1/3/2023 0 Comments Clean text column in r![]() #Replacing “Not Provided” with “Not Available”ĭata$Parking<-str_replace(data$Parking,”Not Provided”,”Not Available”) CLEAN TEXT COLUMN IN R CODEIf we want to replace a particular word or letter under a column we can do so using the code below: #Installing and loading the required packages If we want to trim the whitespaces in the next under a column we need to use the code shown below: In order to change all the text to uppercase or lowercase in a particular column we need to execute the code shown below: String manipulation in R comes in handy when you are working with datasets that have a lot of text based elements. ![]() There are a wide array of type conversions you can carry out in R. In such a case we can change the type of column by using the code shown below:ĭata$Dist_Taxi<-as.character(data$Dist_Taxi) For example, a column containing text elements stored as a numeric column. Sometimes columns have an incorrect type associated with them. In the code above we renamed the Carpet column as “Carpet_area”. If we want to change the name of our data frame we can do so using the code shown below: This step focuses on the methods that you can use to correct all the errors that you have seen. In order to visualize a box plot we need to use the code shown below: BoxPlots are the best way of spotting outliers in your data frame. In order to plot a histogram for any particular column we need to use the code shown below:īoxplots are super useful because it shows you the median, along with the first, second and third quartiles. We can also use Histograms to figure out if there are outliers in the particular numerical column under study. We can determine if the distribution of data is normal or bi-modal or unimodal or any other kind of distribution of interest. The histogram is very useful in visualizing the overall distribution of a numeric column. There are 2 types of plots that you should use during your cleaning process –The Histogram and the BoxPlot We can view the summary statistics for all the columns of the data frame using the code shown below: Here we can see that the data frame has 932 rows and 10 columns. Next, we want to check the number of rows and columns the data frame has. This renders an output as shown below in which we can clearly see that our dataset is saved as a data frame. The first thing that you should do is check the class of your data frame: ![]() Setwd(“ C:/Users/NAGRAJ/Desktop/House Pricing“)ĭata<-read.csv(“ Regression-Analysis-House Pricing.csv“,na.strings = “”) It is very important to understand how you can import data into R and save it as a data frame. The first step to the overall data cleaning process involves an initial exploration of the data frame that you have just imported into R. R has a set of comprehensive tools that are specifically designed to clean data in an effective and comprehensive manner. It is aimed at improving the content of statistical statements based on the data as well as their reliability. Data cleaning may profoundly influence the statistical statements based on the data. Data Cleaning is the process of transforming raw data into consistent data that can be analyzed. ![]()
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