How To Add New Column To Data Frame In R

Dataframes practise not ever come up pre-formatted as we would like them. Consequently, it is often necessary to add additional columns of information to an existing dataframe. When this happens, R makes information technology very easy to execute. It even offers more than 1 way of formatting your data manipulation code.

Why Would Y'all Add Columns?

Why would you demand to know how to add a new column for information analysis in R? At that place are numerous reasons why yous would add columns to an existing dataframe. The ability to add columns gives you the flexibility to format the data, that otherwise would not be. Without this flexibility, the data format cannot adapt to dissimilar situations.

One reason to add column to a dataframe in r is to add data that yous summate based on the existing data set up. Some other reason would be to add supplementary data from another source. When embedding data in an commodity, you may also need to add row labels. These are but three examples of the many reasons you may want to add new cavalcade.

Adding and removing columns maximizes your flexibility on how you tin can utilise data. This feature allows you to adjust the existing data frame to any arrangement yous want, providing almost unlimited flexibility and the way data is displayed.

How To Add A Column

There are three forms to this way of adding a new dataframe column to a data frame in r.

          data-frame$column-name = vector information-frame[["cavalcade-proper noun"]] = vector data-frame[,"cavalcade-name"] = vector

Each of these works the same, they are simply different ways of adding a new column to an existing data frame.

          df = head(morley)    df$Alph = c("a", "b", "c", "d", "due east", "f")    df    Expt Run Speed Alph    001 i 1 850 a    002 1 2 740 b    003 1 3 900 c    004 1 4 1070 d    005 i 5 930 e    006 ane 6 850 f

In this example, we simply added an alphabetic label to each row under the column name Alpha. In an actual chart, this approach could be used as a reference in the presentation of the information manipulation. Of course, there are many other uses, but this is a simple 1.

Reorganizing Columns

Sometimes, a multiple column dataframe object does not come to y'all in the order you lot would like it, and the default value in the original dataframe may not piece of work quite correct for your data analysis. Past saving the contents of an original dataframe column, you can delete the column first so add information technology back to go far the concluding new dataframe column. This process can be repeated as oftentimes equally necessary to obtain the desired sequence.

          df = head(morley)    X = df$Run    df$Run = NULL    df[["Run"]]= X    df    Expt Speed Run    001 one 850 1    002 1 740 ii    003 1 900 three    004 1 1070 iv    005 1 930 v    006 1 850 vi

In this example, we are moving the second dataframe column value to the last position. Offset, we save its contents, then delete the quondam columns. Next, we add together it back in, thereby moving an existing column. Earlier re-adding the cavalcade headers, yous are moving, yous can practice additional processing such equally taking care of missing values.

Adding A Cavalcade Of Calculated Values.

One reason to add together column to a dataframe object is to exist able to include calculated values from existing data. This process makes it possible to brandish relationships that are not easy to see. This approach has more opportunities when yous take multiple values in each row.

          df = head(morley)    10 = df$Speed-mean(df$Speed)    df[,"Departure"]= x    df    Expt Run Speed Deviation    001 ane 1 850 -40    002 1 2 740 -150    003 1 3 900 x    004 1 iv 1070 180    005 1 5 930 twoscore    006 1 half dozen 850 -40

In this example, nosotros have added a cavalcade value based on the deviation of each speed from the hateful speed. This is a very straightforward calculation only information technology tin be more than complicated involving different columns besides as more than ane add new column.

Calculation A Placeholding Column.

A place belongings column is a single column that is put in place so that values tin can be added later. If instead of equating your new cavalcade to a vector, you equate it to a unmarried value that value will exist duplicated across the unabridged cavalcade. This procedure is perfect for setting up a placeholder.

add column to dataframe r how to add new column to r dataframe r add new column to dataframe r add calculated column to dataframe

As yous tin meet from this process effectively adds a new variable to the dataframe. This gives you a place to shop additional values. The value that is entered tin be whatever number, grapheme, or string. It just needs to be the aforementioned information type you programme on putting in that place.

Using The cbind() Function.

This is a completely unlike way of adding columns to the information frame. Unlike the previous method, this method allows multiple columns to be added at the same time. This multiple cavalcade approach has the drawback of just using the vector names as the cavalcade names.

          df = head(morley)    Alph = c("a", "b", "c", "d", "e", "f")    Deviation = df$Speed-mean(df$Speed)    df = cbind(df, Departure, Alph)    df    Expt Run Speed Deviation Alph    001 1 1 850 -xl a    002 1 2 740 -150 b    003 ane 3 900 10 c    004 ane 4 1070 180 d    005 i v 930 twoscore east    006 1 half-dozen 850 -xl f

In this example, we take added ii columns to the original data frame. Because the cbind() office also combines data frames, information technology makes information technology very easy to add new columns.

Adding columns to data frames is a uncomplicated procedure. Depending on how you handle information technology, this procedure tin provide yous with smashing flexibility in using a new data frame. Combining the approaches, we accept used opens up a wide variety of options. These options will give you a lot of flexibility in your programming, even if there is a missing value, column characterization, or other default value or constant value outcome with your new data frame cavalcade.

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