Statistics 251: Statistical Methods
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1 Statistics 251: Statistical Methods Summaries and Graphs in R Module R file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 1/14
2 Summary Statistics First thing to do with data is summarize and graph it Doing so also helps validating the data before use Important (and common) statistics: Mean, Median, Variance, Standard deviation, 1st and 3rd quartiles Many, MANY more 2/14 file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 2/14
3 Summaries In R there are many ways to get these values mean: mean(x) median: median(x) 5# summary (min,q1,median,q3,max) plus mean: summary(x) summary() does not provide variance or standard deviation can use sd(x) and var(x) other methods for different summary statistics are available 3/14 file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 3/14
4 Summary Statistics with mean() and median() mean(eruptions); mean(waiting) [1] [1] 70.9 median(eruptions); median(waiting) [1] 4 [1] 76 4/14 file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 4/14
5 Summary Statistics with sd() and var() var(eruptions); var(waiting) [1] [1] sd(eruptions); sd(waiting) [1] [1] /14 file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 5/14
6 Using summary() # summary can be used on the whole dataset summary(faithful) eruptions waiting Min. :1.60 Min. :43.0 1st Qu.:2.16 1st Qu.:58.0 Median :4.00 Median :76.0 Mean :3.49 Mean :70.9 3rd Qu.:4.45 3rd Qu.:82.0 Max. :5.10 Max. :96.0 6/14 file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 6/14
7 Graphs Most commonly used graphs: Quantitative (continuous) data: Histogram Boxplot Stemplot (not used in class) Scatterplot Qualitative (categorical) data: Barplot Pie chart 7/14 file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 7/14
8 Graph Syntax I Histogram hist(x,breaks=,freq=,main='',xlab='',ylab='',col=,...) x: vector of values breaks: vector of breaks, function to compute breaks, single number, more freq: logical; default is TRUE, FALSE will give relative frequencies main: a title (in quotes) xlab, ylab: titles for axes col: specifies color of bars (Google R colors) : more options 8/14 file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 8/14
9 Graph Syntax II Boxplot Barplot boxplot(x,data=,...) Many of the same options as hist() and other graphs x: can be vector or formula barplot(height,xlim=, ylim=,...) Many of the same options as hist() height: either vector or matrix of values xlim, ylim: limits of the axes 9/14 file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 9/14
10 Graphing 1 Histograms hist(waiting) 10/14 file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 10/14
11 Graphing 2 hist(eruptions) 11/14 file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 11/14
12 Graphing 3 Boxplots boxplot(waiting) 12/14 file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 12/14
13 Graphing 4 boxplot(eruptions) 13/14 file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 13/14
14 Graphing 2 colors=c('red','blue','green','orange','yellow','brown') observed=c(92,157,102,190,91,101) barplot(observed,names.arg=colors,col=colors,ylim=c(0,200), ylab='counts',main="distribution of M&M Colors") 14/14 file:///u:/documents/classes/lectures/251301/renae/markdown/master%20versions/summary_graphs.html#1 14/14
> glucose = c(81, 85, 93, 93, 99, 76, 75, 84, 78, 84, 81, 82, 89, + 81, 96, 82, 74, 70, 84, 86, 80, 70, 131, 75, 88, 102, 115, + 89, 82, 79, 106)
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