People remain confused when it comes to summarizing data real quick in R. There are various options. But, which one is the best ? I’ve answered this question below. You must choose one at first. And, become expert at it. That’s how you should move to the next.
People who transition from SAS or SQL are used to writing simple queries on these languages to summarize data sets. For such audience, the biggest concern is to how do we do the same thing on R.
In this article I will cover primary ways to summarize data sets. Hopefully this will make your journey much easier than it looks like.
Generally, summarizing data means finding statistical figures such as mean, median, box plot etc. If understand well with scatter plots & histogram, you can refer to guide on data visualization in R.
Apply function returns a vector or array or list of values obtained by applying a function to either rows or columns. This is the simplest of all the function which can do this job. However this function is very specific to collapsing either row or column.
m <-
matrix
(
c
(1:10, 11:20), nrow = 10, ncol = 2)
apply
(m, 1, mean)
[1] 6 7 8 9 10 11 12 13 14 15
apply
(m, 2, mean)
[1] 5.5 15.5
“lapply” returns a list of the same length as X, each element of which is the result of applying FUN to the corresponding element of X.”
l <-
list
(a = 1:10, b = 11:20)
lapply
(l, mean)
$a
[1] 5.5
$b
[1] 15.5
“sapply” does the same thing as apply but returns a vector or matrix. Let’s consider the last example again.
l <-
list
(a = 1:10, b = 11:20)
l.mean <-
sapply
(l, mean)
class
(l.mean)
[1]
"numeric"
Till now, all the function we discussed cannot do what Sql can achieve. Here is a function which completes the palette for R. Usage is “tapply(X, INDEX, FUN = NULL, …, simplify = TRUE)”, where X is “an atomic object, typically a vector” and INDEX is “a list of factors, each of same length as X”. Here is an example which will make the usage clear.
attach
(iris)
# mean petal length by species
tapply
(iris$Petal.Length, Species, mean)
setosa versicolor virginica
1.462 4.260 5.552
Now comes a slightly more complicated algorithm. Function ‘by’ is an object-oriented wrapper for ‘tapply’ applied to data frames. Hopefully the example will make it more clear.
attach
(iris)
by
(iris[, 1:4], Species, colMeans)
Species: setosa
Sepal.Length Sepal.Width Petal.Length Petal.Width
5.006 3.428 1.462 0.246
------------------------------------------------------------
Species: versicolor
Sepal.Length Sepal.Width Petal.Length Petal.Width
5.936 2.770 4.260 1.326
------------------------------------------------------------
Species: virginica
Sepal.Length Sepal.Width Petal.Length Petal.Width
6.588 2.974 5.552 2.026
What did the function do? It simply splits the data by a class variable, which in this case is the specie. And then it creates a summary at this level. So it does apply function on split frames. The returned object is of class “by”.
If you found any of the above statements difficult, don’t panic. I bring you a life line which you can use anytime. Let’s fit in the SQL queries in R. Here is a way you can do the same.
attach
(iris)
summarization <- sqldf(select Species, mean(Petal.Length) from Petal.Length_mean where Species is not null group by Species’)
library(plyr)attach
(iris)
# mean petal length by species
ddply(iris,"Species",summarise, Petal.Length_mean = mean (Petal.Length))
Additional Notes: You can also use packages such as dplyr, data.table to summarize data. Here’s a complete tutorial on useful packages for data manipulation in R – Faster Data Manipulation with these 7 R Packages.
In general if you are trying to add this summarisation step in the middle of a process and need a table as output, you need to go for sqldf or ddply. “ddply” in these cases is faster but will not give you options beyond just grouping. “sqldf” has all features you need to summarize the data in SQL statements.
In case you are interested in using function similar to pivot tables or transposing the tables, you can consider using “reshape”. We have covered a few examples of the same in our article – comprehensive guide for data exploration in R.
Challenge : Here is a simple problem you can attempt to solve using all the methods we have discussed. You have a table for all school kids marks in a particular city.
Write a code to find the mean marks of each school for both class 1 and 2, for students with roll no less than 6. And print only the class whose mean score comes out to be higher for the school. For instance, if school A has a mean score of 6 for class 1 and 4 for class 2, you will reject class 2 and only take class 1 mean score for the school. In cases of tie, you can make a random choice. Assume that the actual table is much bigger and keep the code as generalized as possible.
Did you find the article useful? Have you used any other function to summarize data on R. If yes, tell us about the function and your views on other functions discussed in this blog.
The Ultimate Swiss Army Knife of ‘apply...
Do Faster Data Manipulation using These 7 R Pac...
Programming in R – From Variables to Visu...
Comprehensive guide for Data Exploration in R
data.table() vs data.frame() – Learn to w...
4 Tricky R interview questions
Master the basics of R Programming
Get to know all about the R Language
Comprehensive Guide to Data Visualization in R
Quick Guide to learn Statistics for R Users (wi...
We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.
Show details
This site uses cookies to ensure that you get the best experience possible. To learn more about how we use cookies, please refer to our Privacy Policy & Cookies Policy.
It is needed for personalizing the website.
Expiry: Session
Type: HTTP
This cookie is used to prevent Cross-site request forgery (often abbreviated as CSRF) attacks of the website
Expiry: Session
Type: HTTPS
Preserves the login/logout state of users across the whole site.
Expiry: Session
Type: HTTPS
Preserves users' states across page requests.
Expiry: Session
Type: HTTPS
Google One-Tap login adds this g_state cookie to set the user status on how they interact with the One-Tap modal.
Expiry: 365 days
Type: HTTP
Used by Microsoft Clarity, to store and track visits across websites.
Expiry: 1 Year
Type: HTTP
Used by Microsoft Clarity, Persists the Clarity User ID and preferences, unique to that site, on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.
Expiry: 1 Year
Type: HTTP
Used by Microsoft Clarity, Connects multiple page views by a user into a single Clarity session recording.
Expiry: 1 Day
Type: HTTP
Collects user data is specifically adapted to the user or device. The user can also be followed outside of the loaded website, creating a picture of the visitor's behavior.
Expiry: 2 Years
Type: HTTP
Use to measure the use of the website for internal analytics
Expiry: 1 Years
Type: HTTP
The cookie is set by embedded Microsoft Clarity scripts. The purpose of this cookie is for heatmap and session recording.
Expiry: 1 Year
Type: HTTP
Collected user data is specifically adapted to the user or device. The user can also be followed outside of the loaded website, creating a picture of the visitor's behavior.
Expiry: 2 Months
Type: HTTP
This cookie is installed by Google Analytics. The cookie is used to store information of how visitors use a website and helps in creating an analytics report of how the website is doing. The data collected includes the number of visitors, the source where they have come from, and the pages visited in an anonymous form.
Expiry: 399 Days
Type: HTTP
Used by Google Analytics, to store and count pageviews.
Expiry: 399 Days
Type: HTTP
Used by Google Analytics to collect data on the number of times a user has visited the website as well as dates for the first and most recent visit.
Expiry: 1 Day
Type: HTTP
Used to send data to Google Analytics about the visitor's device and behavior. Tracks the visitor across devices and marketing channels.
Expiry: Session
Type: PIXEL
cookies ensure that requests within a browsing session are made by the user, and not by other sites.
Expiry: 6 Months
Type: HTTP
use the cookie when customers want to make a referral from their gmail contacts; it helps auth the gmail account.
Expiry: 2 Years
Type: HTTP
This cookie is set by DoubleClick (which is owned by Google) to determine if the website visitor's browser supports cookies.
Expiry: 1 Year
Type: HTTP
this is used to send push notification using webengage.
Expiry: 1 Year
Type: HTTP
used by webenage to track auth of webenagage.
Expiry: Session
Type: HTTP
Linkedin sets this cookie to registers statistical data on users' behavior on the website for internal analytics.
Expiry: 1 Day
Type: HTTP
Use to maintain an anonymous user session by the server.
Expiry: 1 Year
Type: HTTP
Used as part of the LinkedIn Remember Me feature and is set when a user clicks Remember Me on the device to make it easier for him or her to sign in to that device.
Expiry: 1 Year
Type: HTTP
Used to store information about the time a sync with the lms_analytics cookie took place for users in the Designated Countries.
Expiry: 6 Months
Type: HTTP
Used to store information about the time a sync with the AnalyticsSyncHistory cookie took place for users in the Designated Countries.
Expiry: 6 Months
Type: HTTP
Cookie used for Sign-in with Linkedin and/or to allow for the Linkedin follow feature.
Expiry: 6 Months
Type: HTTP
allow for the Linkedin follow feature.
Expiry: 1 Year
Type: HTTP
often used to identify you, including your name, interests, and previous activity.
Expiry: 2 Months
Type: HTTP
Tracks the time that the previous page took to load
Expiry: Session
Type: HTTP
Used to remember a user's language setting to ensure LinkedIn.com displays in the language selected by the user in their settings
Expiry: Session
Type: HTTP
Tracks percent of page viewed
Expiry: Session
Type: HTTP
Indicates the start of a session for Adobe Experience Cloud
Expiry: Session
Type: HTTP
Provides page name value (URL) for use by Adobe Analytics
Expiry: Session
Type: HTTP
Used to retain and fetch time since last visit in Adobe Analytics
Expiry: 6 Months
Type: HTTP
Remembers a user's display preference/theme setting
Expiry: 6 Months
Type: HTTP
Remembers which users have updated their display / theme preferences
Expiry: 6 Months
Type: HTTP
Used by Google Adsense, to store and track conversions.
Expiry: 3 Months
Type: HTTP
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
Expiry: 2 Years
Type: HTTP
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
Expiry: 2 Years
Type: HTTP
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
Expiry: 2 Years
Type: HTTP
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
Expiry: 2 Years
Type: HTTP
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
Expiry: 2 Years
Type: HTTP
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
Expiry: 2 Years
Type: HTTP
These cookies are used for the purpose of targeted advertising.
Expiry: 6 Hours
Type: HTTP
These cookies are used for the purpose of targeted advertising.
Expiry: 1 Month
Type: HTTP
These cookies are used to gather website statistics, and track conversion rates.
Expiry: 1 Month
Type: HTTP
Aggregate analysis of website visitors
Expiry: 6 Months
Type: HTTP
This cookie is set by Facebook to deliver advertisements when they are on Facebook or a digital platform powered by Facebook advertising after visiting this website.
Expiry: 4 Months
Type: HTTP
Contains a unique browser and user ID, used for targeted advertising.
Expiry: 2 Months
Type: HTTP
Used by LinkedIn to track the use of embedded services.
Expiry: 1 Year
Type: HTTP
Used by LinkedIn for tracking the use of embedded services.
Expiry: 1 Day
Type: HTTP
Used by LinkedIn to track the use of embedded services.
Expiry: 6 Months
Type: HTTP
Use these cookies to assign a unique ID when users visit a website.
Expiry: 6 Months
Type: HTTP
These cookies are set by LinkedIn for advertising purposes, including: tracking visitors so that more relevant ads can be presented, allowing users to use the 'Apply with LinkedIn' or the 'Sign-in with LinkedIn' functions, collecting information about how visitors use the site, etc.
Expiry: 6 Months
Type: HTTP
Used to make a probabilistic match of a user's identity outside the Designated Countries
Expiry: 90 Days
Type: HTTP
Used to collect information for analytics purposes.
Expiry: 1 year
Type: HTTP
Used to store session ID for a users session to ensure that clicks from adverts on the Bing search engine are verified for reporting purposes and for personalisation
Expiry: 1 Day
Type: HTTP
Cookie declaration last updated on 24/03/2023 by Analytics Vidhya.
Cookies are small text files that can be used by websites to make a user's experience more efficient. The law states that we can store cookies on your device if they are strictly necessary for the operation of this site. For all other types of cookies, we need your permission. This site uses different types of cookies. Some cookies are placed by third-party services that appear on our pages. Learn more about who we are, how you can contact us, and how we process personal data in our Privacy Policy.
Edit
Resend OTP
Resend OTP in 45s
Add "data.table" methods as well, more effective ones :)
Thank you, very nice overview ! For anyone interested in more details on the apply-functions, I recommend this rather old (but as far as I can tell still valid) additional blog post: https://nsaunders.wordpress.com/2010/08/20/a-brief-introduction-to-apply-in-r/ One remark from my side: for "real coders" the one-liners from the example are probably more elegant. For daily work with data frames, I prefer in the meantime using not ddply() from the plyr package, but rather using the dplyr-package with its piping. The resulting code is more verbose, but more readable and accessible, like e.g. this pseudo code working on the Titanic dataset: df % group_by(Class, Sex, Age, Survived) %>% summarise(Count = n()) where basically any function can be used in combination with the summarise()-function.
How is this? --- library(dplyr) d <- read.csv('school.csv', stringsAsFactors=F) dd <- d[d$rollno % group_by(school, class) %>% summarise(marks=mean(marks)) sapply(seq(1, nrow(dd)-1, 2), FUN=function(i) { if (dd$marks[i] == dd$marks[i + 1]) { dd[sample(c(i, i+1), 1), ] } else { dd[ifelse(dd$marks[i] > dd$marks[i + 1], i, i+1), ] } }) %>% t
The formatting in the first comment was broken. Please refer to the link below. http://justpaste.it/po5s
Solution to the given Challenge can be done in multiple ways. One short way without using sqldf is as below- ddply(aggregate(Marks~School+Class,df[Rollno<6,],mean),.(School),function(x) x[which.max(x$Marks),]) School Class Marks 1 A 1 6 2 B 1 4 3 C 2 6 Note- One of the most most useful data summarisation function is aggregate
Any one who knows any shortest way than mine mentioned above kindly provide the solution which will be helpful as part of knowledge sharing.
Solution to the given Challenge can be done in multiple ways. One short way without using sqldf is as below- ddply(aggregate(Marks~School+Class,df[Rollno<6,],mean),.(School),function(x) x[which.max(x$Marks),]) School Class Marks 1 A 1 6 2 B 1 4 3 C 2 6 Note- One of the most most useful data summarisation function is aggregate
There is a comma missing at the beginning of the sqldf statement. The argument should be surrounded by quotes, sqldf will parse the character input for evaluation.
Hello, would you happen to know if there is a stream substitute for attach(iris)? Or is R powerful enough to code with for managing realtime/time-series? Any examples or projects going on where the dataframes are realtime/time-series? I am not sure if I am saying that correctly but hopefully you get the gist of it. Thanks
Hi Friends, Find the below is my code. mx=read.csv("D:/Material/Analytics Vidhya/av_test.csv") head(mx) str(mx) mx1=subset(mx,Roll_No<=6) library(reshape2) max_c = dcast(mx1,School ~ Class,mean,na.rm=T) head(max_c) max=data.frame(max_c,Class=apply(max_c[,c(2:3)],1, function(x){names(which.max(x))}),Maxmean_Score=apply(max_c[,c(2:3)],1,max)) head(max)
#Roll Number is less than 6. mx1=subset(mx,Roll_No<6)
library(plyr) AV_test$Class <- as.factor(AV_test$Class) df1 <- ddply(AV_test[AV_test$Roll_No<6 & AV_test$Class %in% c("1","2") ,], .(School,Class), summarise, Avg_Marks = mean(Marks,na.rm = TRUE)) df2 <- ddply(df1,.(School),function(x)x[which.max(x$Avg_Marks),])