### R Tutorial 8.0

I won't write much just to fill in the space, better you go inside, as learn sorting !

Let's use an inbuilt dataset in R for understanding sorting.

###

Data_1 = mtcars

Sorting a Vector

Since vector is uni-variate data i.e. contains only one column, so it doesn't require mentioning a sorting variable.HP_sorted_asc = sort(Data_1$hp)

HP_sorted_dsc = sort(Data_1$hp, decreasing = TRUE)

Option

*is TRUE be default !*

**decreasing**### Sorting a Data Frame

attach(Data_1)

**# Let's sort the data in the increasing order of weight (wt)**

Data_sorted = Data_1[order(wt), ]

**# Let's now sort the data in the increasing order of Horse power (hp) and weight (wt)**

Data_sorted = Data_1[order(hp,wt), ]

**# For changing the default ascending order to descending order, put a minus (-) sign before**

**# column name**

Data_sorted = Data_1[order(hp,-wt), ]

### What if there are missing values in sorting column

First, we create missing values for demo :

rm(list = ls()) # Let's clear work space

Data_1 = mtcars

Data_1 = mtcars

**# Suppose, we create few missing values in hp column**

Data_1[1:5,4] = NA

**# I am jumbling the missing values, for testing purpose**

attach(Data_1)

detach(Data_1)

rm(Data_1)

**# would now use the Data_new, which has missing values in hp column, that too jumbed**

**# Let's now sort the data on the basis of column having missing values**

Data_sorted_1 = Data_new[order(hp),]

Data_sorted_2 = Data_new[order(hp, na.last=TRUE),]

Data_sorted_3 = Data_new[order(hp, na.last=FALSE),]

Data_sorted_4 = Data_new[order(hp, na.last=NA),]

###
**Let's see how the results differ :**

**Let's see how the results differ :**

**Data_sorted_1 :**Data is sorted on hp column in ascending order of hp and all the observations with missing values are left in the last. Even if we consider the descending order while sorting, the observations with missing values in the sorting column by default are left in the end of dataset.

**Data_sorted_2 :**Data is same as Data_sorted_1, the only difference is in this statement we have instructed to keep the observation with missing values in the sorting column in the last.

Data_sorted_3 |

**Data_sorted_3 :**In this code, we have instructed R to keep the observation with missing values in the sorting column in the starting, and R obeys.

**Data_sorted_4 :**In this code, we have instructed R to drop all the observations with missing values in the sorting column, Hence all the 5 observations that have missing hp are deleted.

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