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Previously, we described the essentials of R programming and provided quick start guides for importing data into R.
Here, we’ll describe how to create strip charts (i.e., one dimensional scatter plots or dot plots) in R. These plots are a good alternative to box plots when sample sizes are small.
Launch RStudio as described here: Running RStudio and setting up your working directory
Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files
Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.
Here, we’ll use the R built-in ToothGrowth data set.
ToothGrowth$dose <- as.factor(ToothGrowth$dose)# Print the first 6 rowshead(ToothGrowth, 6)## len supp dose## 1 4.2 VC 0.5## 2 11.5 VC 0.5## 3 7.3 VC 0.5## 4 5.8 VC 0.5## 5 6.4 VC 0.5## 6 10.0 VC 0.5stripchart(x, data = NULL method = "overplot", jitter = 0.1)- x: the data from which the plots are to be produced. Allowed values are one or a list of numeric vector, each corresponding to a component plot.
- data: a data.frame (or list) from which the variables in x should be taken.
- Method: the method to be used to separate coincident points. Allowed values are one of “overplot”, “jitter” or “stack”.
- jitter: when method = “jitter” is used, jitter gives the amount of jittering applied.
# Plot len by dosestripchart(len ~ dose, data = ToothGrowth, pch = 19, frame = FALSE)
# Vertical plot using method = "jitter"stripchart(len ~ dose, data = ToothGrowth, pch = 19, frame = FALSE, vertical = TRUE, method = "jitter")
# Change point shapes (pch) and colors by groups# add main title and axis labelsstripchart(len ~ dose, data = ToothGrowth, frame = FALSE, vertical = TRUE, method = "jitter", pch = c(21, 18, 16), col = c("#999999", "#E69F00", "#56B4E9"), main = "Length by dose", xlab = "Dose", ylab = "Length")
- Creating and Saving Graphs in R
- Scatter Plots
- Scatter Plot Matrices
- Box Plots
- Bar Plots
- Line Plots
- Pie Charts
- Histogram and Density Plots
- Dot Charts
- Plot Group Means and Confidence Intervals
- Graphical Parameters
- Lattice Graphs
- ggplot2 Graphs
This analysis has been performed using R statistical software (ver. 3.2.4).
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