The main difference is that, unlike base graphics, ggplot works with dataframes and not individual vectors.
# Setup
options(scipen=999) # turn off scientific notation like 1e+06
library(ggplot2)
data("midwest", package = "ggplot2") # load the data
# Init Ggplot
ggplot(midwest, aes(x=area, y=poptotal)) # area and poptotal are columns in 'midwest'
aes() function is used to specify the X and Y axes. Even though the x and y are specified, there are no points or lines in it. This is because, ggplot doesn’t assume that you meant a scatterplot or a line chart to be drawn. .
2. How to Make a Simple Scatterplot:
Adding points using a geom layer called geom_point. More about geom_xxx :
Like geom_point(), there are many such geom layers which we will see in list.
Example: a smoothing layer using geom_smooth(method='lm').
g <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point() +
geom_smooth(method="lm") # set se=FALSE to turnoff confidence bands
plot(g)
The majority of points lie in the bottom of the chart which doesn’t really look nice. So, let’s change the Y-axis limits to focus on the lower half.
3. Adjusting the X and Y axis limits:
Method 1: By deleting the points outside the range
g <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point() +
geom_smooth(method="lm") # set se=FALSE to turnoff confidence bands
# Delete the points outside the limits
g + xlim(c(0, 0.1)) + ylim(c(0, 1000000)) # deletes points
Method 2: Zooming In
g <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point() +
geom_smooth(method="lm") # set se=FALSE to turnoff confidence bands
# Zoom in without deleting the points outside the limits.
# As a result, the line of best fit is the same as the original plot.
g1 <- g +
coord_cartesian(xlim=c(0,0.1), ylim=c(0, 1000000)) # zooms in
plot(g1)
4. How to Change the Title and Axis Labels:
# set se=FALSE to turnoff confidence bands
g <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point() +
geom_smooth(method="lm")
# zooms in
g1 <- g +
coord_cartesian(xlim=c(0,0.1), ylim=c(0, 1000000))
# Add Title and Labels
g1 +
labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
# or
g1 +
ggtitle("Area Vs Population", subtitle="From midwest dataset") +
xlab("Area") +
ylab("Population")
ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(col="steelblue", size=3) + # Set static color and size for points
geom_smooth(method="lm", col="green") + # change the color of line
coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) +
labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
#How to Change the Color To Reflect Categories in Another Column?
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state), size=3) + # Set color to vary based on state categories.
geom_smooth(method="lm", col="firebrick", size=2) +
coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) +
labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
plot(gg)
As an added benefit, the legend is added automatically. If needed, it can be removed by setting the legend.position to None from within a theme() function.
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state), size=3) + # Set color to vary based on state categories.
geom_smooth(method="lm", col="firebrick", size=2) +
coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) +
labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
plot(gg)
gg + theme(legend.position="None") # remove legend
Also, You can change the color palette entirely.
gg + scale_colour_brewer(palette = "Set1") # change color palette
More of such palettes can be found in the RColorBrewer package
library(RColorBrewer)
head(brewer.pal.info, 10) # show 10 palettes
#> maxcolors category colorblind
#> BrBG 11 div TRUE
#> PiYG 11 div TRUE
#> PRGn 11 div TRUE
#> PuOr 11 div TRUE
#> RdBu 11 div TRUE
#> RdGy 11 div FALSE
#> RdYlBu 11 div TRUE
#> RdYlGn 11 div FALSE
#> Spectral 11 div FALSE
#> Accent 8 qual FALSE
6. How to Change the X Axis Texts and Ticks Location:
#How to Change the X and Y Axis Text and its Location?
This involves two aspects: breaks and labels.
Step 1: Set the breaks
scale_x_continuous because, the X axis variable is a continuous variable. Had it been a date variable, scale_x_date could be used. Like scale_x_continuous() an equivalent scale_y_continuous() is available for Y axis.
# Base plot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state), size=3) + # Set color to vary based on state categories.
geom_smooth(method="lm", col="firebrick", size=2) +
coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) +
labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
# Change breaks
gg + scale_x_continuous(breaks=seq(0, 0.1, 0.01))
Step 2: Change the labels
#Let me demonstrate by setting the labels to alphabets
#from a to k (though there is no meaning to it in this context).
# Base Plot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state), size=3) + # Set color to vary based on state categories.
geom_smooth(method="lm", col="firebrick", size=2) +
coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) +
labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
# Change breaks + label
gg + scale_x_continuous(breaks=seq(0, 0.1, 0.01), labels = letters[1:11])
If you need to reverse the scale, use scale_x_reverse().
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state), size=3) + # Set color to vary based on state categories.
geom_smooth(method="lm", col="firebrick", size=2) +
coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) +
labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
# Reverse X Axis Scale
gg + scale_x_reverse()
#How to Write Customized Texts for Axis Labels, by Formatting the Original Values?
* Method 1: Using sprintf()
* Method 2: Using a custom user defined function. (Formatted 1000’s to 1K scale)
# Base Plot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state), size=3) + # Set color to vary based on state categories.
geom_smooth(method="lm", col="firebrick", size=2) +
coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) +
labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
# Change Axis Texts
gg + scale_x_continuous(breaks=seq(0, 0.1, 0.01), labels = sprintf("%1.2f%%", seq(0, 0.1, 0.01))) +
scale_y_continuous(breaks=seq(0, 1000000, 200000), labels = function(x){paste0(x/1000, 'K')})
#How to Customize the Entire Theme in One Shot using Pre-Built Themes?
Finally, instead of changing the theme components individually. We can change the entire theme itself using pre-built themes. The help page ?theme_bw shows all the available built-in themes. We have 2 methode: * Use the theme_set() and * Draw the ggplot and then add the overall theme setting (eg. theme_bw())
# Base plot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state), size=3) + # Set color to vary based on state categories.
geom_smooth(method="lm", col="firebrick", size=2) +
coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) +
labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
gg <- gg + scale_x_continuous(breaks=seq(0, 0.1, 0.01))
# method 1: Using theme_set()
theme_set(theme_classic()) # not run
gg
# method 2: Adding theme Layer itself.
gg + theme_bw() + labs(subtitle="BW Theme")
gg + theme_classic() + labs(subtitle="Classic Theme")
