> For the complete documentation index, see [llms.txt](https://r-pedia.gitbook.io/cevi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://r-pedia.gitbook.io/cevi/r-graphics-cookbook/untitled.md). # ggplot2 intro ## 1. Understanding the Ggplot Syntax: {% hint style="info" %} The main difference is that, unlike base graphics, ggplot works with dataframes and not individual vectors. {% endhint %} ```r # 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' ``` {% hint style="info" %} `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. [More about aes](https://ggplot2.tidyverse.org/articles/ggplot2-specs.html). {% endhint %} ## 2. How to Make a Simple Scatterplot: Adding points using a geom layer called `geom_point`. More about geom\_xxx : [List of geom\_xxx](https://ggplot2.tidyverse.org/reference/index.html) ``` ggplot(midwest, aes(x=area, y=poptotal)) + geom_point() ``` ![](/files/-LNqw9rmuwUJSoSAd2n-) Like `geom_point()`, there are many such geom layers which we will see in list. Example: a smoothing layer using `geom_smooth(method='lm')`. ```r g <- ggplot(midwest, aes(x=area, y=poptotal)) + geom_point() + geom_smooth(method="lm") # set se=FALSE to turnoff confidence bands plot(g) ``` ![](/files/-LNqwkSiQSPnUz7ElL3A) 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 ```r 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 ```r 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: ```r # 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") ``` **Here is the full function:** ```r ggplot(midwest, aes(x=area, y=poptotal)) + geom_point() + geom_smooth(method="lm") + 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") ``` ## 5. How to Change the Color and Size of Points**:** ### #How to Change the Color and Size To Static? ```r 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") ``` ![](/files/-LNteK4I32tMN2F9iWxc) ### #How to Change the Color To Reflect Categories in Another Column? ```r 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) ``` ![](/files/-LNteTb72QiS69YTYV5q) 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. ```r 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 ``` ![](/files/-LNtfEbXAbl_bZ1IvlE5) Also, You can change the color palette entirely. ```r gg + scale_colour_brewer(palette = "Set1") # change color palette ``` ![](/files/-LNtj-EH-lEJ0Jk3Bf06) ```r 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 ``` ![](/files/-LNtjG27f35ua7pykKtY) ![](/files/-LNtjNExiha7XOWM4szW) ## 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. ```r # 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()`. ```r 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) ```r # 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()`) ```r # 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") ``` ![](/files/-LNuDl0A_gom8PUHMeEE)