Introduction to programming with R

Boris Reiss

Overview of the lab

Background on programming
R concepts
Exercise: Basic data analysis
Help

Learning objectives

Understand RStudio on a very basic level
Write and run a R script
Being able to survive cryptic error messages
Import and clean up data
Have some knowledge of data structures and packages
Analyse and graph data
Able to ask for help, and find help.

Programming (Wikipedia)

Formulation of a problem that leads to a program

Tasks:

analysis,
developing understanding of the problem,
generating algorithm,
verification of algorithm, and
implementation (coding)

Purpose:

Find a sequence of instructions (algorithm) that will automate performing a specific task to solve a problem

Remember

Programming is something you do and not something you know.
It requires active practice. (Reading is not an active practice.)

Programming structures

Variables:
- numeric, character, factor...
Control Structures:
- if(), ifelse(), while(), for()...
Data Structures:
- data.frame, class, matrix...
Syntax:
- set of rules that defines the correct combinations of symbols in that language
Tools:
- library, functions...

R

R is a statistical programming language and a super advanced calculator
> 2 million users
based on functions that can be expanded through packages
someone has already solved your problem: install.packages()
it is an interpreter language each command is executed in the console
"uses vectors to store data"

Help

help()

reference cards

books:

Beginning Data Science with R by Manas A. Pathak (free at UW)
The Art of R Programming: A Tour of Statistical Software Design N. Matloff (free at UW)

Function

some INPUT - computation - some OUTPUT
e.g. log10(): 10^3 = 1000
Thus log10(1000)=3

Helper functions

help()
example()
c()
data.frame()
head();tail()
table()
summary()
str()
unique()
length()
subset()

assign:
  to <- from
  a <- 5

Make your own function

invmean <- function(data)
{
  
  rslt <- 1/mean(data)
  return(rslt)
}

Important functions

apply()

sapply()

lapply()

by()

%in%

reshape()

Workflow of a typical program

get
review
clean
restructure
summarize
analyse
present results

Getting data

External
Internal
- Existing
- New
Simulations

External

read.csv() # won't work with a lot of EU data
read.tsv() 
read.table()
read.xlsx() # requires foreign package

Internal

# Existing
data()

# create
c()
seq()

#simulation - distributions
rnorm(); rlnorm(); runif()

Variables

#1-D
as.factor()
as.numeric()
as.character()

# multi-D
c()
as.data.frame()
as.list()
as.matrix()

data.table() # -> through external package

Special variable: time

# time

as.POSIXct()
as.POSIXlt()

# packages: lubridate, chron

Always use ISO time format

2015-01-12 23:17:12
yyyy-mm-dd hh:mm:ss
may include timezone

Exercise

Always start with a clean work space

rm(list=ls())
library(ggplot2)
library(doBy)

## Loading required package: survival

Get data

data()

data(iris)

#current <- getwd()
#setwd(newdir)

dt <- iris

write.csv(dt, file = "irisdata.csv")

Read the data back

read.csv(file = "irisdata.csv", sep = ",", nrows=5)

##   X Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 1          5.1         3.5          1.4         0.2  setosa
## 2 2          4.9         3.0          1.4         0.2  setosa
## 3 3          4.7         3.2          1.3         0.2  setosa
## 4 4          4.6         3.1          1.5         0.2  setosa
## 5 5          5.0         3.6          1.4         0.2  setosa

Review

head(dt); tail(dt)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 145          6.7         3.3          5.7         2.5 virginica
## 146          6.7         3.0          5.2         2.3 virginica
## 147          6.3         2.5          5.0         1.9 virginica
## 148          6.5         3.0          5.2         2.0 virginica
## 149          6.2         3.4          5.4         2.3 virginica
## 150          5.9         3.0          5.1         1.8 virginica

Review

str(dt)

## 'data.frame':	150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

Review

summary(dt)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##

Review

unique(dt$Species)

## [1] setosa     versicolor virginica 
## Levels: setosa versicolor virginica

Review

table(dt$Species)

## 
##     setosa versicolor  virginica 
##         50         50         50

Review

nrow(dt)

## [1] 150

Create some data

dt$LLOD <- ifelse(dt$Sepal.Length < 5, "y", "n")

str(dt)

## 'data.frame':	150 obs. of  6 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ LLOD        : chr  "n" "y" "y" "y" ...

table(dt$LLOD)

## 
##   n   y 
## 128  22

Create contextual data: location, date, rain

location <- c("A", "B", "C")

date <- c("2015-11-01", "2016-01-12")

rain <- c("lots", "some", "little", "no", "NA")

Modify

dt$loc <- rep(location, 150 / 3)

dt$date <- rep(sample(date), 150 / 2)

dt$rain <- rep(rain, nrow(dt) / length(rain))

dt$rh <- runif(150, min = 37, max = 75)

Modify

str(dt)

## 'data.frame':	150 obs. of  10 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ LLOD        : chr  "n" "y" "y" "y" ...
##  $ loc         : chr  "A" "B" "C" "A" ...
##  $ date        : chr  "2016-01-12" "2015-11-01" "2016-01-12" "2015-11-01" ...
##  $ rain        : chr  "lots" "some" "little" "no" ...
##  $ rh          : num  41.5 56.2 49.8 42.6 69.6 ...

Summarize

aggregate(Sepal.Length ~ loc + date, data = dt, FUN = "mean")

##   loc       date Sepal.Length
## 1   A 2015-11-01        5.972
## 2   B 2015-11-01        5.744
## 3   C 2015-11-01        5.824
## 4   A 2016-01-12        5.712
## 5   B 2016-01-12        5.900
## 6   C 2016-01-12        5.908

Summarize

tbltst <-
  aggregate(cbind(Sepal.Length,rh) ~ loc + date + rain, 
            data = subset(dt, LLOD != "n"), FUN = "mean")
head(tbltst)

##   loc       date   rain Sepal.Length       rh
## 1   A 2015-11-01 little          4.9 58.29639
## 2   B 2015-11-01 little          4.9 46.44283
## 3   C 2015-11-01 little          4.6 59.95322
## 4   A 2016-01-12 little          4.6 65.26345
## 5   B 2016-01-12 little          4.6 51.59490
## 6   C 2016-01-12 little          4.7 49.77592

Summarize

tblsdt <-
  summaryBy(Sepal.Length ~ loc + date + rain,
            data = dt, FUN = c(mean, sd))
head(tblsdt)

##   loc       date   rain Sepal.Length.mean Sepal.Length.sd
## 1   A 2015-11-01 little              6.12       1.1189281
## 2   A 2015-11-01   lots              6.48       1.2437845
## 3   A 2015-11-01     NA              5.70       0.9027735
## 4   A 2015-11-01     no              5.50       0.7176350
## 5   A 2015-11-01   some              6.06       0.7368853
## 6   A 2016-01-12 little              5.80       1.1467345

Summarize

colnames(tblsdt) <- c("lc", "dt", "rn", "msl", "sdsl")
str(tblsdt)

## 'data.frame':	30 obs. of  5 variables:
##  $ lc  : chr  "A" "A" "A" "A" ...
##  $ dt  : chr  "2015-11-01" "2015-11-01" "2015-11-01" "2015-11-01" ...
##  $ rn  : chr  "little" "lots" "NA" "no" ...
##  $ msl : num  6.12 6.48 5.7 5.5 6.06 5.8 5.46 5.84 5.94 5.52 ...
##  $ sdsl: num  1.119 1.244 0.903 0.718 0.737 ...

Basic plots

boxplot(Sepal.Length ~ loc,data = dt)

ggplot: The beauty of plotting

p <- ggplot(dt)

p <- p + geom_point(aes(x = Petal.Width, y = Sepal.Length, 
                        color = loc))

ggplot: The beauty of plotting

p <- ggplot(dt, aes(x = Petal.Width , y = Sepal.Length))

p <- p + geom_point()

p <- p + geom_smooth(method = "lm", se = T)

p <- p + facet_grid(loc ~ date)

ggplot: The beauty of plotting

Analysis

lm(Sepal.Length ~ loc + date + rain, data = tbltst)

## 
## Call:
## lm(formula = Sepal.Length ~ loc + date + rain, data = tbltst)
## 
## Coefficients:
##    (Intercept)            locB            locC  date2016-01-12  
##        4.77508         0.02826        -0.10744        -0.06404  
##       rainlots          rainNA          rainno        rainsome  
##        0.05694         0.10173        -0.29401         0.03217

Analysis

lf <- lm(Sepal.Length ~ loc + date + rain, data = tbltst)

Analysis

summary(lf)

## 
## Call:
## lm(formula = Sepal.Length ~ loc + date + rain, data = tbltst)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.20934 -0.06850  0.02318  0.09341  0.12853 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     4.77508    0.07763  61.509  2.6e-15 ***
## locB            0.02826    0.07780   0.363  0.72329    
## locC           -0.10744    0.08074  -1.331  0.21018    
## date2016-01-12 -0.06404    0.06127  -1.045  0.31835    
## rainlots        0.05694    0.11691   0.487  0.63581    
## rainNA          0.10173    0.08590   1.184  0.26125    
## rainno         -0.29401    0.09383  -3.133  0.00952 ** 
## rainsome        0.03217    0.08590   0.374  0.71516    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1319 on 11 degrees of freedom
## Multiple R-squared:  0.6637,	Adjusted R-squared:  0.4498 
## F-statistic: 3.102 on 7 and 11 DF,  p-value: 0.04582

Presentation

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knitr

Files

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File metadata and controls

Introduction to programming with R

Overview of the lab

Learning objectives

Programming (Wikipedia)

Remember

Programming structures

R

Help

Function

Helper functions

Make your own function

Important functions

Workflow of a typical program

Getting data

External

Internal

Variables

Special variable: time

Always use ISO time format

Exercise

Always start with a clean work space

Get data

Read the data back

Review

Review

Review

Review

Review

Review

Create some data

Create contextual data: location, date, rain

Modify

Modify

Summarize

Summarize

Summarize

Summarize

Basic plots

ggplot: The beauty of plotting

ggplot: The beauty of plotting

ggplot: The beauty of plotting

ggplot: The beauty of plotting

Analysis

Analysis

Analysis

Presentation