Exploratory Data Analysis in R: Quick Dive into Data Visualization
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In this video, I continue on the topic of exploratory data analysis and provide a quick dive into data visualization using the R base plot functions.
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⭕ Timeline
0:57 Fire up RStudio or RStudio.cloud
1:08 Open Iris-data-understanding.R file
1:14 Load in the Iris dataset
1:32 Scroll to “Quick data visualization”
1:43 Generate panel plot using plot(iris)
2:10 Add the col argument to set color, e.g. plot(iris, col=”red”)
2:57 To make a scatter plot use plot(var1, var2)
3:07 For the Iris dataset use plot(iris$Sepal.Width, iris$Sepal.Length)
3:51 To customize the x-axis label, add the xlab argument
4:40 To construct a histogram for sepal width: hist(iris$Sepal.Width)
5:54 Feature plot shows the Box plots for 4 variables as a function of 3 classes
8:04 Quick recap and next video’s topic
⭕ Code:
https://github.com/dataprofessor/code/blob/master/iris/iris-data-understanding.R
Note: Please excuse the spacing error at 1:32 “Quick data visualization”
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QUESTION OF THE DAY: Instead of specifying the color argument as col = 'red' what would happen if we use col = iris$Species ❓
Try it and comment down below! 😃
Amazing content….Please interpret the first pair plot,the big one. What does that represent.
I need to do install.packages to run featurePlot, otherwise it showed an error.
how can fix this error
Error in plot.window(…) : need finite 'ylim' values
In addition: Warning messages:
1: In xy.coords(x, y, xlabel, ylabel, log) : NAs introduced by coercion
2: In min(x) : no non-missing arguments to min; returning Inf
3: In max(x) : no non-missing arguments to max; returning -Inf
when we plotting the iris data set ?
Nice explanation
Need these videos with python implementation !!!!
Amazing content! Even if you already know the stuff it's good to refresh or get some additional tricks 🙂