This document provides an R tutorial for an undergraduate climate workshop. It introduces key concepts in R including data types, arrays, matrices, data frames, packages, and basic plotting. It demonstrates how to perform calculations, subset data, install and load packages, create different plot types like histograms and maps, and use functions like quantile and quilt.plot. Exercises include drawing a histogram of ozone values and calculating quantiles.

1. R tutorial for CLIM undergraduate workshop
R tutorial for CLIM undergraduate
workshop
Huang Huang, SAMSI, Oct 23, 2017
About R
R is a free software environment for statistical computing and graphics.
A different implementation of S developed at Bell Lab
Provides a wide variety of statistical and graphical techniques, and is highly extensible
Open source
Powerful IDE: such as Rstudio
Open R
Data types
We can use variables without definition in advance
numbers
num=3.14;
print(num)
## [1] 3.14
print(num+1)
## [1] 4.14
print(typeof(num))
## [1] "double"
num.int=as.integer(num);
print(num.int)
## [1] 3
print(typeof(num.int))

2. R tutorial for CLIM undergraduate workshop
## [1] "integer"
We can use R as a calculater to calculate
x=2;
print(x*2);
## [1] 4
print(log(x));
## [1] 0.6931472
print(sqrt(x));
## [1] 1.414214
print(x**3);
## [1] 8
array
x=10:12;
print(x);
## [1] 10 11 12
y=c(1,3,5);
print(y);
## [1] 1 3 5
print(y[1])
## [1] 1
matrix=cbind(x,y);
print(matrix);
2 ∗ 2, log(2), ,2
–√ 23

3. R tutorial for CLIM undergraduate workshop
## x y
## [1,] 10 1
## [2,] 11 3
## [3,] 12 5
Extract a subset of the matrix
print(matrix[2,])
## x y
## 11 3
print(matrix[c(2,3),])
## x y
## [1,] 11 3
## [2,] 12 5
print(matrix[matrix[,1]<12,])
## x y
## [1,] 10 1
## [2,] 11 3
print(matrix[,2])
## [1] 1 3 5
data frame
df=data.frame(x=x,y=y);
print(df)
## x y
## 1 10 1
## 2 11 3
## 3 12 5
print(df$x);
## [1] 10 11 12
print(df$y);

4. R tutorial for CLIM undergraduate workshop
## [1] 1 3 5
Packages
How to install a package
install.packages('fields')
How to load functions from a package
library('fields')
Basic plots
Use of “plot” function
x=1:1000/10;
y=sin(x);
print(x[1:10]);
## [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
print(y[1:10]);
## [1] 0.09983342 0.19866933 0.29552021 0.38941834 0.47942554 0.56464247
## [7] 0.64421769 0.71735609 0.78332691 0.84147098
plot(x,y)

5. R tutorial for CLIM undergraduate workshop
plot(x,y,type='l')
plot(x[1:20],y[1:20],type='b',col='red',lwd=5,ylim=c(0,2))

6. R tutorial for CLIM undergraduate workshop
Use of “hist” function
set.seed(2017)
data=rnorm(1000);
set.panel(1,2);
## plot window will lay out plots in a 1 by 2 matrix
plot(data,type='l');
hist(data)
“help” a function
help(hist)
Use the above command to see the description of function “hist”, and change some parameter(s) to
generate the figure below

7. R tutorial for CLIM undergraduate workshop
help(quantile)
Calculate the percent quantile, median, and percent quantile as follows.
## 5% 50% 95%
## -1.59585243 0.01210508 1.60853804
Use of “quilt.plot” function
quilt.plot is function in package “fields” to produce an image-like plot
library(fields)
data(ozone2)
plot(ozone2$lon.lat[,1],ozone2$lon.lat[,2],xlab='lon',ylab='lat')
US( add=TRUE, col="grey", lwd=2)
5% 95%

8. R tutorial for CLIM undergraduate workshop
quilt.plot( ozone2$lon.lat, ozone2$y[16,],xlab='lon',ylab='lat')
US( add=TRUE, col="grey", lwd=2)

9. R tutorial for CLIM undergraduate workshop
Exercise
Draw the histogram of ozone values at all the sites in the specific time period as before, and get the and
quantiles.
10%
90%