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If you want to get a quick numerical summary of a data set, the summary function gives a nice overview for data frames:
> require(ggplot2)
Loading required package: ggplot2
> data(diamonds)
> summary(diamonds)
carat cut color clarity depth table
Min. :0.2000 Fair : 1610 D: 6775 SI1 :13065 Min. :43.00 Min. :43.00
1st Qu.:0.4000 Good : 4906 E: 9797 VS2 :12258 1st Qu.:61.00 1st Qu.:56.00
Median :0.7000 Very Good:12082 F: 9542 SI2 : 9194 Median :61.80 Median :57.00
Mean :0.7979 Premium :13791 G:11292 VS1 : 8171 Mean :61.75 Mean :57.46
3rd Qu.:1.0400 Ideal :21551 H: 8304 VVS2 : 5066 3rd Qu.:62.50 3rd Qu.:59.00
Max. :5.0100 I: 5422 VVS1 : 3655 Max. :79.00 Max. :95.00
J: 2808 (Other): 2531
price x y z
Min. : 326 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.: 950 1st Qu.: 4.710 1st Qu.: 4.720 1st Qu.: 2.910
Median : 2401 Median : 5.700 Median : 5.710 Median : 3.530
Mean : 3933 Mean : 5.731 Mean : 5.735 Mean : 3.539
3rd Qu.: 5324 3rd Qu.: 6.540 3rd Qu.: 6.540 3rd Qu.: 4.040
Max. :18823 Max. :10.740 Max. :58.900 Max. :31.800
But if you'd prefer a visual overview of your data, Andrew Barr suggests the tableplot function (included in the tabplot package) for a graphical version:
tableplot(diamonds, cex = 1.8)
Andrew explains how to use the tabplot function in the post linked below.
W. Andrew Barr's Paleoecology Blog: Quickly Visualize Your Whole Dataset (via @JacquelynGill)
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