R notes 1

data types

1. Scaler - scalers in R are vectors of size 1
   1. double
   2. int
   3. character
   4. logical (true/false)
2. Vector - Vectors are one-dimensional homogeneous data structures.
   1. array
   2. matrix
   3. data frame

>v1 <- c(1, 2, 3)           # Numeric vector
v2 <- c("a", "b", "c")     # Character vector
v3 <- c(TRUE, FALSE, TRUE) # Logical vector

A scalar is treated as a numeric vector of length 1

x <- 5     
length(x)  # Output: 1

Declaring vectors

x = c(1,2,3,4,5,6,7,8,9,10); y = c(8,0,4,6,2,10,20,14,8,-9); z = x+y

print(z); [1] 9 2 7 10 7 16 27 22 17 1 min(y) [1] -9 max(y) [1] 20 min(x) [1] 1 mix(y) range_y = max(y) - min(y) [1] 29

ls() [1] “max_x” “max_y” “min_x” “min_y” “range_y” “x” “y” [8] “z”

summary(y) Min. 1st Qu. Median Mean 3rd Qu. Max. -9.0 2.5 7.0 6.3 9.5 20.0

plot graphs

plot(x,y) barplot(x,y) plot(x,y,type=“l”)

write to a text file

When you run your script with Rscript, redirect its output to a file:

Rscript myscript.R > output.txt

• > overwrites the file every time.

• Use >> to append instead:

  Rscript myscript.R >> output.txt OR

sink("output.txt")     # Start capturing console
.
.
.
sink()

read csv

data <- read.csv("students.csv")

setwd("C:/Users/YourName/Documents")
data <- read.csv("students.csv")

data <- read.csv(file.choose())
head(data)

read excel file

install.packages("readxl")
find.package("readxl")
library(readxl)
data_file = file.choose()
patient_data = read_xlsx(data_file)
patient_data

operations on data

metadata

str(patient_data)

accessing individual columns

patient_data$“weight”

find first n or last n elements

head(patient_data) // first 6 rows tail(patient_data) // last 6 rows

random access to data

student_data[1:3, 1:4]
# here , first parameter is the range of rows and 2nd parameter is the range of columns

when only one parameter is passed, then its for the columns

patient_data[2]

here, its is rows range and column

patient_data[1:3, 2]

you can specify multiple columns using this

patient_Data[, c(3,5)]

patient_data[c(1,4), c(1,3,5)]

patient_weight = patient_data$“weight”

using conditions

find the average weight of smokers in the given dataset

smoking_patients = patients_data[patients_data$"smoking" == TRUE , ]