dplyr - Assignment 1

Install DPLYR

 install.packages('dplyr')

The dplyr package offers functionality that is easy to understand when working with data tables.

  • Offer a package of functions that has been well optimised.
  • R does not gain any additional capabilities as a result of this.
  • The functionality is really effective.
  • Carries out the data processing tasks that are most frequently used

Dataset: https://drive.google.com/file/d/1KVMo0BhkIQkgO_YU7QjVLJ4BLie5VoJK/view?usp=sharing

Code

#select

library(dplyr)
mydata <- read.csv('murders.csv')
mydata
dim(mydata)
str(mydata)
summary(mydata)

apply(X=mydata[,c(4,6)], MARGIN = 2, FUN = mean,na.rm=T)

mydata[c(1,4,5)]
#mydata[c(mydata, state,population,total)]#cannot use
names(mydata)
names(mydata)[c(1,4,5)]
subset <- select(mydata, state:population) 
subset
subset <- select(mydata, state,population,total) 
subset
subset <- select(mydata, -(abb:region)) 
subset

#filter

library(dplyr)
mydata <- read.csv('murders.csv')
mydata
dim(mydata)
str(mydata)
names(mydata)

subset <- filter(mydata,total>=100)
subset
subset <- filter(mydata,total>=100 & population >= 10,000,000)
subset
subset1 <- select(subset, state,population,total) 
subset1
subset1 <- select(subset, -(abb:region)) 
subset1
summary(subset)
summary(subset1)
summary(subset1$population)
summary(subset1$total)

#Arrange 

library(dplyr)
mydata <- read.csv('murders.csv')
mydata
names(mydata)

subset <- arrange(mydata,population)
subset
subset1 <- select(subset,state,population,total)
subset1
head(subset1,5)
tail(subset1,5)

subset <- arrange(mydata,desc(population))
subset
subset1 <- select(subset,state,population,total)
subset1

head(subset1,5)
tail(subset1,5)

#rename

library(dplyr)
mydata <- read.csv('murders.csv')
mydata
dim(mydata)
str(mydata)
names(mydata)

mydata1 <- rename(mydata, abbrivation = abb, homocide = total)
mydata1

#mutate data frame

library(dplyr)
mydata <- read.csv('murders.csv')
names(mydata)

mydata1 <- mutate(mydata,ratio=murders/population)
names(mydata1)
mydata1
subset <- select(mydata1, state,population,murders,ratio) 
subset

mydata1 <- transmute(mydata,ratio=murders/population)
names(mydata1)
mydata1

mydata1 <- transmute(mydata,state = state, ratio=total/population)
names(mydata1)
mydata

#Generate Summary Statistics

library(dplyr)
mydata <- read.csv('murders.csv')
mydata
dim(mydata)
str(mydata)
names(mydata)

mydata1 <- group_by(mydata,region)
summarize(mydata1,sum(murders))

#Pipeline Operator

library(dplyr)
mydata <- read.csv('murders.csv')
names(mydata)

group_by(mydata,region) %>% summarize(sum(total))

mutate(mydata,ratio=total/population) %>% select(state,population,total,ratio)

mutate(mydata,ratio=total/population) %>% arrange(ratio) %>% select(state,total,ratio)

mutate(mydata,ratio=total/population) %>% arrange(ratio) %>% select(state,total,ratio) %>% head(5)

Comments

Post a Comment

Popular posts from this blog

Probability: Binomial Distribution

 Binomial Distribution The binomial distribution is a discrete probability distribution that describes the number of successes in a fixed number of independent trials, where each trial has a constant probability of success. The binomial distribution has two parameters: n, the number of trials, and p, the probability of success in each trial. The probability of getting exactly k successes in n trials is given by the formula: P(X=k) = (n choose k) * p^k * (1-p)^(n-k) where (n choose k) is the binomial coefficient, which is equal to n! / (k! * (n-k)!) and represents the number of ways to choose k successes out of n trials. The mean or expected value of a binomial distribution is given by E(X) = n * p, and the variance is given by Var(X) = n * p * (1-p). The binomial distribution is widely used in statistics and probability theory, particularly in fields such as genetics, engineering, and finance, where it is used to model the probability of success or failure in a series of trials. Co...
Read more

Probability: Normal Distribution

 Normal Distribution DataSet: https://drive.google.com/file/d/1eggLsBKx_RIK8_m0CHLHu2zAtZlrR3aG/view?usp=share_link Generate a Normal Distribution We can use the rnorm() function in R to generate random values from a normal distribution with a specified mean and standard deviation. The syntax of the function is: rnorm (n, mean = 0 , sd = 1 ) where n is the number of values to generate, mean is the mean of the distribution, and sd is the standard deviation of the distribution. For example, to generate 1000 random values from a normal distribution with a mean of 0 and a standard deviation of 1, we can use the following code: set .seed( 123 ) # set a seed for reproducibility x <- rnorm( 1000 , mean = 0 , sd = 1 ) The set.seed() function is used to set a seed value for the random number generator, which ensures that the same set of random numbers will be generated each time the code is run. This can be useful for reproducibility. Plot a Histogram of the Distribution We can us...
Read more

Hypothesis testing

Drive:  https://drive.google.com/drive/folders/1XSJoE-3rCDDJ6S4mRb8rYCgw5CQ_PYxP?usp=share_link Problem Statement Paired dataset: Suppose we have a dataset of 20 students who took a math test before and after attending a tutoring program. We want to test if the tutoring program had a significant effect on the students' math scores. Our null hypothesis is that there is no significant difference between the mean scores before and after the tutoring program, and our alternative hypothesis is that there is a significant difference. Data is stored in a file called "math_scores.csv" with two columns: "pre_tutoring_scores" and "post_tutoring_scores".  Load the data: math_scores <- read.csv("math_scores.csv") Calculate the sample mean and standard deviation for both pre-tutoring and post-tutoring scores: pre_tutoring_mean <- mean(math_scores$pre_tutoring_scores) pre_tutoring_sd <- sd(math_scores$pre_tutoring_scores) post_tutoring_mean <- ...
Read more