From spreadsheet thinking to R thinking
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.
Add two columns
Figure 1 shows some numbers in two columns and the start of adding those two columns to each other in a third column.
Figure 1: Adding two columns in a spreadsheet.
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 casesensitive — 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 filldown 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(1p, 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(1p, 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, nb, 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 evalparsetext 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)
Nice post! I think you touched one of the most negative sides of R: the default recycling to the length of the longer vector (or column). I could guess that many errors stay unnoticed when doing columnwise summary statistics, i.e. the mean will be completely wrong if the original entry was of shorter length. I think, the default should be to fill with NA’s… and I heard many other people feel the same.
Cheers,
Andrej
Thank you. Your post finally helped me to have an overview of an excel functions…
I do not really like it and I use R. At home… However at work I mostly do VBA and while it is possible to have nice repository for more frequently used functions, some excel functions are handy. Say all distributions used for hypothesis testing. I would rather use excel version that is most likely slow than code it up in vba.
I think just about anything is better than Excel for complex modeling, and R is a very good alternative. However, one main problem with R is that it presents itself in linear, procedural line code. Working through a comprehensive understanding of a model with a client, such as a CFO, is very difficult.
I attempted to address these issues in my introductory tutorial “Business Case Analysis with R” (https://leanpub.com/bizanalysiswithr) by introducing the idea of using an influence diagram in parallel with communicating the flow of R logic in complex business case models.
However, the modeling application Analytica (http://www.lumina.com) actually resolves just about all of the problems with Excel and many of the remaining problems with R, namely that Analytica uses an integrated influence diagram to demonstrate the flow of logic of a model and that Analytica uses a technology called Intelligent Arrays that operates much more intuitively than R’s array system. I wrote a review for INFORMS here (http://www.incitedecisiontech.com/anareviewredirect.html).
Of course, Analytica doesn’t have anywhere near the broad array of libraries and classes that R does, and it is sold commercially (although there is a free version), but as far as communicating logic, auditing logic, and extending logic once a base model has been created, Analytica is definitely worth the time to consider.
After thinking a bit more about my previous comment, I think it’s worth adding that we should always try to use the best tool available for the task/problem at hand. If data analysis is the task at hand, R is probably among the best of tools available. However, if business simulation/systems engineering is the task at hand, and those analyses require recursive time dependencies, multiple parallel threads of logic, multidimensional spaces that may need to be extended (both in size and number of dimensions) easily with little to no additional programming, something like Analytica is the best tool. When it comes to the graphical presentation of results, I would recommend marrying both R and Analytica together, as Analytica’s charting environment is still a little primitive (although very useful). The number and scale of quality of useful R charting packages available to produce beautiful and compelling graphics is unparalleled.
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