## Overview

Teaching: 20 min
Exercises: 0 min
Questions
• What are the different methods for accessing parts of a data frame?

Objectives
• Understand the three different ways R can address data inside a data frame.

• Combine different methods for addressing data with the assignment operator to update subsets of data.

R is a powerful language for data manipulation. There are three main ways for addressing data inside R objects.

• By index (subsetting)
• By logical vector
• By name

Lets start by loading some sample data:

``````dat <- read.csv(file = 'data/sample.csv', header = TRUE, stringsAsFactors = FALSE)
``````

## Interpreting Rows as Headers

The first row of this csv file is a list of column names. We used the header = TRUE argument to `read.csv` so that R can interpret the file correctly. We are using the stringsAsFactors = FALSE argument to override the default behaviour for R. Using factors in R is covered in a separate lesson.

Lets take a look at this data.

``````class(dat)
``````
``````[1] "data.frame"
``````

R has loaded the contents of the .csv file into a variable called `dat` which is a `data frame`.

We can compactly display the internal structure of a data frame using the structure function `str`.

``````str(dat)
``````
``````'data.frame':	100 obs. of  9 variables:
\$ ID           : chr  "Sub001" "Sub002" "Sub003" "Sub004" ...
\$ Gender       : chr  "m" "m" "m" "f" ...
\$ Group        : chr  "Control" "Treatment2" "Treatment2" "Treatment1" ...
\$ BloodPressure: int  132 139 130 105 125 112 173 108 131 129 ...
\$ Age          : num  16 17.2 19.5 15.7 19.9 14.3 17.7 19.8 19.4 18.8 ...
\$ Aneurisms_q1 : int  114 148 196 199 188 260 135 216 117 188 ...
\$ Aneurisms_q2 : int  140 209 251 140 120 266 98 238 215 144 ...
\$ Aneurisms_q3 : int  202 248 122 233 222 320 154 279 181 192 ...
\$ Aneurisms_q4 : int  237 248 177 220 228 294 245 251 272 185 ...
``````

The `str` function tell us that the data has 100 rows and 9 columns. It is also tell us that the data frame is made up of character `chr`, integer `int` and `numeric` vectors.

``````head(dat)
``````
``````      ID Gender      Group BloodPressure  Age Aneurisms_q1 Aneurisms_q2
1 Sub001      m    Control           132 16.0          114          140
2 Sub002      m Treatment2           139 17.2          148          209
3 Sub003      m Treatment2           130 19.5          196          251
4 Sub004      f Treatment1           105 15.7          199          140
5 Sub005      m Treatment1           125 19.9          188          120
6 Sub006      M Treatment2           112 14.3          260          266
Aneurisms_q3 Aneurisms_q4
1          202          237
2          248          248
3          122          177
4          233          220
5          222          228
6          320          294
``````

The data is the results of an (not real) experiment, looking at the number of aneurysms that formed in the eyes of patients who undertook 3 different treatments.

### Addressing by Index

Data can be accessed by index. We have already seen how square brackets `[` can be used to subset data (sometimes also called “slicing”). The generic format is `dat[row_numbers,column_numbers]`.

## Selecting Values

What will be returned by `dat[1, 1]`? Think about the number of rows and columns you would expect as the result.

## Solution

``````dat[1, 1]
``````
``````[1] "Sub001"
``````

If we leave out a dimension R will interpret this as a request for all values in that dimension.

## Selecting More Values

What will be returned by `dat[, 2]`?

## Solution

``````dat[, 2]
``````
``````  [1] "m" "m" "m" "f" "m" "M" "f" "m" "m" "f" "m" "f" "f" "m" "m" "m" "f" "m"
[19] "m" "F" "f" "m" "f" "f" "m" "M" "M" "f" "m" "f" "f" "m" "m" "m" "m" "f"
[37] "f" "m" "M" "m" "f" "m" "m" "m" "f" "f" "M" "M" "m" "m" "m" "f" "f" "f"
[55] "m" "f" "m" "m" "m" "f" "f" "f" "f" "M" "f" "m" "f" "f" "M" "m" "m" "m"
[73] "F" "m" "m" "f" "M" "M" "M" "f" "m" "M" "M" "m" "m" "f" "f" "f" "m" "m"
[91] "f" "m" "F" "f" "m" "m" "F" "m" "M" "M"
``````

The colon `:` can be used to create a sequence of integers.

``````6:9
``````
``````[1] 6 7 8 9
``````

Creates a vector of numbers from 6 to 9.

This can be very useful for addressing data.

## Subsetting with Sequences

Use the colon operator to index just the aneurism count data (columns 6 to 9).

## Solution

``````dat[, 6:9]
``````
``````    Aneurisms_q1 Aneurisms_q2 Aneurisms_q3 Aneurisms_q4
1            114          140          202          237
2            148          209          248          248
3            196          251          122          177
4            199          140          233          220
5            188          120          222          228
6            260          266          320          294
7            135           98          154          245
8            216          238          279          251
9            117          215          181          272
10           188          144          192          185
11           134          155          247          223
12           152          177          323          245
13           112          220          225          195
14           109          150          177          189
15           146          140          239          223
16            97          172          203          207
17           165          157          200          193
18           158          265          243          187
19           178          109          206          182
20           107          188          167          218
21           174          160          203          183
22            97          110          194          133
23           187          239          281          214
24           188          191          256          265
25           114          199          242          195
26           115          160          158          228
27           128          249          294          315
28           112          230          281          126
29           136          109          105          155
30           103          148          219          228
31           132          151          234          162
32           118          154          260          160
33           166          176          253          233
34           152          105          197          299
35           191          148          166          185
36           152          178          158          170
37           161          270          232          284
38           239          184          317          269
39           132          137          193          206
40           168          255          273          274
41           140          184          239          202
42           166           85          179          196
43           141          160          179          239
44           161          168          212          181
45           103          111          254          126
46           231          240          260          310
47           192          141          180          225
48           178          180          169          183
49           167          123          236          224
50           135          150          208          279
51           150          166          153          204
52           192           80          138          222
53           153          153          236          216
54           205          264          269          207
55           117          194          216          211
56           199          119          183          251
57           182          129          226          218
58           180          196          250          294
59           111          111          244          201
60           101           98          178          116
61           166          167          232          241
62           158          171          237          212
63           189          178          177          238
64           189          101          193          172
65           239          189          297          300
66           185          224          151          182
67           224          112          304          288
68           104          139          211          204
69           222          199          280          196
70           107           98          204          138
71           153          255          218          234
72           118          165          220          227
73           102          184          246          222
74           188          125          191          157
75           180          283          204          298
76           178          214          291          240
77           168          184          184          229
78           118          170          249          249
79           169          114          248          233
80           156          138          218          258
81           232          211          219          246
82           188          108          180          136
83           169          168          180          211
84           241          233          292          182
85            65          207          234          235
86           225          185          195          235
87           104          116          173          221
88           179          158          216          244
89           103          140          209          186
90           112          130          175          191
91           226          170          307          244
92           228          221          316          259
93           209          142          199          184
94           153          104          194          214
95           111          118          173          191
96           148          132          200          194
97           141          196          322          273
98           193          112          123          181
99           130          226          286          281
100          126          157          129          160
``````

Finally we can use the `c()` (combine) function to address non-sequential rows and columns.

``````dat[c(1, 5, 7, 9), 1:5]
``````
``````      ID Gender      Group BloodPressure  Age
1 Sub001      m    Control           132 16.0
5 Sub005      m Treatment1           125 19.9
7 Sub007      f    Control           173 17.7
9 Sub009      m Treatment2           131 19.4
``````

Returns the first 5 columns for patients in rows 1,5,7 and 9

## Subsetting Non-Sequential Data

Write code to return the age and gender values for the first 5 patients.

## Solution

``````dat[1:5, c(5, 2)]
``````
``````   Age Gender
1 16.0      m
2 17.2      m
3 19.5      m
4 15.7      f
5 19.9      m
``````

### Addressing by Name

Columns in an R data frame are named.

``````colnames(dat)
``````
``````[1] "ID"            "Gender"        "Group"         "BloodPressure"
[5] "Age"           "Aneurisms_q1"  "Aneurisms_q2"  "Aneurisms_q3"
[9] "Aneurisms_q4"
``````

## Default Names

If column names are not specified e.g. using `headers = FALSE` in a `read.csv()` function, R assigns default names `V1, V2, ..., Vn`

We usually use the `\$` operator to address a column by name

``````dat\$Gender
``````
``````  [1] "m" "m" "m" "f" "m" "M" "f" "m" "m" "f" "m" "f" "f" "m" "m" "m" "f" "m"
[19] "m" "F" "f" "m" "f" "f" "m" "M" "M" "f" "m" "f" "f" "m" "m" "m" "m" "f"
[37] "f" "m" "M" "m" "f" "m" "m" "m" "f" "f" "M" "M" "m" "m" "m" "f" "f" "f"
[55] "m" "f" "m" "m" "m" "f" "f" "f" "f" "M" "f" "m" "f" "f" "M" "m" "m" "m"
[73] "F" "m" "m" "f" "M" "M" "M" "f" "m" "M" "M" "m" "m" "f" "f" "f" "m" "m"
[91] "f" "m" "F" "f" "m" "m" "F" "m" "M" "M"
``````

When we extract a single column from a data frame using the `\$` operator, R will return a vector of that column class and not a data frame.

``````class(dat\$Gender)
``````
``````[1] "character"
``````
``````class(dat\$BloodPressure)
``````
``````[1] "integer"
``````

Named addressing can also be used in square brackets.

``````head(dat[, c('Age', 'Gender')])
``````
``````   Age Gender
1 16.0      m
2 17.2      m
3 19.5      m
4 15.7      f
5 19.9      m
6 14.3      M
``````

## Best Practice

Best practice is to address columns by name. Often, you will create or delete columns and the column position will change.

Rows in an R data frame can also be named, and rows can also be addressed by their names.
By default, row names are indices (i.e. position of each row in the data frame):

``````rownames(dat)
``````
``````  [1] "1"   "2"   "3"   "4"   "5"   "6"   "7"   "8"   "9"   "10"  "11"  "12"
[13] "13"  "14"  "15"  "16"  "17"  "18"  "19"  "20"  "21"  "22"  "23"  "24"
[25] "25"  "26"  "27"  "28"  "29"  "30"  "31"  "32"  "33"  "34"  "35"  "36"
[37] "37"  "38"  "39"  "40"  "41"  "42"  "43"  "44"  "45"  "46"  "47"  "48"
[49] "49"  "50"  "51"  "52"  "53"  "54"  "55"  "56"  "57"  "58"  "59"  "60"
[61] "61"  "62"  "63"  "64"  "65"  "66"  "67"  "68"  "69"  "70"  "71"  "72"
[73] "73"  "74"  "75"  "76"  "77"  "78"  "79"  "80"  "81"  "82"  "83"  "84"
[85] "85"  "86"  "87"  "88"  "89"  "90"  "91"  "92"  "93"  "94"  "95"  "96"
[97] "97"  "98"  "99"  "100"
``````

We can add row names as we read in the file with the `row.names` parameter in `read.csv`.
In the following example, we choose the first column ID to become the vector of row names of the data frame, with `row.names = 1`.

``````dat2 <- read.csv(file = 'data/sample.csv', header = TRUE, stringsAsFactors = FALSE, row.names=1)
rownames(dat2)
``````
``````  [1] "Sub001" "Sub002" "Sub003" "Sub004" "Sub005" "Sub006" "Sub007" "Sub008"
[9] "Sub009" "Sub010" "Sub011" "Sub012" "Sub013" "Sub014" "Sub015" "Sub016"
[17] "Sub017" "Sub018" "Sub019" "Sub020" "Sub021" "Sub022" "Sub023" "Sub024"
[25] "Sub025" "Sub026" "Sub027" "Sub028" "Sub029" "Sub030" "Sub031" "Sub032"
[33] "Sub033" "Sub034" "Sub035" "Sub036" "Sub037" "Sub038" "Sub039" "Sub040"
[41] "Sub041" "Sub042" "Sub043" "Sub044" "Sub045" "Sub046" "Sub047" "Sub048"
[49] "Sub049" "Sub050" "Sub051" "Sub052" "Sub053" "Sub054" "Sub055" "Sub056"
[57] "Sub057" "Sub058" "Sub059" "Sub060" "Sub061" "Sub062" "Sub063" "Sub064"
[65] "Sub065" "Sub066" "Sub067" "Sub068" "Sub069" "Sub070" "Sub071" "Sub072"
[73] "Sub073" "Sub074" "Sub075" "Sub076" "Sub077" "Sub078" "Sub079" "Sub080"
[81] "Sub081" "Sub082" "Sub083" "Sub084" "Sub085" "Sub086" "Sub087" "Sub088"
[89] "Sub089" "Sub090" "Sub091" "Sub092" "Sub093" "Sub094" "Sub095" "Sub096"
[97] "Sub097" "Sub098" "Sub099" "Sub100"
``````

We can now extract one or more rows using those row names:

``````dat2["Sub072", ]
``````
``````       Gender   Group BloodPressure  Age Aneurisms_q1 Aneurisms_q2 Aneurisms_q3
Sub072      m Control           116 17.4          118          165          220
Aneurisms_q4
Sub072          227
``````
``````dat2[c("Sub009", "Sub072"), ]
``````
``````       Gender      Group BloodPressure  Age Aneurisms_q1 Aneurisms_q2
Sub009      m Treatment2           131 19.4          117          215
Sub072      m    Control           116 17.4          118          165
Aneurisms_q3 Aneurisms_q4
Sub009          181          272
Sub072          220          227
``````

Note that row names must be unique!
For example, if we try and read in the data setting the Group column as row names, R will throw an error because values in that column are duplicated:

``````dat2 <- read.csv(file = 'data/sample.csv', header = TRUE, stringsAsFactors = FALSE, row.names=3)
``````
``````Error in read.table(file = file, header = header, sep = sep, quote = quote, : duplicate 'row.names' are not allowed
``````

### Addressing by Logical Vector

A logical vector contains only the special values `TRUE` and `FALSE`.

``````c(TRUE, TRUE, FALSE, FALSE, TRUE)
``````
``````[1]  TRUE  TRUE FALSE FALSE  TRUE
``````

## Truth and Its Opposite

Note the values `TRUE` and `FALSE` are all capital letters and are not quoted.

Logical vectors can be created using `relational operators` e.g. `<, >, ==, !=, %in%`.

``````x <- c(1, 2, 3, 11, 12, 13)
x < 10
``````
``````[1]  TRUE  TRUE  TRUE FALSE FALSE FALSE
``````
``````x %in% 1:10
``````
``````[1]  TRUE  TRUE  TRUE FALSE FALSE FALSE
``````

We can use logical vectors to select data from a data frame. This is often referred to as logical indexing.

``````index <- dat\$Group == 'Control'
dat[index,]\$BloodPressure
``````
`````` [1] 132 173 129  77 158  81 137 111 135 108 133 139 126 125  99 122 155 133  94
[20]  98  74 116  97 104 117  90 150 116 108 102
``````

Often this operation is written as one line of code:

``````plot(dat[dat\$Group == 'Control', ]\$BloodPressure)
``````

## Using Logical Indexes

1. Create a scatterplot showing BloodPressure for subjects not in the control group.
2. How many ways are there to index this set of subjects?

## Solution

1. The code for such a plot:

`````` plot(dat[dat\$Group != 'Control', ]\$BloodPressure)
``````

2. In addition to `dat\$Group != 'Control'`, one could use `dat\$Group %in% c("Treatment1", "Treatment2")`.

### Combining Addressing and Assignment

The assignment operator `<-` can be combined with addressing.

``````x <- c(1, 2, 3, 11, 12, 13)
x[x < 10] <- 0
x
``````
``````[1]  0  0  0 11 12 13
``````

## Updating a Subset of Values

In this dataset, values for Gender have been recorded as both uppercase `M, F` and lowercase `m, f`. Combine the addressing and assignment operations to convert all values to lowercase.

## Solution

``````dat[dat\$Gender == 'M', ]\$Gender <- 'm'
dat[dat\$Gender == 'F', ]\$Gender <- 'f'
``````

## Key Points

• Data in data frames can be addressed by index (subsetting), by logical vector, or by name (columns only).

• Use the `\$` operator to address a column by name.