R has an extensive range of builtin datasets, which are useful for experimenting with the language. The RDatasets package makes many of these available within Julia. We’ll see another way of accessing R’s datasets in a couple of days’ time too. In the meantime though, check out the documentation for RDatasets and then read on below.
As always, the first thing that we need to do is load the package.
julia> using RDatasets
We can get a list of the R packages which are supported by RDatasets.
julia> RDatasets.packages() 33x2 DataFrame | Row | Package | Title | |-----|----------------|---------------------------------------------------------------------------| | 1 | "COUNT" | "Functions, data and code for count data." | | 2 | "Ecdat" | "Data sets for econometrics" | | 3 | "HSAUR" | "A Handbook of Statistical Analyses Using R (1st Edition)" | | 4 | "HistData" | "Data sets from the history of statistics and data visualization" | | 5 | "ISLR" | "Data for An Introduction to Statistical Learning with Applications in R" | | 6 | "KMsurv" | "Data sets from Klein and Moeschberger (1997), Survival Analysis" | | 7 | "MASS" | "Support Functions and Datasets for Venables and Ripley's MASS" | | 8 | "SASmixed" | "Data sets from \"SAS System for Mixed Models\"" | | 9 | "Zelig" | "Everyone's Statistical Software" | | 10 | "adehabitatLT" | "Analysis of Animal Movements" | | 11 | "boot" | "Bootstrap Functions (Originally by Angelo Canty for S)" | | 12 | "car" | "Companion to Applied Regression" | | 13 | "cluster" | "Cluster Analysis Extended Rousseeuw et al." | | 14 | "datasets" | "The R Datasets Package" | | 15 | "gap" | "Genetic analysis package" | | 16 | "ggplot2" | "An Implementation of the Grammar of Graphics" | | 17 | "lattice" | "Lattice Graphics" | | 18 | "lme4" | "Linear mixed-effects models using Eigen and S4" | | 19 | "mgcv" | "Mixed GAM Computation Vehicle with GCV/AIC/REML smoothness estimation" | | 20 | "mlmRev" | "Examples from Multilevel Modelling Software Review" | | 21 | "nlreg" | "Higher Order Inference for Nonlinear Heteroscedastic Models" | | 22 | "plm" | "Linear Models for Panel Data" | | 23 | "plyr" | "Tools for splitting, applying and combining data" | | 24 | "pscl" | "Political Science Computational Laboratory, Stanford University" | | 25 | "psych" | "Procedures for Psychological, Psychometric, and Personality Research" | | 26 | "quantreg" | "Quantile Regression" | | 27 | "reshape2" | "Flexibly Reshape Data: A Reboot of the Reshape Package." | | 28 | "robustbase" | "Basic Robust Statistics" | | 29 | "rpart" | "Recursive Partitioning and Regression Trees" | | 30 | "sandwich" | "Robust Covariance Matrix Estimators" | | 31 | "sem" | "Structural Equation Models" | | 32 | "survival" | "Survival Analysis" | | 33 | "vcd" | "Visualizing Categorical Data" |
Next we’ll get a list of all datasets supported across all of those R packages. There are a lot of them! Also we see some specific statistics about the number of records and fields in each of them.
julia> sets = RDatasets.datasets(); julia> size(sets) (733,5) julia> head(sets) 6x5 DataFrame | Row | Package | Dataset | Title | Rows | Columns | |-----|---------|-------------|-------------|------|---------| | 1 | "COUNT" | "affairs" | "affairs" | 601 | 18 | | 2 | "COUNT" | "azdrg112" | "azdrg112" | 1798 | 4 | | 3 | "COUNT" | "azpro" | "azpro" | 3589 | 6 | | 4 | "COUNT" | "badhealth" | "badhealth" | 1127 | 3 | | 5 | "COUNT" | "fasttrakg" | "fasttrakg" | 15 | 9 | | 6 | "COUNT" | "lbw" | "lbw" | 189 | 10 |
Or we can find out what datasets are available from a particular R package.
julia> RDatasets.datasets("vcd")
31x5 DataFrame
| Row | Package | Dataset | Title | Rows | Columns |
|-----|---------|-------------------|--------------------------------------------|-------|---------|
| 1 | "vcd" | "Arthritis" | "Arthritis Treatment Data" | 84 | 5 |
| 2 | "vcd" | "Baseball" | "Baseball Data" | 322 | 25 |
| 3 | "vcd" | "BrokenMarriage" | "Broken Marriage Data" | 20 | 4 |
| 4 | "vcd" | "Bundesliga" | "Ergebnisse der Fussball-Bundesliga" | 14018 | 7 |
| 5 | "vcd" | "Bundestag2005" | "Votes in German Bundestag Election 2005" | 16 | 6 |
| 6 | "vcd" | "Butterfly" | "Butterfly Species in Malaya" | 24 | 2 |
| 7 | "vcd" | "CoalMiners" | "Breathlessness and Wheeze in Coal Miners" | 32 | 4 |
| 8 | "vcd" | "DanishWelfare" | "Danish Welfare Study Data" | 180 | 5 |
| 9 | "vcd" | "Employment" | "Employment Status" | 24 | 4 |
| 10 | "vcd" | "Federalist" | "'May' in Federalist Papers" | 7 | 2 |
| 11 | "vcd" | "Hitters" | "Hitters Data" | 154 | 4 |
| 12 | "vcd" | "HorseKicks" | "Death by Horse Kicks" | 5 | 2 |
| 13 | "vcd" | "Hospital" | "Hospital data" | 3 | 4 |
| 14 | "vcd" | "JobSatisfaction" | "Job Satisfaction Data" | 8 | 4 |
| 15 | "vcd" | "JointSports" | "Opinions About Joint Sports" | 40 | 5 |
| 16 | "vcd" | "Lifeboats" | "Lifeboats on the Titanic" | 18 | 8 |
| 17 | "vcd" | "NonResponse" | "Non-Response Survey Data" | 12 | 4 |
| 18 | "vcd" | "OvaryCancer" | "Ovary Cancer Data" | 16 | 5 |
| 19 | "vcd" | "PreSex" | "Pre-marital Sex and Divorce" | 16 | 5 |
| 20 | "vcd" | "Punishment" | "Corporal Punishment Data" | 36 | 5 |
| 21 | "vcd" | "RepVict" | "Repeat Victimization Data" | 8 | 9 |
| 22 | "vcd" | "Saxony" | "Families in Saxony" | 13 | 2 |
| 23 | "vcd" | "SexualFun" | "Sex is Fun" | 4 | 5 |
| 24 | "vcd" | "SpaceShuttle" | "Space Shuttle O-ring Failures" | 24 | 6 |
| 25 | "vcd" | "Suicide" | "Suicide Rates in Germany" | 306 | 6 |
| 26 | "vcd" | "Trucks" | "Truck Accidents Data" | 24 | 5 |
| 27 | "vcd" | "UKSoccer" | "UK Soccer Scores" | 5 | 6 |
| 28 | "vcd" | "VisualAcuity" | "Visual Acuity in Left and Right Eyes" | 32 | 4 |
| 29 | "vcd" | "VonBort" | "Von Bortkiewicz Horse Kicks Data" | 280 | 4 |
| 30 | "vcd" | "WeldonDice" | "Weldon's Dice Data" | 11 | 2 |
| 31 | "vcd" | "WomenQueue" | "Women in Queues" | 11 | 2 |
Finally, the most interesting bit: accessing data from a particular dataset. Below we load up the women dataset from the vcd package.
julia> women = dataset("datasets", "women")
15x2 DataFrame
| Row | Height | Weight |
|-----|--------|--------|
| 1 | 58 | 115 |
| 2 | 59 | 117 |
| 3 | 60 | 120 |
| 4 | 61 | 123 |
| 5 | 62 | 126 |
| 6 | 63 | 129 |
| 7 | 64 | 132 |
| 8 | 65 | 135 |
| 9 | 66 | 139 |
| 10 | 67 | 142 |
| 11 | 68 | 146 |
| 12 | 69 | 150 |
| 13 | 70 | 154 |
| 14 | 71 | 159 |
| 15 | 72 | 164 |
From these data we learn that the average mass of American women of height 66 inches is around 139 pounds. If you are from a country which uses the Metric system (like me!) then these numbers might seem a little mysterious. Come back in a couple of days and we’ll see how Julia can convert pounds and inches in metres and kilograms.
That’s all for now. Code for today is available on github.
The post #MonthOfJulia Day 17: Datasets from R appeared first on Exegetic Analytics.