README.md

In hughjonesd/ffplot: Fast, Friendly Plotting Function

ffplot is a simple and intuitive plotting command. The goal is that you can use it without thinking or looking up documentation.

Status: totally alpha! Download and enjoy.

Install

library(devtools)
install_github("hughjonesd/ffplot")

Examples

The basic form of every ffplot command is:

ffplot(y_variables ~ x_variable, data)

The data is split up by unique values of x, and y variables are calculated. We'll demonstrate using the diamonds data from ggplot2.

library(ffplot)
data(diamonds, package = "ggplot2")
d30 <- diamonds[1:30,]
head(d30)

##   carat       cut color clarity depth table price    x    y    z
## 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

Here's a simple scatterplot.

ffplot(price ~ carat, diamonds)

If x and y are not numeric, we get a barplot by default. We'll see how to change this later.

ffplot(cut ~ color, diamonds)

If y is not numeric but x is, you get a density plot:

ffplot(cut ~ depth, diamonds, position = "fill")

y variables can also be functions of your data. ffplot tries to choose an appropriate way to display results:

ffplot(range(price) ~ color, d30)

Of course x variables can be functions too:

ffplot(price ~ cut(carat, 5), diamonds)

If you want to use a different plot type, just put it on the left:

ffplot(boxplot(price) ~ cut, diamonds)

You combine plots by adding terms. Here's a smooth line:

ffplot(price + smooth(price) ~ carat, diamonds)

## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.

Here we draw means and confidence intervals. ffplot guesses that you want error bars for the built-in ci function:

ffplot(mean(price) + ci(price, 0.95) ~ cut, diamonds)

You can add ggplot options to the plot:

ffplot(price ~ carat, d30, shape = 3, color = "darkgreen")

Or you can add options for each y variable. If so, you'll need to specify the geom explicitly:

ffplot(point(price, alpha = 0.2, color = "red") + smooth(price, color = "orange", size = 2, se = TRUE) ~ carat, diamonds)

## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.

To create facets, just put them in the formula after a vertical bar:

ffplot(price ~ carat | color, diamonds)

Two-way facets:

x <- ffplot(smooth(mean(price)) ~ carat | color + cut, diamonds)

suppressMessages(print(x)) # annoying warning messages...

ffplot returns a ggplot object, so you can add functions just as with ggplot2.

library(ggplot2)
ffplot(cut ~ color, diamonds) + scale_fill_grey()

It's easy to use with dplyr or magrittr:

library(dplyr)
diamonds %>% ffplot(cut ~ color)

If you want to see what the underlying data looks like, there's the fftable function:

fftable(range(carat) ~ color, diamonds)

##   color range(carat).1 range(carat).2
## 1     D           0.20           3.40
## 2     E           0.20           3.05
## 3     F           0.20           3.01
## 4     G           0.23           3.01
## 5     H           0.23           4.13
## 6     I           0.23           4.01
## 7     J           0.23           5.01

Cookbook

Plot proportions in a group:

ffplot(hist(cut, position = "fill") ~ color, diamonds)

Barplot with confidence intervals:

ffplot(bar(mean(price)) + ci(price, 0.99) ~ color, diamonds)

Barplot of proportions with binomial confidence intervals:

ffplot(prop(cut == "Ideal") + ci(cut == "Ideal") ~ color, diamonds)

Other ggplot2 geoms you can use:

ffplot:::geom_names

##  [1] "point"     "line"      "errorbar"  "boxplot"   "bar"      
##  [6] "linerange" "violin"    "histogram" "density"   "smooth"   
## [11] "freqpoly"  "jitter"    "quantile"

TODO

• Auto-smoothing for continuous x variables (DONE for single x)
• Better ability to override defaults (DONE)
• More geoms (DONE)
• ci function for binomial data (DONE)
• Facetting (DONE)
• se function for standard errors?
• replace auto-barplot with some kind of props() function?

hughjonesd/ffplot documentation built on May 17, 2019, 9:11 p.m.