Manipulating Datasets#
Often we need to manipulate or extract parts of our dataset prior to doing any analysis or plotting with it.
Learning Objectives#
Learn how to subset a dataset using conditional subsetting.
Understand different methods for comparing and matching datasets in R.
Learn how to combine one and two dimensional datasets using
paste(),cbind(), andrbind().Learn how to rename, add and remove rows and/or columns in a two dimensional dataset.
Learn how to use the
seq()function to generate regular sequences of numbers.
Conditional Subsetting#
We have already looked at slicing subsets, where we knew the indexs of the rows or columns of the entries we wanted. There may be times when, instead we want to select rows based on a specific condition. This would require a conditional statement. Conditional commands check if criteria is met and return either TRUE or FALSE in response.
Let’s find which rows of iris have a Sepal.Length less than 7.
%%R
iris$Sepal.Length < 6
[1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[13] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[25] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[37] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[49] TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE
[61] TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE TRUE FALSE
[73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE
[85] TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[97] TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE
[109] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[121] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[145] FALSE FALSE FALSE FALSE FALSE TRUE
Where is says TRUE means the criteria have been met and FALSE not. We can use this to subset the rows of iris
%%R
iris[iris$Sepal.Length < 6,]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5.0 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3.0 1.4 0.1 setosa
14 4.3 3.0 1.1 0.1 setosa
15 5.8 4.0 1.2 0.2 setosa
16 5.7 4.4 1.5 0.4 setosa
17 5.4 3.9 1.3 0.4 setosa
18 5.1 3.5 1.4 0.3 setosa
19 5.7 3.8 1.7 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
21 5.4 3.4 1.7 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
23 4.6 3.6 1.0 0.2 setosa
24 5.1 3.3 1.7 0.5 setosa
25 4.8 3.4 1.9 0.2 setosa
26 5.0 3.0 1.6 0.2 setosa
27 5.0 3.4 1.6 0.4 setosa
28 5.2 3.5 1.5 0.2 setosa
29 5.2 3.4 1.4 0.2 setosa
30 4.7 3.2 1.6 0.2 setosa
31 4.8 3.1 1.6 0.2 setosa
32 5.4 3.4 1.5 0.4 setosa
33 5.2 4.1 1.5 0.1 setosa
34 5.5 4.2 1.4 0.2 setosa
35 4.9 3.1 1.5 0.2 setosa
36 5.0 3.2 1.2 0.2 setosa
37 5.5 3.5 1.3 0.2 setosa
38 4.9 3.6 1.4 0.1 setosa
39 4.4 3.0 1.3 0.2 setosa
40 5.1 3.4 1.5 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
42 4.5 2.3 1.3 0.3 setosa
43 4.4 3.2 1.3 0.2 setosa
44 5.0 3.5 1.6 0.6 setosa
45 5.1 3.8 1.9 0.4 setosa
46 4.8 3.0 1.4 0.3 setosa
47 5.1 3.8 1.6 0.2 setosa
48 4.6 3.2 1.4 0.2 setosa
49 5.3 3.7 1.5 0.2 setosa
50 5.0 3.3 1.4 0.2 setosa
54 5.5 2.3 4.0 1.3 versicolor
56 5.7 2.8 4.5 1.3 versicolor
58 4.9 2.4 3.3 1.0 versicolor
60 5.2 2.7 3.9 1.4 versicolor
61 5.0 2.0 3.5 1.0 versicolor
62 5.9 3.0 4.2 1.5 versicolor
65 5.6 2.9 3.6 1.3 versicolor
67 5.6 3.0 4.5 1.5 versicolor
68 5.8 2.7 4.1 1.0 versicolor
70 5.6 2.5 3.9 1.1 versicolor
71 5.9 3.2 4.8 1.8 versicolor
80 5.7 2.6 3.5 1.0 versicolor
81 5.5 2.4 3.8 1.1 versicolor
82 5.5 2.4 3.7 1.0 versicolor
83 5.8 2.7 3.9 1.2 versicolor
85 5.4 3.0 4.5 1.5 versicolor
89 5.6 3.0 4.1 1.3 versicolor
90 5.5 2.5 4.0 1.3 versicolor
91 5.5 2.6 4.4 1.2 versicolor
93 5.8 2.6 4.0 1.2 versicolor
94 5.0 2.3 3.3 1.0 versicolor
95 5.6 2.7 4.2 1.3 versicolor
96 5.7 3.0 4.2 1.2 versicolor
97 5.7 2.9 4.2 1.3 versicolor
99 5.1 2.5 3.0 1.1 versicolor
100 5.7 2.8 4.1 1.3 versicolor
102 5.8 2.7 5.1 1.9 virginica
107 4.9 2.5 4.5 1.7 virginica
114 5.7 2.5 5.0 2.0 virginica
115 5.8 2.8 5.1 2.4 virginica
122 5.6 2.8 4.9 2.0 virginica
143 5.8 2.7 5.1 1.9 virginica
150 5.9 3.0 5.1 1.8 virginica
Matching#
There are several circumstances we will need to check for matching and use that information. There are several ways we can do this using R depending on what we need.
Using identical(), we can check if values or collections of values are identical.
%%R
# Checking if the first and second row values in the "Species" column of iris are identical
identical(iris$Species[1], iris$Species[2])
[1] TRUE
%%R
# Checking if the first and 51st row values in the "Species" column of iris are identical
identical(iris$Species[1], iris$Species[51])
[1] FALSE
Using all.equal() is similar to identical(), but allows for some tolerance in how similar values can be. For example, we may want to check two numbers with lots of decimal places, but only need them to be within 0.01 of each other. Therefore we can give a tolerance of 0.01
%%R
x1 <- 1.232529
x2 <- 1.23366
all.equal(x1, x2, tolerance=0.01)
[1] TRUE
%%R
all.equal(x1, x2, tolerance=0.0001)
[1] "Mean relative difference: 0.0009176255"
We can use “==” as a selector to pull all matching entries. We can give a numeric value or a character in quotations.
%%R
iris[iris$Species == "setosa", ]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5.0 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3.0 1.4 0.1 setosa
14 4.3 3.0 1.1 0.1 setosa
15 5.8 4.0 1.2 0.2 setosa
16 5.7 4.4 1.5 0.4 setosa
17 5.4 3.9 1.3 0.4 setosa
18 5.1 3.5 1.4 0.3 setosa
19 5.7 3.8 1.7 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
21 5.4 3.4 1.7 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
23 4.6 3.6 1.0 0.2 setosa
24 5.1 3.3 1.7 0.5 setosa
25 4.8 3.4 1.9 0.2 setosa
26 5.0 3.0 1.6 0.2 setosa
27 5.0 3.4 1.6 0.4 setosa
28 5.2 3.5 1.5 0.2 setosa
29 5.2 3.4 1.4 0.2 setosa
30 4.7 3.2 1.6 0.2 setosa
31 4.8 3.1 1.6 0.2 setosa
32 5.4 3.4 1.5 0.4 setosa
33 5.2 4.1 1.5 0.1 setosa
34 5.5 4.2 1.4 0.2 setosa
35 4.9 3.1 1.5 0.2 setosa
36 5.0 3.2 1.2 0.2 setosa
37 5.5 3.5 1.3 0.2 setosa
38 4.9 3.6 1.4 0.1 setosa
39 4.4 3.0 1.3 0.2 setosa
40 5.1 3.4 1.5 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
42 4.5 2.3 1.3 0.3 setosa
43 4.4 3.2 1.3 0.2 setosa
44 5.0 3.5 1.6 0.6 setosa
45 5.1 3.8 1.9 0.4 setosa
46 4.8 3.0 1.4 0.3 setosa
47 5.1 3.8 1.6 0.2 setosa
48 4.6 3.2 1.4 0.2 setosa
49 5.3 3.7 1.5 0.2 setosa
50 5.0 3.3 1.4 0.2 setosa
We can use objects or parts of objects to select rows and columns within [ ] using the “%in%”.
%%R
select <- "versicolor"
iris[iris$Species %in% select, ]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
51 7.0 3.2 4.7 1.4 versicolor
52 6.4 3.2 4.5 1.5 versicolor
53 6.9 3.1 4.9 1.5 versicolor
54 5.5 2.3 4.0 1.3 versicolor
55 6.5 2.8 4.6 1.5 versicolor
56 5.7 2.8 4.5 1.3 versicolor
57 6.3 3.3 4.7 1.6 versicolor
58 4.9 2.4 3.3 1.0 versicolor
59 6.6 2.9 4.6 1.3 versicolor
60 5.2 2.7 3.9 1.4 versicolor
61 5.0 2.0 3.5 1.0 versicolor
62 5.9 3.0 4.2 1.5 versicolor
63 6.0 2.2 4.0 1.0 versicolor
64 6.1 2.9 4.7 1.4 versicolor
65 5.6 2.9 3.6 1.3 versicolor
66 6.7 3.1 4.4 1.4 versicolor
67 5.6 3.0 4.5 1.5 versicolor
68 5.8 2.7 4.1 1.0 versicolor
69 6.2 2.2 4.5 1.5 versicolor
70 5.6 2.5 3.9 1.1 versicolor
71 5.9 3.2 4.8 1.8 versicolor
72 6.1 2.8 4.0 1.3 versicolor
73 6.3 2.5 4.9 1.5 versicolor
74 6.1 2.8 4.7 1.2 versicolor
75 6.4 2.9 4.3 1.3 versicolor
76 6.6 3.0 4.4 1.4 versicolor
77 6.8 2.8 4.8 1.4 versicolor
78 6.7 3.0 5.0 1.7 versicolor
79 6.0 2.9 4.5 1.5 versicolor
80 5.7 2.6 3.5 1.0 versicolor
81 5.5 2.4 3.8 1.1 versicolor
82 5.5 2.4 3.7 1.0 versicolor
83 5.8 2.7 3.9 1.2 versicolor
84 6.0 2.7 5.1 1.6 versicolor
85 5.4 3.0 4.5 1.5 versicolor
86 6.0 3.4 4.5 1.6 versicolor
87 6.7 3.1 4.7 1.5 versicolor
88 6.3 2.3 4.4 1.3 versicolor
89 5.6 3.0 4.1 1.3 versicolor
90 5.5 2.5 4.0 1.3 versicolor
91 5.5 2.6 4.4 1.2 versicolor
92 6.1 3.0 4.6 1.4 versicolor
93 5.8 2.6 4.0 1.2 versicolor
94 5.0 2.3 3.3 1.0 versicolor
95 5.6 2.7 4.2 1.3 versicolor
96 5.7 3.0 4.2 1.2 versicolor
97 5.7 2.9 4.2 1.3 versicolor
98 6.2 2.9 4.3 1.3 versicolor
99 5.1 2.5 3.0 1.1 versicolor
100 5.7 2.8 4.1 1.3 versicolor
Merging and Binding#
We will often need to bring multiple two-dimensional objects together. We can do this multiple ways.
Using rbind() and cbind(), we can combine objects together. rbind() allows us to bind together rows.
%%R
# First we look at the dimension of "iris"
dim(iris)
[1] 150 5
%%R
# Using rbind() to put together two copies of causing double rows
rbind(iris, iris) -> iris.r
dim(iris.r)
[1] 300 5
%%R
iris.r[ ,1]
[1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1
[19] 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0
[37] 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5
[55] 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1
[73] 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3 5.6 5.5
[91] 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3
[109] 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2
[127] 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8
[145] 6.7 6.7 6.3 6.5 6.2 5.9 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8
[163] 4.8 4.3 5.8 5.7 5.4 5.1 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7
[181] 4.8 5.4 5.2 5.5 4.9 5.0 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6
[199] 5.3 5.0 7.0 6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7
[217] 5.6 5.8 6.2 5.6 5.9 6.1 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0
[235] 5.4 6.0 6.7 6.3 5.6 5.5 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8
[253] 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0
[271] 6.9 5.6 7.7 6.3 6.7 7.2 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4
[289] 6.0 6.9 6.7 6.9 5.8 6.8 6.7 6.7 6.3 6.5 6.2 5.9
cbind() allows us to bind together columns.
%%R
# Using cbind() to put together two copies of iris causing double columns
cbind(iris, iris) -> iris.c
dim(iris.c)
[1] 150 10
%%R
iris.c[1,]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length
1 5.1 3.5 1.4 0.2 setosa 5.1
Sepal.Width Petal.Length Petal.Width Species
1 3.5 1.4 0.2 setosa
Both rbind and cbind will work, if you try to combine vectors of different lengths. In this case it will recycle the shorted vector until it matches the length of the longer vector. You will get a warning in this situation.
Using merge(), we can merge objects together assigning what we bind by using “by =”. For example, we can bind using the rownames of our objects using “by = row.names”, we can merge by a specific column present in both objects (e.g. by = “Name”), or different columns in each object (by.x = “Species”, by.y = “Name”).
%%R
# Merging iris by row names
merge(iris, iris, by = "row.names") -> iris.double
dim(iris.double)
[1] 150 11
%%R
iris.double[1, ]
Row.names Sepal.Length.x Sepal.Width.x Petal.Length.x Petal.Width.x Species.x
1 1 5.1 3.5 1.4 0.2 setosa
Sepal.Length.y Sepal.Width.y Petal.Length.y Petal.Width.y Species.y
1 5.1 3.5 1.4 0.2 setosa
Activity#
Create two objects, one containing numbers 1-10, one containing numbers 11-20
Bind them together to make an object of two rows, row 1 being 1:10, row 2 being 11-20
Bind them together to make an object of two columns, columns 1 being 11-20, column 2 being 1-10
Paste#
The function paste() is a way to concatenating together vectors. It can be applied to a characters or numbers,
vector and column(s) of a data frame or matrix. You can define what you want the separator to be (sep =), or use
paste0() or paste() with the argument sep = “” for no space. You can also prodvide a string as an argument
to add the same component to a character or vector.
%%R
# Adding text to a value in iris
paste("Species", iris$Species[1])
[1] "Species setosa"
%%R
# Pasting together two columns of iris
paste(iris$Species[1:10], iris$Sepal.Length[1:10], sep = ":")
[1] "setosa:5.1" "setosa:4.9" "setosa:4.7" "setosa:4.6" "setosa:5"
[6] "setosa:5.4" "setosa:4.6" "setosa:5" "setosa:4.4" "setosa:4.9"
Renaming columns and rows#
By using rownames() and colnames(), we can look at what the rownames and colnames of an object are. We can also use this to replace the rownames and colnames of the object by assigning using <-.
%%R
# Renaming the colnames in iris
iris -> iris2
colnames(iris2)
[1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
%%R
colnames(iris2) <- c("S.Length", "S.Width", "P.Length", "P.Width", "Type")
colnames(iris2)
[1] "S.Length" "S.Width" "P.Length" "P.Width" "Type"
Adding and removing variables#
Adding data to your objects can be very useful. Adding an extra column is very easy using the assignment operator and giving the new column a name.
%%R
# Adding a new column
iris -> iris2
iris2$new.column <- 1:nrow(iris2)
head(iris2)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species new.column
1 5.1 3.5 1.4 0.2 setosa 1
2 4.9 3.0 1.4 0.2 setosa 2
3 4.7 3.2 1.3 0.2 setosa 3
4 4.6 3.1 1.5 0.2 setosa 4
5 5.0 3.6 1.4 0.2 setosa 5
6 5.4 3.9 1.7 0.4 setosa 6
Removing a column can be done by assigning the relevant column the “NULL” value.
%%R
# Removing a column
iris2$new.column <- NULL
Generating a sequence of numbers#
To generate regular sequences, we can use seq(). We provide it a value to start from (from =), and where to
end (to =) abd then a value to increase by (by =).
%%R
# Create a sequence from 0 to 100 increasing by 5 each time
seq(from = 0, to = 100, by = 5)
[1] 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
[20] 95 100
Activity#
Create a copy of iris
Rename the columns of
irisby prefixing with the word “flower” and the separator “_”In your copy, duplicate the
SpeciescolumnAdd a column to your copy which contains the numbers from 4 to 600 increasing by 4 each time