Week 3 Tutorial: Control Flow in R

POP77001 Computer Programming for Social Scientists

Note on Code Formatting

Use consistent style and indentation (RStudio indents by 2 whitespaces, Jupyter Notebook by 4)
Even though it does not affect how programs are executed in R

# Good style
is_positive <- function(num) {
  if (num > 0) {
    res <- TRUE
  } else {
    res <- FALSE
  }
  return(res)
}

# Bad style
is_positive <- function(num) {
if (num > 0) {
res <- TRUE
}
else {
res <- FALSE
}
return(res)
}

Exercise 1: Conditional Statements

Below you will find a code snippet for finding the maximum value in vector v using exhaustive enumeration.
Modify it in such a way that it finds the minimum (rather than maximum) value.
Check that your code works correctly by applying the built-in function min().

set.seed(2025)
v <- sample(1:1000, 50)
max_val <- v[1]
for (i in v[2:length(v)]) {
  if (i > max_val) {
    max_val <- i
  }
}
max_val

[1] 983

Now let’s make this code more robust.
Re-write the code above so that it can handle vectors that contain NAs in them.
Test your code on the vector below.

set.seed(2025)
v <- sample(c(1:750, rep(NA, 250)), 50)
v

 [1]  NA 460  NA  NA  NA 279 510 187 266 461  NA  NA  NA 539  NA  NA 373 407 571
[20]  NA 159 142 549  NA 575  NA  NA  NA  NA 630  88 195 686  NA 371 306  59 317
[39] 528  NA 564 186 144  NA  49 399  50 626 185 107

Exercise 2: Iteration

Below you see a matrix of random 30 observations of 5 variables
Inspect visually the matrix
Which variable(s) do you think has(ve) the highest standard deviation?
First, try subsetting individual rows and columns from this matrix
Check the dimensionality of the matrix using dim(), nrow() and ncol() functions
Write a loop that goes over each variable and calculates its standard deviation
You can use sd() function to calculate the standard deviation
Save these calculated standard deviations in a vector
Find the variable with the maximum standard deviation using max() or which.max() functions
Is it the one you thought it would be?

# When dealing with random number generation it's always a good idea to make your code replicable
# by setting the seed with set.seed(function)
set.seed(2025)
# Here we create a matrix of 30 observations of 5 variables
# where each variable is a random draw from a normal distribution with mean 0
# and standard deviation drawn from a uniform distribution between 0 and 10
mat <- mapply(
  function(x) cbind(rnorm(n = 30, mean = 0, sd = x)),
  runif(n = 5, min = 0, max = 10)
)

mat

             [,1]        [,2]        [,3]        [,4]         [,5]
 [1,]   0.0780885  3.48089343   2.9553545   3.2248615  -4.66712911
 [2,]  -8.3269637 -2.05680059   6.5670977   8.3727181 -13.86628154
 [3,]   1.2834069 -4.25078791   3.0311714  -3.6736945   2.20685092
 [4,]  12.2189488 12.51048821   2.2500211   5.8992348  12.81071523
 [5,] -14.5079803 -0.83591818  -0.8329266   1.0321989   5.73323413
 [6,]   7.8277431 -0.04931062   1.4101378   6.7058639   2.63359979
 [7,]  -2.5172557  1.27351635  -5.6927903 -12.9850971  -8.19349948
 [8,]  -9.0808601  1.07779111   3.0050245  -4.1673004   0.51438396
 [9,]   6.5582896 -3.70133731   7.1498934  -8.8572673   0.05485376
[10,]   4.1685004 -6.50775660   9.0180872  -4.5555447  -4.32601307
[11,]  10.2156894 -1.84773737   2.4000462   6.2044559  -6.12311135
[12,]  -6.1880010 -4.12393197  -2.1740754  -0.4008144   3.40181041
[13,]  -4.7220233  0.21939243   3.2602544   1.7233189  -1.59565895
[14,]  -7.7975071  7.67400612   5.9990204   7.2296638  -0.54940831
[15,]   0.8389551  6.27673537   5.4250108   4.9929334  -4.11237985
[16,]  -8.8303620  2.01744395  -1.2485669   7.0326504   7.41427192
[17,]  -6.7987871  3.87487181 -11.5966579  -4.1382655  12.93379615
[18,]   9.6151288 -2.91265614   5.0533075   2.0702789  -1.76367895
[19,]  10.8374659  8.50027774  -2.1024975  -2.2128037   9.27726204
[20,]   6.5590893  4.15403829  -3.4412653   0.2689806  -1.02485010
[21,]   6.5197367  4.07635880   6.7820067  -3.9887753   5.01717503
[22,]  -8.2150378 -0.79340248  -5.5809099   0.7797501   1.38873142
[23,]   0.7504032  7.01848922   0.4065937 -10.0721259  -4.42487986
[24,]   4.6233436 -5.21899205   0.6368000  -8.9976512   9.75800935
[25,]  -8.1970619  4.13052036   3.6410426  13.1384188  -0.18438540
[26,]  -2.6565373 -5.58620440   1.8889740   8.9695624  12.56154387
[27,]   1.6696565  2.71295623  -6.5482715 -10.2190913   0.64368393
[28,]   1.8708354 -4.61167206  -1.1712393   0.6024034   4.14227228
[29,]   4.7594528  2.03420819   6.3618553  -2.6569757  -3.66269350
[30,]  -5.7215014  0.63822093   6.7654597   5.9124287  -8.75833965

Week 3: Assignment 1

Practice subsetting, conditional statements and iterations in R
Due by 12:00 on Monday, 6th October