R

Question 1

Modulo operation

Answer 1

The modulo returns the remainder of the division of the number to the left by the number on its right, for example 5 modulo 3 or 5 %% 3 is 2.

Question 2

Check the data type of a variable

Answer 2

class()

Question 2

assign value to variable

Answer 2

var <- value

Question 3

how to create a vector

Answer 3

with the combine function c()

Question 4

assign names to vector

Answer 4

names()

Question 5

[2:5] –> which values does this include?

Answer 5

includes the second and fifth value of a vector

Question 5

Define a new variable based on a selection from a vector

Answer 5

new_var <- some_vector[c(…,3, 4, …)] or [:]

Question 6

Construct a matrix with 3 rows that contain the numbers 1 up to 9

Answer 6

matrix(1:9, byrow = TRUE, nrow = 3)

Question 7

Name the columns and rows of a matrix

Answer 7

rownames()
colnames()

Question 8

calculate sums of rows of matrix or of columns

Answer 8

rowSums() or colSums

Question 9

Merge matrices and/or vectors together by column (right) or below

Answer 9

cbind() or for below: rbind()

Question 10

in console, check out contents of workspace

Answer 10

ls()

Question 11

data on the rows 1, 2, 3 and columns 2, 3, 4.

Answer 11

my_matrix[1:3,2:4]

Question 12

encode the vector as a factor –> and, optional, also give them an order

Answer 12

factor() –> factor(temperature_vector, order = TRUE, levels = c(“Low”, “Medium”, “High”))

Question 12

change factor levels of a factor vector to …

Answer 12

levels(factor_vector) <- c(“”, “”) –> the order with which you assign the levels is important. If you don’t specify the levels of the factor when creating the vector, R will automatically assign them alphabetically.

Question 13

quick overview of the contents of a variable

Answer 13

summary()

Question 13

see first or last rows of a built-in dataframe

Answer 13

head() or tail()

Question 13

select a subset based on a certain condition from your dataset

Answer 13

subset(dataframe, condition)

Question 13

order a vector

Answer 13

order()

Question 13

Call order() on a dataframe by ordering it based on certain column

Answer 13

dataframe$column

Question 14

see structure of a dataframe

Answer 14

str()

Question 14

create a dataframe

Answer 14

data.frame()

Question 14

add components to a list, then assign names to the components

Answer 14

my_list <- list(your_comp1, your_comp2)
names(my_list) <- c(“name1”, “name2”)

or

my_list <- list(name1 = your_comp1,
name2 = your_comp2)

Question 14

filter dataframe

Answer 14

filter()

Question 14

sort the rows of a df based on a positions vector

Answer 14

planets_df[positions, ]

Question 15

sort values in a dataset

Answer 15

arrange(column_to_use) or arrange(desc(column_to_use)) for descending

Question 15

pipe

Answer 15

%>%

Question 16

change values in a dataframe

Answer 16

mutate(what_is_replaced = what_is_calculated)

Question 16

package for data visualization

Answer 16

ggplot2

Question 17

create visualization

Answer 17

ggplot(dataset, aes(aesthetic mapping of variables) + type of graph)

Question 17

function for creating scatterplot with ggplot

Answer 17

geom_point()

Question 18

to ggplot, add color and size of dots

Answer 18

ggplot(dataset, aes(aesthetic mapping of variables, color = …, size = …) + type of graph)

Question 18

divide one plot into multiple smaller plots

Answer 18

faceting: facet_wrap(~…)

Question 19

turn groups into one row each before summarize()

Answer 19

group_by()

Question 19

after specifying type of graph, also specify log scale

Answer 19

+ scale_x_log10()

Question 20

turn many rows into one with pipe

Answer 20

… %>% summarize(… = mean(…))

Question 20

make a line plot

Answer 20

geom_line()

Question 21

make a bar plot

Answer 21

geom_col()

Question 21

make a histogram

Answer 21

geom_histogram(binwidth = … or bins = …)

Question 22

make a boxplot

Answer 22

geom_boxplot()

Question 22

how are the lines going up and down from the boxplot called?

Answer 22

“whiskers”

Question 23

add title to ggplot

Answer 23

+ggtitle(“…”)

Question 24

Data visualization points of consideration

Answer 24

Question 25

Add a smooth geom to the plot

Answer 25

geom_smooth()

Question 26

geom_point() has an alpha argument - what does it mean?

Answer 26

controls the opacity of the points. A value of 1 (the default) means that the points are totally opaque; a value of 0 means the points are totally transparent (and therefore invisible). Values in between specify transparency.

Question 26

when only is it necessary to map onto the color aesthetic in geom?

Answer 26

When all layers should NOT inherit the same aesthetics or when mixing different data sources

Question 26

how is fill distinct from color?

Answer 26

fill differs that color usually, but not always, refers to the outline of a shape

Question 27

change line pattern

Answer 27

linetype

Question 27

text on a plot or axes

Answer 27

geom_text() layer and aes(label = …)

Question 27

attributes vs aesthetics

Answer 27

attributes are always called in the geom layer, for example, it’s color attribute is set by the color argument, its size by the size argument

Question 28

get rownames of dataframe

Answer 28

rownames()

Question 29

default position for dataplot

Answer 29

identity (put position exactly where it originally should be)

Question 29

add random noise to points to counteract overplotting

Answer 29

position = “jitter” or before everything set posn_j <- position_jitter(…) and alter position = posn_j

Question 30

all aesthetics are a scale, so how can be access that scale?

Answer 30

with scale_…*()

Question 30

most common scale arguments

Answer 30

limits, breaks, expand, labels

Question 31

set the x- and y-axis labels

Answer 31

You’ll also make use of some functions for improving the appearance of the plot.

labs() to set the x- and y-axis labels. It takes strings for each argument.

Question 31

define properties of the color scale (i.e. axis). The first argument sets the legend title. values is a named vector of colors to use.

Answer 31

scale_fill_manual()

Question 32

set y limits of axis

Answer 32

+ ylim()

Question 33

to which axes are the independent and dependent variables mapped?

Answer 33

Typically, the dependent variable is mapped onto the the y-axis and the independent variable is mapped onto the x-axis.

Question 34

Markdown: Bold, italics, both, headers, inline link, reference link, images, block quote, lists, soft break

Answer 34

(1-6 # possible)

Bold: …
Italics: …
Both: …
some_text_displayed
instead of (link) directly, use […], and at the end, […]: link
images: same as links, just enter ! before []
block quote: simply enter the symbol > before
Lists: either just * or 1., 2., etc.
Soft break: with 2 spaces ( )

Question 34

picture of different geometries

Answer 34

Question 34

how to offset bars in a histogram? How to “use the complete space top to bottom”?

Answer 34

position = “dodge”, position = “fill”

Question 35

for plotting, count number of cases at each x position

Answer 35

geom_bar()

Question 36

plot errorbars e.g. for a mean

Answer 36

geom_errorbar(aes(ylim = avg - stdev, ymax = avg + stdev))

Question 36

in aes, set different line types

Answer 36

linetype = …

Question 37

modify visual elements not part of the data (text, line, rectangle)

Answer 37

element_…()

Question 38

aesthetics for categorical variables

Answer 38

Question 39

in a plot, how to change e.g. the axis title colour

Answer 39

+ theme(axis.title = element_text(color = …))

Question 40

do we need to modify each e.g. text item individually to e.g. change the colour?

Answer 40

no, they inherent from each other in a hierarchy. All text elements inherit from text, so if we changed that argument, all downstream arguments would be affected. The same goes for line and rectangle.

Question 41

remove legend

Answer 41

theme(legend.position = “none”) - also: “top”, “bottom”, “left”, or “right’”: place it at that side of the plot.

Question 42

remove an element in a plot

Answer 42

eg line: line = element_blank()

Question 43

look inside data how each column looks

Answer 43

glimpse()

Question 44

Basic data types

Answer 44

Character, Numeric (Double/Integer), Logical

Question 45

vector vs matrix vs array vs dataframe

Answer 45

row in excel, excel sheet, stacked excel sheets, 2-D array which can hold different data types down each column

Question 46

Tibble

Answer 46

2-D array with less functionality than a dataframe to limit user mistakes - tibble is the unifying feature of tidyverse (data is expected to be in tibble)

Question 47

Feature matrix X c R^(NXD)

Answer 47

feature matrix X which contains N observations and D features (and R = real numbers)

Question 48

Dimensionality of data - what counts?

Answer 48

When we talk about ‘dimensionality’ we typically mean ‘how many independent variables do I have for analysis’?

Question 48

A single observations forms a row of data - correct?

Answer 48

Yes

Question 49

is a number (and the opposite)

Answer 49

is.na (is not a number) and !is.na (opposite)

Question 50

how do tidy up names of dataframe

Answer 50

clean_names()

Question 51

you need to convert characters to numbers - how to do that

Answer 51

by creating a factor - function called factor()

Question 52

each variable forms a column, each observation forms a row, each cell is a single value - correct?

Answer 52

yes

Question 53

which format in tidyverse is considered tidy?

Answer 53

long format

Question 54

function to change date format - and package in tidyverse

Answer 54

dmy() - Lubridate package

Question 55

create tidy format in tidyverse - function

Answer 55

pivot_longer()

Question 56

select columns of interest

Answer 56

select() –> no need for “” to select column, just write column_name

Question 57

select rows of interest

Answer 57

filter()

Question 58

select all columns in a dataframe except one

Answer 58

select(-column_name)

Question 59

instead of using mutate: drop all non-transfomred variables - function

Answer 59

transmute()

Question 60

mutate vs transmutate

Answer 60

mutate() keeps all variables in the original dataframe (unless otherwise specified in the .keep argument.) transmute() returns a dataframe with only the newly computed or modified variables.

Question 61

filter in a df from specific date to specific date

Answer 61

filter(date >= as.Date(“2020-01-01”)) %>%
filter(date < as.Date(“2021-01-01”)) %>%

Question 62

in timeseries, use e.g. previous value, what function?

Answer 62

lag()

Question 63

mutate datetime into year

Answer 63

mutate(year = year(date))

Question 64

join two dataframes - function

Answer 64

inner_join(df1, df2, by = …)

Question 65

for pivot_longer, use all available columns

Answer 65

pivot_longer(cols = everything(), …, …)

Question 66

how to in ggplot access different infos/settings for plot

Answer 66

+ theme(…)

Question 67

draw a straight line in a ggplot

Answer 67

+ geom_smooth(method = “lm”, color = …) - other line would e.g. be “loess” -> works basically like a local regression

Question 68

define some sort of baseline in a plot

Answer 68

geom_hline(yintercept = 0, color = …)

Question 69

filter filters rows, select selects columns - correct?

Answer 69

yes

Question 70

get probability of z-value - function

Answer 70

xpnorm(probability, mean =…, sd = …)

Question 71

given a particular probability of 𝑍 < 𝑧, what is the corresponding value 𝑧?

Answer 71

qnorm(value z, mean = …, sd = …)

Question 72

Function for correlation

Answer 72

cor(X,Y)

Question 73

Get summary statistics about variables in tibbles

Answer 73

skim() of the skimr package - skim() works with (grouped) tibbles

Question 74

get some statistics on some data (e.g. tibbles)

Answer 74

favstats(column, data)

Question 75

USe the dplyr package to select to columns from e.g. a tibble/data set and plot correlations with one (simple/nice) function

Answer 75

… %>%
dplyr::select(…, …) %>%
ggpairs()

Question 76

add a trendline over an existing plot, and use a linear model, and control whether the standard error (confidence interval) of the fitted line should be displayed - do not display it

Answer 76

geom_smooth(method=lm, se=FALSE, color=…)

Question 77

Which library to plot 3 essential plots for residuals? Code for that?

Answer 77

• ggfortify
• model1 %>% autoplot(which = 1:3) + theme_bw()

Question 78

What does :: do?

Answer 78

:: is operator which helps to access a specific function from a specific package

Question 79

Function/plots that allow to check whether assumptions of a Linear Regression model have been satisfied (i.e. examination of the behavior of the residuals for model inadequacies)

Answer 79

• autoplot()
• from ggfortify library to plot 3 essential plots for residuals

Question 80

Build a multiple regression model

Answer 80

model <- lm(y_variable ~ 1st_var + 2nd_var + 3rd_var)

Question 81

use a library to compare valid models (regression) to chose the best one

Answer 81

Question 82

Test in RStudio for Multicollinearity

Answer 82