{"id":75903,"date":"2020-01-28T14:24:10","date_gmt":"2020-01-28T08:54:10","guid":{"rendered":"https:\/\/techvidvan.com\/tutorials\/?p=75903"},"modified":"2020-01-28T14:24:10","modified_gmt":"2020-01-28T08:54:10","slug":"data-reshaping-in-r","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/data-reshaping-in-r\/","title":{"rendered":"Data Reshaping in R \u2013 Popular Functions to Organise Data"},"content":{"rendered":"

In this TechVidvan tutorial, discover why data reshaping is required in R and\u00a0learn how to use different functions in R to do this.<\/strong><\/p>\n

For an analysis project, the gathered data is messy and unstructured most of the time. It is collected from different sources, has different variables, and has irregular formats.<\/p>\n

With this tutorial, learn how to change the gathered data and conform it to our desired format in R. We will take a look at the functions in R that allow us to do this and much more. So, get ready for the ride!<\/p>\n

What is Data Reshaping in R?<\/h2>\n

Before we can perform any kind of analysis, we first need to shape the gathered data into a regular and processable format. We need to ensure that all of the data fits into proper variables.<\/p>\n

We also need to take care of missing values and put placeholders in their place that our analysis tools can understand.<\/p>\n

This is the first step in any analysis project. We call this process as data reshaping.<\/strong><\/p>\n

Now let’s learn popular functions used for data reshaping in R.<\/p>\n

The cbind(), rbind(), and t() Functions<\/h3>\n

There are many functions in R that allow us to manipulate data objects in many ways.<\/p>\n

cbind()<\/strong>, rbind()<\/strong>, and t()<\/strong> are the most commonly used functions for data reshaping. We will be going through them one-by-one:<\/p>\n

1. cbind():<\/strong> The cbind() function allows us to join objects as column<\/strong>. We can combine matrices, data-frames, vectors<\/strong><\/a> or any combination of these.<\/p>\n

Code:<\/strong><\/p>\n

vec1 <- c(1,2,3,4,5)\nvec2 <- c(6,7,8,9,10)\nmat1 <- matrix(c(1:15),c(5,3))\nmat2 <- matrix(c(16:30),c(5,3))\ndf1 <- data.frame(matrix(c(1:30),nrow=5))\ndf2 <- data.frame(matrix(c(31:60),nrow=5))\ncbind(vec1,vec2)<\/pre>\n
Output:<\/strong><\/p>\n
\"cbind()<\/a><\/strong><\/p>\n
Code:<\/strong><\/p>\n
cbind(mat1,mat2)<\/pre>\n
Code:<\/strong><\/p>\n
cbind(df1,df2)<\/pre>\n
Output:<\/strong><\/p>\n
\"cbind()<\/a><\/p>\n
Code:<\/strong><\/p>\n
cbind(vec1,mat2)<\/pre>\n
Code:<\/strong><\/p>\n
cbind(vec2,df1)<\/pre>\n
Output:<\/strong><\/p>\n
\"data<\/a><\/p>\n
Code:<\/strong><\/p>\n
cbind(mat1,df2)<\/pre>\n
Output:<\/strong><\/p>\n
\"cbind()<\/a><\/p>\n
Note:<\/strong> The number of rows should be the same for the cbind()<\/code><\/strong> function to work.<\/p>\n
Wait! before proceeding ahead get a clear understanding of user-defined functions in R<\/strong><\/a>.<\/p>\n
2. rbind():<\/strong> The rbind()<\/code><\/strong> function allows us to join objects as rows<\/strong>.<\/p>\n
Code:<\/strong><\/p>\n
rbind(vec1,vec2)<\/pre>\n
Code:<\/strong><\/p>\n
rbind(vec1,df2)<\/pre>\n
Output:<\/strong><\/p>\n
\"data<\/a><\/strong><\/p>\n
Note:<\/strong> The number of columns should be the same for the rbind()<\/code><\/strong> function to work.<\/p>\n
3. t()<\/strong>: Thet()<\/code><\/strong> function transposes a matrix<\/strong><\/a> that is it turns the rows into columns and columns into rows.<\/strong><\/p>\n
Code:<\/strong><\/p>\n
t(mat1)<\/pre>\n
Code:<\/strong><\/p>\n
t(mat2)<\/pre>\n
Output:<\/strong><\/p>\n
\"t()<\/a><\/p>\n
The Tidyr package<\/h3>\n
The tidyr<\/code><\/strong> package is the most commonly used<\/strong> R package for data reshaping in R. tidyr<\/code><\/strong> helps you tidy your data.<\/p>\n
It allows you to convert it into the desired format and make it easier to process and analyze. Tidyr<\/a> simplifies the process of data reshaping.<\/p>\n
To install tidyr<\/code><\/strong>, use the following command:<\/p>\n
install.packages(\"tidyr\")<\/pre>\n
\"installing<\/a><\/strong><\/p>\n
Once installed, include it into your current R session by using the library()<\/code><\/strong> command:<\/p>\n
library(tidyr)<\/pre>\n
\"library(tidyr)8<\/a><\/p>\n
These functions of tidyr<\/code><\/strong> are very useful in data reshaping and keeping your data tidy.<\/p>\n
    \n
  1. gather()<\/li>\n
  2. spread()<\/li>\n
  3. unite()<\/li>\n
  4. separate()<\/li>\n<\/ol>\n
    \"data<\/a><\/p>\n
    Let\u2019s look at these functions<\/strong> and their usage:<\/p>\n
    1. gather() Function<\/h4>\n
    The gather()<\/code><\/strong> function helps us in reshaping wide-format data-frames to long-format.<\/p>\n
    Sometimes, datasets have attributes of common concern spread across different columns. This creates unnecessary variables. Such a dataset is said to be in the wide-format.<\/p>\n
    It would be more efficient to stack similar attributes together and turning the dataset into long-format. The gather()<\/code><\/strong> function allows us to do that.<\/p>\n
    Code:<\/strong><\/p>\n
    month <- month.abb[1:3]\ndelhi <- sample(seq(-5,47,by=0.01),3,rep=TRUE)\nmumbai <-sample(seq(-5,47,by=0.01),3,rep=TRUE)\nchennai <-sample(seq(-5,47,by=0.01),3,rep=TRUE)\nbangalore <- sample(seq(-5,47,by=0.01),3,rep=TRUE)\nkolkata <- sample(seq(-5,47,by=0.01),3,rep=TRUE)\ndata <- data.frame(month,delhi,mumbai,bangalore,chennai,kolkata)\ndata<\/pre>\n
    Output:<\/strong><\/p>\n
    \"tidyr<\/a><\/strong><\/p>\n
    Code:<\/strong><\/p>\n
    gathered_data <- gather(data,key=\"city\",value=\"avg.temp\",-month)\ngathered_data<\/pre>\n
    Output:<\/strong><\/p>\n
    \"tidyr<\/a><\/strong><\/span><\/p>\n
    2. spread() Function<\/h4>\n
    The spread()<\/code><\/strong> function is the complement to the gather()<\/code><\/strong> function. It spreads long-format data-frames to wide-format.<\/p>\n
    Code:<\/strong><\/p>\n
    spread_data <- spread(gathered_data,key=\"city\",value=\"avg.temp\")<\/pre>\n
    Output:<\/strong><\/p>\n
    \"tidyr<\/a><\/strong><\/p>\n
    3. unite() Function<\/h4>\n
    Take a look at the following dataset:<\/p>\n\n\n\n\n\n\n\n
    S.no<\/b><\/td>\nMonth<\/b><\/td>\nYear<\/b><\/td>\nTemp.<\/b><\/td>\n<\/tr>\n
    1<\/span><\/td>\njan<\/span><\/td>\n2018<\/span><\/td>\n4.64<\/span><\/td>\n<\/tr>\n
    2<\/span><\/td>\nfeb<\/span><\/td>\n2018<\/span><\/td>\n19.68<\/span><\/td>\n<\/tr>\n
    3<\/span><\/td>\njan<\/span><\/td>\n2019<\/span><\/td>\n2.56<\/span><\/td>\n<\/tr>\n
    4<\/span><\/td>\nmar<\/span><\/td>\n2019<\/span><\/td>\n36.74<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    In the dataset, the month and year have separate columns. It looks inefficient, doesn\u2019t it?<\/p>\n
    The two variables month and year can be in the same column without affecting the information conveyed by the data. This is exactly what the unite()<\/code><\/strong> function does.<\/p>\n
    Code:<\/strong><\/p>\n
    months <- c(\"jan\",\"feb\",\"jan\",\"mar\")\nyear <- c(\"2018\",\"2018\",\"2019\",\"2019\")\ntemp <- c(4.64,19.68,2.56,36.74)\ndelhi_temp <- data.frame(months,year,temp)\ndelhi_temp<\/pre>\n
    Output:<\/strong><\/p>\n
    \"tidyr<\/a><\/p>\n
    Code:<\/strong><\/p>\n
    united_delhi_temp <- unite(delhi_temp,\"interval\",months,year)\nunited_delhi_temp<\/pre>\n
    Output:<\/strong><\/p>\n
    \"tidyr<\/a><\/p>\n
    4. separate() Function<\/h4>\n
    The separate()<\/code><\/strong> function is the complement to the unite()<\/code><\/strong> function. It separates values into separate columns.<\/p>\n
    Code:<\/strong><\/p>\n
    sep_delhi_temp <- separate(united_delhi_temp,\ninterval,c(\"month\",\"year\"))\nsep_delhi_temp<\/pre>\n
    Output:<\/strong><\/p>\n
    \"tidyr<\/a><\/strong><\/p>\n
    The Reshape2 Package<\/h3>\n
    Reshape2 is another R package that is used for data reshaping. Reshape2 can be considered as an older version of the tidyr<\/code><\/strong> package.<\/p>\n
    The development of the reshape2 package has stopped. The most commonly used reshape2 functions are the melt()<\/code><\/strong> and merge()<\/code><\/strong> functions.<\/p>\n
    1. melt() Function<\/h4>\n
    The melt()<\/code><\/strong> function is very similar to the gather()<\/code><\/strong> function from the tidyr<\/code><\/strong> package. It melts the input data frame<\/strong><\/a> and converts wide-format data into long-format. For example:<\/p>\n
    Code:<\/strong><\/p>\n
    mdata <- melt(data,id=c(\"month\"),variable.name=\"city\", value.name=\"avg.temp\")\nmdata<\/pre>\n
    Output:<\/strong><\/p>\n
    \"data<\/a><\/strong><\/p>\n
    2. merge() Function<\/h4>\n
    The merge()<\/code><\/strong> function can merge data frames. The function merges the input data frames horizontally, therefore, the data frames must have the same variable that is column names. For example:<\/p>\n
    Code:<\/strong><\/p>\n
    months2 <- c(\"apr\", \"mar\", \"feb\", \"jun\")\nyear2 <- c(\"2018\",\"2018\",\"2019\",\"2019\")\ntemp2 <- c(38.75,37.68,28.56,41.74)\ndelhi_temp2 <- data.frame(months2,year2,temp2)\ncolnames(delhi_temp2) <- c(\"months\",\"year\",\"temp\")\ndelhi_temp2<\/pre>\n
    Output:<\/strong><\/p>\n
    \"reshape2<\/a><\/strong><\/p>\n
    Code:<\/strong><\/p>\n
    merge_delhi_temp <- merge(delhi_temp,delhi_temp2,by=\"year\")\nmerge_delhi_temp<\/pre>\n
    Output:<\/strong><\/p>\n
    \"eshape2<\/a><\/p>\n
    Summary<\/h2>\n
    Data reshaping is the first step of any data analysis project. It is also called data formatting<\/strong> and data cleaning<\/strong>.<\/p>\n
    In this article, we looked at the functions in base R that allow us to reshape and transform our data.<\/p>\n
    We also looked at the most popular data processing and transformation package, the tidyr<\/code><\/strong> package. The tidyr<\/code><\/strong> package is a part of the tidyverse collection of R packages. It is a must-have for beginner, intermediate and advanced R programmers.<\/p>\n
    Still, have some doubts about the data reshaping in R?<\/strong> Ask us and our TechVidvan<\/strong> experts will be happy to help you.<\/p>\n
    Keep Visiting\u00a0?<\/p>\n","protected":false},"excerpt":{"rendered":"
    In this TechVidvan tutorial, discover why data reshaping is required in R and\u00a0learn how to use different functions in R to do this. For an analysis project, the gathered data is messy and unstructured...<\/p>\n","protected":false},"author":1,"featured_media":75954,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1020],"tags":[1522,1523,1524,1525,1526,1527,1528,1529,1530,1531],"class_list":["post-75903","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-r","tag-data-reshaping-in-r","tag-gather-r","tag-r-melt","tag-r-merge","tag-r-reshape2","tag-r-unite","tag-reshape-r-package","tag-spread-r","tag-tidyr-package","tag-tidyr-r"],"yoast_head":"\nData Reshaping in R \u2013 Popular Functions to Organise Data - TechVidvan<\/title>\n<meta name=\"description\" content=\"Data Reshaping in R - In this tutorial, learn to change the gathered data and conform it to our desired format with the help of functions in R programming.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/techvidvan.com\/tutorials\/data-reshaping-in-r\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Reshaping in R \u2013 Popular Functions to Organise Data - 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