mean + sd),
          colour = 'red',
          width = 0.4
  )

# We will now demonstrate some of the ways in which we can use the ggpipe
# to our advantage when creating plots, particularly with respect to groups.

# Let us start by creating a grouped set of data similar to the one above
data <- data.frame(
  gp = rep(rep(letters[1:3], each = 10),3),
  set = factor(rep(LETTERS[1:3], each = 30)),
  y = rnorm(90)
) %>%
  nest('df'=-set)%>%
  mutate('ds'=map(df,function(x){
    x %>%
      group_by(gp)%>%
      summarise(mean=mean(y),
                sd=sd(y))
  }))

# Let's recreate the plot from above for each of our sets

example4 <- data %>%
  ggpipe(df) %>%
  gglayer(geom_point,aes(gp,y)) %>%
  gglayer(geom_point,data=ds,aes(gp,mean),colour='red',size=3)%>%
  gglayer(geom_errorbar, data = ds,
          aes(gp, mean, ymin = mean - sd, ymax = mean + sd),
          colour = 'red',
          width = 0.4
  )

example4$plot

# As we have 3 similar plots it is difficult to tell them apart. We can use
# gglayer to define titles for each plot, however first we need to ensure the
# data frame is grouped - if the data frame is not grouped then gglayer will
# produce an error.
#
# We have defined a column "title" within the main (unnested) part of the
# data frame and we can call this within "gglayer" to produce a title.

example5 <- example4 %>%
  group_by(set)%>%
  mutate('title'=sprintf('Plot of %s',set))%>%
  gglayer(labs,title=title)

example5$plot

# We can also do the same with colours, again ensuring that the data table
# is grouped
# N.B. ggpipe will currently overwrite any column called plot, so if you
# want to keep the plot it is best to use "rename" to preserve plots
example6 <- example5 %>%
  mutate('colour'=case_when(set=='A'~'red',
                            set=='B'~'green',
                            set=='C'~'blue'))%>%
  ggpipe(df) %>%
  gglayer(geom_point,aes(gp,y)) %>%
  gglayer(geom_point,data=ds,aes(gp,mean),colour=colour,size=3)%>%
  gglayer(geom_errorbar, data = ds,
          aes(gp, mean, ymin = mean - sd, ymax = mean + sd),
          colour = colour,
          width = 0.4
  )%>%
  gglayer(labs,title=title)

example6$plot

statisticiansix/ggpipe documentation built on Dec. 9, 2019, 3:22 p.m.