Data Cleaning, Merging, and Wrangling in R | by Michael Grogan | Towards Data Science

One of the big issues when it comes to working with data in any context is the issue of data cleaning and merging of datasets, since it is often the case that you will find yourself having to collate data across multiple files, and will need to rely on R to carry out functions that you would normally carry out using commands like VLOOKUP in Excel.
Here are some useful examples of how R can be used for data manipulation. While Python’s pandas library has traditionally been regarded as the winner in this area, the data manipulation techniques frequently used with R can actually be quite powerful.
Let’s take a look at some of them.
For examples 1–7, we have two datasets:
sales: This file contains the variables Date, ID (which is Product ID), and Sales. We load this into R under the name mydata.
customers: This file contains the variables ID, Age, and Country. We load this into R under the name mydata2.
These datasets are available at the following GitHub repository.
The following are examples of popular techniques employed in R to clean a dataset, along with how to format variables effectively to facilitate analysis. The below functions work particularly well with panel datasets, where we have a mixture of cross-sectional and time series data.
To start off with a simple example, let us choose the customers dataset. Suppose that we only wish to include the variables ID and Age in our data. To do this, we define our data frame as follows:
dataframe<-data.frame(ID,Age)
Often times, it is necessary to combine two variables from different datasets similar to how VLOOKUP is used in Excel to join two variables based on certain criteria. If you are unfamiliar with the VLOOKUP function, then you might find this guide from Spreadsheeto particularly helpful.
In R, this can be done using the merge function.
For instance, suppose that we wish to link the Date variable in the sales dataset with the Age and Country variables in the customers dataset — with the ID variable being the common link.
Therefore, we do as follows:
mergeinfo<-merge(mydata[, c("ID", "Sales")],mydata2[, c("ID", "Age", "Country")])
Upon doing this, we see that a new dataset is formed in R joining our chosen variables:
3. Using as.date to format dates and calculate duration
Suppose that we now wish to calculate the number of days between the current date and the date of sale as listed in the sales file. In order to accomplish this, we can use as.date as follows:
currentdate=as.Date('2016-12-15')dateinfile=as.Date(Date)Duration=currentdate-dateinfile
Going back to the example above, suppose that we now wish to combine this duration variable with the rest of our data.
Hence, we can now combine our new Duration variable with the merge function as above, and can do this as follows:
durationasdouble=as.double.difftime(Duration, units='days')updateddataframe=data.frame(ID,Sales,Date,durationasdouble)updateddataframe
4. Using as.POSIXct and format to calculate differences between seconds
While it is not the case in the above example, a situation can often occur where we have dates which include the time, e.g. “2016–10–13 19:30:55”.
There may be times where we wish to find differences between seconds of two dates. In this regard, as.POSIXct is a more suitable option than as.Date. For instance, we can first format our date as follows:
date_converted<-format(Date, format="%Y-%m-%d %H:%M:%S")new_date_variable<-as.POSIXct(date_converted)seconds<-diff(new_date_variable,1)
When we define our seconds variable, it will now give us the difference between two dates in seconds. Then, it is a matter of simple arithmetic to obtain the difference in minutes and seconds.
minutes<-seconds/60hours<-minutes/60
5. grepl: Remove instances of a string from a variables
Let us look to the Country variable. Suppose that we wish to remove all instances of “Greenland” from our variable. This is accomplished using the grepl command:
countryremoved<-mydata2[!grepl("Greenland", mydata2$Country),]
6. Delete observations using head and tail functions
The head and tail functions can be used if we wish to delete certain observations from a variable, e.g. Sales. The head function allows us to delete the first 30 rows, while the tail function allows us to delete the last 30 rows.
When it comes to using a variable edited in this way for calculation purposes, e.g. a regression, the as.matrix function is also used to convert the variable into matrix format:
Salesminus30days←head(Sales,-30)X1=as.matrix(Salesminus30days)X1 Salesplus30days<-tail(Sales,-30)X2=as.matrix(Salesplus30days)X2
7. Replicate SUMIF using the “aggregate” function
names <- c("John", "Elizabeth", "Michael", "John", "Elizabeth", "Michael")webvisitsframe <- cbind("24","32","40","71","65","63")webvisits=as.numeric(webvisitsframe)minutesspentframe <- cbind("20", "41", "5", "6", "48", "97")minutesspent=as.numeric(minutesspentframe)
Let us suppose that we have created the following table as below, and want to obtain the sum of web visits and minutes spent on a website in any particular period:
In this instance, we can replicate the SUMIF function in Excel (where the values associated with a specific identifier are summed up) by using the aggregate function in R. This can be done as follows (where raw_table is the table specified as above):
sumif_table<-aggregate(. ~ names, data=raw_table, sum)sumif_table
Thus, the values associated with identifiers (in this case, names) are summed up as follows:
As per the examples at Stack Overflow, the plyr and data.table libraries can also be used to accomplish the same result as follows:
library(plyr)ddply(nametable, .(names), summarise, Sum_webvisits = sum(webvisits), Sum_minutesspent = sum(minutesspent)) library(data.table)DT <- as.data.table(nametable)DT[ , lapply(.SD, sum), by = "names"]
8. Calculate lags using the diff() function
When it comes to doing time series analysis, often times it is necessary to calculate lags for a specific variable. To do this in R, we use the diff() function.
For the purposes of this example, we create a matrix with price data for the column names, along with years as our row names:
pricedata <- matrix(c(102, 90, 84, 130, 45), ncol=1)colnames(pricedata) <- c('Price')rownames(pricedata) <- c('2012', '2013', '2014', '2015', '2016')pricedata.table <- as.table(pricedata)pricedata.tableYear	Price2012	1022013	902014	842015	1302016	452. Lag = 1diff(pricedata.table,1)Year	Price2013	-122014	-62015	462016	-853. Lag = 2diff(pricedata.table,2)Year	Price2014	-182015	402016	-394. Differences = 2diff(pricedata.table,differences=2)Year	Price2014	62015	522016	131
9. Separating by list (useful for panel datasets)
Suppose we have a dataset that needs to be separated, e.g. by ID. Doing this manually would make for quite a messy process. Instead, we can do so using the unique and split functions to form a list. Here is an example of how this would be done.
Using a data frame of dates, names, and IDs:
> Date<-c("20/02/2017","21/02/2017","22/02/2017","20/02/2017","21/02/2017","22/02/2017")> ID<-c("20","20","20","40","40","40")> Name<-c("Brian","Brian","Brian","Adam","Adam","Adam")> df<-data.frame(Date,ID,Name)> df        Date ID  Name1 20/02/2017 20 Brian2 21/02/2017 20 Brian3 22/02/2017 20 Brian4 20/02/2017 40  Adam5 21/02/2017 40  Adam6 22/02/2017 40  Adam
However, we would like to separate the observations into two separate lists by filtering by ID. We would do this as below:
> listofids=as.character(unique(df$ID))> mylist <- split(df, df$ID)> mylist $`20`        Date ID  Name1 20/02/2017 20 Brian2 21/02/2017 20 Brian3 22/02/2017 20 Brian$`40`        Date ID Name4 20/02/2017 40 Adam5 21/02/2017 40 Adam6 22/02/2017 40 Adam
This is the list in its entirety. If we wished to call one at a time (by ID as our unique identifier, we can do so as follows:
> mylist[1]$`20`        Date ID  Name1 20/02/2017 20 Brian2 21/02/2017 20 Brian3 22/02/2017 20 Brian> mylist[2]$`40`        Date ID Name4 20/02/2017 40 Adam5 21/02/2017 40 Adam6 22/02/2017 40 Adam
The above examples have illustrated various ways in which we can conduct data manipulation procedures in R, and techniques for replicating common Excel functions such as VLOOKUP. Many thanks for your time, and once again the associated GitHub repository for the above examples is available here.
You can also find more of my data science content at michael-grogan.com.
Disclaimer: This article is written on an “as is” basis and without warranty. It was written with the intention of providing an overview of data science concepts, and should not be interpreted as professional advice in any way.