What in the world is R programming language?
The R programming language created by Ross Ihaka and Robert Gentleman in 1993 is an open-source language primarily used for statistical computing, data analysis, and graphical visualization. It’s widely used by statisticians, data scientists, and researchers. And of course, a student who just got an assignment by their lecturer.
How to install and use the R programming language
- First, head to the CRAN website and pick a version based on your current operating system
- Next, head to the POSIT website to grab RStudio, the recommended IDE for R. Alternatively, you can setup R in visual studio code by following these steps.
- To run an entire R script, click on the “Source” button or Ctrl-Shift-Enter. To run selected line, click “Run” or Ctrl-Enter
Basic arithmetic operations in R
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# ==========================================
# BASIC ARITHMETIC OPERATIONS IN R
# ==========================================
# 1. Addition (+)
5 + 3 # Output: 8
# 2. Subtraction (-)
10 - 4 # Output: 6
# 3. Multiplication (*)
6 * 7 # Output: 42
# 4. Division (/)
20 / 5 # Output: 4
# 5. Exponentiation (^ or **)
2 ^ 3 # Output: 8 (2 cubed)
3 ** 2 # Output: 9 (3 squared)
# 6. Modulus (Remainder, %%)
10 %% 3 # Output: 1 (10 ÷ 3 leaves remainder 1)
# 7. Integer Division (%/%)
10 %/% 3 # Output: 3 (3 fits fully into 10 three times)
# ==========================================
# USING VARIABLES
# ==========================================
x <- 15
y <- 4
sum <- x + y # 15 + 4 = 19
difference <- x - y # 15 - 4 = 11
product <- x * y # 15 * 4 = 60
cat("Sum:", sum, "\n") # \n adds a newline
cat("Difference:", difference, "\n")
cat("Product:", product, "\n")
# ==========================================
# ORDER OF OPERATIONS (PEMDAS/BODMAS)
# ==========================================
(2 + 3) * 4 # Output: 20 (parentheses first)
2 + 3 * 4 # Output: 14 (multiplication before addition)
# ==========================================
# PRACTICAL EXAMPLE: AREA OF A CIRCLE
# ==========================================
radius <- 5
area <- pi * radius ^ 2 # pi is a built-in constant in R
cat("Area of circle with radius", radius, "=", area, "\n") # Output: ~78.53982
Importing a data in R
the R programming language can read many data formats (.csv, .tsv, .xlsx to name a few).
- for this assignment, I was given the task to find the mean, median, and modus from nutrient.csv
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data <- read.csv("path\\to\\file\\nutrient.csv") # reading the CSV file
head(data) # is used to get the first rows of the DataFrame, Vector, or compatible object
modus <- function(x) {
uniq_x <- unique(x)
uniq_x[which.max(tabulate(match(x, uniq_x)))]
}
print("Mean")
print(sapply(data, mean))
print("Median")
print(sapply(data, median))
print("Modus")
print(sapply(data, modus))
which outputs to
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> data <- read.csv("C:\\Users\\USER\\Desktop\\Rprogramming\\nutrient.csv") # reading the CSV file
> head(data) # is used to get the first rows of the DataFrame, Vector, or compatible object
id calcium iron protein vitamin.A vitamin.C
1 1 522.29 10.188 42.561 349.13 54.141
2 2 343.32 4.113 67.793 266.99 24.839
3 3 858.26 13.741 59.933 667.90 155.455
4 4 575.98 13.245 42.215 792.23 224.688
5 5 1927.50 18.919 111.316 740.27 80.961
6 6 607.58 6.800 45.785 165.68 13.050
> modus <- function(x) {
+ uniq_x <- unique(x)
+ uniq_x[which.max(tabulate(match(x, uniq_x)))]
+ }
> print("Mean")
[1] "Mean"
> print(sapply(data, mean))
id calcium iron protein vitamin.A vitamin.C
464.25645 624.04925 11.12990 65.80344 839.63535 78.92845
> print("Median")
[1] "Median"
> print(sapply(data, median))
id calcium iron protein vitamin.A vitamin.C
457.000 548.290 10.033 61.072 524.030 53.594
> print("Modus")
[1] "Modus"
> print(sapply(data, modus))
id calcium iron protein vitamin.A vitamin.C
1.000 522.290 8.950 89.235 0.000 0.000
Conclusion
I find R interesting. I didn’t know a programming language for such a specific task to exist. I always thought the same could be done by something like Python which seems to be the jack-of-all-trades language these days. I’ve always been interested in statistics but this.. along with many other data-science related lecture only proves that I have much to learn.
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