# Regression (Explanatory) in R

Hi,

I have written about Regression – Predictive model in my earlier post Regression testing in R. Following posts are useful if you want to know what is regression.

Previous post talks about predicting unknown values using known values. This post would explain about how much change is observed between IV(s) and DV.

> setwd("D:/gandhari/videos/Advanced Business Analytics")
> student_data <- read.csv("student_data.csv") > student_data
id gender sup.help sup.under sup.appre adv.comp adv.access tut.prof tut.sched val.devel val.meet sat.glad sat.expe loy.proud loy.recom loy.pleas scholarships job
1   1 female        7         1         7        5          5        5         4         5        6        7        7         7         7         7           no  no
2   2   male        7         1         7        6          6        6         6         6        7        7        7         7         7         7           no yes
3   3 female        6         1         7        6          6        6         6         6        7        7        6         7         7         7           no  no
4   4   male        1         7         1        1          2        3         2         1        1        1        1         1         1         1          yes  no
5   5 female        6         5         7        7          6        7         7         7        7        7        7         7         7         7           no yes
6   6   male        3         1         7        7          7        6         7         6        6        7        6         7         7         7          yes  no
7   7 female        5         2         7        7          6        6         7         4        3        7        7         7         7         7          yes  no
8   8   male        6         1         7        7          7        7         5         7        6        7        7         5         6         7          yes yes
9   9 female        7         1         7        6          6        5         5         5        5        7        6         6         7         7           no yes
10 10   male        2         4         7        7          6        6         6         4        2        5        4         4         7         7           no  no
> str(student_data)
'data.frame': 10 obs. of 18 variables:
$id : int 1 2 3 4 5 6 7 8 9 10$ gender : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2
$sup.help : int 7 7 6 1 6 3 5 6 7 2$ sup.under : int 1 1 1 7 5 1 2 1 1 4
$sup.appre : int 7 7 7 1 7 7 7 7 7 7$ adv.comp : int 5 6 6 1 7 7 7 7 6 7
$adv.access : int 5 6 6 2 6 7 6 7 6 6$ tut.prof : int 5 6 6 3 7 6 6 7 5 6
$tut.sched : int 4 6 6 2 7 7 7 5 5 6$ val.devel : int 5 6 6 1 7 6 4 7 5 4
$val.meet : int 6 7 7 1 7 6 3 6 5 2$ sat.glad : int 7 7 7 1 7 7 7 7 7 5
$sat.expe : int 7 7 6 1 7 6 7 7 6 4$ loy.proud : int 7 7 7 1 7 7 7 5 6 4
$loy.recom : int 7 7 7 1 7 7 7 6 7 7$ loy.pleas : int 7 7 7 1 7 7 7 7 7 7
$scholarships: Factor w/ 2 levels "no","yes": 1 1 1 2 1 2 2 2 1 1$ job : Factor w/ 2 levels "no","yes": 1 2 1 1 2 1 1 2 2 1<span 				data-mce-type="bookmark" 				id="mce_SELREST_start" 				data-mce-style="overflow:hidden;line-height:0" 				style="overflow:hidden;line-height:0" 			>﻿</span>


Sometimes, the dataset is not completely visible in wordpress. Hence I’m giving it as an image below.

support, advice, satisfaction and loyalty has multiple variables in the above data set as sup.help, sup.under etc.

Let’s make it as a single variable (mean) for easy analysis.

> #get sing score for support advice satisfaction loyalty
> student_data$support <- apply(student_data[,3:5],1,mean) > summary (student_data$support)
Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
3.000   4.417   4.667   4.600   5.000   6.000
> student_data$value <- rowMeans(student_data[,10:11]) > student_data$sat <- rowMeans(student_data[,12:13])
> mtcars5by5$total [1] 300.90 300.90 231.65 398.48 564.85 > #total is added a new variable in our data set > mtcars5by5 mpg cyl disp hp drat total Mazda RX4 21.0 6 160 110 3.90 300.90 Mazda RX4 Wag 21.0 6 160 110 3.90 300.90 Datsun 710 22.8 4 108 93 3.85 231.65 Hornet 4 Drive 21.4 6 258 110 3.08 398.48 Hornet Sportabout 18.7 8 360 175 3.15 564.85 > #column sum > apply(mtcars5by5, 2, sum) mpg cyl disp hp drat total 104.90 30.00 1046.00 598.00 17.88 1796.78  ### Transform Transform() helps us to prepare data. Using this, we shall create n number of new variables. > transform(mtcars5by5,tot=sum(mtcars5by5[,1:5]),mtper=mpg/drat,ntprod=mpg/hp) mpg cyl disp hp drat total tot mtper ntprod Mazda RX4 21.0 6 160 110 3.90 300.90 1796.78 5.384615 0.1909091 Mazda RX4 Wag 21.0 6 160 110 3.90 300.90 1796.78 5.384615 0.1909091 Datsun 710 22.8 4 108 93 3.85 231.65 1796.78 5.922078 0.2451613 Hornet 4 Drive 21.4 6 258 110 3.08 398.48 1796.78 6.948052 0.1945455 Hornet Sportabout 18.7 8 360 175 3.15 564.85 1796.78 5.936508 0.1068571  I have added new variables tot, mtper, ntprod above. ### lapply This help us to issue a function over a list. It loops over a list and evaluate a function on each element > lapply(mtcars5by5, mean)$mpg
[1] 20.98

$cyl [1] 6$disp
[1] 209.2

$hp [1] 119.6$drat
[1] 3.576

$total [1] 359.356  It went through the complete list to provide the mean of each variable. ### tapply tapply() helps us to apply the function in a ragged array or a subset of vector. > tapply(mtcars5by5$mpg, mtcars5by5$cyl, mean) 4 6 8 22.80000 21.13333 18.70000  Consider our mtcars data set. I need to find out mean and maximum horse power hp grouped by different gears > mtcars mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 > ctmean v s automatic 15.05 20.74286 manual 19.75 28.37143 > tapply(mtcars$hp, mtcars$gear, mean) 3 4 5 176.1333 89.5000 195.6000 > tapply(mtcars$hp, mtcars$gear, max) 3 4 5 245 123 335  We got mean horse power for 3, 4 and 5 gears. We may even provide a list to group the mean operation. In the below given example, we shall calculate the mean for different transmission model am (0 – automatic; 1 – manual) and v/s. > list(mtcars$am,mtcars$vs) [[1]] [1] 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 [[2]] [1] 0 0 1 1 0 1 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 0 0 1 > ctmean <- tapply(mtcars$hp, list(mtcars$am,mtcars$vs), mean)
> rownames(ctmean) <- c("automatic", "manual")
> colnames(ctmean) <- c("v", "s")
> ctmean
v         s
automatic 194.1667 102.14286
manual    180.8333  80.57143


I think I shall stop here. See you in another interesting post.

Have a leisurely weekend.

# Exploring data files with R

I have written about data types and data structures of R in my previous post Working with data types of R. We shall explore a data set in this post.

mtcars is a dataset exists in R already

> mtcars
mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2


### File structure

Very first question would be about the size of the data set.

> dim(mtcars)
[1] 32 11


It contains 32 rows and 11 columns.

Now, how many variables we have in mtcars?

> names(mtcars)
[1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear" "carb"


We shall preview the data using head and tail commands.

> 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
> tail(mtcars)
mpg cyl  disp  hp drat    wt qsec vs am gear carb
Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.7  0  1    5    2
Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.9  1  1    5    2
Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.5  0  1    5    4
Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.5  0  1    5    6
Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.6  0  1    5    8
Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.6  1  1    4    2


Similar to unix tail, head commands, you would see first and last 6 records in R.

This is the time to know about the structure of the data set.

> str(mtcars)
'data.frame':	32 obs. of  11 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 ...  So, this is a data frame. Each variables are explained above. How the data is being stored? > mode(mtcars) [1] "list"  It is stored as a list. Let’s take another dataset available with R – airquality. > head(airquality, n=10) Ozone Solar.R Wind Temp Month Day 1 41 190 7.4 67 5 1 2 36 118 8.0 72 5 2 3 12 149 12.6 74 5 3 4 18 313 11.5 62 5 4 5 NA NA 14.3 56 5 5 6 28 NA 14.9 66 5 6 7 23 299 8.6 65 5 7 8 19 99 13.8 59 5 8 9 8 19 20.1 61 5 9 10 NA 194 8.6 69 5 10  You may parameterize head commands as I have shown above. Let’s omit the records with NA. > aqNoNA=na.omit(airquality) > dim(aqNoNA) [1] 111 6 > dim(airquality) [1] 153 6  So, out new object aqNoNA contains the records without missing cases, totally 111 rows. We have is.na command to check if the data is NA. > is.na(airquality) Ozone Solar.R Wind Temp Month Day [1,] FALSE FALSE FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE FALSE FALSE FALSE [3,] FALSE FALSE FALSE FALSE FALSE FALSE [4,] FALSE FALSE FALSE FALSE FALSE FALSE [5,] TRUE TRUE FALSE FALSE FALSE FALSE [6,] FALSE TRUE FALSE FALSE FALSE FALSE > sum(is.na(airquality)) [1] 44  So totally 44 NAs found. Summary command gives us minimum, quartile 1, median, mean, 3rd quartile, maximum and number of NAa. > summary(airquality) Ozone Solar.R Wind Temp Month Min. : 1.00 Min. : 7.0 Min. : 1.700 Min. :56.00 Min. :5.000 1st Qu.: 18.00 1st Qu.:115.8 1st Qu.: 7.400 1st Qu.:72.00 1st Qu.:6.000 Median : 31.50 Median :205.0 Median : 9.700 Median :79.00 Median :7.000 Mean : 42.13 Mean :185.9 Mean : 9.958 Mean :77.88 Mean :6.993 3rd Qu.: 63.25 3rd Qu.:258.8 3rd Qu.:11.500 3rd Qu.:85.00 3rd Qu.:8.000 Max. :168.00 Max. :334.0 Max. :20.700 Max. :97.00 Max. :9.000 NA's :37 NA's :7 Day Min. : 1.0 1st Qu.: 8.0 Median :16.0 Mean :15.8 3rd Qu.:23.0 Max. :31.0  1st quartile meant of 24% data 2nd quartile meant of 50 percentile of data 3rd quartile meant of 75 percentile of data We shall filter the summary commands as given below. > summary(airquality$Ozone)
Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
1.00   18.00   31.50   42.13   63.25  168.00      37


We have given the summary of only one variable Ozone above.

We shall apply other mathematical functions as given below

> sd(aqNoNA$Ozone) [1] 33.27597  We have calculated the standard deviation for Ozone above. I have taken the data without NA, as my sd operation will fail if I have NA. ### Finding the Outlier > myNumbers <- rep(c(1,4,10, 100), c(5, 5, 5, 1)) > myNumbers [1] 1 1 1 1 1 4 4 4 4 4 10 10 10 10 10 100 > mean(myNumbers) [1] 10.9375 > sd(myNumbers) [1] 24.04293 > scale(myNumbers) [,1] [1,] -0.41332316 [2,] -0.41332316 [3,] -0.41332316 [4,] -0.41332316 [5,] -0.41332316 [6,] -0.28854636 [7,] -0.28854636 [8,] -0.28854636 [9,] -0.28854636 [10,] -0.28854636 [11,] -0.03899275 [12,] -0.03899275 [13,] -0.03899275 [14,] -0.03899275 [15,] -0.03899275 [16,] 3.70431136 attr(,"scaled:center") [1] 10.9375 attr(,"scaled:scale") [1] 24.04293  scaling of the metrics (z score) is calculated using the formula each sample data – mean / standard deviation After scaling, if you find any values is greater than ±2, they are called outliers (odd points located away from the central measures). If SD > mean, it is outlier. Other way to test the normality is Shapiro test > shapiro.test(myNumbers) Shapiro-Wilk normality test data: myNumbers W = 0.40055, p-value = 3.532e-07  If the p-value is greater than 0.05, it is normalized data. Here it is not. ### Data Analysis Here comes another interesting part. How to analyze the data, after you upload your files. To explain this, I’d prepare sample data set first. > #Employee ID > sn <- seq(1, 10, 1) > sn [1] 1 2 3 4 5 6 7 8 9 10 > #Employee gender > gender <- rep(c("male", "female"), c(6,4)) > gender [1] "male" "male" "male" "male" "male" "male" "female" "female" "female" [10] "female" > #available departments > dept <- rep(c("Admin", "HR", "Prod", "Contractor"), c(1, 3, 3, 3)) > dept [1] "Admin" "HR" "HR" "HR" "Prod" "Prod" [7] "Prod" "Contractor" "Contractor" "Contractor" > #Employee Salary > sal <- rnorm(10, 1000, 200); > sal [1] 888.2026 876.6272 919.7453 1005.9058 1084.4704 1337.7696 909.4302 801.1482 [9] 1025.9457 1182.9774 > #Now our data set > mydataset <- data.frame(sn, gender, sal, dept) > mydataset sn gender sal dept 1 1 male 888.2026 Admin 2 2 male 876.6272 HR 3 3 male 919.7453 HR 4 4 male 1005.9058 HR 5 5 male 1084.4704 Prod 6 6 male 1337.7696 Prod 7 7 female 909.4302 Prod 8 8 female 801.1482 Contractor 9 9 female 1025.9457 Contractor 10 10 female 1182.9774 Contractor  So we have serial number, gender, salary and department. Which gender is majority in the given data? we shall use table function to arrive at a simple frequency distribution table. > #which gender is more in each dept > #this is frequency distribution > table(mydataset$gender)

female   male
4      6


So we have 4 females and 6 males. Let’s group the above FD by department now.

> table(mydataset$gender, mydataset$dept)

female     0          3  0    1
male       1          0  3    2
> #assign the results to an object
> freqDis <- table(mydataset$gender, mydataset$dept);
> #transpose the table
> t(freqDis)

female male
Contractor      3    0
HR              0    3
Prod            1    2


So all departments except contractors, have male as majority. The function t() stands for transpose.

Let’s do a proportion of the data using prop.table()

> #proportion
> prop.table(freqDis)

female   0.0        0.3 0.0  0.1
male     0.1        0.0 0.3  0.2


Rather than proportion, % of males and females would give us a better visibility. hence we multiply proportion by 100.

> #Percentage
> prop.table(freqDis)*100

female     0         30  0   10
male      10          0 30   20


Column sum and row sums are frequently asked in our day today life.

> #sum
> colSums(freqDis)
1          3          3          3


Row sum shall be calculated as below.

> rowSums(freqDis)
female   male
4      6


Salary is interesting part in our profession. I’d like to see who earns more using aggregate() function? Male or Female?

> #who earns more - male or female?
> aggregate(sal~gender, mean, data = mydataset)
gender       sal
1 female  979.8754
2   male 1018.7868
> #sal is continuous variable
> #gender is categorical variable
> #mean is the function
> #data is our data source


We used mean salary above. Lets use sum now.

> aggregate(sal~gender, sum, data = mydataset)
gender      sal
1 female 3919.501
2   male 6112.721


Similarly, we use standard deviation.

> aggregate(sal~gender, sd, data = mydataset)
gender      sal
1 female 163.5837
2   male 175.1006


So salary package for females looks to be more consistent than that of males.

We calculated all the above functions individually. psych package helps to calculate everything in one command.

> install.packages("psych")
Installing package into ‘D:/gandhari/documents/R/win-library/3.4’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.4/psych_1.7.5.zip'
Content type 'application/zip' length 3966969 bytes (3.8 MB)

package ‘psych’ successfully unpacked and MD5 sums checked

> library(psych)
> describe (mydataset)
vars  n    mean     sd median trimmed    mad    min     max  range  skew
sn         1 10    5.50   3.03   5.50    5.50   3.71   1.00   10.00   9.00  0.00
gender*    2 10    1.60   0.52   2.00    1.62   0.00   1.00    2.00   1.00 -0.35
sal        3 10 1003.22 162.35 962.83  986.66 119.22 801.15 1337.77 536.62  0.71
dept*      4 10    2.80   1.03   3.00    2.88   1.48   1.00    4.00   3.00 -0.20
kurtosis    se
sn         -1.56  0.96
gender*    -2.05  0.16
sal        -0.72 51.34
dept*      -1.42  0.33


n is total samples, sd is standard deviation etc. All the aggregated functions are calculated for serial numbmer, gender, salary and department.

In the above data, I get only the salary data, which is in 3rd row.

> describe (mydataset[,3])
vars  n    mean     sd median trimmed    mad    min     max  range skew kurtosis
X1    1 10 1003.22 162.35 962.83  986.66 119.22 801.15 1337.77 536.62 0.71    -0.72
se
X1 51.34


Lets group the above described data by gender, ie, mean, sd for males and females separately.

> describe.by(mydataset, mydataset$gender) Descriptive statistics by group group: female vars n mean sd median trimmed mad min max range skew sn 1 4 8.50 1.29 8.50 8.50 1.48 7.00 10.00 3.00 0.00 gender* 2 4 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.00 NaN sal 3 4 979.88 163.58 967.69 979.88 166.64 801.15 1182.98 381.83 0.14 dept* 4 4 2.50 1.00 2.00 2.50 0.00 2.00 4.00 2.00 0.75 kurtosis se sn -2.08 0.65 gender* NaN 0.00 sal -2.04 81.79 dept* -1.69 0.50 --------------------------------------------------------------- group: male vars n mean sd median trimmed mad min max range skew sn 1 6 3.50 1.87 3.50 3.50 2.22 1.00 6.00 5.00 0.00 gender* 2 6 2.00 0.00 2.00 2.00 0.00 2.00 2.00 0.00 NaN sal 3 6 1018.79 175.10 962.83 1018.79 119.22 876.63 1337.77 461.14 0.83 dept* 4 6 3.00 1.10 3.00 3.00 0.74 1.00 4.00 3.00 -0.76 kurtosis se sn -1.80 0.76 gender* NaN 0.00 sal -1.02 71.48 dept* -0.92 0.45 Warning message: describe.by is deprecated. Please use the describeBy function  # Working with data types of R I have discussed about package management in the previous post. This post concentrates on data type, data structure and data coercion. ### Data types & Data Structure We have different Object types in R as given below. • Vectors • Lists • Matrices • Arrays • Factors • Data Frames We have the following data types in R. • Logical myvar <- TRUE  • Numeric myvar <- 23.5  • Integer myvar <- 2L  • Complex myvar <- 2+5i  • Character myvar <- "GOOD"  • Raw myvar <- charToRaw("GOOD")  We could see them with examples below. I have already written about c command in Basic functions in R language > id1 <- c(1,2,3,4) > id1+10 [1] 11 12 13 14  rep command helps us to repeat the values. > gender <- rep(c("male", "female"), c(6,4)) > gender [1] "male" "male" "male" "male" "male" "male" "female" "female" [9] "female" "female"  male is repeated 6 times where as female repeated 4 times. Serial number is given by the command sn. > sn <- seq(1, 10, 1) > sn [1] 1 2 3 4 5 6 7 8 9 10 > sn <-seq(1, 10, 2) > sn [1] 1 3 5 7 9  we passed start value, end value and interval. cbind meant for column bind. > #vector to matrix - cbind > slno_gender <- cbind(sn, gender) > slno_gender sn gender [1,] "1" "male" [2,] "2" "male" [3,] "3" "male" [4,] "4" "male" [5,] "5" "male" [6,] "6" "male" [7,] "7" "female" [8,] "8" "female" [9,] "9" "female" [10,] "10" "female"  This gives column wise data, in matrix. Matrix takes single data type only. Hence serial number is changed to character data type. Let’s look at similar example using DataFrame. We shall overcome this data type problem. > #data frame > df <- data.frame(sn, gender) > df sn gender 1 1 male 2 2 male 3 3 male 4 4 male 5 5 male 6 6 male 7 7 female 8 8 female 9 9 female 10 10 female  Let’s create another object called names > name <- c("Sophia","Jackson","Isabella","Lucas","Charlotte","Oliver","Amelia","Benjamin","Sarah","Julian") > name [1] "Sophia" "Jackson" "Isabella" "Lucas" "Charlotte" "Oliver" [7] "Amelia" "Benjamin" "Sarah" "Julian"  Let’s create another object called salary. when we specify number of samples (10), mean (1000) and standard deviation (20), this command would generate random numbers to satisfy this. > sal <- rnorm(10, 1000, 200); > sal [1] 898.5731 1105.1638 757.7067 934.9025 1006.0053 1014.4837 1188.6763 611.0265 [9] 643.5498 1121.6005 > mean(sal) [1] 928.1688 > sd(sal) [1] 200.4666  Let’s combine 3 objects together now to form a new object, using data.frame > mydataset <- data.frame(sn, gender, sal) > mydataset sn gender sal 1 1 male 898.5731 2 2 male 1105.1638 3 3 male 757.7067 4 4 male 934.9025 5 5 male 1006.0053 6 6 male 1014.4837 7 7 female 1188.6763 8 8 female 611.0265 9 9 female 643.5498 10 10 female 1121.6005  After showing the above example for data frame, lets switch to list. > mydatalist <- list(v1=id1, v2=sn, v3=slno_gender, v4=mydataset) > mydatalist$v1
[1] 1 2 3 4

$v2 [1] 1 2 3 4 5 6 7 8 9 10$v3
sn   gender
[1,] "1"  "male"
[2,] "2"  "male"
[3,] "3"  "male"
[4,] "4"  "male"
[5,] "5"  "male"
[6,] "6"  "male"
[7,] "7"  "female"
[8,] "8"  "female"
[9,] "9"  "female"
[10,] "10" "female"

$v4 sn gender sal 1 1 male 898.5731 2 2 male 1105.1638 3 3 male 757.7067 4 4 male 934.9025 5 5 male 1006.0053 6 6 male 1014.4837 7 7 female 1188.6763 8 8 female 611.0265 9 9 female 643.5498 10 10 female 1121.6005  I put different object types together in a single collection and stored it in one object. We shall refer to individual data as variables as shown below. > mydatalist$v1
[1] 1 2 3 4


Cool isn’t it!

### Data Coercion

Let’s look at data coercion now. mode command helps is to find out the data type of an object

> mode(mydatalist$v1) [1] "numeric" > mode(mydatalist$v3)
[1] "character"
> mode(gender)
[1] "character"


Let’s convert this character type to factor type now.


gender1<-as.factor(gender)



How is this possible to represent the text  as numeric? Let’s look at the class, which shows us the data structure.

> class(gender1)
[1] "factor


Okay, let’s unclass gender1 now to know how is it stored.

> unclass(gender1)
[1] 2 2 2 2 2 2 1 1 1 1
attr(,"levels")
[1] "female" "male"


So 1 represents female, 2 represents male.

See you in another interest post.

# Package management in R

Hi,

We have seen how to load the data into R language in my previous post Loading Data into R. It is an important part of this blog series. Let’s talk about packages now.

Packages are not new to programmers. Any programming language comes with packages, of course limited set of packages. Additional packages are added a la carte. We shall see same behavior in R as well. The default installation of is a thin solution, which has only basic packages. If needed we need to add additional packages. Lets see how.

### Viewing the packages

search() would help us to check the list of loaded packages.

> search()
[5] "package:graphics"  "package:grDevices" "package:utils"     "package:datasets"
[9] "package:methods"   "Autoloads"         "package:base"

installed.packages() shows us the packages installed but not loaded.

> installed.packages()
Package        LibPath                                   Version
BH           "BH"           "D:/gandhari/documents/R/win-library/3.4" "1.65.0-1"
hms          "hms"          "D:/gandhari/documents/R/win-library/3.4" "0.3"
R6           "R6"           "D:/gandhari/documents/R/win-library/3.4" "2.2.2"
Rcpp         "Rcpp"         "D:/gandhari/documents/R/win-library/3.4" "0.12.12"
rlang        "rlang"        "D:/gandhari/documents/R/win-library/3.4" "0.1.2"
tibble       "tibble"       "D:/gandhari/documents/R/win-library/3.4" "1.3.4"
base         "base"         "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
boot         "boot"         "C:/Program Files/R/R-3.4.1/library"      "1.3-19"
class        "class"        "C:/Program Files/R/R-3.4.1/library"      "7.3-14"
cluster      "cluster"      "C:/Program Files/R/R-3.4.1/library"      "2.0.6"
codetools    "codetools"    "C:/Program Files/R/R-3.4.1/library"      "0.2-15"
compiler     "compiler"     "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
datasets     "datasets"     "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
foreign      "foreign"      "C:/Program Files/R/R-3.4.1/library"      "0.8-69"
graphics     "graphics"     "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
grDevices    "grDevices"    "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
grid         "grid"         "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
KernSmooth   "KernSmooth"   "C:/Program Files/R/R-3.4.1/library"      "2.23-15"
lattice      "lattice"      "C:/Program Files/R/R-3.4.1/library"      "0.20-35"
MASS         "MASS"         "C:/Program Files/R/R-3.4.1/library"      "7.3-47"
Matrix       "Matrix"       "C:/Program Files/R/R-3.4.1/library"      "1.2-10"
methods      "methods"      "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
mgcv         "mgcv"         "C:/Program Files/R/R-3.4.1/library"      "1.8-17"
nlme         "nlme"         "C:/Program Files/R/R-3.4.1/library"      "3.1-131"
nnet         "nnet"         "C:/Program Files/R/R-3.4.1/library"      "7.3-12"
parallel     "parallel"     "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
rpart        "rpart"        "C:/Program Files/R/R-3.4.1/library"      "4.1-11"
spatial      "spatial"      "C:/Program Files/R/R-3.4.1/library"      "7.3-11"
splines      "splines"      "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
stats        "stats"        "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
stats4       "stats4"       "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
survival     "survival"     "C:/Program Files/R/R-3.4.1/library"      "2.41-3"
tcltk        "tcltk"        "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
tools        "tools"        "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
translations "translations" "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
utils        "utils"        "C:/Program Files/R/R-3.4.1/library"      "3.4.1"
Priority      Depends
BH           NA            NA
hms          NA            NA
R6           NA            "R (>= 3.0)"
Rcpp         NA            "R (>= 3.0.0)"
rlang        NA            "R (>= 3.1.0)"
tibble       NA            "R (>= 3.1.0)"
base         "base"        NA
boot         "recommended" "R (>= 3.0.0), graphics, stats"
class        "recommended" "R (>= 3.0.0), stats, utils"
cluster      "recommended" "R (>= 3.0.1)"
codetools    "recommended" "R (>= 2.1)"
compiler     "base"        NA
datasets     "base"        NA
foreign      "recommended" "R (>= 3.0.0)"
graphics     "base"        NA
grDevices    "base"        NA
grid         "base"        NA
KernSmooth   "recommended" "R (>= 2.5.0), stats"
lattice      "recommended" "R (>= 3.0.0)"
MASS         "recommended" "R (>= 3.1.0), grDevices, graphics, stats, utils"
Matrix       "recommended" "R (>= 3.0.1)"
methods      "base"        NA
mgcv         "recommended" "R (>= 2.14.0), nlme (>= 3.1-64)"
nlme         "recommended" "R (>= 3.0.2)"
nnet         "recommended" "R (>= 2.14.0), stats, utils"
parallel     "base"        NA
rpart        "recommended" "R (>= 2.15.0), graphics, stats, grDevices"
spatial      "recommended" "R (>= 3.0.0), graphics, stats, utils"
splines      "base"        NA
stats        "base"        NA
stats4       "base"        NA
survival     "recommended" "R (>= 2.13.0)"
tcltk        "base"        NA
tools        "base"        NA
translations NA            NA
utils        "base"        NA
BH           NA                                                 NA
hms          "methods"                                          NA
R6           NA                                                 NA
Rcpp         "methods, utils"                                   NA
readr        "Rcpp (>= 0.12.0.5), tibble, hms, R6"              "Rcpp, BH"
rlang        NA                                                 NA
tibble       "methods, rlang, Rcpp (>= 0.12.3), utils"          "Rcpp"
base         NA                                                 NA
boot         NA                                                 NA
class        "MASS"                                             NA
cluster      "graphics, grDevices, stats, utils"                NA
codetools    NA                                                 NA
compiler     NA                                                 NA
datasets     NA                                                 NA
foreign      "methods, utils, stats"                            NA
graphics     "grDevices"                                        NA
grDevices    NA                                                 NA
grid         "grDevices, utils"                                 NA
KernSmooth   NA                                                 NA
lattice      "grid, grDevices, graphics, stats, utils"          NA
MASS         "methods"                                          NA
Matrix       "methods, graphics, grid, stats, utils, lattice"   NA
methods      "utils, stats"                                     NA
mgcv         "methods, stats, graphics, Matrix"                 NA
nlme         "graphics, stats, utils, lattice"                  NA
nnet         NA                                                 NA
parallel     "tools, compiler"                                  NA
rpart        NA                                                 NA
spatial      NA                                                 NA
splines      "graphics, stats"                                  NA
stats        "utils, grDevices, graphics"                       NA
stats4       "graphics, methods, stats"                         NA
survival     "graphics, Matrix, methods, splines, stats, utils" NA
tcltk        "utils"                                            NA
tools        NA                                                 NA
translations NA                                                 NA
utils        NA                                                 NA
Suggests
BH           NA
hms          "testthat, lubridate"
R6           "knitr, microbenchmark, pryr, testthat, ggplot2, scales"
Rcpp         "RUnit, inline, rbenchmark, highlight, pkgKitten (>= 0.1.2)"
readr        "curl, testthat, knitr, rmarkdown, stringi, covr"
rlang        "knitr, rmarkdown (>= 0.2.65), testthat, covr"
tibble       "covr, dplyr, knitr (>= 1.5.32), microbenchmark, nycflights13,\ntestthat, rmarkdown, withr"
base         "methods"
boot         "MASS, survival"
class        NA
cluster      "MASS"
codetools    NA
compiler     NA
datasets     NA
foreign      NA
graphics     NA
grDevices    "KernSmooth"
grid         "lattice"
KernSmooth   "MASS"
lattice      "KernSmooth, MASS, latticeExtra"
MASS         "lattice, nlme, nnet, survival"
Matrix       "expm, MASS"
methods      "codetools"
mgcv         "splines, parallel, survival, MASS"
nlme         "Hmisc, MASS"
nnet         "MASS"
parallel     "methods"
rpart        "survival"
spatial      "MASS"
splines      "Matrix, methods"
stats        "MASS, Matrix, SuppDists, methods, stats4"
stats4       NA
survival     NA
tcltk        NA
tools        "codetools, methods, xml2, curl"
translations NA
utils        "methods, XML"
BH           NA                                      "BSL-1.0"
hms          NA                                      "GPL-3"
R6           NA                                      "MIT + file LICENSE"
Rcpp         NA                                      "GPL (>= 2)"
rlang        NA                                      "GPL-3"
tibble       NA                                      "MIT + file LICENSE"
base         NA                                      "Part of R 3.4.1"
boot         NA                                      "Unlimited"
class        NA                                      "GPL-2 | GPL-3"
cluster      NA                                      "GPL (>= 2)"
codetools    NA                                      "GPL"
compiler     NA                                      "Part of R 3.4.1"
datasets     NA                                      "Part of R 3.4.1"
foreign      NA                                      "GPL (>= 2)"
graphics     NA                                      "Part of R 3.4.1"
grDevices    NA                                      "Part of R 3.4.1"
grid         NA                                      "Part of R 3.4.1"
KernSmooth   NA                                      "Unlimited"
lattice      "chron"                                 "GPL (>= 2)"
MASS         NA                                      "GPL-2 | GPL-3"
Matrix       "MatrixModels, graph, SparseM, sfsmisc" "GPL (>= 2) | file LICENCE"
methods      NA                                      "Part of R 3.4.1"
mgcv         NA                                      "GPL (>= 2)"
nlme         NA                                      "GPL (>= 2) | file LICENCE"
nnet         NA                                      "GPL-2 | GPL-3"
parallel     "snow, nws, Rmpi"                       "Part of R 3.4.1"
rpart        NA                                      "GPL-2 | GPL-3"
spatial      NA                                      "GPL-2 | GPL-3"
splines      NA                                      "Part of R 3.4.1"
stats        NA                                      "Part of R 3.4.1"
stats4       NA                                      "Part of R 3.4.1"
survival     NA                                      "LGPL (>= 2)"
tcltk        NA                                      "Part of R 3.4.1"
tools        NA                                      "Part of R 3.4.1"
translations NA                                      "Part of R 3.4.1"
utils        NA                                      "Part of R 3.4.1"
BH           NA              NA                    NA      NA     "no"
hms          NA              NA                    NA      NA     "no"
R6           NA              NA                    NA      NA     "no"
Rcpp         NA              NA                    NA      NA     "yes"
readr        NA              NA                    NA      NA     "yes"
rlang        NA              NA                    NA      NA     "yes"
tibble       NA              NA                    NA      NA     "yes"
base         NA              NA                    NA      NA     NA
boot         NA              NA                    NA      NA     "no"
class        NA              NA                    NA      NA     "yes"
cluster      NA              NA                    NA      NA     "yes"
codetools    NA              NA                    NA      NA     "no"
compiler     NA              NA                    NA      NA     NA
datasets     NA              NA                    NA      NA     NA
foreign      NA              NA                    NA      NA     "yes"
graphics     NA              NA                    NA      NA     "yes"
grDevices    NA              NA                    NA      NA     "yes"
grid         NA              NA                    NA      NA     "yes"
KernSmooth   NA              NA                    NA      NA     "yes"
lattice      NA              NA                    NA      NA     "yes"
MASS         NA              NA                    NA      NA     "yes"
Matrix       NA              NA                    NA      NA     "yes"
methods      NA              NA                    NA      NA     "yes"
mgcv         NA              NA                    NA      NA     "yes"
nlme         NA              NA                    NA      NA     "yes"
nnet         NA              NA                    NA      NA     "yes"
parallel     NA              NA                    NA      NA     "yes"
rpart        NA              NA                    NA      NA     "yes"
spatial      NA              NA                    NA      NA     "yes"
splines      NA              NA                    NA      NA     "yes"
stats        NA              NA                    NA      NA     "yes"
stats4       NA              NA                    NA      NA     NA
survival     NA              NA                    NA      NA     "yes"
tcltk        NA              NA                    NA      NA     "yes"
tools        NA              NA                    NA      NA     "yes"
translations NA              NA                    NA      NA     NA
utils        NA              NA                    NA      NA     "yes"
Built
BH           "3.4.1"
hms          "3.4.1"
R6           "3.4.1"
Rcpp         "3.4.1"
rlang        "3.4.1"
tibble       "3.4.1"
base         "3.4.1"
boot         "3.4.1"
class        "3.4.1"
cluster      "3.4.1"
codetools    "3.4.1"
compiler     "3.4.1"
datasets     "3.4.1"
foreign      "3.4.1"
graphics     "3.4.1"
grDevices    "3.4.1"
grid         "3.4.1"
KernSmooth   "3.4.1"
lattice      "3.4.1"
MASS         "3.4.1"
Matrix       "3.4.1"
methods      "3.4.1"
mgcv         "3.4.1"
nlme         "3.4.1"
nnet         "3.4.1"
parallel     "3.4.1"
rpart        "3.4.1"
spatial      "3.4.1"
splines      "3.4.1"
stats        "3.4.1"
stats4       "3.4.1"
survival     "3.4.1"
tcltk        "3.4.1"
tools        "3.4.1"
translations "3.4.1"
utils        "3.4.1"

R Studio IDE has a tab which shows the loaded/not loaded packages.

### Installing new packages

To install a new package we shall use Install packages option in R Studio, or install.packages() command.

> install.packages("regress")
Installing package into ‘D:/gandhari/documents/R/win-library/3.4’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.4/regress_1.3-15.zip'
Content type 'application/zip' length 32695 bytes (31 KB)

package ‘regress’ successfully unpacked and MD5 sums checked

C:\Users\pandian\AppData\Local\Temp\Rtmpq29bYI\downloaded_packages

### Removing packages

Removing a package using R Studio is as easy as clicking the x mark.

To do it from console, we shall use remove.packages()

> remove.packages("regress")
Removing package from ‘D:/gandhari/documents/R/win-library/3.4’
(as ‘lib’ is unspecified)

To load a package, we shall just check ✅ the needed package in package tab. The same task shall be performed using library() command in console.

> library("BH", lib.loc="~/R/win-library/3.4")

See in another interesting post. 💖 you all.

I have written about storing and retrieving objects in R language in my previous post. Lets see how to load data in R language here.

### c Command

R offers a command called c, which stands for combine. It used to enter numeric, alphanumeric data.

> marks = c (100, 80, 85, 70, 35)

Following commands show how to load the numeric, alphabetic and alphanumeric data. See how R responds when you give alphanumeric data.

> marks = c (100, 80, 85, 70, 35)
> marks
[1] 100  80  85  70  35
> names = c("sun", "moon", "earth")
> names
[1] "sun"   "moon"  "earth"
> alphanu = c("sun", "moon", "earth", 2, 3)
> alphanu
[1] "sun"   "moon"  "earth" "2"     "3"
> #append data
> marks = c(marks, 10, 20)
> marks
[1] 100  80  85  70  35  10  20
> #combine two objects
> combo = c(names, marks)
> combo
[1] "sun"   "moon"  "earth" "100"   "80"    "85"    "70"    "35"    "10"    "20"

### Scan command

We give the complete data as CSV when we use c command. Scan command helps us to enter the data interactively. Double enter to complete the data loading process.

> #scan numbers
> scan()
1: 10
2: 20
3: 30
4:
[1] 10 20 30
> scan(what='character')
1: tamil
2: english
3: maths
4:
[1] "tamil"   "english" "maths"

Here is the way, we shall read the values.

> marks = scan(file = 'D:/gandhari/videos/Advanced Business Analytics/marks.txt')
> marks;
[1]  80  90 100 100  90  70  85  67  74  76  50  55  57  62  51  35  30  27  40  39

So scan forms everything as single dimension array.

I have given the complete path of the file in the above example. If you have multiple file in a same folder, it would be easier to change the working directory to ease the loading process. We shall give only the file name instead of the complete path.

> getwd()
[1] "D:/gandhari/documents"

> marks = scan(file="marks.txt")

This is my input file.

> marks<-read.csv(file = 'marks.csv', header = FALSE, sep = ",")
> marks
V1 V2  V3  V4 V5
1 80 90 100 100 90
2 70 85  67  74 76
3 50 55  57  62 51
4 35 30  27  40 39

v1, v2, … v5 are variables

1, 2, … 5 are rows

### R Studio data import

R Studio has an option to import the CSV files interactively using GUI.

Following is our input data

# Basic functions in R language

I have written about R installation in my previous post R language, R studio – Installation. Let’s do something more in this post.

### Basic commands

> 1+2
[1] 3
> log(4)
[1] 1.386294
> tan(45)
[1] 1.619775
> atan(5)
[1] 1.373401
> 10+15
[1] 25
> #Subtraction
> 450-300
[1] 150
> #Multiplication
> 3 * 4;
[1] 12
> #Division
> 5/2
[1] 2.5
> #Expressions
> 1+(4/2)/3
[1] 1.666667
> #Exponentiation
> 3^2
[1] 9
> #Square root
> sqrt(25)
[1] 5
> #Constants
> pi
[1] 3.141593
> oth<-1:100
> oth
[1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19
[20]  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38
[39]  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57
[58]  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76
[77]  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95
[96]  96  97  98  99 100
> letters
[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t"
[21] "u" "v" "w" "x" "y" "z"
> letters[1:10]
[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j"
> letters[26:1]
[1] "z" "y" "x" "w" "v" "u" "t" "s" "r" "q" "p" "o" "n" "m" "l" "k" "j" "i" "h" "g"
[21] "f" "e" "d" "c" "b" "a"
> LETTERS
[1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T"
[21] "U" "V" "W" "X" "Y" "Z"

() – Function bracket

[] – data set contains row and columns [row, column]

{} – user defined functions

> matrix(1:30)
[,1]
[1,]    1
[2,]    2
[3,]    3
[4,]    4
[5,]    5
[6,]    6
[7,]    7
[8,]    8
[9,]    9
[10,]   10
[11,]   11
[12,]   12
[13,]   13
[14,]   14
[15,]   15
[16,]   16
[17,]   17
[18,]   18
[19,]   19
[20,]   20
[21,]   21
[22,]   22
[23,]   23
[24,]   24
[25,]   25
[26,]   26
[27,]   27
[28,]   28
[29,]   29
[30,]   30
> matrix(1:30, nrow=3)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]    1    4    7   10   13   16   19   22   25    28
[2,]    2    5    8   11   14   17   20   23   26    29
[3,]    3    6    9   12   15   18   21   24   27    30
> matrix(1:30, ncol=3)
[,1] [,2] [,3]
[1,]    1   11   21
[2,]    2   12   22
[3,]    3   13   23
[4,]    4   14   24
[5,]    5   15   25
[6,]    6   16   26
[7,]    7   17   27
[8,]    8   18   28
[9,]    9   19   29
[10,]   10   20   30

### Store the results to environment

All the results shown above are not stored. So they are cleared from memory. So we’ll store the results to the R environment and retrieve it.

> result = 1+2+3+4+5
> result2 = 6+7+8+9+10
> final = result + result2
> avg = mean(result, result2)

The assignment may also use <- arrow.

> tamil<-80
> tamil
[1] 80

Everything is object in R. tamil is on object, so as avg, final, result, result2.

We shall use : operator to mention a series as given below

> 1:10
[1]  1  2  3  4  5  6  7  8  9 10
> one_to_ten<-1:10
> one_to_ten
[1]  1  2  3  4  5  6  7  8  9 10

Got it? If you are using R Studio, you would have seen that, the values are being stored.

We shall print it on the console like the following.

> result
[1] 15
> result2
[1] 40
> final
[1] 55
> avg
[1] 15

### Object management

This returns all the user-defined variables in memory

> #view all objects in use
> ls()
[1] "avg"     "final"   "result"  "result2" "tamil"
> objects()
[1] "avg"     "final"   "result"  "result2" "tamil"

Lets see how to remove those objects from memory.

> #remove one object
> rm(avg)
> remove(tamil)
> ls()
[1] "final"   "result"  "result2"
> #remove multiple objects
> rm(final, result)
> ls()
[1] "result2"
> rm(list=ls())
> ls()
character(0)

I’ll see you in another post with interesting subject.

# R language, R studio – Installation

After completing the series of statistical post in this blog post series, I’d be writing about language R in this post.

R is a OpenSource statistical computing and graphics. This is a software environment which gives an interactive workspace called console.

### Setup

Open Source License which is free to use.

Install R-3.4.1-win.exe

Open R console, by clicking the above icon.

### Get familiarized with R console

Let’s get familiarize ourselves by executing some basic commands.

> 2+3
[1] 5
> 10*12+4
[1] 124
> 10^3
[1] 1000
> sqrt(4)
[1] 2
> pi
[1] 3.141593
> 10 + (2*3)
[1] 16
> pi * 2^2
[1] 12.56637
> 100 + 6/3
[1] 102

### R Studio

We can give the same commands to get it working. See you in another detailed post regarding R programming.