From spreadsheet thinking to R thinking

January 7, 2014
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(This article was first published on Burns Statistics » R language, and kindly contributed to R-bloggers)

Towards the basic R mindset.

Previously

The post “A first step towards R from spreadsheets” provides an introduction to switching from spreadsheets to R.  It also includes a list of additional posts (like this one) on the transition.

Figure 1 shows some numbers in two columns and the start of adding those two columns to each other in a third column.

The next step is to fill the addition formula down the column.

It is not so different to do the same thing in R.  First create two objects that are equivalent to the two columns in the spreadsheet:

A <- c(32.5, -3.8, 15.9, 22.5)
B <- c(48.1, 19.4, 46.8, 14.7)

In those commands you used the c function which combines objects.  You have created two vectors.  The rules for a vector are:

• it can be any length (up to a very large value)
• all the elements are of the same type — all numbers, all character strings or all logicals
• the order matters (just like it matters which row a number is in within a spreadsheet)

To summarize: they’re in little boxes and they all look just the same.

You have two R vectors holding your numbers.  Now just add them together (and assign that value into a third name):

C <- A + B

This addition is precisely what is done in the spreadsheet: the first value in C is the first value in A plus the first value in B, the second value in C is the second value in A plus the second value in B, and so on.

See the values in an object by typing its name:

> C
[1] 80.6 15.6 62.7 37.2

The “> ” is the R prompt, you type only what is after that: ‘C‘ (and the return or enter key).

Also note that R is case-sensitive – C and c are different things:

> c
function (..., recursive = FALSE)  .Primitive("c")

(Don’t try to make sense of what this means other than that c is a function.)

Multiply by a constant

One way of multiplying a column by a constant is to multiply the values in the column by the value in a single cell.  This is illustrated in Figure 2.

Figure 2: Multiply a column times the value in a single cell, shown before filling down column E.

Another way of doing the same thing is to fill the value in D1 down column D and then multiply the two columns.

Do this operation in R with:

> C * 33
[1] 2659.8  514.8 2069.1 1227.6

In this command you didn’t create a new object to hold the answer.

You can think of R as doing either of the spreadsheet methods, but the fill-down image might be slightly preferable.

Recycling in R

The R recycling rule generalizes the idea of a single value expanding to the length of the vector.  It is possible to do operations with vectors of different lengths where both have more than one element:

> 1:6 + c(100, 200)
[1] 101 202 103 204 105 206

Figure 3 illustrates how R got to its answer.

Figure 3: Equivalent of the example of R’s recycling rule.

Column F shows how column G was created: use the ROW function and fill it down the column.  That sequence of numbers was created in R with the : operator.

Note how the shorter vector is replicated to the length of the longer one.  Each value is used in order, and when it reaches the end it goes back to the beginning again.

You are free to think this is weird.  However, it is often useful.

Functions

Table 1 translates between spreadsheet and R functions. The spreadsheets consulted were Excel, Works and OpenOffice. Note there is some variation between spreadsheets.

Table 1: Equivalent functions between spreadsheets and R.

 spreadsheet R comment ABS abs ADDRESS perhaps assign but there is probably a better way AND all more literally would be the & and && R operators AVERAGE mean danger: mean accepts only one data argument AVG mean this danger of mean is discussed in Circle 3 of The R Inferno AVERAGEIF subscript before using mean BESSELI besselI BESSELJ besselJ BESSELK besselK BESSELY besselY BETADIST pbeta BETAINV qbeta BINOMDIST pbinom or dbinom pbinom when cumulative, dbinom when not CEILING ceiling CELL str is sort of the same idea CHIDIST pchisq CHIDIST(x, df) is pchisq(x, df, lower.tail=FALSE) CHIINV qchisq CHIINV(p, df) is qchisq(1-p, df) CHISQDIST pchisq or dchisq pchisq when cumulative, dchisq when not CHISQINV qchisq CHITEST chisq.test CHOOSE switch CLEAN gsub COLS ncol (Works) COLUMNS ncol (Excel, OpenOffice) COLUMN col or probably more likely : or seq COMBIN choose CONCATENATE paste CONFIDENCE CONFIDENCE(alpha, std, n) is -qnorm(alpha/2) * std / sqrt(n) CORREL cor COUNT length COUNTIF get length of a subscripted object COVAR cov CRITBINOM qbinom CRITBINOM(n, p, a) is qbinom(a, n, p) DELTA all.equal or identical all.equal allows for slight differences, and note that it does not return a logical if there’s a pertinent difference — you can wrap it in isTRUE if you want DGET use subscripting in R ERF see the example in ?"Normal" ERFC see the example in ?"Normal" EXACT == EXACT is specific to text, == is not EXP exp EXPONDIST pexp or dexp pexp when cumulative, dexp when not FACT factorial FACTDOUBLE dfactorial dfactorial is in the phangorn package FDIST pf FDIST(x, df1, df2) is pf(x, df1, df2, lower.tail=FALSE) FIND regexpr FINV qf FINV(p, df1, df2) is qf(1-p, df1, df2) FISHER atanh FISHERINV tanh FIXED format or sprintf or formatC FLOOR floor FORECAST predict on an lm object FREQUENCY you probably want to use cut and/or table FTEST var.test GAMMADIST pgamma or dgamma GAMMADIST(x, a, b, TRUE) is pgamma(x, a, scale=b) GAMMADIST(x, a, b, FALSE) is dgamma(x, a, scale=b) GAMMAINV qgamma GAMMAINV(p, a, b) is qgamma(p, a, scale=b) GAMMALN lgamma GAUSS GAUSS(x) is pnorm(x) - 0.5 GCD gcd gcd is in the schoolmath package (and others). For more than two numbers you can do: Reduce(gcd, numVector) GEOMEAN exp(mean(log(x))) GESTEP >= GESTEP(x, y) is as.numeric(x >= y) but R often coerces automatically if needed HARMEAN harmonic.mean harmonic.mean is in the psych package HLOOKUP use subscripting in R HYPGEOMDIST dhyper HYPGEOMDIST(x, a, b, n) is dhyper(x, b, n-b, a) IF if or ifelse see Circle 3.2 of The R Inferno on if versus ifelse IFERROR try or tryCatch INDEX [ use subscripting in R INDIRECT get or possibly the eval-parse-text idiom, or (better) make changes that simplify the situation INT floor danger: not the same as as.integer for negative numbers INTERCEPT (usually) the first element of coef of an lm object ISLOGICAL is.logical ISNUMBER is.numeric ISTEXT is.character KURT kurtosis kurtosis is in the moments package LARGE you can use subscripting after sort LCM scm scm is in the schoolmath package. For more than two numbers you can do: Reduce(scm, numVector) LEFT substr LEN nchar (Excel, OpenOffice) LENGTH nchar (Works) LINEST use lm LN log danger: the default base in R for log is e LOG log danger: the default base in spreadsheets for log is 10 LOG10 log10 LOGINV qlnorm LOGNORMDIST plnorm LOWER tolower MATCH match or which match only does exact matches. Given that MATCH demands a sorted set of values for type 1 or -1, then MATCH(x, vec, 1) is sum(x <= vec) and MATCH(x, vec, -1) is sum(x >= vec) when vec is sorted as MATCH assumes. MAX max or pmax max returns one value, pmax returns a vector MDETERM det MEDIAN median MID substr MIN min or pmin min returns one value, pmin returns a vector MINVERSE solve MMULT %*% MOD %% MODE the table function does the hard part. A crude approximation to MODE(x) is as.numeric(names(which.max(table(x)))) MUNIT diag diag is much more general N as.numeric the correspondence is for logicals, as.numeric is more general NEGBINOMDIST dnbinom NORMDIST, NORMSDIST pnorm or dnorm pnorm when cumulative is true, dnorm when false NORMINV, NORMSINV qnorm NOT ! NOW date or Sys.time OR any the or operators in R are | and || PEARSON cor PERCENTILE quantile PERCENTRANK similar to ecdf but the argument is removed from the distribution in PERCENTRANK PERMUT function(n,k) {choose(n,k) * factorial(k)} PERMUTATIONA PERMUTATIONA(n, k) is n^k PHI dnorm POISSON ppois or dpois ppois if cumulative, dpois if not POWER ^ PROB you can use the Ecdf function in the Hmisc package (the probabilities in the spreadsheet are the weights in Ecdf), then you can get the difference of that on the two limits PRODUCT prod PROPER see example in ?toupper QUARTILE use quantile QUOTIENT %/% RAND runif see an introduction to random generation in R RANDBETWEEN use sample RANK rank RANK has the "min" tie.method and defaults to biggest first. rank only has smallest first. To get biggest first in R you can do: length(x) + 1 - rank(x) REPLACE sub or gsub REPT use rep and paste or paste0 RIGHT substring you'll also need nchar to count the characters. Alternatively you can use str_sub in the stringr package with negative limits ROUND round note: round rounds exact halves to even (which avoids bias) ROUNDDOWN trunc trunc only goes to integers ROW row or probably more likely : or seq ROWS nrow RSQ in summary of an lm object SEARCH regexpr also see grep SIGN sign SKEW skewness skewness is in the moments package SLOPE in coef of an lm object SMALL you can use subscripting after sort SQRT sqrt STANDARDIZE scale STD sd (Works) STDEV sd (Excel, OpenOffice) STEYX predict on an lm object STRING format or sprintf or formatC or prettyNum (Works) SUBSTITUTE sub or gsub or possibly paste SUM sum sum is one of the few R functions that allow multiple data arguments SUMIF subscript before using sum SUMPRODUCT crossprod TDIST pt TDIST(abs(x), df, tails) is pt(-abs(x), df) * tails TEXT format or sprintf or formatC or prettyNum TINV TINV(x, df) is abs(qt(x/2, df)) TODAY Sys.Date TRANSPOSE t TREND fitted of an lm object TRIM sub TRIMMEAN mean TRIMMEAN(x, tr) is mean(x, trim=tr/2) TRUNC trunc TTEST t.test TYPE similar concepts in R are typeof, mode, class. Use str to understand the structure of objects UPPER toupper VALUE as.numeric VAR var VLOOKUP use subscripting in R WEEKDAY weekdays WEIBULL pweibull or dweibull pweibull when cumulative, dweibull when not ZTEST use pnorm on the calculated statistic

The trigonometric functions, like cos, acos, acosh are the same, except the R functions are all in lowercase.

Arguments

Spreadsheets show you the arguments of a function.  The args function in R provides similar information.  For example:

> args(sample)
function (x, size, replace = FALSE, prob = NULL)
NULL

This shows that replace and prob both have default values, and so are not required.  Actually size is not required either -- x is the only mandatory argument.

You will learn to not even see the NULL on the final line of the result of args.

Help

You can get help for a function with the question mark operator:

?sample

This will show you the help file for the object -- sample in this case.  It is best not to let yourself be overwhelmed by a help file.

R vectorization

Most of the R functions are vectorized.

This is like creating a new spreadsheet column where an argument of the function is a value from the same row but a different column.  Think of putting =EXP(A1) in cell B1 and then filling it down.

Figure 4: EXP example of the vectorization idea, shown before column K is filled down.

Giving a vector to exp returns the exponential of each of the values in the input vector:

> exp(0:5)
[1]   1.000000   2.718282   7.389056  20.085537
[5]  54.598150 148.413159

The result is a vector of length 6 -- the same length as the input. The number in square brackets at the start of each line of output is the index number of the first item on the line.

Some R resources

“Impatient R” provides a grounding in how to use R.

“Some hints for the R beginner” suggests additional ways to learn R.

Epilogue

And they're all made out of ticky tacky
And they all look just the same

from "Little Boxes" by Malvina Reynolds (1900 - 1978)

The post From spreadsheet thinking to R thinking appeared first on Burns Statistics.