This means you can actually access the minimum with: This reminds us that, in R, there are often several ways to arrive at the same result. Now that you have For example, apply() the function is used to compute the number of observations in the data set using … We’ll first start with loading the dataset into R. This function takes 3 arguments: the numeric variable, a categorical grouping variable and the function to apply. Note that the output of the range() function is actually an object containing the minimum and maximum (in that order). For this, we leverage the ftable() function to print the results more attractively. Like boxplots, scatterplots are even more informative when differentiating the points according to a factor, in this case the species: Line plots, particularly useful in time series or finance, can be created by adding the type = "l" argument in the plot() function: In order to check the normality assumption of a variable (normality means that the data follow a normal distribution, also known as a Gaussian distribution), we usually use histograms and/or QQ-plots.1 See an article discussing about the normal distribution and how to evaluate the normality assumption in R if you need a refresh on that subject. Length and width of the sepal and petal are numeric variables and the species is a factor with 3 levels (indicated by num and Factor w/ 3 levels after the name of the variables). The idea is to break the range of values into intervals and count how many observations fall into each interval. For your information, a mosaic plot can also be done via the mosaic() function from the {vcd} package: Barplots can only be done on qualitative variables (see the difference with a quantitative variable here). If well presented, descriptive statistics is already a good starting point for further analyses. individually calculate skewness and kurtosis should look like this. For instance, if we go back to table3 which is the cross classication counts for gender by marital status: We can compute the marginal frequencies with margin.table() and the percentages for these marginal frequencies with prop.table() using the margin argument: Bar charts are most often used to visualize categorical variables. However, you need to enter a list in such functions instead of a data Another package The IQR criterion means that all observations above $$q_{0.75} + 1.5 \cdot IQR$$ and below $$q_{0.25} - 1.5 \cdot IQR$$ (where $$q_{0.25}$$ and $$q_{0.75}$$ correspond to first and third quartile respectively) are considered as potential outliers by R. The minimum and maximum in the boxplot are represented without these suspected outliers. Furthermore, results do not dramatically change between the two methods. Studying your data closely gives you answers to all However, in practice, normality tests are often considered as too conservative in the sense that sometimes a very limited number of observations may cause the normality condition to be violated. Note that there are multiple ways to reorder bar charts, just search “Order Bars in ggplot2 bar graph” in Stackoverflow. Furthermore, results do not dramatically change between the two methods. ... frequency tables can be used to determine if your data follows a pattern or a relationship against another variable… The skew, for instance, The functions plot() and density() are used together to draw a density plot: The last type of descriptive plot is a correlation plot, also called a correlogram. These are not the only things you can plot using R. You can easily generate a pie chart for categorical data in r. Look at the pie function. In fact, statisticians often Descriptive statistics is often the first step and an important part in any statistical analysis. For instance, when drawing a scatterplot of the length of the sepal and the length of the petal: There seems to be a positive association between the two variables. The coefficient of variation can be found with stat.desc() (see the line coef.var in the table above) or by computing manually (remember that the coefficient of variation is the standard deviation divided by the mean): To my knowledge there is no function to find the mode of a variable. The p-value is close to 0 so we reject the null hypothesis of independence between the two variables. For instance, we compare the length of the sepal across the different species: A dotplot is more or less similar than a boxplot, except that observations are represented as points and there is no summary statistics presented on the plot: Scatterplots allow to check whether there is a potential link between two quantitative variables. The following reproduces the previous tables but calculates the proportions rather than counts: Marginals show the total counts or percentages across columns or rows in a contingency table. The dataset iris has only one qualitative variable so we create a new qualitative variable just for this example. This function takes 3 arguments: the numeric variable, a categorical grouping variable and the function to apply.

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