Help:Demonstrations
From Stikir
(Redirected from Help:Tutorials)
This page contains some simple analyses that demonstrate key functionality of Stikir
Some analyses may depend on uploaded data (see - Special:upload), and on data previously loaded and saved in an R workspace. Refer to Help:Saving R Workspaces (Warning: more than 3 min load time) and Help:Loading a Workspace for more information on Loading and Saving datasets.
Contents |
Reading in a data file and running an R command that returns text
Summary Statistics of foreign exchange rates (and gold)
This code executes to produce the following table.
FX_Data<-readdataSK(name="FX_Data.csv",format="csv",na.strings="#N/A") summary(FX_Data) #
Date CAD EUR GBP
1-Apr-02: 1 Min. :1.099 Min. : 0.7341 Min. :0.4990
1-Apr-03: 1 1st Qu.:1.197 1st Qu.: 0.8088 1st Qu.:0.5695
1-Apr-04: 1 Median :1.347 Median : 0.8652 Median :0.6138
1-Apr-05: 1 Mean :1.335 Mean : 0.8973 Mean :0.6100
1-Apr-88: 1 3rd Qu.:1.453 3rd Qu.: 0.9488 3rd Qu.:0.6453
1-Apr-91: 1 Max. :1.613 Max. : 1.2087 Max. :0.7283
(Other) :4752 NA's :1281.0000
JPY AUD CNY DKK
Min. : 81.07 Min. : 1.221 Min. : 5.705 Min. : 5.353
1st Qu.:108.28 1st Qu.: 1.320 1st Qu.: 8.276 1st Qu.: 5.995
Median :118.25 Median : 1.395 Median : 8.278 Median : 6.410
Mean :118.88 Mean : 1.479 Mean : 8.125 Mean : 6.618
3rd Qu.:127.82 3rd Qu.: 1.579 3rd Qu.: 8.292 3rd Qu.: 6.949
Max. :159.91 Max. : 2.070 Max. : 8.740 Max. : 8.999
NA's :770.000 NA's :1306.000 NA's :766.000
NLG XAU MXN NZD
Min. : 1.518 Min. :1.379e-03 Min. : 3.092 Min. : 1.343
1st Qu.: 1.747 1st Qu.:2.573e-03 1st Qu.: 7.757 1st Qu.: 1.526
Median : 1.877 Median :2.829e-03 Median : 9.365 Median : 1.741
Mean : 1.942 Mean :2.892e-03 Mean : 8.718 Mean : 1.772
3rd Qu.: 2.055 3rd Qu.:3.376e-03 3rd Qu.: 10.583 3rd Qu.: 1.912
Max. : 2.664 Max. :3.966e-03 Max. : 11.632 Max. : 2.551
NA's :770.000 NA's :1.024e+03 NA's :1423.000 NA's :788.000
PLN RUB ZAR KRW
Min. : 2.459 Min. : 4.528 Min. : 3.625 Min. : 667.2
1st Qu.: 3.220 1st Qu.: 6.875 1st Qu.: 5.732 1st Qu.: 774.3
Median : 3.664 Median : 27.904 Median : 6.365 Median : 890.8
Mean : 3.618 Mean : 22.423 Mean : 6.674 Mean : 967.6
3rd Qu.: 4.035 3rd Qu.: 29.101 3rd Qu.: 7.554 3rd Qu.:1177.7
Max. : 4.706 Max. : 31.950 Max. : 13.471 Max. :1952.7
NA's :2041.000 NA's :2034.000 NA's :2020.000 NA's : 64.0
CHF TWD THB TRY
Min. :1.117 Min. : 24.50 Min. : 22.59 Min. :2.892e-03
1st Qu.:1.286 1st Qu.: 26.93 1st Qu.: 25.43 1st Qu.:3.649e-02
Median :1.429 Median : 28.98 Median : 38.56 Median :2.908e-01
Mean :1.423 Mean : 29.93 Mean : 35.60 Mean :6.178e-01
3rd Qu.:1.519 3rd Qu.: 32.99 3rd Qu.: 41.47 3rd Qu.:1.351e+00
Max. :1.821 Max. : 35.11 Max. : 55.80 Max. :1.764e+00
NA's :208.00 NA's :1292.00 NA's :7.540e+02
SEK USD
Min. : 5.086 Min. :1
1st Qu.: 6.844 1st Qu.:1
Median : 7.647 Median :1
Mean : 7.681 Mean :1
3rd Qu.: 8.220 3rd Qu.:1
Max. : 11.019 Max. :1
NA's :505.000
Loading a Library and running an R command that plots an image
This code produces the following "dendogram" illustrating the degree of similarity in currency's historical rates
dates<-FX_Data[,1] FX_Rates<- as.ts(FX_Data[,3:ncol(FX_Data)-1]) FX_Rates.Correlation<- cor(FX_Rates, use="pairwise.complete.obs") FX_Rates.DistanceMeasure <- as.dist(1-FX_Rates.Correlation) library(cluster) FX_Rates.Clusters <- hclust(FX_Rates.DistanceMeasure, method="ward", members=NULL) pdf(rpdf, width=7, height=5) plot(FX_Rates.Clusters) #
Loading a previously saved workspace
This code:
<R output="display" workspace="FX">
pdf(rpdf, width=5, height=4)
plot(FX_Rates[,c("GBP","CAD","XAU")], main="GBP, EUR, and Gold time series plots")
</R>
does this:
Creating a "Rform" using html controls
This code:
<Rform name="binom"> n: <input name="n" type="text" size="5" maxlength="5" value="12"> prob: <input name="prob" type="text" size="5" maxlength="5" value="0.2"> <input type="submit" value=" Submit "> </Rform>
does this:
Enter the parameters of a Binomial distribution
Passing user input from the Rform to an R analysis
And you will get the probability mass function plotted here
This code:
<R output="display" name="binom" iframe="height:400px;">
if (exists("prob")) prob <- as.numeric(prob) else prob <- 0.2
if (exists("n")) n <- as.numeric(n) else n <- 12
x <- seq(0, n, 1)
p <- dbinom(x, n, prob)
param <- list(n, prob)
main <- c("Binomial Distribution P.M.F.", paste (c("Trials n", "Probability p"), param, sep="="))
pdf(rpdf, width=4, height=4)
plot(x,p, type="h", main=main)
</R>
does this:
--WikiSysop 12:00, 25 April 2007 (EDT)
