Lecture 2: Data Frame, Matrix, List

Abhijit Dasgupta

September 19, 2018

Preamble

Practice makes perfect

Start using RSeek
Other resources on website http://www.araastat.com/BIOF339_PracticalR
Beg, Borrow, Steal code that you need
- R is open-source, so is meant to be shared

R coding conventions

# This is a comment, which doesn't get evaluated

1:3 # This is also a comment

## [1] 1 2 3

# Multi-line code

x <- c(1, 2, 
       3, 4, 5, 6,
       7)
x

## [1] 1 2 3 4 5 6 7

Google has a style guide for how to write R code

R packages

R packages live on CRAN and its mirrors. To install an R package:

install.packages('dplyr', repos='http://cran.r-project.org')

knitr::include_graphics('lecturedataframe_img/install_package.png')

R Packages

To use a package, or rather, use the functions from the package, you have to load it into R

library(dplyr)

We’ll talk about packages later in the semester.

We will concentrate now on what is known as Base R, that is, the functions that are available when R is installed

Loading data

We will usually load CSV files, since they are the easiest for R. The typical suggestion if you have Excel data is to save the sheet as a CSV and then import it into R.

You can also load Excel files directly using either the readxl or rio packages

The structure of data sets

Tables

Data is typically in a rectangular format
- spreadsheet, database table
- CSV (comma-separated values) or TSV (tab-separated values) files
Characteristic
- Rows are observations
- Columns are variables
- Each column has the same number of observations

Tidy data is a particularly amenable format for data analysis.

An example [GEO](http://www.ncbi.nlm.nih.gov/geo/) dataset

Lower back pain symptoms dataset on Kaggle.com

Breast Cancer Proteome dataset on Kaggle.com

Let’s look at a dataset

Let’s look at a dataset (Link)

data_spine <- read.csv('lecturedataframe_data/Dataset_spine.csv')

head(data_spine)

##   Pelvic.incidence Pelvic.tilt Lumbar.lordosis.angle Sacral.slope
## 1         63.02782   22.552586              39.60912     40.47523
## 2         39.05695   10.060991              25.01538     28.99596
## 3         68.83202   22.218482              50.09219     46.61354
## 4         69.29701   24.652878              44.31124     44.64413
## 5         49.71286    9.652075              28.31741     40.06078
## 6         40.25020   13.921907              25.12495     26.32829
##   Pelvic.radius Degree.spondylolisthesis Pelvic.slope Direct.tilt
## 1      98.67292                -0.254400    0.7445035     12.5661
## 2     114.40543                 4.564259    0.4151857     12.8874
## 3     105.98514                -3.530317    0.4748892     26.8343
## 4     101.86850                11.211523    0.3693453     23.5603
## 5     108.16872                 7.918501    0.5433605     35.4940
## 6     130.32787                 2.230652    0.7899929     29.3230
##   Thoracic.slope Cervical.tilt Sacrum.angle Scoliosis.slope
## 1        14.5386      15.30468   -28.658501         43.5123
## 2        17.5323      16.78486   -25.530607         16.1102
## 3        17.4861      16.65897   -29.031888         19.2221
## 4        12.7074      11.42447   -30.470246         18.8329
## 5        15.9546       8.87237   -16.378376         24.9171
## 6        12.0036      10.40462    -1.512209          9.6548
##   Class.attribute
## 1        Abnormal
## 2        Abnormal
## 3        Abnormal
## 4        Abnormal
## 5        Abnormal
## 6        Abnormal

Ignore the first ##; it denotes that this is R output

Let’s look at a dataset

Assumes that the first row has variable names
Replaces spaces with .
Keeps numeric and character variables together

Let’s look at a dataset

View(data_spine)  ## It looks like a matrix

Let’s look at a dataset

str(data_spine) ## Structure of a dataset

## 'data.frame':    310 obs. of  13 variables:
##  $ Pelvic.incidence        : num  63 39.1 68.8 69.3 49.7 ...
##  $ Pelvic.tilt             : num  22.55 10.06 22.22 24.65 9.65 ...
##  $ Lumbar.lordosis.angle   : num  39.6 25 50.1 44.3 28.3 ...
##  $ Sacral.slope            : num  40.5 29 46.6 44.6 40.1 ...
##  $ Pelvic.radius           : num  98.7 114.4 106 101.9 108.2 ...
##  $ Degree.spondylolisthesis: num  -0.254 4.564 -3.53 11.212 7.919 ...
##  $ Pelvic.slope            : num  0.745 0.415 0.475 0.369 0.543 ...
##  $ Direct.tilt             : num  12.6 12.9 26.8 23.6 35.5 ...
##  $ Thoracic.slope          : num  14.5 17.5 17.5 12.7 16 ...
##  $ Cervical.tilt           : num  15.3 16.78 16.66 11.42 8.87 ...
##  $ Sacrum.angle            : num  -28.7 -25.5 -29 -30.5 -16.4 ...
##  $ Scoliosis.slope         : num  43.5 16.1 19.2 18.8 24.9 ...
##  $ Class.attribute         : Factor w/ 2 levels "Abnormal","Normal": 1 1 1 1 1 1 1 1 1 1 ...

So this is a data.frame object with 310 observations and 13 variables, of which one is a factor and the rest are numeric

It looks like a list of things

Dataframes

Dataframes are the primary mode of storing datasets in R

They were revolutionary in that they kept heterogeneous data together

They share properties of both a matrix and a list

class(data_spine)

## [1] "data.frame"

Technically, a data.frame is a list of vectors (or objects, generally) of the same length

Matrices

A matrix is a rectangular array of data of the same type

matrix(0, nrow=2, ncol=4)

##      [,1] [,2] [,3] [,4]
## [1,]    0    0    0    0
## [2,]    0    0    0    0

matrix(letters, nrow=2)

##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] "a"  "c"  "e"  "g"  "i"  "k"  "m"  "o"  "q"  "s"   "u"   "w"   "y"  
## [2,] "b"  "d"  "f"  "h"  "j"  "l"  "n"  "p"  "r"  "t"   "v"   "x"   "z"

matrix(letters, nrow=2, byrow=T)

##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] "a"  "b"  "c"  "d"  "e"  "f"  "g"  "h"  "i"  "j"   "k"   "l"   "m"  
## [2,] "n"  "o"  "p"  "q"  "r"  "s"  "t"  "u"  "v"  "w"   "x"   "y"   "z"

Matrices

You can create a matrix from a set of vectors of the same length

x <- c(1,2,3,4)
y <- c(10,20,30,40)

Put columns together

cbind(c(1,2,3,4), c(10,20,30,40)) ## Column bind

##      [,1] [,2]
## [1,]    1   10
## [2,]    2   20
## [3,]    3   30
## [4,]    4   40

Matrices

You can create a matrix from a set of vectors of the same length

x <- c(1,2,3,4)
y <- c(10,20,30,40)

Put rows together

example_matrix <- rbind(c(1,2,3,4), c(10,20,30,40)) ## Row bind
example_matrix

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]   10   20   30   40

Extracting elements

example_matrix

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]   10   20   30   40

example_matrix[1,] ## Extracts 1st row

## [1] 1 2 3 4

example_matrix[,2:3] ## extracts 2nd & 3rd columns

##      [,1] [,2]
## [1,]    2    3
## [2,]   20   30

example_matrix[1,4]

## [1] 4

Matrix properties

example_matrix

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]   10   20   30   40

nrow(example_matrix) ## Number of rows

## [1] 2

ncol(example_matrix) ## Number of columns

## [1] 4

dim(example_matrix) ## shortcut for above

## [1] 2 4

Matrix arithmetic

example_matrix

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]   10   20   30   40

example_matrix + 5 ## Add 5 to each element

##      [,1] [,2] [,3] [,4]
## [1,]    6    7    8    9
## [2,]   15   25   35   45

example_matrix * 2 ## Multiply each element by 2

##      [,1] [,2] [,3] [,4]
## [1,]    2    4    6    8
## [2,]   20   40   60   80

Two matrices

example_matrix

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]   10   20   30   40

example_matrix2 <- rbind(3:6, 9:12)
example_matrix2

##      [,1] [,2] [,3] [,4]
## [1,]    3    4    5    6
## [2,]    9   10   11   12

example_matrix + example_matrix2

##      [,1] [,2] [,3] [,4]
## [1,]    4    6    8   10
## [2,]   19   30   41   52

Two matrices

example_matrix

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]   10   20   30   40

example_matrix2

##      [,1] [,2] [,3] [,4]
## [1,]    3    4    5    6
## [2,]    9   10   11   12

example_matrix * example_matrix2 ## Not matrix multiplication, but element-wise multiplication

##      [,1] [,2] [,3] [,4]
## [1,]    3    8   15   24
## [2,]   90  200  330  480

Two matrices

rbind(example_matrix, example_matrix2)

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]   10   20   30   40
## [3,]    3    4    5    6
## [4,]    9   10   11   12

cbind(example_matrix, example_matrix2)

##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,]    1    2    3    4    3    4    5    6
## [2,]   10   20   30   40    9   10   11   12

Two matrices

dim(example_matrix2)

## [1] 2 4

t(example_matrix2) ## Transpose of a matrix

##      [,1] [,2]
## [1,]    3    9
## [2,]    4   10
## [3,]    5   11
## [4,]    6   12

example_matrix %*% t(example_matrix2) ## Matrix multiplication

##      [,1] [,2]
## [1,]   50  110
## [2,]  500 1100

Lists

Lists are collections of arbitrary objects in R

example_list <- list(c('Andy','Brian','Harry'), 
                     c(12, 16, 16), 
                     c(TRUE, TRUE, FALSE), 
                     matrix(1, nrow=2, ncol=3))
example_list

## [[1]]
## [1] "Andy"  "Brian" "Harry"
## 
## [[2]]
## [1] 12 16 16
## 
## [[3]]
## [1]  TRUE  TRUE FALSE
## 
## [[4]]
##      [,1] [,2] [,3]
## [1,]    1    1    1
## [2,]    1    1    1

Extracting elements from lists

example_list[[3]]

## [1]  TRUE  TRUE FALSE

example_list[1:2]

## [[1]]
## [1] "Andy"  "Brian" "Harry"
## 
## [[2]]
## [1] 12 16 16

Extracting elements from lists

example_list[[4]]

##      [,1] [,2] [,3]
## [1,]    1    1    1
## [2,]    1    1    1

class(example_list[[4]])

## [1] "matrix"

example_list[[4]][1,]

## [1] 1 1 1

Named lists

example_named_list <- list('Names' = c('Andy','Brian','Harry'), 
                     "YearsOfEducation" = c(12, 16, 16), 
                     "Married" = c(TRUE, TRUE, FALSE), 
                     'something' = matrix(1, nrow=2, ncol=3))

example_named_list[['Names']]

## [1] "Andy"  "Brian" "Harry"

example_named_list$Names

## [1] "Andy"  "Brian" "Harry"

example_named_list$Names[3]

## [1] "Harry"

Back to a Data Frame

A data.frame object is a named list where each element is of the same length

You can use both matrix and list functions to operate on data.frame objects!!

Data Frames

head(data_spine)

##   Pelvic.incidence Pelvic.tilt Lumbar.lordosis.angle Sacral.slope
## 1         63.02782   22.552586              39.60912     40.47523
## 2         39.05695   10.060991              25.01538     28.99596
## 3         68.83202   22.218482              50.09219     46.61354
## 4         69.29701   24.652878              44.31124     44.64413
## 5         49.71286    9.652075              28.31741     40.06078
## 6         40.25020   13.921907              25.12495     26.32829
##   Pelvic.radius Degree.spondylolisthesis Pelvic.slope Direct.tilt
## 1      98.67292                -0.254400    0.7445035     12.5661
## 2     114.40543                 4.564259    0.4151857     12.8874
## 3     105.98514                -3.530317    0.4748892     26.8343
## 4     101.86850                11.211523    0.3693453     23.5603
## 5     108.16872                 7.918501    0.5433605     35.4940
## 6     130.32787                 2.230652    0.7899929     29.3230
##   Thoracic.slope Cervical.tilt Sacrum.angle Scoliosis.slope
## 1        14.5386      15.30468   -28.658501         43.5123
## 2        17.5323      16.78486   -25.530607         16.1102
## 3        17.4861      16.65897   -29.031888         19.2221
## 4        12.7074      11.42447   -30.470246         18.8329
## 5        15.9546       8.87237   -16.378376         24.9171
## 6        12.0036      10.40462    -1.512209          9.6548
##   Class.attribute
## 1        Abnormal
## 2        Abnormal
## 3        Abnormal
## 4        Abnormal
## 5        Abnormal
## 6        Abnormal

Data Frames

dim(data_spine)

## [1] 310  13

nrow(data_spine)

## [1] 310

data_spine_small <- data_spine[1:4,] ## Matrix operation

Data Frames

data_spine_small[,2] ## Matrix extraction by position

## [1] 22.55259 10.06099 22.21848 24.65288

data_spine_small[[2]] ## List extraction by position

## [1] 22.55259 10.06099 22.21848 24.65288

Data Frames

data_spine_small[['Pelvic.tilt']] ## Named list extraction

## [1] 22.55259 10.06099 22.21848 24.65288

data_spine_small[,'Pelvic.tilt'] ## Data frame named column extraction

## [1] 22.55259 10.06099 22.21848 24.65288

data_spine_small$Pelvic.tilt ## Dollar sign extraction

## [1] 22.55259 10.06099 22.21848 24.65288

Data Frames

My preference is for

data frame named column extraction data_spine_small[,'Pelvic.tilt'],
named list extraction data_spine_small[['Pelvic.tilt']]
Dollar-based extraction data_spine_small$Pelvic.tilt

Data Frames

names(data_spine_small)

##  [1] "Pelvic.incidence"         "Pelvic.tilt"             
##  [3] "Lumbar.lordosis.angle"    "Sacral.slope"            
##  [5] "Pelvic.radius"            "Degree.spondylolisthesis"
##  [7] "Pelvic.slope"             "Direct.tilt"             
##  [9] "Thoracic.slope"           "Cervical.tilt"           
## [11] "Sacrum.angle"             "Scoliosis.slope"         
## [13] "Class.attribute"

data_spine_small[,c('Pelvic.tilt', 'Pelvic.slope','Class.attribute')]

##   Pelvic.tilt Pelvic.slope Class.attribute
## 1    22.55259    0.7445035        Abnormal
## 2    10.06099    0.4151857        Abnormal
## 3    22.21848    0.4748892        Abnormal
## 4    24.65288    0.3693453        Abnormal

Filtering data frames

Boolean operators

Operator	Meaning
`\|`	Or
`&`	And
`!`	Not

Filtering data frames

data_spine[data_spine$Pelvic.tilt > 20, ]

##     Pelvic.incidence Pelvic.tilt Lumbar.lordosis.angle Sacral.slope
## 1           63.02782    22.55259              39.60912     40.47523
## 3           68.83202    22.21848              50.09219     46.61354
## 4           69.29701    24.65288              44.31124     44.64413
## 14          53.57217    20.46083              33.10000     33.11134
## 15          57.30023    24.18888              47.00000     33.11134
## 17          63.83498    20.36251              54.55243     43.47247
## 22          54.91944    21.06233              42.20000     33.85711
## 23          63.07361    24.41380              54.00000     38.65981
## 25          36.12568    22.75875              29.00000     13.36693
## 26          54.12492    26.65049              35.32975     27.47443
## 29          44.55101    21.93115              26.78592     22.61986
## 30          66.87921    24.89200              49.27860     41.98721
## 35          59.59554    31.99824              46.56025     27.59730
## 39          55.84329    28.84745              47.69054     26.99584
## 44          66.28539    26.32784              47.50000     39.95755
## 46          50.91244    23.01517              47.00000     27.89727
## 47          48.33264    22.22778              36.18199     26.10485
## 51          55.28585    20.44012              34.00000     34.84573
## 52          74.43359    41.55733              27.70000     32.87626
## 53          50.20967    29.76012              36.10401     20.44955
## 61          74.37768    32.05310              78.77201     42.32457
## 62          89.68057    32.70443              83.13073     56.97613
## 64          77.69058    21.38064              64.42944     56.30993
## 65          76.14721    21.93619              82.96150     54.21103
## 66          83.93301    41.28631              62.00000     42.64670
## 67          78.49173    22.18180              60.00000     56.30993
## 72          86.90079    32.92817              47.79435     53.97263
## 73          84.97413    33.02117              60.85987     51.95296
## 74          55.51221    20.09516              44.00000     35.41706
## 75          72.22233    23.07771              91.00000     49.14462
## 76          70.22145    39.82272              68.11840     30.39873
## 77          86.75361    36.04302              69.22104     50.71059
## 81          77.10657    30.46999              69.48063     46.63658
## 82          74.00554    21.12240              57.37950     52.88314
## 83          88.62391    29.08945              47.56426     59.53446
## 84          81.10410    24.79417              77.88702     56.30993
## 85          76.32600    42.39620              57.20000     33.92980
## 90          71.18681    23.89620              43.69667     47.29061
## 91          81.65603    28.74887              58.23282     52.90716
## 92          70.95273    20.15993              62.85911     50.79280
## 96          57.52236    33.64708              50.90986     23.87528
## 99          77.65512    22.43295              93.89278     55.22217
## 101         84.58561    30.36168              65.47949     54.22392
## 105         77.40933    29.39655              63.23230     48.01279
## 106         65.00796    27.60261              50.94752     37.40536
## 108         78.42595    33.42595              76.27744     45.00000
## 112         84.99896    29.61010              83.35219     55.38886
## 115         80.98807    36.84317              86.96060     44.14490
## 118         86.04128    38.75067              47.87140     47.29061
## 119         65.53600    24.15749              45.77517     41.37852
## 122         83.87994    23.07743              87.14151     60.80251
## 123         80.07491    48.06953              52.40344     32.00538
## 127         70.67690    21.70440              59.18116     48.97250
## 129         90.51396    28.27250              69.81394     62.24146
## 133         69.62628    21.12275              52.76659     48.50353
## 134         81.75442    20.12347              70.56044     61.63095
## 136         77.12134    30.34987              77.48108     46.77147
## 137         88.02450    39.84467              81.77447     48.17983
## 138         83.39661    34.31099              78.42329     49.08562
## 139         72.05403    24.70074              79.87402     47.35330
## 140         85.09550    21.06990              91.73479     64.02561
## 142         89.50495    48.90365              72.00342     40.60129
## 144         60.62622    20.59596              64.53526     40.03026
## 146         85.64379    42.68920              78.75066     42.95459
## 147         85.58171    30.45704              78.23138     55.12467
## 150         79.24967    23.94482              40.79670     55.30485
## 151         81.11260    20.69044              60.68701     60.42216
## 157         79.47698    26.73227              70.65098     52.74471
## 161         92.02631    35.39267              77.41696     56.63363
## 163        118.14465    38.44950              50.83852     79.69515
## 164        115.92326    37.51544              76.80000     78.40782
## 166         83.70318    20.26823              77.11060     63.43495
## 169         95.38260    24.82263              95.15763     70.55997
## 175         61.41174    25.38436              39.09687     36.02737
## 179         80.65432    26.34438              60.89812     54.30994
## 180         68.72191    49.43186              68.05601     19.29005
##     Pelvic.radius Degree.spondylolisthesis Pelvic.slope Direct.tilt
## 1        98.67292               -0.2544000  0.744503464     12.5661
## 3       105.98514               -3.5303173  0.474889164     26.8343
## 4       101.86850               11.2115234  0.369345264     23.5603
## 14      110.96670                7.0448029  0.081930993     15.0580
## 15      116.80659                5.7669469  0.416721511     16.5158
## 17      112.30949               -0.6225266  0.560675371     10.7690
## 22      125.21272                2.4325614  0.175244572     23.0791
## 23      106.42433               15.7796968  0.666388008     11.9696
## 25      115.57712               -3.2375625  0.126473707     25.6206
## 26      121.44701                1.5712048  0.928687869     14.6686
## 29      111.07292                2.6523206  0.527891438     32.4275
## 30      113.47702               -2.0058917  0.677267795     12.4271
## 35      119.33035                1.4742858  0.477087978      8.6051
## 39      123.31184                2.8124269  0.142325313     12.6634
## 44      121.21968               -0.7996245  0.647625866      9.0466
## 46      117.42226               -2.5267015  0.319204878     30.6389
## 47      117.38463                6.4817091  0.062276845     23.5538
## 51      115.87702                3.5583724  0.680654711     16.7110
## 52      107.94930                5.0000888  0.606767772     32.6283
## 53      128.29251                5.7406141  0.031139167     36.1431
## 61      143.56069               56.1259060  0.159377926     35.9529
## 62      129.95548               92.0272768  0.527584239     26.3756
## 64      114.81875               26.9318410  0.046225827     24.5548
## 65      123.93201               10.4319719  0.252796128     21.5934
## 66      115.01233               26.5881002  0.614766530      8.8345
## 67      118.53033               27.3832131  0.008485564      7.5647
## 72      135.07536              101.7190919  0.459674217     25.0986
## 73      125.65953               74.3334086  0.600115713     25.6364
## 74      122.64875               34.5529464  0.672041297     35.0909
## 75      137.73665               56.8040928  0.826532439     32.3379
## 76      148.52556              145.3781432  0.946610646     10.3840
## 77      139.41450              110.8607824  0.640487619     16.5571
## 81      112.15160               70.7590831  0.156502675     10.8490
## 82      120.20596               74.5551659  0.406965314     10.5895
## 83      121.76478               51.8058992  0.770613922     13.1962
## 84      151.83986               65.2146161  0.972005589     10.5715
## 85      124.26701               50.1274569  0.583098014     33.1635
## 90      119.86494               27.2839845  0.402241133      9.1296
## 91      114.76986               30.6091484  0.832811085     23.1811
## 92      116.17793               32.5223310  0.054842915      7.3173
## 96      140.98171              148.7537109  0.597457003     21.5943
## 99      123.05571               61.2111866  0.924902858     14.9502
## 101     108.01022               25.1184785  0.341664609     30.4108
## 105     118.45073               93.5637373  0.375287380     11.2385
## 106     116.58111                7.0159779  0.867324121     12.1292
## 108     138.55411               77.1551724  0.580604336     36.6285
## 112     126.91299               71.3211754  0.998826684      7.0551
## 115     141.08815               85.8721522  0.496180717     27.5223
## 118     122.09295               61.9882771  0.035210491     27.5499
## 119     136.44030               16.3780856  0.377683884     25.0670
## 122     124.64607               80.5556053  0.436932625      7.2994
## 123     110.70991               67.7273160  0.099940503     20.2822
## 127     103.00835               27.8101478  0.039655293     15.7748
## 129     100.89216               58.8236482  0.881441257     13.5739
## 133     116.80309               54.8168673  0.286894092     18.5916
## 134     119.42509               55.5068891  0.265889494     15.3790
## 136     110.61115               82.0936070  0.278327565     21.9069
## 137     116.60154               56.7660832  0.238984543     14.6182
## 138     110.46652               49.6720956  0.771860278     24.9264
## 139     107.17236               56.4261587  0.296152366      7.7545
## 140     109.06231               38.0328311  0.481861846     17.1681
## 142     134.63429              118.3533701  0.039380359     19.8712
## 144     117.22555              104.8592474  0.386903046     17.0217
## 146     105.14408               42.8874258  0.844294085     16.9272
## 147     114.86605               68.3761218  0.624974835     26.5001
## 150      98.62251               36.7063954  0.672569597     29.0324
## 151      94.01878               40.5109823  0.530804498     11.4265
## 157     118.58867               61.7005982  0.642084751      8.2975
## 161     115.72353               58.0575416  0.302441921     30.0162
## 163      81.02454               74.0437674  0.599392845     35.8563
## 164     104.69860               81.1989271  0.542816002     22.3317
## 166     125.48017               69.2795710  0.735958830     33.8124
## 169      89.30755               57.6608413  0.268276345     28.6901
## 175     103.40460               21.8434069  0.697750246      8.4084
## 179     120.10349               52.4675518  0.931603620     20.0845
## 180     125.01852               54.6912893  0.379895400     17.4281
##     Thoracic.slope Cervical.tilt Sacrum.angle Scoliosis.slope
## 1          14.5386      15.30468   -28.658501         43.5123
## 3          17.4861      16.65897   -29.031888         19.2221
## 4          12.7074      11.42447   -30.470246         18.8329
## 14         12.8127      12.00109    -1.734117         15.6205
## 15         18.6222       8.51898   -33.441303         13.2498
## 17         16.8116      11.41344     2.676002         17.3859
## 22         14.2195      14.14196     3.780394         24.9278
## 23         17.6891       7.63771   -14.183602         44.2338
## 25         15.7438      11.55610   -18.108941         24.1151
## 26         13.5700      16.12951   -17.630363         28.1902
## 29         10.2244      11.71324   -28.506125         28.0470
## 30          8.2495       7.58784    -3.963385         27.3587
## 35          8.3058       8.53700    -0.029028         40.5823
## 39          8.8550      10.55193   -16.404668         15.2954
## 44         10.2636      13.50349     1.138079         34.3683
## 46         18.6181      15.55901     2.537043          9.4310
## 47         11.0942      13.15072    -4.200276         20.0348
## 51         15.9714      14.37627     4.779509         43.2610
## 52          9.8062      11.62142   -10.028289          9.3141
## 53         13.9907      12.90967   -30.430498         31.6999
## 61         15.3975      11.71169   -18.628293         22.5623
## 62         18.6012      16.09596   -18.701330         35.8729
## 64         15.3209      11.10896   -23.279118         16.1132
## 65          7.8098      11.07095   -34.897660         43.1487
## 66          7.2405       9.79573   -20.130727         22.4032
## 67         12.6737       8.03422   -22.037558         32.0972
## 72          8.7655       8.93510   -21.318960         12.8518
## 73         17.7501       7.88600     4.442569         13.4605
## 74          8.6569      14.35843   -33.990906         25.5218
## 75          7.4437      16.44128    -9.487619          9.6867
## 76          9.5742      11.22353     4.641629          9.8472
## 77         17.3635      12.70134   -15.088499          7.0079
## 81         13.2216      11.55132    -1.860646         18.6033
## 82         12.5946      15.87462   -10.624659         31.9699
## 83         13.6590      15.27164    -4.208953         32.9340
## 84         11.2339      13.29506   -12.139219         11.8487
## 85          8.3830      13.75752   -32.106343         18.6868
## 90         16.7172       7.03060    -4.667443         14.5344
## 91         11.2491      11.69024   -25.011107         21.9180
## 92         16.3676      12.21365    -5.091336         17.2601
## 96          7.5666       7.81812   -27.570464         17.8768
## 99         15.0493       7.57722     0.307904         33.7201
## 101        15.7092      11.58279    -1.273566         29.6399
## 105        12.9197      13.82148     6.079425         11.8698
## 106        13.8536      11.95397   -20.735613          9.7675
## 108        16.6264       7.96524   -19.123087         16.1431
## 112         9.0119       9.85541   -19.314135         43.0086
## 115        13.5136      11.60893   -13.600284         34.3656
## 118        12.0080       9.62750     5.603229         36.2899
## 119        13.7801      14.63875   -15.898046         19.5298
## 122        11.1917      16.28150    -8.553212         24.8562
## 123        10.3082      15.89258   -14.156070         39.9730
## 127        14.8568      11.45991   -18.475476         19.8407
## 129        16.3289      16.52676   -10.917156         35.6543
## 133        15.4963      15.92252     1.320769         34.8665
## 134        18.1885      14.66728    -3.994716         33.2091
## 136        17.0071      16.22809    -0.055790         27.6595
## 137        12.1692       7.39996   -11.029157         40.7572
## 138         7.6245      13.23600   -21.449617         16.2153
## 139        10.8824       7.23178    -2.990023         29.9404
## 140         8.4727      11.98150   -25.387556          8.3163
## 142         8.9861      14.77008     6.868423         29.1844
## 144         8.8097      16.82108   -30.591567         35.4529
## 146         8.0109      15.08030    -1.056403         11.4148
## 147        11.2204      12.33663   -15.774389         40.4629
## 150        14.5804      16.56784    -0.269590         31.7726
## 151         8.3428       7.26048   -15.985527         29.5574
## 157         8.3840      11.96108     5.237386         10.8006
## 161         9.8318      11.21248   -19.264777         19.9972
## 163        10.9266      13.11354   -17.520810         21.2408
## 164         8.8519      11.48960    -6.754004         32.5082
## 166        15.1125      14.38085   -20.214168         11.7348
## 169         7.2124      13.13055    -6.412477         19.9792
## 175        15.6980      12.90325   -27.124475         22.9564
## 179        18.4914       8.98429   -19.329173         31.0862
## 180        16.0009      14.48126   -31.852569         30.5330
##     Class.attribute
## 1          Abnormal
## 3          Abnormal
## 4          Abnormal
## 14         Abnormal
## 15         Abnormal
## 17         Abnormal
## 22         Abnormal
## 23         Abnormal
## 25         Abnormal
## 26         Abnormal
## 29         Abnormal
## 30         Abnormal
## 35         Abnormal
## 39         Abnormal
## 44         Abnormal
## 46         Abnormal
## 47         Abnormal
## 51         Abnormal
## 52         Abnormal
## 53         Abnormal
## 61         Abnormal
## 62         Abnormal
## 64         Abnormal
## 65         Abnormal
## 66         Abnormal
## 67         Abnormal
## 72         Abnormal
## 73         Abnormal
## 74         Abnormal
## 75         Abnormal
## 76         Abnormal
## 77         Abnormal
## 81         Abnormal
## 82         Abnormal
## 83         Abnormal
## 84         Abnormal
## 85         Abnormal
## 90         Abnormal
## 91         Abnormal
## 92         Abnormal
## 96         Abnormal
## 99         Abnormal
## 101        Abnormal
## 105        Abnormal
## 106        Abnormal
## 108        Abnormal
## 112        Abnormal
## 115        Abnormal
## 118        Abnormal
## 119        Abnormal
## 122        Abnormal
## 123        Abnormal
## 127        Abnormal
## 129        Abnormal
## 133        Abnormal
## 134        Abnormal
## 136        Abnormal
## 137        Abnormal
## 138        Abnormal
## 139        Abnormal
## 140        Abnormal
## 142        Abnormal
## 144        Abnormal
## 146        Abnormal
## 147        Abnormal
## 150        Abnormal
## 151        Abnormal
## 157        Abnormal
## 161        Abnormal
## 163        Abnormal
## 164        Abnormal
## 166        Abnormal
## 169        Abnormal
## 175        Abnormal
## 179        Abnormal
## 180        Abnormal
##  [ reached 'max' / getOption("max.print") -- omitted 29 rows ]

subset(data_spine, Pelvic.tilt > 20) ## is equivalent

Filtering data frames

data_spine[data_spine$Pelvic.tilt > 20 & data_spine$Pelvic.slope > 0.85, ]

##     Pelvic.incidence Pelvic.tilt Lumbar.lordosis.angle Sacral.slope
## 26          54.12492    26.65049              35.32975     27.47443
## 76          70.22145    39.82272              68.11840     30.39873
## 84          81.10410    24.79417              77.88702     56.30993
## 99          77.65512    22.43295              93.89278     55.22217
## 106         65.00796    27.60261              50.94752     37.40536
## 112         84.99896    29.61010              83.35219     55.38886
## 129         90.51396    28.27250              69.81394     62.24146
## 179         80.65432    26.34438              60.89812     54.30994
## 231         65.61180    23.13792              62.58218     42.47388
## 303         54.60032    21.48897              29.36022     33.11134
##     Pelvic.radius Degree.spondylolisthesis Pelvic.slope Direct.tilt
## 26       121.4470                 1.571205    0.9286879     14.6686
## 76       148.5256               145.378143    0.9466106     10.3840
## 84       151.8399                65.214616    0.9720056     10.5715
## 99       123.0557                61.211187    0.9249029     14.9502
## 106      116.5811                 7.015978    0.8673241     12.1292
## 112      126.9130                71.321175    0.9988267      7.0551
## 129      100.8922                58.823648    0.8814413     13.5739
## 179      120.1035                52.467552    0.9316036     20.0845
## 231      124.1280                -4.083298    0.9972475     30.0422
## 303      118.3433                -1.471067    0.9629075     30.8554
##     Thoracic.slope Cervical.tilt Sacrum.angle Scoliosis.slope
## 26         13.5700      16.12951   -17.630363         28.1902
## 76          9.5742      11.22353     4.641629          9.8472
## 84         11.2339      13.29506   -12.139219         11.8487
## 99         15.0493       7.57722     0.307904         33.7201
## 106        13.8536      11.95397   -20.735613          9.7675
## 112         9.0119       9.85541   -19.314135         43.0086
## 129        16.3289      16.52676   -10.917156         35.6543
## 179        18.4914       8.98429   -19.329173         31.0862
## 231        17.6222      13.39076   -16.369970         21.4495
## 303        11.4198      13.82322    -5.606449         18.5514
##     Class.attribute
## 26         Abnormal
## 76         Abnormal
## 84         Abnormal
## 99         Abnormal
## 106        Abnormal
## 112        Abnormal
## 129        Abnormal
## 179        Abnormal
## 231          Normal
## 303          Normal

subset(data_spine, Pelvic.tilt > 20 & Pelvic.slope > 0.85)

Filtering data frames and selecting variables

data_spine[data_spine$Pelvic.tilt > 20 & data_spine$Pelvic.slope > 0.85, 
           c('Direct.tilt', 'Class.attribute')]

##     Direct.tilt Class.attribute
## 26      14.6686        Abnormal
## 76      10.3840        Abnormal
## 84      10.5715        Abnormal
## 99      14.9502        Abnormal
## 106     12.1292        Abnormal
## 112      7.0551        Abnormal
## 129     13.5739        Abnormal
## 179     20.0845        Abnormal
## 231     30.0422          Normal
## 303     30.8554          Normal

Adding a variable

data_spine_small[,'bad.angle'] <- c('No','Yes','No','No')
data_spine_small

##   Pelvic.incidence Pelvic.tilt Lumbar.lordosis.angle Sacral.slope
## 1         63.02782    22.55259              39.60912     40.47523
## 2         39.05695    10.06099              25.01538     28.99596
## 3         68.83202    22.21848              50.09219     46.61354
## 4         69.29701    24.65288              44.31124     44.64413
##   Pelvic.radius Degree.spondylolisthesis Pelvic.slope Direct.tilt
## 1      98.67292                -0.254400    0.7445035     12.5661
## 2     114.40543                 4.564259    0.4151857     12.8874
## 3     105.98514                -3.530317    0.4748892     26.8343
## 4     101.86850                11.211523    0.3693453     23.5603
##   Thoracic.slope Cervical.tilt Sacrum.angle Scoliosis.slope
## 1        14.5386      15.30468    -28.65850         43.5123
## 2        17.5323      16.78486    -25.53061         16.1102
## 3        17.4861      16.65897    -29.03189         19.2221
## 4        12.7074      11.42447    -30.47025         18.8329
##   Class.attribute bad.angle
## 1        Abnormal        No
## 2        Abnormal       Yes
## 3        Abnormal        No
## 4        Abnormal        No

data_spine_small$bad.angle <- ...
data_spine_small[['bad.angle']] <- ...

Removing a variable

data_spine_small[, -c(13,14)]

data_spine_small[,-c('Class.attribute', 'bad.angle')]

## The next two commands change the original data set

data_spine_small[c('Class.attribute','bad.angle')] <- NULL

data_spine_small[['bad.angle']] <- NULL

Creating derived variables

data_spine_small$bad.angle <- ifelse(data_spine_small$Sacrum.angle > 80, 
                                     'Yes','No')

Creating derived variables

data_spine_small <- transform(data_spine_small, 
                              bad.angle = ifelse(Sacrum.angle > 80, 
                                                 'Yes','No'))

For deriving multiple variables into a data frame

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

For deriving multiple variables into a data frame

mtcars <- transform(mtcars, 
                    kmpg = mpg * 1.6, ## Numerical vector
                    low.mpg = ifelse(mpg < 16, 'Yes','No')  ## Factor
                    )

For deriving multiple variables into a data frame

str(mtcars)

## 'data.frame':    32 obs. of  13 variables:
##  $ mpg    : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl    : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp   : num  160 160 108 258 360 ...
##  $ hp     : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat   : num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt     : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec   : num  16.5 17 18.6 19.4 17 ...
##  $ vs     : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am     : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear   : num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb   : num  4 4 1 1 2 1 4 2 2 4 ...
##  $ kmpg   : num  33.6 33.6 36.5 34.2 29.9 ...
##  $ low.mpg: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 2 1 1 1 ...

Adding new data to a data frame

You can concatenate two data frames using rbind as long as the variable names and orders are the same

new_data = rbind(data_spine[1:4,], data_spine[c(8,22),])
new_data

##    Pelvic.incidence Pelvic.tilt Lumbar.lordosis.angle Sacral.slope
## 1          63.02782    22.55259              39.60912     40.47523
## 2          39.05695    10.06099              25.01538     28.99596
## 3          68.83202    22.21848              50.09219     46.61354
## 4          69.29701    24.65288              44.31124     44.64413
## 8          45.36675    10.75561              29.03835     34.61114
## 22         54.91944    21.06233              42.20000     33.85711
##    Pelvic.radius Degree.spondylolisthesis Pelvic.slope Direct.tilt
## 1       98.67292                -0.254400    0.7445035     12.5661
## 2      114.40543                 4.564259    0.4151857     12.8874
## 3      105.98514                -3.530317    0.4748892     26.8343
## 4      101.86850                11.211523    0.3693453     23.5603
## 8      117.27007               -10.675871    0.1319726     28.8165
## 22     125.21272                 2.432561    0.1752446     23.0791
##    Thoracic.slope Cervical.tilt Sacrum.angle Scoliosis.slope
## 1         14.5386      15.30468   -28.658501         43.5123
## 2         17.5323      16.78486   -25.530607         16.1102
## 3         17.4861      16.65897   -29.031888         19.2221
## 4         12.7074      11.42447   -30.470246         18.8329
## 8          7.7676       7.60961   -25.111459         26.3543
## 22        14.2195      14.14196     3.780394         24.9278
##    Class.attribute
## 1         Abnormal
## 2         Abnormal
## 3         Abnormal
## 4         Abnormal
## 8         Abnormal
## 22        Abnormal

Adding new data to a data frame

You can add columns of a new data frame to an existing data frame using cbind as long as the columns have no common names

new_data2 <- cbind(data_spine[1:4, c('Pelvic.slope','Class.attribute')], 
                   data.frame(Sex = c('M','F','M','M'),    ## Creating a new data frame on the fly
                              Race = c('W','B','As','B'))
                   )
new_data2

##   Pelvic.slope Class.attribute Sex Race
## 1    0.7445035        Abnormal   M    W
## 2    0.4151857        Abnormal   F    B
## 3    0.4748892        Abnormal   M   As
## 4    0.3693453        Abnormal   M    B