Every ggplot2 chart in R follows the same three-step pattern: name your data, map your variables, pick a geom. Here is the minimum working example for a scatter plot:
library(ggplot2)ggplot(data = mtcars, aes(x = wt, y = mpg)) + geom_point()
Three lines. One chart. The rest of this cheat sheet covers the 6 chart types that show up in nearly every R assignment, the formatting tweaks that turn a default chart into a submission-ready one, and how to save your chart at the right resolution for a PDF or Word document.
All examples below use the mtcars dataset built into R, so every block below copies, pastes into RStudio, and runs without downloading anything.
The grammar of graphics in three lines
ggplot2 builds every chart from three ingredients: a dataset, an aesthetic mapping, and a geom. Once those three are clear, the rest is layering.
ggplot(data = mtcars, aes(x = wt, y = mpg)) + geom_point()# ^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^# the dataset the aesthetic the geom
The data argument names the data frame. The aes() call maps columns of that data frame to visual properties (x-axis, y-axis, colour, size, shape). The geom_*() call decides what kind of chart appears.
The plus sign between layers is part of the syntax. ggplot2 charts add features by combining layers with +, which is why a complete chart often spans several lines:
The data argument names the data frame. The aes() call maps columns of that data frame to visual properties (x-axis, y-axis, colour, size, shape). The geom_*() call decides what kind of chart appears.
The plus sign between layers is part of the syntax. ggplot2 charts add features by combining layers with +, which is why a complete chart often spans several lines:
ggplot(data = mtcars, aes(x = wt, y = mpg)) +geom_point() +labs(title = "Fuel economy by car weight",x = "Weight (1000 lbs)",y = "Miles per gallon") +theme_minimal()
This is the structure every cheat sheet example below follows. Change the geom, change the aesthetics, change the labels. The skeleton stays the same.
The 6 chart types your R assignments use most
The list below covers the 6 chart types graded most often across undergraduate statistics, social science, biology, and economics assignments. For each one, the cheat sheet block names the geom, shows the minimum code, and explains what to change for your own data.
Chart 1: Scatter plot with geom_point()
A scatter plot displays the relationship between two continuous variables. Each dot is one observation.
ggplot(data = mtcars, aes(x = wt, y = mpg)) +geom_point()
What to change for your data
Replace mtcars with the name of your data frame, wt with the column you want on the x-axis, and mpg with the column you want on the y-axis.
Common upgrades
Add colour by a third variable with colour = factor(cyl) inside aes(). Add a regression line on top with + geom_smooth(method = “lm”). Increase dot size with geom_point(size = 3).
Chart 2: Bar chart with geom_bar() or geom_col()
A bar chart displays one value per category. The choice between geom_bar() and geom_col() is the most common ggplot2 confusion for beginners.
For counting how many rows fall in each category
ggplot(data = mtcars, aes(x = factor(cyl))) +geom_bar()
Use geom_bar() when ggplot2 needs to count the rows for each category. No y variable is supplied. The function counts the rows for you.
For plotting a value you already calculated
avg_mpg <- aggregate(mpg ~ cyl, data = mtcars, mean)
ggplot(data = avg_mpg, aes(x = factor(cyl), y = mpg)) +geom_col()
Use geom_col() when you already have the values you want to plot in your dataset. The y variable is explicit.
A faster fix that avoids switching functions: keep geom_bar() and add stat = “identity” inside it. Both approaches produce the same chart.
Chart 3: Line chart with geom_line()
A line chart displays change over a continuous variable, usually time. Each point connects to the next.
# Build a tiny time-series datasetdf <- data.frame(year = 2015:2024,sales = c(120, 135, 150, 142, 168, 175, 190, 205, 220, 240))
ggplot(data = df, aes(x = year, y = sales)) +geom_line() +geom_point()
Common upgrades
Add multiple lines from different groups with colour = group_name inside aes(). Thicken the line with geom_line(size = 1.2). Add data points on top of the line with + geom_point(), which the example above already does.
Chart 4: Histogram with geom_histogram()
A histogram displays the distribution of one continuous variable. The x-axis is the variable, the y-axis is the count of observations in each bin.
ggplot(data = mtcars, aes(x = mpg)) +geom_histogram(binwidth = 2)
What to change for your data
The binwidth argument sets the size of each bar in the units of your variable. For mpg, a binwidth of 2 means each bar covers 2 mpg of range. Always set binwidth explicitly. The default of 30 bins is rarely what you want, and ggplot2 prints a warning if you leave it unset.
Common upgrades
Add a clean outline with geom_histogram(binwidth = 2, fill = “steelblue”, colour = “white”). Overlay a density curve with + geom_density(aes(y = ..count.. * 2)) where 2 matches the binwidth.
Chart 5: Box plot with geom_boxplot()
A box plot displays the distribution of a continuous variable across groups. The box shows the interquartile range, the line inside the box shows the median, and the whiskers extend to the most extreme values within 1.5 times the IQR.
ggplot(data = mtcars, aes(x = factor(cyl), y = mpg)) +geom_boxplot()
This is the chart that appears in almost every statistics assignment that involves comparing groups. It is the visual equivalent of a t-test or ANOVA.
Common upgrades
Add individual data points on top of the boxes with + geom_jitter(width = 0.2, alpha = 0.5). Colour the boxes by group with fill = factor(cyl) inside aes(). Flip the orientation to horizontal with + coord_flip().
Chart 6: Density plot with geom_density()
A density plot displays the distribution of a continuous variable as a smooth curve. It is the underused alternative to a histogram, and it works better than a histogram when comparing two or more groups on the same axis.
ggplot(data = mtcars, aes(x = mpg, fill = factor(cyl))) +geom_density(alpha = 0.4)
Why it beats a histogram for group comparison
Two or more histograms overlaid on each other become unreadable. Two or more density curves with alpha = 0.4 (40 percent transparency) overlap cleanly. Use the density plot whenever the assignment asks you to compare distributions between groups.
Making your chart look academic
Default ggplot2 charts have a grey background and no title. Most academic submissions look better with a clean white background, proper titles, and labelled axes. Four functions handle this:
Adding titles and axis labels with labs()
ggplot(data = mtcars, aes(x = wt, y = mpg)) +geom_point() +labs(title = "Fuel economy by car weight",subtitle = "Data: 32 cars from Motor Trend (1974)",x = "Weight (1000 lbs)",y = "Miles per gallon",caption = "Source: mtcars dataset")
Every chart in an academic assignment needs at minimum a title and labelled axes. The caption argument is useful for citing the data source, which several rubrics specifically check for.
Switching to a clean theme
Four themes work well for academic charts: theme_minimal(), theme_classic(), theme_bw(), and theme_light().
ggplot(data = mtcars, aes(x = wt, y = mpg)) +geom_point() +theme_minimal()
Of the four, theme_minimal() is the safest default. It removes the grey background, keeps light gridlines for reading values, and lets the data take centre stage. theme_classic() removes the gridlines entirely, which suits cleaner submissions but is harder to read precise values from.
Formatting axes for academic precision
Two scale functions cover most academic formatting needs:
+ scale_x_continuous(breaks = seq(0, 6, by = 1)) +scale_y_continuous(limits = c(0, 40), breaks = seq(0, 40, by = 10))
Use breaks to control where the axis tick marks appear, and limits to set the visible range. Setting these explicitly is what separates a default chart from one that looks designed.
Faceting: multiple panels in one chart
Faceting splits one chart into multiple smaller panels by a categorical variable. The technique is invaluable for comparing the same relationship across groups.
facet_wrap() for one categorical variable
ggplot(data = mtcars, aes(x = wt, y = mpg)) +geom_point() +facet_wrap(~ cyl)
This produces three side-by-side scatter plots, one per cylinder count. The tilde ~ before the variable name is part of the syntax. Read it as “by”.
facet_grid() for two categorical variables
ggplot(data = mtcars, aes(x = wt, y = mpg)) +geom_point() +facet_grid(am ~ cyl)
This produces a grid of panels with rows for each value of am (transmission type) and columns for each value of cyl (cylinder count). The format is facet_grid(rows ~ columns).
Saving your chart for assignment submission
The ggsave() function saves your most recent chart to a file. It handles every format students need.
ggsave("my_chart.png", width = 6, height = 4, dpi = 300)
Width and height are in inches. A dpi of 300 produces print-quality output suitable for a PDF or Word submission. The default 72 dpi from screenshots looks pixelated when graders zoom in.
Choosing the right file format
Three formats cover every assignment scenario:
PNG is the safe default for any chart submitted inside a Word document. Use .png for screenshots, dashboards, and anything with text overlays.
PDF is the right choice for charts that go into an R Markdown report or a LaTeX document. PDF charts stay sharp at any zoom level because they are vector-based. Use .pdf when your final submission is a PDF.
SVG is the format for web display and editable charts. Most assignments do not need SVG, but if your supervisor or marker asks for an editable graphic, this is the format.
Three ggplot2 errors students hit constantly
The three errors below cover the vast majority of ggplot2 failures in beginner R coursework. Each one has a one-line fix.
Error 1: could not find function “ggplot”
The ggplot2 package is not loaded into the current R session. Add library(ggplot2) at the top of your script. If the package is not installed at all, run install.packages(“ggplot2”) first. The full troubleshooting walkthrough for this error category lives in Fix the ‘Could Not Find Function’ Error in R.
Error 2: object ‘mpg’ not found
The variable name inside aes() does not exist in your data frame. Check the spelling and the case (ggplot2 is case-sensitive). Run names(your_data) to see the actual column names. Common cause: a column that looks like mpg in your spreadsheet got imported as MPG or Mpg depending on the source.
Error 3: stat_bin() using bins = 30. Pick better value with binwidth
This is a warning, not an error. ggplot2 is telling you that geom_histogram() used 30 bins by default and you have not set binwidth yourself. Add binwidth = 2 (or whatever makes sense for your data) and the warning goes away.
Once your chart is ready for submission
With a clean chart saved at 300 dpi, the rest of your analysis continues from there. If your chart visualises a regression you fitted, Linear regression in R walks through the model output that pairs with your scatter plot or line chart. If your chart visualises data you imported from a CSV, How to Read CSV Files in R covers the data import workflow including the most common errors that surface during import.
ggplot2 has many more geoms beyond the 6 covered here. Once these 6 are solid, geom_violin() for distribution comparison, geom_tile() for heatmaps, and geom_smooth() for trend lines are the natural next layer. The grammar of graphics structure stays the same. Only the geom changes.
Stuck on a ggplot2 chart for your assignment?
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