Basic R

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Lesson Objectives

Specific Achievements

What is R?

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Console

The interpreter accepts commands interactively through the console.

Basic math, as you would type it on a calculator, is usually a valid command in the R language:

> 1 + 2
[1] 3
> 4^2
[1] 16
Question
Why is the output prefixed by [1]?
Answer
That’s the index, or position in a vector, of the first result.

A command giving a vector of results shows this clearly:

> seq(1, 100)
  [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17
 [18]  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34
 [35]  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51
 [52]  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68
 [69]  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85
 [86]  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100

The interpreter understands more than arithmatic operations. That last command told it to use (or “call”) the function seq().

Most of “learning R” involves getting to know a whole lot of functions, the effect of each function’s arguments (e.g. the input values 1 and 100), and what each function returns (e.g. the output vector).

R as Calculator

A good place to begin learning R is with its built-in mathematical functionality.

Arithmatic Operators

Try +, -, *, /, and ^ (for raising to a power).

> 5/3
[1] 1.666667

Logical Tests

Test equality with == and inequality with <=, <, !=, >, or >=.

> 1/2 == 0.5
[1] TRUE

Math Functions

Common mathematical functions like sin, log, and sqrt, exist along side some universal constants.

> sin(2 * pi)
[1] -2.449294e-16

Generic Functions

Functions do more than math! Functions like ‘rep’, ‘sort’, and ‘range’ are pre-packaged instructions for processing user input.

> rep(2, 5)
[1] 2 2 2 2 2

Parentheses

Sandwiching something with ( and ) has two possible meanings.

Group sub-expressions by parentheses where needed.

> (1 + 2) / 3
[1] 1

Call functions by typing their name and comma-separated arguments between parentheses.

> logb(2, 2)
[1] 1

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Environment

In the RStudio IDE, the environment tab displays any variables added to R’s vocabulary in the current session. In a brand new session, the R interpreter already recognizes many things, despite the environment being “empty”.

With an “empty” environment, the interpreter still recognizes:

To reference a number or function just type it in as above. To referece a string of characters, surround them in quotation marks.

> 'ab.cd'
[1] "ab.cd"

Without quotation marks, the interpreter checks for things in the environment named ab.cd and doesn’t find anything:

> ab.cd
Error in eval(expr, envir, enclos): object 'ab.cd' not found
Question
Is it better to use ' or "?
Answer
Neither one is better. You will often encounter stylistic choices like this, so if you don’t have a personal preference try to mimic existing styles.

Assignment

You can expand the vocabulary known to the R interpreter by creating a new variable. Using the symbol <- is referred to as assignment: the output of any command to the right of <- gets the name given on its left.

> x <- seq(0, 100)

You’ll notice that nothing prints to the console, because we assigned the output to a variable. We can print the value of x by evaluating it without assignment.

> x
  [1]   0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16
 [18]  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33
 [35]  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50
 [52]  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67
 [69]  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84
 [86]  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100

Assigning values to new variables (to the left of a <-) is the only time you can reference something previously unknown to the interpreter. All other commands must reference things already in the interpreter’s vocabulary.

Once assigned to a variable, a value becomes known to R and you can refer to it in other commands.

> plot(x, sin(x * 2 * pi / 100))

The environment is dynamic, but under your control!

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Editor

The console is for evaluating commands you don’t intend to keep or reuse. It’s useful for testing commands and poking around. The environment represents the state of a current session. The editor reads and writes files–it is where you head to compose your R scripts.

R scripts are simple text files that contain code you intend to run again and again; code to process data, perform analyses, produce visualizations, and even generate reports. The editor and console work together in the RStudio IDE, which gives you multiple ways to send parts of the script you are editing to the console for immediate evaluation. Alternatively you can “source” the entire script or run it from a shell with Rscript.

Open up “worksheet.R” in the editor, and follow along by replacing the ... placeholders with the code here. Then evalute just this line (Ctrl+Enter on Windows, ⌘+Enter on macOS)

vals <- seq(1, 100)

Our call to the function seq could have been much more explicit. We could give the arguments by the names that seq is expecting.

vals <- seq(from = 1,
  to = 100)

Run that code by moving your cursor anywhere within those two lines and clicking “Run”, or by using the keyboard shortcut Ctrl-Return or ⌘ Return.

Question
What’s an advantage of naming arguments?
Answer
One advantage is that you can put them in any order. A related advantage is that you can then skip some arguments, which is fine to do if each skipped argument has a default value. A third advantage is code readability, which you should always be conscious of while writing in the editor.

Readability

Code readability in the editor cuts both ways: sometimes verbosity is useful, sometimes it is cumbersome.

The seq() function has an alternative form available when only the from and to arguments are needed.

> 1:100
  [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17
 [18]  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34
 [35]  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51
 [52]  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68
 [69]  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85
 [86]  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100

The : operator should be used whenever possible because it replaces a common, cumbersome function call with an brief, intuitive syntax. Likewise, the assign function duplicates the functionallity of the <- symbol, but is never used when the simpler operator will suffice.

Function documentation

How would you get to know these properties and the names of a function’s arguments?

> ?seq

How would you even know what function to call?

> ??sequence

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Load Data

We will use the function read.csv() to load data from a Comma Separated Value file. The essential argument for the function to read in data is the path to the file, other optional arguments adjust how the file is read.

Additional file types can be read in using read.table(); in fact, read.csv() is a simple wrapper for the read.table() function having set some default values for some of the optional arguments (e.g. sep = ",").

Use the assignment operator “<-“ to read data into a variable for subsequent operations.

Type read.csv( into the editor and then press tab to see what arguments this function takes. Hovering over each item in the list will show a description of that argument from the help documentation about that function. Specify the values to use for an argument using the syntax name = value.

storm <- read.csv('data/StormEvents.csv')
Question
How does read.csv determine the field names?
Answer
The read.csv command assumes the first row in the file contains column names. Look at ?read.csv to see the default header = TRUE argument. What exactly that means is described down in the “Arguments” section.

After reading in the “StormEvents.csv” file, you can explore what types of data are in each column with the str function, short for “structure”.

> str(storm)
'data.frame':	100 obs. of  42 variables:
 $ BEGIN_YEARMONTH  : int  200604 200601 200601 200601 200601 200601 200601 200601 200601 200601 ...
 $ BEGIN_DAY        : int  7 1 1 1 1 30 30 28 28 28 ...
 $ BEGIN_TIME       : int  1515 0 0 0 0 500 500 800 1400 800 ...
 $ END_YEARMONTH    : int  200604 200601 200601 200601 200601 200601 200601 200601 200601 200601 ...
 $ END_DAY          : int  7 31 31 31 31 31 31 29 29 29 ...
 $ END_TIME         : int  1515 2359 2359 2359 2359 1400 1400 1300 500 1600 ...
 $ EPISODE_ID       : int  207534 202408 202409 202409 202409 202394 202394 202395 202396 202397 ...
 $ EVENT_ID         : int  5501658 5482479 5482480 5482481 5482482 5482324 5482325 5482326 5482327 5482328 ...
 $ STATE            : Factor w/ 19 levels "ALASKA","ARKANSAS",..: 6 3 17 17 17 3 3 3 3 3 ...
 $ STATE_FIPS       : int  18 8 49 49 49 8 8 8 8 8 ...
 $ YEAR             : int  2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 ...
 $ MONTH_NAME       : Factor w/ 4 levels "April","February",..: 1 3 3 3 3 3 3 3 3 3 ...
 $ EVENT_TYPE       : Factor w/ 14 levels "Avalanche","Coastal Flood",..: 11 4 4 4 4 14 14 13 14 14 ...
 $ CZ_TYPE          : Factor w/ 2 levels "C","Z": 1 2 2 2 2 2 2 2 2 2 ...
 $ CZ_FIPS          : int  51 12 23 29 24 4 13 13 5 4 ...
 $ CZ_NAME          : Factor w/ 63 levels "ALAMANCE","ANIMAS RIVER BASIN",..: 27 63 20 9 19 21 23 23 60 21 ...
 $ WFO              : Factor w/ 13 levels "AFC","DTX","GJT",..: 9 3 3 3 3 3 3 3 3 3 ...
 $ BEGIN_DATE_TIME  : Factor w/ 53 levels "01-JAN-06 00:00:00",..: 14 1 1 1 1 52 52 49 50 49 ...
 $ CZ_TIMEZONE      : Factor w/ 6 levels "AST","CST","CST-6",..: 2 5 5 5 5 5 5 5 5 5 ...
 $ END_DATE_TIME    : Factor w/ 55 levels "01-JAN-06 09:20:00",..: 13 55 55 55 55 54 54 51 49 52 ...
 $ INJURIES_DIRECT  : int  0 0 0 0 0 0 0 0 0 0 ...
 $ INJURIES_INDIRECT: int  0 0 0 0 0 0 0 0 0 0 ...
 $ DEATHS_DIRECT    : int  0 0 0 0 0 0 0 0 0 0 ...
 $ DEATHS_INDIRECT  : int  0 0 0 0 0 0 0 0 0 0 ...
 $ DAMAGE_PROPERTY  : Factor w/ 6 levels "0","0.00K","200K",..: 5 NA NA NA NA NA NA NA NA NA ...
 $ DAMAGE_CROPS     : Factor w/ 3 levels "0","0.00K","UNKNOWN": NA NA NA NA NA NA NA NA NA NA ...
 $ SOURCE           : Factor w/ 14 levels "AMATEUR RADIO",..: 7 8 8 8 8 2 2 2 14 2 ...
 $ MAGNITUDE        : num  61 NA NA NA NA NA NA NA NA NA ...
 $ MAGNITUDE_TYPE   : Factor w/ 2 levels "EG","MG": 1 NA NA NA NA NA NA NA NA NA ...
 $ BEGIN_RANGE      : int  4 NA NA NA NA NA NA NA NA NA ...
 $ BEGIN_AZIMUTH    : Factor w/ 8 levels "E","N","NE","NW",..: 1 NA NA NA NA NA NA NA NA NA ...
 $ BEGIN_LOCATION   : Factor w/ 20 levels "ATLANTA","COLUMBUS",..: 13 NA NA NA NA NA NA NA NA NA ...
 $ END_RANGE        : int  NA NA NA NA NA NA NA NA NA NA ...
 $ END_AZIMUTH      : Factor w/ 7 levels "E","N","NE","NW",..: NA NA NA NA NA NA NA NA NA NA ...
 $ END_LOCATION     : Factor w/ 19 levels "ATLANTA","COLUMBUS",..: 13 NA NA NA NA NA NA NA NA NA ...
 $ BEGIN_LAT        : num  38.4 NA NA NA NA ...
 $ BEGIN_LON        : num  -87.5 NA NA NA NA ...
 $ END_LAT          : num  38.3 NA NA NA NA ...
 $ END_LON          : num  -87.3 NA NA NA NA ...
 $ EPISODE_NARRATIVE: Factor w/ 37 levels "3 CHICKEN HOUSES DESTROYED.",..: NA 36 35 35 35 7 7 37 15 8 ...
 $ EVENT_NARRATIVE  : Factor w/ 6 levels "1.3 inches of rain fell in 30 minutes.",..: 2 NA NA NA NA NA NA NA NA NA ...
 $ DATA_SOURCE      : Factor w/ 2 levels "CSV","PDS": 2 2 2 2 2 2 2 2 2 2 ...

The data viewer, opened with View or the spreadsheet icon a data frame’s record in the Environment, is useful despite not being a full spreadsheet application.

> View(storm)

Missing data, as interpreted by the read.csv function, is controlled by the na.strings argument. Override the default value of 'NA' with a character vector.

You often need to specify multiple strings to interpret as missing values, such as na.strings = c("missing", "no data", "< 0.05 mg/L", "XX").

storm <- read.csv(
  'data/StormEvents.csv',
  na.strings = c('NA', 'UNKNOWN'))

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Data Structures

A data frame is a compound object, built up from (eventually) a few basic data types, but there are intermediate objects to understand. Like all data frames, storm is actually a “list”.

> typeof(storm)
[1] "list"

The “list” is one of three one-dimensional data structurs you will regularly encounter.

Lists

Lists are one-dimensional and each element is entirely unrestricted; you can put anything in a list.

Create a list called x with a string, a sequence, and a function.

x <- list('abc', 1:3, sin)
Question
Compare the structure of storm and x while thinking about the length of each of their elements. Do the elements within list x have a length? The same length?
Answer
The elements of x all have lengths, and are not all the same. Note that the command length('abc') yields 1.

When you enter a single number or character string in R, you are actually creating a one-dimensional data structure of length 1. There are not really 0-dimensional “scalars” in R. The kind of one-dimensional structure created in this was is called a “vector”.

Vectors

Vectors are an array of values of the same data type. Create a vector by combining elements of the same type together using the function c().

> c(1, 2, 3)
[1] 1 2 3

All elements of an vector must be the same type, so when you attempt to combine different types they will be coerced to the most flexible type.

> c(1, 2, 'c')
[1] "1" "2" "c"

The difference between c(1, 2, 3) and c(1, 2, 'c') isn’t just in the third element. To understand the difference, we need to recognize data types.

Data Types

A data frame has now been recognized as a list, and the str command gives an indication that each field has a data type. The final key property is its dimension.

> dim(storm)
[1] 100  42

A data frame is essentially a list of vectors, with an important constraint. The vectors must all be of the same length, giving a rectangular data structure.

Here is a summary of the data types you frequently encounter in data frames.

Type Example
double 3.1, 4, Inf, NaN
integer 4L, length(…)
character ‘a’, ‘4’, ‘👏’
logical TRUE, FALSE

Both the double and integer data types are considered numeric, and while the str function tells you that a double is “num”, the typeof function will properly identify either numeric type. Missing data created with NA actually have a variant for each data type. So you can put NA in any vector without breaking the fule that the elements of a vector have the same data type.

Factors

A factor is a vector that can contain only predefined values, and is used to store categorical data. Factors are like integer vectors, but posess a levels attribute that assigns names to each level, or possible value in the vector.

Use factor() to create a vector with predefined values, which are often character strings.

education <- factor(
    c('college', 'highschool', 'college', 'middle', 'middle'),
    levels = c('middle', 'highschool', 'college'))

The str function notes the labels, but prints the integers assigned in their stead.

> str(education)
 Factor w/ 3 levels "middle","highschool",..: 3 2 3 1 1

Data Frames

One last property makes data frames stand out from lists: a data frame is a list of equal-length vectors having unique names.

The “columns” visible in the data viewer are accessed by the names function.

> names(storm)
 [1] "BEGIN_YEARMONTH"   "BEGIN_DAY"         "BEGIN_TIME"       
 [4] "END_YEARMONTH"     "END_DAY"           "END_TIME"         
 [7] "EPISODE_ID"        "EVENT_ID"          "STATE"            
[10] "STATE_FIPS"        "YEAR"              "MONTH_NAME"       
[13] "EVENT_TYPE"        "CZ_TYPE"           "CZ_FIPS"          
[16] "CZ_NAME"           "WFO"               "BEGIN_DATE_TIME"  
[19] "CZ_TIMEZONE"       "END_DATE_TIME"     "INJURIES_DIRECT"  
[22] "INJURIES_INDIRECT" "DEATHS_DIRECT"     "DEATHS_INDIRECT"  
[25] "DAMAGE_PROPERTY"   "DAMAGE_CROPS"      "SOURCE"           
[28] "MAGNITUDE"         "MAGNITUDE_TYPE"    "BEGIN_RANGE"      
[31] "BEGIN_AZIMUTH"     "BEGIN_LOCATION"    "END_RANGE"        
[34] "END_AZIMUTH"       "END_LOCATION"      "BEGIN_LAT"        
[37] "BEGIN_LON"         "END_LAT"           "END_LON"          
[40] "EPISODE_NARRATIVE" "EVENT_NARRATIVE"   "DATA_SOURCE"      

In summary, a data frame is the data structure most similar to a spreadsheet, with a few key differences:

Creating a data frame from scratch can be done by combining vectors with the data.frame() function.

income <- c(32000, 28000, 89000, 0, 0)
df <- data.frame(education, income)

Remember to use these functions when getting to know a data frame:

dim() dimensions
nrow(), ncol() number of rows, columns
names() (column) names
str() structure
summary() summary info
head() shows beginning rows

Matrices

One way to understand the need for another data structure, the matrix, is that a matrix differs from a data frame in terms of the underlying data type.

Dimensions Homogeneous Heterogeneous
1-D c() list()
2-D matrix() data.frame()
n-D array()  

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Parts and Subsets

Any single part of a data structure is always accessible, either by its name or by its position, using double square brackets: [[ and ]].

Position

The first element:

> income[[1]]
[1] 32000

The third element:

> income[[3]]
[1] 89000

Names

Parts of an object may also have a name. The names can be given when you are creating a vector or afterwards using the names() function.

> df[['education']]
[1] college    highschool college    middle     middle    
Levels: middle highschool college
names(df) <- c('ed', 'in')
> df[['ed']]
[1] college    highschool college    middle     middle    
Levels: middle highschool college
Question
This use of <- with names(x) on the left is a little odd. What’s going on?
Answer
We are overwriting an existing variable, but one that is accessed through the output of the function on the left rather than the global environment.

For a multi-dimensional array, separate the dimensions within which a part is requested with a comma.

> df[[3, 'ed']]
[1] college
Levels: middle highschool college

It’s fine to mix names and indices when selecting parts of an object.

The $ sign is an additional operator for quick access to a single, named part of some objects. It’s most useful when used interactively with “tab completion” on the columns of a data frame.

> df$ed
[1] college    highschool college    middle     middle    
Levels: middle highschool college

Subsets

Multiple parts of a data structure are similarly accessed using single square brackets: [ and ]. What goes between the brackets, to specify the positions or names of the desired subset, may be of multiple forms.

Parts Result
positives elements at given positions
negatives given positions omitted
logicals elements where the corresponding position is TRUE
nothing all the elements
days <- c(
  'Sunday', 'Monday', 'Tuesday',
  'Wednesday', 'Thursday', 'Friday',
  'Saturday')
weekdays <- days[2:6]
weekend <- days[c(1, 7)]
> weekdays
[1] "Monday"    "Tuesday"   "Wednesday" "Thursday"  "Friday"   
> weekend
[1] "Sunday"   "Saturday"

A logical test applied to a single column produces a vector of TRUE and FALSE values that’s the right length for subsetting the data.

> df[df$ed == 'college', ]
       ed    in
1 college 32000
3 college 89000

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Functions

Functions package up a batch of commands. There are several reasons to develop functions in R for data analysis:

Writing functions to use multiple times within a project prevents you from duplicating code, a real time-saver when you want to update what the function does. If you see blocks of similar lines of code through your project, those are usually candidates for being moved into functions.

Anatomy of a function

Like all programming languages, R has keywords that are reserved for import activities, like creating functions. Keywords are usually very intuitive, the one we need is function.

function(...) {
    ...
    return(...)
}

Three components:

We’ll make a function to extract the first row of its argument, which we give a name to use inside the function:

function(a) {
    result <- a[1, ]
    return(result)
}

Note that a doesn’t exist until we call the function, which merely contains the instructions for how any a will be handled.

Finally, we need to give the function a name so we can use it like we used c() and seq() above.

first <- function(a) {
    result <- a[1, ]
    return(result)
}
> first(df)
       ed    in
1 college 32000
Question
Can you explain the result of entering first(income) into the console?
Answer
The function caused an error, which prompted the interpreter to print a helpful error message. Never ignore an error message. (It’s okay to ignore a “warning”.)

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Flow Control

A generic term for causing the interpreter to repeat or skip certain lines, using concepts like “for loops” and conditionals.

The R interpreter’s “focus” flows through a script (or any section of code you run) line by line. Without additional instruction, every line is processed from the top to bottom. Flow control refers mostly to the two main ways of directing the interpreter’s focus, via loops and conditions.

Flow control happens within blocks of code isolated between curly braces { and }, known as “statements”.

if (...) {
    ...
} else {
    ...
}

The keyword if must be followed by a logical test which determines, at runtime, what to do next. The R interpreter goes to the first statement if the logical value is TRUE and to the second statement if it’s FALSE.

An if/else conditional would allow the first function to avoid the error thrown by calling first(counts).

first <- function(dat) {
    if (is.vector(dat)) {
        result <- dat[[1]]
    } else {
        result <- dat[1, ]
    }
    return(result)
}
> first(df)
       ed    in
1 college 32000
> first(income)
[1] 32000

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Distributions and Statistics

Since it was designed by statisticians, R can easily draw random numbers from probability distributions and calculate probabilities.

To generate random numbers from a normal distribution, use the function rnorm()

rnorm(n = 10)
 [1] -0.2262330 -1.0254656  1.2444628 -0.7458895 -1.0402514  1.5143953
 [7] -0.5442489  0.8525838  0.6985059 -0.8708866
Function Returns Notes
rnorm() Draw random numbers from normal distribution Specify n, mean, sd
dnorm() Probability density at a given number  
pnorm() Cumulative probability up to a given number left-tailed by default
qnorm() The quantile given a cumulative probability opposite of pnorm

Statistical distributions and their functions. See Table 14.1 in R for Everyone by Jared Lander for a full table.

Distribution Functions
Normal *norm
Binomial *binom
Poisson *pois
Gamma *gamma
Exponential *exp
Uniform *unif
Logistic *logis

R has built in functions for handling many statistical tests.

x <- rnorm(n = 100, mean = 15, sd = 7)
y <- rbinom(n = 100, size = 20, prob = .85)

The samples above are drawn from different distributions with different means. The T-Test should easilly distinguish them, although it does not check assumptions!

> t.test(x, y)

	Welch Two Sample t-test

data:  x and y
t = -3.0895, df = 107.92, p-value = 0.00255
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -3.3562439 -0.7327582
sample estimates:
mean of x mean of y 
  15.2155   17.2600 

Shapiro’s test of normality provides one routine for verifying assumptions.

> shapiro.test(y)

	Shapiro-Wilk normality test

data:  y
W = 0.94755, p-value = 0.0005739

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Review

In this introduction to R, we touched on several key parts of scripting for data analysis.

Special characters in R

Perhaps more than most languages, an R script can appear like a jumble of archaic symbols. Here is a little table of characters to recognize as having special meaning.

Symbol Meaning
? get help
# comment
: sequence
::, ::: access namespaces (advanced)
<- assignment
$, [ ], [[ ]] subsetting
% % infix operators, e.g. %*%
{ } statements
.  
@ slot (advanced)

The . in R has no fixed meaning and is often used as _ might be used to separate words in a variable name.

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Exercises

Exercise 1

Use the quadratic formula to find that satisfies the equation .

Exercise 2

By default, all character data is read in to a data.frame as factors. Use the read.csv() argument stringsAsFactors to suppress this behavior, then subsequently modify the STATE column in storm to make it a factor. Remember that columns of a data.frame are identified to the R interpreter with the $ operator, e.g. storm$STATE.

Exercise 3

Use the typeof function to inspect the data type of income, and do the same for another variable to which you assign a list of numbers. Why are they different? Use c to combine income with the new variable you just created and inspect the result with typeof. Does c always create vectors?

Exercise 4

Create a data frame with two columns, one called “species” with four strings and another called “abund” with four numbers. Store your data frame as a variable called data.

Exercise 5

  1. Get weekdays using negative integers.
  2. Get M-W-F using a vector of postitions generated by seq() that uses the by argument (don’t forget to ?seq for help).

Exercise 6

The keywords else and if can be combined to allow flow control among more than two statements, as below. Expand the first function once again to differentiate between dat provided as a matrix and as a data.frame. It’s up to you what the “first” element of a matrix should be!

if (...) {
  ...
} else if {
  ...
} else {
  ...
}

Solutions

Solution 1

(-0.3 + sqrt(0.3 ^ 2 - 4 * 1.5 * -2.9)) / (2 * 1.5)
[1] 1.294035

Solution 2

storm <- read.csv(
  'data/StormEvents.csv',
  stringsAsFactors = TRUE)
storm$STATE <- factor(storm$STATE)

Solution 3

x <- list(3, 4, 5, 7)
typeof(x)
[1] "list"

The variable x has a data type of list, so R does not restrict its elements to a particular type as it does for vectors.

typeof(c(income, x))
[1] "list"

The result of combining a list and vector is a list, because the list is the more flexible data structure.

Solution 4

species <- c('ape', 'bat', 'cat', 'dog')
abund <- 1:4
data <- data.frame(species, abund)

Solution 5

days[c(-1, -7)]
[1] "Monday"    "Tuesday"   "Wednesday" "Thursday"  "Friday"   
days[seq(2, 7, 2)]
[1] "Monday"    "Wednesday" "Friday"   

Solution 6

first <- function(dat) {
    if (is.vector(dat)) {
        result <- dat[[1]]
    } else if (is.matrix(dat)) {
        result <- dat[[1, 1]]
    } else {
        result <- dat[1, ]
    }
    return(result)
}
m <- matrix(1:9, nrow = 3, ncol = 3)
first(m)
[1] 1

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