Melbourne April 26 and May 3
Sticky notes on the top of your laptop:
- Yellow: I'm ok!
- Pink: I need some assistance!
You need to download some files to follow this lesson:
Download and move the file to your Desktop.
Unzip/extract the file (ask your instructor if you need help with this step). You should end up with a new folder called data-shell on your Desktop.
cd
Notes for today's Bash lesson: http://swcarpentry.github.io/shell-novice/
Which of these are valid variable names?
min_height Valid!
max.height Valid!
_age NO, can't start with _
.mass Valid, but hidden (don't do this)
MaxLength Valid
min-length Invalid
2widths Can't start with a number
celsius2kelvin Valid
mass <- 47.5
age <- 122
mass <- mass * 2.3
age <- age - 20
Run the code from the previous challenge, and writ
Je a command to compare mass to age. Is mass larger than age?
Clean up your working environment by deleting the mass and age variables.
Start by making a vector with the numbers 1 through 26. Multiply the vector by 2, and give the resulting vector names A through Z (hint: there is a built in vector called LETTERS)
Bec
vector <- 1:26
vector <- vector * 2
names(vector) <- LETTERS
Mathilda
task <-(1:26)
task
task <- task *2
names(task) <- (LETTERS)
Payal
x <- 1:26
x <- x*2
names(x) <- letters
Sarah
quiz<-seq(1,26)
doublequiz <- quiz*2
names(doublequiz) <-LETTERS
Alyssa
a <- 1:26
a <- a*2
names(a) <- LETTERS
Pengxing
a1 <- (1:26)*2
names(a1) <- LETTERS
a1
Dom
numbers <- 2*seq(26)
names(numbers) <- letters
Make a list. Item 1 will be a vector of data types, item 2 will be a vector of data structure,
item 3 your favourite number. Give each item in the list a name.
dataTypes <- c("Logical", "Int", "Double", "Complex", "Chr")
dataStructures <- c("Vector", "Factor", "List")
faveNum <- 47
Stuff <- list(dataTypes, dataStructures, faveNum)
names(Stuff) <- c("Data Types", "Data Structures", "My favourite number")
z <- c("double","integer","character","logical","complex")
z2 <- c("vector","factor","list")
z3 <- 7
my_list <- list(z,z2,z3)
names(my_list) = c("types","structures","number")
datatypes <- c("logical", "integer", "double", "complex", "character")
datatypes
datastructure <- c(1:15)
datastructure
number <- 16
my_list2 <- list(datatypes, datastructure, number)
my_list2
test_list <- list(dataType = c("double", "integer", "complex", "character", "logical"),
dataStructure = c("vector", "factor", "list"),
myNumber =3)
data_types <- c("Numeric","character","logical")
data_structures <- c("vector","factor","list")
combine_Data_list<-list(datatypes = data_types,
datastr = data_structures,
favorite_num = 3
)
combine_Data_list
item_1 <- list("character", "numeric", "integer", "logical", "complex")
item_2 <- list("vector", "factor", "list", "matrix", "dataframe" )
item_3 <- 7
list_of_items<- list(title="THE LIST", data_types=item_1,data_structure=item_2,fave_num=item_3)
list_of_items
x <- (1:26) * 2
names(x) <- letters
typeslist <- list(1:26, letters, TRUE, rnorm(10,5,1), factor(x))
names(typeslist) <- c("int", "chr", "logi", "num","fac")
What do you think will be the result of length(matrix_example)? Try it. Were you right? Why / why not?
Did the matrix function fill your matrix by column, or by row, as its default behaviour?
See if you can figure out how to change this. (hint: read the documentation for matrix!)
my_matrix <- matrix(data=1:50,nrow=10,ncol = 5,byrow = TRUE)
Consider the R output of the matrix below:
[,1] [,2]
[1,] 4 1
[2,] 9 5
[3,] 10 7
What was the correct command used to write this matrix? Examine each command and try to figure out the correct one before typing them. Think about what matrices the other commands will produce.
matrix(c(4, 1, 9, 5, 10, 7), nrow = 3)
matrix(c(4, 9, 10, 1, 5, 7), ncol = 2, byrow = TRUE)
matrix(c(4, 9, 10, 1, 5, 7), nrow = 2)
matrix(c(4, 1, 9, 5, 10, 7), ncol = 2, byrow = TRUE)
Given the following code:
m <- matrix(1:18, nrow=3, ncol=6)
print(m)
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 4 7 10 13 16
[2,] 2 5 8 11 14 17
[3,] 3 6 9 12 15 18
Which of the following commands will extract the values 11 and 14?
A. m[2,4,2,5]
B. m[2:5]
C. m[4:5,2]
D. m[2,c(4,5)]
Add your name here
Luke
Kristy
Mathilda
Alyssa
Charis
David
Nynke
Pengxing
Tharkeshi
Create a new data.frame called gapminder_eurasia from the Gapminder data
Hints
Put answers down here v
temp <- gapminder[gapminder$year > 1960,]
gapminder_Eurasia <- temp[(temp$continent == "Europe" | temp$continent == "Asia"),]
gapminder_Eurasia$popMillions <- gapminder_Eurasia$pop / (10^6)
gapminder.1960 <- gapminder[gapminder$year > 1960,]
Continents <- c("Europe", "Asia")
gapminder.1960.continents <- gapminder.1960[gapminder.1960$continent %in% Continents,]
gapminder.1960.continents$popMillions <- gapminder.1960.continents$pop / 1000000
temp1 <- gapminder[gapminder$continent %in% c("Europe","Asia"), ]
gapminder.part <- temp1[temp1$year > 1960, ]
gapminder.part$popMillions <- gapminder.part$pop/1e6
chal<-gapminder[gapminder$continent=="Asia"| gapminder$continent=="Europe",]
chaln<-chal[chal$year>1960,]
chaln$popMillions<-chaln$pop/1000000
chaln
gapminder_eurasia <- subset.data.frame(gapminder,continent %in% c("Asia","Europe"))
gapminder_eurasia <-subset.data.frame(gapminder_eurasia,year>1960)
gapminder_eurasia$popMillions <- gapminder_eurasia$pop / 1000000
gapminder_recent <- gapminder[gapminder$year > 1960, ]
gapminder_eurasia <- gapminder_recent[gapminder_recent$continent == c("Asia", "Europe"), ]
gapminder_eurasia$popMillions <- gapminder_eurasia$pop / 1000000
gapminder_eurasia <- gapminder[gapminder$continent %in% c("Asia", "Europe"),]
gapminder_eurasia <- gapminder_eurasia[gapminder_eurasia$year > 1960,]
gapminder_eurasia$popMillions <- gapminder_eurasia$pop / 10^6
x <- gapminder[gapminder$continent == c("Europe", "Asia"),
gapminder_eurasia <- x[x$year > 1960, ]
gapminder_eurasia$popMillions <- gapminder_eurasia$pop/1000000
#### From the tidyverse.
#### Weird syntax - but looks kinda nice.
#### https://www.tidyverse.org/
gapmind_eurasia <- gapminder %>%
dplyr::filter(Continent %in% c("Europe", "Asia")) %>%
dplyr::filter(year > 1960) %>%
dplyr::mutate(popmillions=pop/1000000)
####
Modify the example
* Make x axis = time
* Make y axis = life expectancy
* Colour points by continent
ggplot(gapminder, aes(year, lifeExp))+
geom_point(aes(colour=continent))
ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
geom_point(alpha = 0.5) +
scale_x_log10() +
geom_smooth(method = "lm", size = 1.5)
Change the shape of the points
Colour by continent
Add trendlines by continent
Hint: Find the different shapes here
ggplot(data = gapminder, aes(x= gdpPercap, y= lifeExp, colour=continent))+
geom_point(alpha = 0.8)+
scale_x_log10()+
geom_smooth(method = "lm")+
scale_shape(solid = T)
ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp, shape = continent, colour = continent)) +
geom_point(alpha = 0.5) +
scale_x_log10() +
geom_smooth(method = "lm", size = 1.5)
Create a density plot of GDP per capita, filled by continent
Advanced:
* Transform the x axis to show the spread
* Add a facet layer by year
* Make your own plot
ggplot(data=gapminder, aes(x = gdpPercap, fill=continent)) + geom_density(alpha=0.6) + facet_wrap(~year) + scale_x_log10()
ggplot(data = gapminder, aes(x = gdpPercap, colour = continent, fill = continent)) +
geom_density() + scale_x_log10() + facet_grid(year~.)
ggplot(data = gap.minder, aes(x = gdpPercap, colour = continent)) +
geom_density()+
scale_x_log10() +
facet_wrap(~ year)
ggplot(data = gap.minder, aes(x = gdpPercap, fill = continent)) +
geom_density(alpha = 0.5, linetype= 0)+
scale_x_log10() +
facet_wrap(~ year)+
labs(
x = "GDP per capita",
y = "Density",
title = "Figure 1",
colour = "Continent"
)
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