Write R Code(S) to Read the Contents of Data.csv and Store Them in a Data Frame Called Data.
Addressing Data
Overview
Pedagogy: twenty min
Exercises: 0 minQuestions
What are the different methods for accessing parts of a data frame?
Objectives
Empathize the three different ways R tin can address information inside a data frame.
Combine unlike methods for addressing data with the assignment operator to update subsets of data.
R is a powerful language for data manipulation. There are three master means for addressing information inside R objects.
- By index (subsetting)
- Past logical vector
- Past proper noun
Lets commencement by loading some sample data:
dat <- read.csv ( file = 'data/sample.csv' , header = TRUE , stringsAsFactors = False ) The outset row of this csv file is a list of cavalcade names. We used the header = TRUE argument to
read.csvand 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 dissever lesson.
Lets take a wait at this data.
R has loaded the contents of the .csv file into a variable chosen dat which is a data frame.
We tin compactly display the internal structure of a data frame using the structure role str.
'data.frame': 100 obs. of ix variables: $ ID : chr "Sub001" "Sub002" "Sub003" "Sub004" ... $ Gender : chr "m" "m" "thousand" "f" ... $ Group : chr "Command" "Treatment2" "Treatment2" "Treatment1" ... $ BloodPressure: int 132 139 130 105 125 112 173 108 131 129 ... $ Historic period : num 16 17.ii 19.5 15.7 19.ix fourteen.3 17.7 xix.viii 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 part tell us that the data has 100 rows and ix columns. It is as well tell united states of america that the data frame is made upwards of grapheme chr, integer int and numeric vectors.
ID Gender Grouping BloodPressure Age Aneurisms_q1 Aneurisms_q2 one Sub001 g Control 132 16.0 114 140 2 Sub002 k Treatment2 139 17.2 148 209 3 Sub003 m Treatment2 130 19.5 196 251 four Sub004 f Treatment1 105 fifteen.seven 199 140 5 Sub005 m Treatment1 125 19.nine 188 120 6 Sub006 M Treatment2 112 14.3 260 266 Aneurisms_q3 Aneurisms_q4 i 202 237 ii 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 iii different treatments.
Addressing by Index
Information can be accessed past index. Nosotros have already seen how square brackets [ tin be used to subset data (sometimes also chosen "slicing"). The generic format is dat[row_numbers,column_numbers].
Selecting Values
What volition be returned by
dat[ane, 1]? Think about the number of rows and columns you lot would expect as the result.Solution
If we leave out a dimension R will interpret this as a asking for all values in that dimension.
Selecting More Values
What will be returned past
dat[, 2]?Solution
[one] "m" "m" "m" "f" "k" "M" "f" "k" "m" "f" "m" "f" "f" "m" "grand" "yard" "f" "m" [xix] "k" "F" "f" "m" "f" "f" "m" "Chiliad" "Chiliad" "f" "m" "f" "f" "m" "k" "1000" "yard" "f" [37] "f" "grand" "M" "chiliad" "f" "g" "m" "m" "f" "f" "One thousand" "M" "m" "m" "m" "f" "f" "f" [55] "m" "f" "m" "1000" "m" "f" "f" "f" "f" "M" "f" "g" "f" "f" "M" "k" "m" "yard" [73] "F" "m" "thousand" "f" "M" "G" "Yard" "f" "chiliad" "M" "Chiliad" "m" "one thousand" "f" "f" "f" "1000" "grand" [91] "f" "one thousand" "F" "f" "m" "m" "F" "m" "M" "One thousand"
The colon : can be used to create a sequence of integers.
Creates a vector of numbers from 6 to 9.
This can be very useful for addressing data.
Subsetting with Sequences
Utilise the colon operator to alphabetize just the aneurism count data (columns 6 to ix).
Solution
Aneurisms_q1 Aneurisms_q2 Aneurisms_q3 Aneurisms_q4 1 114 140 202 237 two 148 209 248 248 3 196 251 122 177 4 199 140 233 220 five 188 120 222 228 vi 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 xiv 109 150 177 189 xv 146 140 239 223 sixteen 97 172 203 207 17 165 157 200 193 eighteen 158 265 243 187 19 178 109 206 182 xx 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 twoscore 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 eighty 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 lx 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 fourscore 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 xc 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 nosotros can utilize the c() (combine) function to address non-sequential rows and columns.
ID Gender Group BloodPressure Age i Sub001 m Control 132 16.0 5 Sub005 1000 Treatment1 125 xix.ix 7 Sub007 f Control 173 17.vii nine Sub009 m Treatment2 131 19.four Returns the offset 5 columns for patients in rows i,5,seven and ix
Subsetting Non-Sequential Data
Write code to render the age and gender values for the showtime 5 patients.
Solution
Age Gender 1 16.0 m 2 17.2 thou 3 xix.5 m four 15.7 f 5 19.9 m
Addressing by Proper name
Columns in an R data frame are named.
[1] "ID" "Gender" "Group" "BloodPressure" [5] "Age" "Aneurisms_q1" "Aneurisms_q2" "Aneurisms_q3" [ix] "Aneurisms_q4" Default Names
If column names are not specified e.g. using
headers = Fakein aread.csv()office, R assigns default namesV1, V2, ..., Vn
We usually employ the $ operator to address a column by name
[1] "g" "m" "thousand" "f" "1000" "G" "f" "m" "one thousand" "f" "m" "f" "f" "k" "m" "m" "f" "thousand" [xix] "yard" "F" "f" "m" "f" "f" "m" "Grand" "M" "f" "m" "f" "f" "g" "thousand" "m" "1000" "f" [37] "f" "grand" "One thousand" "m" "f" "grand" "one thousand" "m" "f" "f" "1000" "M" "one thousand" "m" "one thousand" "f" "f" "f" [55] "m" "f" "m" "m" "m" "f" "f" "f" "f" "Yard" "f" "yard" "f" "f" "M" "g" "thousand" "m" [73] "F" "m" "yard" "f" "M" "M" "G" "f" "m" "M" "M" "m" "chiliad" "f" "f" "f" "thousand" "m" [91] "f" "m" "F" "f" "g" "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.
Named addressing can likewise be used in foursquare brackets.
caput ( dat [, c ( 'Age' , 'Gender' )]) Age Gender i xvi.0 m 2 17.2 thousand 3 19.five m four fifteen.7 f 5 19.9 m 6 fourteen.3 K Best Practise
Best practice is to address columns past name. Often, you will create or delete columns and the cavalcade position will modify.
Rows in an R information frame can also be named, and rows can also be addressed by their names.
Past default, row names are indices (i.e. position of each row in the data frame):
[i] "1" "ii" "three" "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" "l" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60" [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "seventy" "71" "72" [73] "73" "74" "75" "76" "77" "78" "79" "eighty" "81" "82" "83" "84" [85] "85" "86" "87" "88" "89" "90" "91" "92" "93" "94" "95" "96" [97] "97" "98" "99" "100" Nosotros can add together row names as we read in the file with the row.names parameter in read.csv.
In the following example, we choose the outset column ID to get the vector of row names of the data frame, with row.names = 1.
dat2 <- read.csv ( file = 'data/sample.csv' , header = True , stringsAsFactors = Simulated , row.names = ane ) rownames ( dat2 ) [i] "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 i or more rows using those row names:
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 Grouping BloodPressure Age Aneurisms_q1 Aneurisms_q2 Sub009 m Treatment2 131 nineteen.4 117 215 Sub072 thousand Control 116 17.iv 118 165 Aneurisms_q3 Aneurisms_q4 Sub009 181 272 Sub072 220 227 Notation that row names must be unique!
For example, if nosotros try and read in the data setting the Group column as row names, R volition throw an error because values in that cavalcade are duplicated:
dat2 <- read.csv ( file = 'information/sample.csv' , header = TRUE , stringsAsFactors = Imitation , row.names = 3 ) Error in read.table(file = file, header = header, sep = sep, quote = quote, : duplicate 'row.names' are non allowed Addressing past Logical Vector
A logical vector contains only the special values True and Faux.
c ( True , True , False , Simulated , TRUE ) [1] True True False FALSE TRUE Truth and Its Opposite
Note the values
TRUEandImitationare all capital letters and are not quoted.
Logical vectors tin can be created using relational operators east.1000. <, >, ==, !=, %in%.
x <- c ( i , 2 , three , eleven , 12 , 13 ) x < ten [1] TRUE Truthful TRUE FALSE FALSE FALSE [i] True TRUE TRUE Faux Simulated Faux We tin can use logical vectors to select data from a data frame. This is often referred to as logical indexing.
index <- dat $ Grouping == 'Control' dat [ index ,] $ BloodPressure [1] 132 173 129 77 158 81 137 111 135 108 133 139 126 125 99 122 155 133 94 [twenty] 98 74 116 97 104 117 90 150 116 108 102 Oftentimes this operation is written as one line of code:
plot ( dat [ dat $ Group == 'Command' , ] $ BloodPressure ) 
Using Logical Indexes
- Create a scatterplot showing BloodPressure for subjects not in the command group.
- How many ways are at that place to index this set of subjects?
Solution
The lawmaking for such a plot:
plot ( dat [ dat $ Group != 'Command' , ] $ BloodPressure )
In addition to
dat$Group != 'Control', 1 could employdat$Group %in% c("Treatment1", "Treatment2").
Combining Addressing and Consignment
The consignment operator <- can be combined with addressing.
x <- c ( 1 , 2 , 3 , xi , 12 , thirteen ) x [ 10 < 10 ] <- 0 x Updating a Subset of Values
In this dataset, values for Gender have been recorded every bit both capital
M, Fand lowercaseyard, f. Combine the addressing and assignment operations to convert all values to lowercase.Solution
dat [ dat $ Gender == 'Grand' , ] $ Gender <- '1000' dat [ dat $ Gender == 'F' , ] $ Gender <- 'f'
Key Points
Data in data frames can be addressed by alphabetize (subsetting), by logical vector, or by name (columns only).
Employ the
$operator to address a column by name.
Source: https://swcarpentry.github.io/r-novice-inflammation/10-supp-addressing-data/
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