Code from demo 2

library(psych)

## 2.1 Data structures 

## vectors
# vectors can be created in multiple ways as we learned last time, 
a <- c(1,2,3,4)
b <- sample(1:20, 4)
c <- seq(1,20, length=4)

## matrices

# you can create matrices with cbind or rbind
mat1 <- cbind(a,b,c)
mat2 <- rbind(a,b,c)

# different commands combine the vectors differently
# resulting in different shapes of matrices
mat1
mat2 

# indexing happens row, column
mat1[2,3]
mat1[2,]

# another method of creating matrices
d <- 1:9
mat3 <- matrix(d, nrow=3)

mat3*2
mat3[c(1,3),c(1,3)]

## lists
# please note that you should not use command names (like 'list' or 'matrix') 
# as variable names, they will mask the relevant commands
list1 <- list('a',a, c('what', 'up'))
str(list1)
str(list1[2])
str(list1[[2]])
list1[[3]][2]

## 2.2 Working with data

## loading data
data <- read.csv('E:\\juulia_R\\demo_data_share.csv')
# oops, need to define that the separator is ; and not ,
data <- read.csv('E:\\juulia_R\\demo_data_share.csv', sep=';')

## taking a look at the data
head(data)
tail(data)
summary(data)
# describe is part of psych package, added loading package to the top of the 
# script file 
describe(data)

## converting sex to factor
data$sex <- factor(data$sex, levels=c(1,2), labels=c('female','male'))


## indexing

# you can use $ to select columns
data$sex

# you can combine $ and [] to select specific rows of a column
data$age[11:20]

# you can select severak rows & columns like this
data[1:10,c('age','sex','weight','height')]

# selecting all rows fulfilling the logical condition, and all columns for those
data[data$sex == 'male',]


## quick plotting
# plot() will give you the type of plot it thinks you might want
plot(data$sex, data$age)
plot(data$age, data$height)
with(data, plot(weight, height))

# you can make the plot a bit more elaborate by adding parameters
with(data, plot(height, weight, main='Height vs weight',
                xlab="Height in cm", ylab="Weight in kg",
                col = as.integer(sex)))

# you can also define the type of plot you want
# e.g. hist(), or boxplot()
hist(data$height)

# to save the figure, put function defining what format you want the
# output in before the call for plot
# this could also be png(), jpeg()
pdf('E:\\juulia_R\\figures\\figure_1.pdf')
# next, you build the plot
with(data, plot(height, weight, main='Height vs weight',
                xlab="Height in cm", ylab="Weight in kg",
                col = as.integer(sex)))
# and then tell R you're done with doing the plot by calling dev.off()
dev.off()

# You can also create the plot as normal and then run
dev.copy(png,'myplot.png')
dev.off()

## 2.3 If-else

# change this value to check how the if-else statement deals with different values
value <- 500

# start with the most specific case
if (value == 0){
  print('value is 0, not sure if that is odd or even')
# then have a more general case
} else if(value %% 2 == 0){
  print(paste(value, 'is even'))
# finally, catch all the other cases which were not outlined above
} else {
  print(paste(value, 'is odd'))
}

 

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Created by Juulia Suvilehto
juulia.suvilehto@iki.fi

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License