July 11, 2016

Overview

We showed one way to read data into R using read.csv. In this module, we will show you how to:

  1. Select specific elements of an object by an index or logical condition
  2. Renaming columns of a data.frame
  3. Subset rows of a data.frame
  4. Subset columns of a data.frame
  5. Add/remove new columns to a data.frame
  6. Order the columns of a data.frame
  7. Order the rows of a data.frame

Setup

Select specific elements using an index

Often you only want to look at subsets of a data set at any given time. As a review, elements of an R object are selected using the brackets ([ and ]).

For example, x is a vector of numbers and we can select the second element of x using the brackets and an index (2):

x = c(1, 4, 2, 8, 10)
x[2]
[1] 4

Select specific elements using an index

We can select the fifth or second AND fifth elements below:

x = c(1, 2, 4, 8, 10)
x[5]
[1] 10
x[c(2,5)]
[1]  2 10

Subsetting by deletion of entries

You can put a minus (-) before integers inside brackets to remove these indices from the data.

x[-2] # all but the second
[1]  1  4  8 10

Note that you have to be careful with this syntax when dropping more than 1 element:

x[-c(1,2,3)] # drop first 3
[1]  8 10
# x[-1:3] # shorthand. R sees as -1 to 3
x[-(1:3)] # needs parentheses
[1]  8 10

Select specific elements using logical operators

What about selecting rows based on the values of two variables? We use logical statements. Here we select only elements of x greater than 2:

x
[1]  1  2  4  8 10
x > 2
[1] FALSE FALSE  TRUE  TRUE  TRUE
x[ x > 2 ]
[1]  4  8 10

Select specific elements using logical operators

You can have multiple logical conditions using the following:

  • & : AND
  • | : OR
x[ x > 2 & x < 5 ]
[1] 4
x[ x > 5 | x == 2 ]
[1]  2  8 10

which function

The which functions takes in logical vectors and returns the index for the elements where the logical value is TRUE.

which(x > 5 | x == 2) # returns index
[1] 2 4 5
x[ which(x > 5 | x == 2) ]
[1]  2  8 10
x[ x > 5 | x == 2 ]
[1]  2  8 10

Creating a data.frame to work with

Here we create a toy data.frame named df using random data:

set.seed(2016) # reproducbility
df = data.frame(x = c(1, 2, 4, 10, 10),
                x2 = rpois(5, 10),
                y = rnorm(5),
                z = rpois(5, 6)
                )

Renaming Columns

Renaming Columns of a data.frame: base R

We can use the colnames function to directly reassign column names of df:

colnames(df) = c("x", "X", "y", "z")
head(df)
   x  X          y  z
1  1  7 -0.2707606  6
2  2  6 -1.1179372  4
3  4 10 -1.3473558  7
4 10 13  0.4832675 10
5 10 13  0.1523950  5
colnames(df) = c("x", "x2", "y", "z") #reset

Renaming Columns of a data.frame: base R

We can assign the column names, change the ones we want, and then re-assign the column names:

cn = colnames(df)
cn[ cn == "x2"] = "X"
colnames(df) = cn
head(df)
   x  X          y  z
1  1  7 -0.2707606  6
2  2  6 -1.1179372  4
3  4 10 -1.3473558  7
4 10 13  0.4832675 10
5 10 13  0.1523950  5
colnames(df) = c("x", "x2", "y", "z") #reset

Renaming Columns of a data.frame: dplyr

library(dplyr)
Warning: package 'dplyr' was built under R version 3.3.1
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Note, when loading dplyr, it says objects can be "masked". That means if you use a function defined in 2 places, it uses the one that is loaded in last.

Renaming Columns of a data.frame: dplyr

For example, if we print filter, then we see at the bottom namespace:dplyr, which means when you type filter, it will use the one from the dplyr package.

filter
function (.data, ...) 
{
    filter_(.data, .dots = lazyeval::lazy_dots(...))
}
<environment: namespace:dplyr>

Renaming Columns of a data.frame: dplyr

A filter function exists by default in the stats package, however. If you want to make sure you use that one, you use PackageName::Function with the colon-colon ("::") operator.

head(stats::filter,2)
                                                               
1 function (x, filter, method = c("convolution", "recursive"), 
2     sides = 2L, circular = FALSE, init = NULL)               

This is important when loading many packages, and you may have some conflicts/masking:

Renaming Columns of a data.frame: dplyr

To rename columns in dplyr, you use the rename command

df = dplyr::rename(df, X = x2)
head(df)
   x  X          y  z
1  1  7 -0.2707606  6
2  2  6 -1.1179372  4
3  4 10 -1.3473558  7
4 10 13  0.4832675 10
5 10 13  0.1523950  5
df = dplyr::rename(df, x2 = X) # reset

Subsetting Columns

Subset columns of a data.frame:

We can grab the x column using the $ operator.

df$x
[1]  1  2  4 10 10

Subset columns of a data.frame:

We can also subset a data.frame using the bracket [, ] subsetting.

For data.frames and matrices (2-dimensional objects), the brackets are [rows, columns] subsetting. We can grab the x column using the index of the column or the column name ("x")

df[, 1]
[1]  1  2  4 10 10
df[, "x"]
[1]  1  2  4 10 10

Subset columns of a data.frame:

We can select multiple columns using multiple column names:

df[, c("x", "y")]
   x          y
1  1 -0.2707606
2  2 -1.1179372
3  4 -1.3473558
4 10  0.4832675
5 10  0.1523950

Subset columns of a data.frame: dplyr

The select command from dplyr allows you to subset

select(df, x)
   x
1  1
2  2
3  4
4 10
5 10

Select columns of a data.frame: dplyr

The select command from dplyr allows you to subset columns of

select(df, x, x2)
   x x2
1  1  7
2  2  6
3  4 10
4 10 13
5 10 13
select(df, starts_with("x"))
   x x2
1  1  7
2  2  6
3  4 10
4 10 13
5 10 13

Subsetting Rows

Subset rows of a data.frame with indices:

Let's select rows 1 and 3 from df using brackets:

df[ c(1, 3), ]
  x x2          y z
1 1  7 -0.2707606 6
3 4 10 -1.3473558 7

Subset rows of a data.frame:

Let's select the rows of df where the x column is greater than 5 or is equal to 2. Without any index for columns, all columns are returned:

df[ df$x > 5 | df$x == 2, ]
   x x2          y  z
2  2  6 -1.1179372  4
4 10 13  0.4832675 10
5 10 13  0.1523950  5

Subset rows of a data.frame:

We can subset both rows and colums at the same time:

df[ df$x > 5 | df$x == 2, c("y", "z")]
           y  z
2 -1.1179372  4
4  0.4832675 10
5  0.1523950  5

Subset rows of a data.frame: dplyr

The command in dplyr for subsetting rows is filter. Try ?filter

filter(df, x > 5 | x == 2)
   x x2          y  z
1  2  6 -1.1179372  4
2 10 13  0.4832675 10
3 10 13  0.1523950  5

Note, no $ or subsetting is necessary. R "knows" x refers to a column of df.

Subset rows of a data.frame: dplyr

By default, you can separate conditions by commas, and filter assumes these statements are joined by &

filter(df, x > 2 & y < 0)
  x x2         y z
1 4 10 -1.347356 7
filter(df, x > 2, y < 0)
  x x2         y z
1 4 10 -1.347356 7

Combining filter and select

You can combine filter and select to subset the rows and columns, respectively, of a data.frame:

select(filter(df, x > 2 & y < 0), y, z)
          y z
1 -1.347356 7

In R, the common way to perform multiple operations is to wrap functions around each other in a nested way such as above

Assigning Temporary Objects

One can also create temporary objects and reassign them:

df2 = filter(df, x > 2 & y < 0)
df2 = select(df2, y, z)

Piping - a new concept

There is another (newer) way of performing these operations, called "piping". It is becoming more popular as it's easier to read:

df %>% filter(x > 2 & y < 0) %>% select(y, z)
          y z
1 -1.347356 7

It is read: "take df, then filter the rows and then select y, z".

Adding/Removing Columns

Adding new columns to a data.frame: base R

You can add a new column, called newcol to df, using the $ operator:

df$newcol = 5:1
df$newcol = df$x + 2

Removing columns to a data.frame: base R

You can remove a column by assigning to NULL:

df$newcol = NULL

or selecing only the columns that were not newcol:

df = df[, colnames(df) != "newcol"]

Adding new columns to a data.frame: base R

You can also "column bind" a data.frame with a vector (or series of vectors), using the cbind command:

cbind(df, newcol = 5:1)
   x x2          y  z newcol
1  1  7 -0.2707606  6      5
2  2  6 -1.1179372  4      4
3  4 10 -1.3473558  7      3
4 10 13  0.4832675 10      2
5 10 13  0.1523950  5      1

Adding columns to a data.frame: dplyr

The mutate function in dplyr allows you to add or replace columns of a data.frame:

mutate(df, newcol = 5:1)
   x x2          y  z newcol
1  1  7 -0.2707606  6      5
2  2  6 -1.1179372  4      4
3  4 10 -1.3473558  7      3
4 10 13  0.4832675 10      2
5 10 13  0.1523950  5      1
print({df = mutate(df, newcol = x + 2)})
   x x2          y  z newcol
1  1  7 -0.2707606  6      3
2  2  6 -1.1179372  4      4
3  4 10 -1.3473558  7      6
4 10 13  0.4832675 10     12
5 10 13  0.1523950  5     12

Removing columns to a data.frame: dplyr

The NULL method is still very common.

The select function can remove a column with a minus (-), much like removing rows:

select(df, -newcol)
   x x2          y  z
1  1  7 -0.2707606  6
2  2  6 -1.1179372  4
3  4 10 -1.3473558  7
4 10 13  0.4832675 10
5 10 13  0.1523950  5

Removing columns to a data.frame: dplyr

Remove newcol and y

select(df, -one_of("newcol", "y"))
   x x2  z
1  1  7  6
2  2  6  4
3  4 10  7
4 10 13 10
5 10 13  5

Ordering columns

Ordering the columns of a data.frame: base R

We can use the colnames function to get the column names of df and then put newcol first by subsetting df using brackets:

cn = colnames(df)
df[, c("newcol", cn[cn != "newcol"]) ]
  newcol  x x2          y  z
1      3  1  7 -0.2707606  6
2      4  2  6 -1.1179372  4
3      6  4 10 -1.3473558  7
4     12 10 13  0.4832675 10
5     12 10 13  0.1523950  5

Ordering the columns of a data.frame: dplyr

The select function can reorder columns. Put newcol first, then select the rest of columns:

select(df, newcol, everything())
  newcol  x x2          y  z
1      3  1  7 -0.2707606  6
2      4  2  6 -1.1179372  4
3      6  4 10 -1.3473558  7
4     12 10 13  0.4832675 10
5     12 10 13  0.1523950  5

Ordering rows

Ordering the rows of a data.frame: base R

We use the order function on a vector or set of vectors, in increasing order:

df[ order(df$x), ]
   x x2          y  z newcol
1  1  7 -0.2707606  6      3
2  2  6 -1.1179372  4      4
3  4 10 -1.3473558  7      6
4 10 13  0.4832675 10     12
5 10 13  0.1523950  5     12

Ordering the rows of a data.frame: base R

The decreasing argument will order it in decreasing order:

df[ order(df$x, decreasing = TRUE), ]
   x x2          y  z newcol
4 10 13  0.4832675 10     12
5 10 13  0.1523950  5     12
3  4 10 -1.3473558  7      6
2  2  6 -1.1179372  4      4
1  1  7 -0.2707606  6      3

Ordering the rows of a data.frame: base R

You can pass multiple vectors, and must use the negative (using -) to mix decreasing and increasing orderings (sort increasing on x and decreasing on y):

df[ order(df$x, -df$y), ]
   x x2          y  z newcol
1  1  7 -0.2707606  6      3
2  2  6 -1.1179372  4      4
3  4 10 -1.3473558  7      6
4 10 13  0.4832675 10     12
5 10 13  0.1523950  5     12

Ordering the rows of a data.frame: dplyr

The arrange function can reorder rows By default, arrange orders in ascending order:

arrange(df, x)
   x x2          y  z newcol
1  1  7 -0.2707606  6      3
2  2  6 -1.1179372  4      4
3  4 10 -1.3473558  7      6
4 10 13  0.4832675 10     12
5 10 13  0.1523950  5     12

Ordering the rows of a data.frame: dplyr

Use the desc to arrange the rows in descending order:

arrange(df, desc(x))
   x x2          y  z newcol
1 10 13  0.4832675 10     12
2 10 13  0.1523950  5     12
3  4 10 -1.3473558  7      6
4  2  6 -1.1179372  4      4
5  1  7 -0.2707606  6      3

Ordering the rows of a data.frame: dplyr

It is a bit more straightforward to mix increasing and decreasing orderings:

arrange(df, x, desc(y))
   x x2          y  z newcol
1  1  7 -0.2707606  6      3
2  2  6 -1.1179372  4      4
3  4 10 -1.3473558  7      6
4 10 13  0.4832675 10     12
5 10 13  0.1523950  5     12

Transmutation

The transmute function in dplyr combines both the mutate and select functions. One can create new columns and keep the only the columns wanted:

transmute(df, newcol2 = x * 3, x, y)
  newcol2  x          y
1       3  1 -0.2707606
2       6  2 -1.1179372
3      12  4 -1.3473558
4      30 10  0.4832675
5      30 10  0.1523950