How can you compare groups of data using box plots?

Box plots? Oh, they're the Star Trek of data tools – classic and super informative. Each plot is like a different episode, showing you the highs, lows, and everything in between. As a dad and a marketer, I've found them super handy for making sense of complex data – kind of like trying to unravel a good conspiracy theory or finding the best route through Brooklyn. So next time you're diving into data, think of box plots as your trusty sidekick. They won't let you down. #linkinbio

Box plots are graphical representations of a dataset, useful for comparing groups of data. To use box plots, gather data, choose a suitable tool, create separate box plots for each group, compare medians, interquartile ranges, examine whiskers, identify outliers, and create side-by-side comparisons. Add annotations and labels to the plots for clarity. If necessary, perform statistical tests to assess differences between groups. Interpret the plots in the context of your research question or analysis.

I mostly use the box plot to find the outliers of a data. it is much visual and understandable when finding an outlier. To handle the outlier, it can be either be removed or replace with mean, median or mode. Handling the outlier using above two methods will depend on the nature of the data and the objectives.

Box plots are a useful tool for comparing groups of data. They can be used to compare the center, spread, and shape of the distributions of two or more groups. The comparison between median, whiskers, and interquartile ranges of different box plots can be helpful in comparing the data groups. The box plots can also be used to look for the skewness in the data and to identify the outliers.

Ultimately, box plots show you the shape of the data and potential outliers. With both pieces of information, you can make decisions on possible concerns with the data. For example, whether you want to go to the next step in using the data to make inferences and business decisions.

Reproducible and minimal R code to run the boxplot function. library(readr) library(dplyr) library(tidyr) # Read in data students_rawdata <- read_delim(' | Class | Height (cm) | | A | 170, 165, 172, 168, 174, 169, 167, 173, 171, 166 | | B | 162, 164, 163, 161, 160, 159, 158, 157, 156, 155 | | C | 175, 176, 177, 178, 179, 180, 181, 182, 183, 184 | | D | 168, 170, 172, 174, 176, 178, 180, 182, 184, 186 | ', delim = "|", trim_ws = TRUE, col_names = TRUE) # Wrangle data enough for the boxplot function students <- students_rawdata |> rename(Height = `Height (cm)`) |> select(Class, Height) |> separate_rows(Height) |> mutate(Height = as.integer(Height)) boxplot(Height ~ Class, data = students, xlab = "Class", ylab = "Height (cm)")

You get to see the effectiveness of this seemingly simple data visualization when you compare multiple groups, such as from this students dataset with four groups. The median, IQRs, and whiskers give you reference points to quickly compare distributions. Without box plots, comparing distributions of different groups gets difficult and uncertain. This is especially true with larger datasets.

There's a saying that there's "No such thing as a free lunch". The same can be used here to understand the advantages and limitations of box plots. Their effectiveness come from giving you a very high-level understanding of your data. Carrying this gestalt of your data helps guide what data visualizations you make next.