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Statistics: > RStudio functions: > Flashcards

RStudio functions: Flashcards

1
Q

A + B + Enter button

A

Answer for A + B

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2
Q

*

A

Multiply

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3
Q

/

A

Divide

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4
Q
A

To the power of

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5
Q

Brackets

A

To separate different functions used in one

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6
Q

A ← 3

A

Store the value 3 in variable A

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7
Q

What is a scalar variable?

A

A variable storing a single value

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8
Q

Variable (eg. A) + Enter

A

Displays value stored in variable

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9
Q

C ← “apples”

A

Stores the word apples in variable C

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10
Q

What are vectors?

A

Variables that can hold more than one value

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11
Q

D ← c(3, 7, 1)

A

Stores vector 3, 7, 1 in variable D

C stands for combine

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12
Q

D[2]

A

Allows access to the second value stored in the vector variable D

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13
Q

What are data frames?

A

A store of large amounts of data

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14
Q

Putting name of data frame

A

Displays the full set of data in data frame

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15
Q

Cars$Mpg

A

Codes for only mpg column in the cars data frame

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16
Q

DateFrameName[RowNumber, ColumnNumber]

A

Helps distinguish between rows and columns in a table

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17
Q

Cars[ , 1]

A

When row number is left blank, it will return all the rows.

In this example, the first column of rows of car will be returned

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18
Q

What is a function?

A

Anything that performs a particular operation on our data

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19
Q

What is the format that all functions follow on R Studio?

A

Function_name(argument)

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20
Q

What is an argument?

A

The input for the function

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21
Q

What would the function “ mean(e) “ do?

A

Give the mean/average of values in vector e

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22
Q

What code would you use to calculate the mean mpg of cars?

A

Mean(cars$mpg)

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23
Q

How do you find the column names in a data frame?

A

By printing the whole data frame

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24
Q

Names(name of data frame )

A

Only shows the column names of a data frame

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25
Q

Head(data frame name )

A

Only shows column names and top few rows of data frame

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26
Q

Why might the function “ head(name of data frame)” function cause the full data frame to be shown?

A

The function shows the first 10 rows, so if data frame has 10 or less rows then all of it will be shown

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27
Q

How are arguments separated in all functions?

A

Using commas

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28
Q

How do you define how many rows you want from a data frame?

A

head(name of date frame, n= )

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29
Q

Hist()

A

Creates a histogram

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30
Q

Draw histogram of horsepower (hp) from cars:

A

hist(cars$hp)

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31
Q

What is the standard layout of data?

A
  • Each column represents a different variable

- Each row represents a different subject or replicate

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32
Q

How is the standard layout of data different from the layout used by people when making spreadsheets?

A

Each condition is put into separate columns

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33
Q

What is an advantage of the standard layout of data?

A

It is much easier to record additional variables in a data frame

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34
Q

What is the advantage of using histograms over box plots?

A

Histograms show more information

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35
Q

What is the advantage of using boxplots over histograms?

A

It is easier to compare data presented as a box plot than as a histogram

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36
Q

Boxplot()

A

Function that plots a boxplot

37
Q

How do you code for a boxplot of only one variable (column) of the data?

A

Boxplot(data-frame$ColumnName)

38
Q

What values are shown on a boxplot?

A
  • Median
  • Upper (1st) quartile
  • Lower (3rd) quartile
  • Lowest value in data
  • Highest value in data
39
Q

How much of the data falls within each interval of a boxplot (generally speaking)?

A

A quarter of the data

40
Q

How much of the data falls within the interquartile range?

A

Half (50%) of the data

41
Q

When is the boxplot() function able to produce multiple plots?

A

If given a variable to group the data by

42
Q

Name the code needed to make multiple plots using the boxplot() function:

A

boxplot(NameOfDataFrame$a ~ NameOfDataFrame$b)

  • Where a is the variable that is plotted
  • Where b is the variable for grouping the data
43
Q

In the function “boxplot(NameOfDataFrame$a ~NameOfDataFrame$b)”, what lies to the left and right of the “ ~ ” symbol?

A
  • The response variables lie to the left

- The explanatory variables lie to the right

44
Q

Summary()

A

This function neatly and easily provides several summary statistics

45
Q

List the function that would be used to make summary statistics for weight from the mice data frame:

A

summary(mice$weight)

46
Q

List the function that would be used to make summary statistics from the whole mice data frame:

A

summary(mice)

47
Q

When sex is a variable in the data frame, what problem can be caused when trying to make summary statistics? What are possible solutions?

A
  • Sex isn’t a numerical variable
  • Any numbers used would have no intrinsic meaning so wouldn’t be very useful
  • Better summaries involve knowing the number of males or females
  • To do this, we would need to make R a categorical variable
48
Q

What are categorical variables called in R?

A

Factors

49
Q

What function is used to convert numerical values to factors?

A

factor()

50
Q

List the function that would be used to convert the sex column from the data frame “mice” to a factor:

A

Factor(mice$sex)

51
Q

When a variable has been converted in to a factor, the outcome will not be stored unless told to be. How do you store the date in this format?

A
  • By assigning the converted data to a column under the name of the converted variable
  • Here, it will overdue the current column contents
52
Q

class()

A

Function helps you check what kind of variable the variable is being saved as

53
Q

dbinom( x, size= , prob= )

A

Function for discrete binomial distributions

Where x, size and probability are the arguments

54
Q

List the function for finding the probability of getting 3 heads after tossing a coin 4 times:

A

dbinom(3, 4, 0.5)

55
Q

pbinom()

A

Function calculates the probability of observing up to a certain number of successes or events

56
Q

What are the arguments for pbinom?

A

pbinom (x, size = , prob = )

  • X= observed number of outcomes
  • Size= sample size
  • Prob= Probability of success
57
Q

What is the probability of getting up to 3 heads after tossing 6 different coins?

A

pbinom( 3, 6, 0.5)

58
Q

15 people are admitted to hospital with a heart attack. 4 in 100 people die of a fatal heart attack. What is the probability that more than 4 people die?

A

1- pbinom(4, 15, 0.04)

59
Q

Pnorm()

A

Calculates cumulative probability of a normal distribution

60
Q

What is a similarity and difference between the pnorm() and pbinom() functions?

A
  • They both calculate cumulative probability

- pnorm() is used for normal distribution and pbinom() is used for binomial distributions

61
Q

What are the arguments for pnorm()?

A

pnorm(x, mean, sd)

  • Where x is the observation made
  • Where mean is the mean of the population
  • Where sd is the standard deviation
62
Q

The probability that two people share the same birthday in a cohort of 150 is being calculated. Explain why the function below is incorrect.
dbinom(2, 150, 0.00273973)

A
  • As two people have the same birthday, size has to be the number of trials and we are comparing 1 birthday to 149 other birthdays
  • This means the number of trials is 149
  • As x is the number of successes, you get a success when 1 other person has the same birthday, so x = 1
63
Q

The probability of sharing a birthday with at least two people in a cohort of 150 is being calculated. Why is the following function incorrect?

Pbinom(2, 149, 0.00273973)

A
  • Mutual exclusivity must be taken into account
  • pbinom(1, 149, 1/365) is the probability of sharing a birthday with none or one other person
  • Taking the value away from 1 finds the probability of sharing a birthday with more than 1 person
64
Q

qbinom()

A

Function calculates critical value of a given distribution at a specific alpha

65
Q

What are the arguments in the function qbinom()?

A

qbinom( alpha, size= , prob=)

  • Alpha is the probability of success
  • size is the number of trials
  • prob is the probability of success
66
Q

Out of 100 tosses of a coin, 59 were heads. With a value of alpha being 0.05, calculate the critical values given a two-tailed test:

A

The Lower critical value:
qbinom(0.025, 100, 0.5)

The upper critical value:
qbinom(0.975, 100, 0.5)

Remember during a two tailed test, the significance level is 0.025 as it is shared between both tails

67
Q

4 in every 100 people suffering with heart attacks die. 15 people are admitted to a hospital and 3 of them die. A doctor is cared this is abnormally high, so finds 4% of 15 (which turns out to be 0.6) but is not sure what this means.

Null hypothesis - hospital does not suffer more fatalities from heart attacks than expected.

Alternative hypothesis - hospital suffers more fatalities from heart attacks then expected.

  1. Calculate the critical value for heart attacks at hospital.

Now as a two tailed test:

  1. Calculate the lower critical value
  2. Calculate the upper critical value
A
  1. qbinom(0.95, 15, 0.04)
  2. qbinom(0.025, 15, 0.04)
  3. qbinom(0.975, 15, 0.04)
68
Q

Why would we use a one-tailed test over a two-tailed test when trying to figure out if the fatality rate from heart attack in a hospital is higher than expected?

A

We are not interested in whether the hospital has a lower than expected fatality rate

69
Q

Binom.test()

A

Function

70
Q

What are the arguments in the binom.test() function?

A

Binom.test( x, size = , prob = )

  • X is the observed number of a particular outcome
  • size is the number of trials
  • prob is the probability of success
71
Q

What is used to compare the mean of one sample to a particular value?

A

One sample t test

72
Q

What is the function for a t-test?

A

t.test()

73
Q

What are the arguments for a one sample t-test?

A

t. test(DataFrameName, mu= )
- Mu is the population mean
- DataFrameName is the name of the data frame

74
Q

What test compares the means of two samples?

A

The two sample t-test

75
Q

The weight of 10 mice for each cohort is stored in data1. The data is arranged with the weights stored in the weight column and a treatment column containing either control or drug.

  1. Code a boxplot of weight grouped by treatment:
  2. Perform a two sample t-test to compare the weights grouped by treatment option:
A
  1. Boxplot(weight ~ treatment, data = data1)
  2. t-test(weight~treatment, data =data1)

As we are doing t-test on the same thing as we made boxplot on, we can use the same arguments in both functions

76
Q

What are the arguments for the function t.test() for a two sample t-test?

A

t. test(OutputVariable ~ VariableThisIsGroupedBy, data= )

- Where data is the name of the data frame

77
Q

Plot()

A

Function plots a scatter graph of data

78
Q

What happens if the grouping variable used in the plot() function is a factor? How do you resolve this?

A
  • If the grouping variable is a factor, R will produce a boxplot
  • Use the function “as.numeric(NameOfGroupingVariable)” to plot a scatter graph
79
Q

Plot a scatter graph of output grouped by day from data2:

A

Plot( output~as.numeric(day), data = data2)

80
Q

How do you colour in points on a graph showing data?

A

By using the argument “col=“ to colour the data points according to the variable you want

81
Q

How do we change the t-test so it becomes a paired t-test?

A

By adding the argument paired=TRUE

82
Q

When is a paired t-test useful?

A
  • With two sample t-tests compare the mean of two groups
  • The variation between replicates overrides the effects of the independent variable (the one we are looking to see has a significant effect)
  • Paired t-test analyses the data in pairs to stop this happening
83
Q

What is required in R for the paired t-test to work?

A

The data must be arranged in the same order in each group (sorted/grouped by the same variable)

84
Q

When is the paired t-test used?

A
  • When observations in one group can be paired with observations in the other group
  • There needs to be a reason why an observation in one group is more closely related to one particular observation than the other observations in the second group
85
Q

When can observations in one group be paired with observations in the other group?

A
  • The observations were performed on the same subject

- The observations were performed at the same time

86
Q

qqnorm()

A

Functions plots data into a graph with sample quantiles against theoretical quantiles (Q-Q plot)

87
Q

qqline()

A

Adds a line to Q-Q plot to see if data is normally distributed

88
Q

R automatically uses the Welch’s t-test, which does not assume equal variance of the two populations from which the samples have been taken. How do you specify in R to change this when variance is equal? What effect does this have?

A

By adding the argument “var.equal =TRUE”

  • It increases the power of the test, but there is little advantage in most situations

Statistics: (5 decks)

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