R for Beginners - R Code File -2
This R code book has been written by Rohit Dhankar . GitHub - https://github.com/RohitDhankar
This is the 2nd in series of R Code Files.
Refer GitHub Repository , for all Data Files –> https://github.com/RohitDhankar/R-Beginners-Online-Virtual-Learning-Session
Its a good practice from time to time to keep a track of our current Working Directory and list out all the Objects in our R ENVIRONMENT - specially so when we are committing changes to a Git Remote.
VECTOR Operations
getwd()## [1] "/home/dhankar/Desktop/R_Own/Proj_1"#
ls()## character(0)We could remove any object with command - rm(“Object Name”)
We can also use print() , to view any objects stored value.
# Code Section -1
a1 <- "FINANCE"
b1 <- "MARKETING"
c1 <- "SALES"
d1 <- 3.1416
char_vector <- c("x","d","c","f")
print(a1)## [1] "FINANCE"#
print(char_vector)## [1] "x" "d" "c" "f"Going further with VECTORS .
We combine two or more vectors to get another vector .
# Code Section -2
num_vector <- c(22,22,33,33,44)
print(num_vector)## [1] 22 22 33 33 44num_vector1 <- c(11,12,13,14,15)
#
num_vector3 <- c(num_vector,num_vector1)
print(num_vector3)## [1] 22 22 33 33 44 11 12 13 14 15#
sort(num_vector3)## [1] 11 12 13 14 15 22 22 33 33 44#
order(num_vector3) # Ascending Order of ELEMENTS without SORTING .## [1] 6 7 8 9 10 1 2 3 4 5#
# The COLON Operator is same as the seq() function seen later in this text.
seq_1<- 55:50
seq_1## [1] 55 54 53 52 51 50#
seq_2<- 50:55
seq_2## [1] 50 51 52 53 54 55#
# While the above - seq_1 and seq_2 are stored as Objects withing persistence storage.
50:55 ## is in Memory Only and Not Stored on any persistence storage. ## [1] 50 51 52 53 54 55## Source -- R Manual -- https://stat.ethz.ch/R-manual/R-devel/library/base/html/Colon.htmlSome basic Maths and Stats with VECTORS.
# Code Section -3
num_vector3 + 5## [1] 27 27 38 38 49 16 17 18 19 20# Adds NUMERIC VALUE = 5 to all ELEMENTS of the Num Vector.
nmv_1<-c(20,21,211,312,413,5114)
nmv_2<-c(20,21,211,313,414,5214)
#
class(nmv_1) # "numeric" Vector ## [1] "numeric"#
print(1/nmv_1)## [1] 0.0500000000 0.0476190476 0.0047393365 0.0032051282 0.0024213075
## [6] 0.0001955417#
min_max_nmv <- c(min(nmv_1),max(nmv_1))
min_max_nmv ## Output - MIN == 20 , MAX ==5114## [1] 20 5114#
### MATHEMATICAL logical operators and Boolean calculus - present in R .
# - <, <=, >, >=, == for exact equality and != for inequality.
### Boolean calculus
# Given - nmv_1 and nmv_2 are logical expressions,
# thus nmv_1 & nmv_2 is intersection ("AND")
# nmv_1 | nmv_2 is union ("OR")
# !nmv_1 is Negation of nmv_1.
nmv_1 == nmv_2## [1] TRUE TRUE TRUE FALSE FALSE FALSE#
nmv_1 != nmv_2## [1] FALSE FALSE FALSE TRUE TRUE TRUE#
# Lets introduce NA's - the data wranglers nightmare
#
nmv_3 <- c(11,NA,22,33,44,NA,NA)
nmv_3## [1] 11 NA 22 33 44 NA NA# We can use function is.na() , to find out the NOT AVAILABLE missing values
# At a letr stage we shall also look at NA management or IMPUTATION of MISSING VALUES
# Here is a prelim resource --
#
is.na(nmv_3)## [1] FALSE TRUE FALSE FALSE FALSE TRUE TRUE#
# Kaggle_Titanic [Multiple Imputation of Missing_Values] --
# http://datasciencewithrandpython.blogspot.in/2017/01/kaggle-titanic-initial-analysis-wip.html
#
# Not a NUMBER = NaN
xx <- 0/0.00
xx## [1] NaN#
is.nan(xx) ## TRUE## [1] TRUE#
is.nan(nmv_3) ## As Many FALSE - as Elements ## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE#
nmv_4 <- nmv_3 + 2 ## Any OPERATION done with a NA value , results in a NA value.
nmv_4## [1] 13 NA 24 35 46 NA NA## Code Section -4
num_vector1 * num_vector3## [1] 242 264 429 462 660 121 144 169 196 225# First 5 elements of - num_vector3 multiplied by the Five Elements
# of num_vector1 and again the Next 5 elements of num_vector3
# multiplied by the Five Elements of num_vector1
# Concatenate Strings - Its ofetn required to PASTE together CHAR VARIABLES
# to create more Complex CHAR VARIABLES
# R has a handy function called PASTE -
# ?paste() # Uncomment to see help
# Concatenate vectors after converting to character.
col_names_1 <- paste(c("N","P","Q","R"), 1:20, sep="")
col_names_1## [1] "N1" "P2" "Q3" "R4" "N5" "P6" "Q7" "R8" "N9" "P10" "Q11"
## [12] "R12" "N13" "P14" "Q15" "R16" "N17" "P18" "Q19" "R20"#
col_names_2 <- paste(c("X","Y","Z"), 1:3, sep="")
col_names_2## [1] "X1" "Y2" "Z3"#
col_names_3 <- paste(c("M","N","P"), col_names_2, sep="")
col_names_3## [1] "MX1" "NY2" "PZ3"#
#
col_names_4 <- paste( col_names_2,c("M","N","P"), sep="")
col_names_4## [1] "X1M" "Y2N" "Z3P"# As seen above very handy for creating COLUMN NAMES or VARIABLE LABELS
# Kindly notice - PASTE() folllows the Order of R OBJECTS provided
# and converts them to CHAR VECTORS. Check out the LENGTH of a VECTOR with length()
# Code Section -5
length(num_vector1 * num_vector3)## [1] 10# Code Section -6
#num_vector1 %*% num_vector3 # Error in num_vector1 %*% num_vector3 : non-conformable arguments
# Vectors are not of same Length above - below they are of same length
nv <- c(1,2,3,4,5)
nv1 <- c(6,7,8,9,10)
nv %*% nv1 # Inner Product of same Length Vectors## [,1]
## [1,] 130# Algeberic Dot Product as defined by WikiPedia - "https://en.wikipedia.org/wiki/Dot_product"Operate upon a ELEMENT of the Vector.
# Code Section -7
log(num_vector3[2]) # Log Base 2 of 22 ## [1] 3.091042#
log(22)## [1] 3.091042#Converting a CHAR Vector into a NUMERIC Vector .
# Code Section -8
ch_v <- c("11","12","13","14","15")
#
class(ch_v)## [1] "character"#ch_v + 2 # Error in ch_v + 2 : non-numeric argument to binary operator
# Cant do a Math operation on CHAR Vector - lets Convert into NUM Vector
#
nm_v <- as.numeric(ch_v)
#
class(nm_v)## [1] "numeric"nm_v + 2 ## [1] 13 14 15 16 17#
#Summary of the Num Vector as below :-
#
summary(nm_v+2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 13 14 15 15 16 17#
summary(nm_v+5)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 16 17 18 18 19 20#
sum(nm_v+5)## [1] 90#
sd(nm_v+5)## [1] 1.581139#
max(nm_v+5)## [1] 20#
min(nm_v+5)## [1] 16#
mean(nm_v+5)## [1] 18#
median(nm_v+5)## [1] 18#
#The Quantile -
#
quantile(nm_v+5)## 0% 25% 50% 75% 100%
## 16 17 18 19 20#
quantile(nm_v+100)## 0% 25% 50% 75% 100%
## 111 112 113 114 115#
#We can also specify the Quantile buckets or Percentiles as an argument to the Quantile function :-
#
nmv_q <- c(10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,100)
percent_1 <- quantile(nmv_q, c(.50,.75,.84, .97, .99))
percent_1 ## 50% 75% 84% 97% 99%
## 52.50 73.75 81.40 94.90 98.30boxplot(percent_1,col = "red",horizontal = TRUE,
main = "Box and Whisker Plot of Quantiles",
xlab = "Quantile Values")
# Kindly note how the ARGUMENTs to boxplot()
# have been bumped to the next row - keeping in mind
# the Horizontal space of our PDF knit of the .Rmd file
# Seen above we have the MEDIAN quartile - 50% and the UPPER
# Quartile - 75% along with THREE more percentiles. Wiki reference – Percentile Rank - “https://en.wikipedia.org/wiki/Percentile_rank” #
Intro to ANOVA and BOXPLOTS
We also carry out ONE Way ANOVA or ANALYSIS of VARIANCE test with the BOX and WHISKERS plots as seen below :-
# Code Section -9
library(graphics)
nmv_q <- c(10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,100)
percent_1 <- quantile(nmv_q, c(.50,.75,.84, .97, .99))
percent_1 ## 50% 75% 84% 97% 99%
## 52.50 73.75 81.40 94.90 98.30percent_2 <- quantile(nmv_q, c(.1, .3, .16, .40, .50))
percent_2 ## 10% 30% 16% 40% 50%
## 18.5 35.5 23.6 44.0 52.5percent_3 <- quantile(nmv_q, c(.16, .40, .50,.75,.84))
percent_3 ## 16% 40% 50% 75% 84%
## 23.60 44.00 52.50 73.75 81.40col_boxes = (c("red","blue","green"))
boxplot(percent_1,percent_2,percent_3,col = col_boxes,
names = c("perc_1","perc_2","perc_3"),horizontal = TRUE,
main = "Box and Whisker Plot of Quantiles",
xlab = "Quantile Values")
# Kindly note the Quantiles are randomly chosen here
# this is not the best way to choose quantiles
# we shall come back for details later in this textrainbow() for Coloring Boxplots -
# Code Section -10
percent_4 <- quantile(nmv_q, c(.16, .40, .50,.95,.99))
percent_4 ## 16% 40% 50% 95% 99%
## 23.6 44.0 52.5 91.5 98.3percent_5 <- quantile(nmv_q, c(.16, .24,.32 ,.40,.75))
percent_5 ## 16% 24% 32% 40% 75%
## 23.60 30.40 37.20 44.00 73.75percent_6 <- quantile(nmv_q, c(.1, .5, .26, .45, .60))
percent_6 ## 10% 50% 26% 45% 60%
## 18.50 52.50 32.10 48.25 61.00percent_7 <- quantile(nmv_q, c(.3, .7, .18, .43, .70))
percent_7 ## 30% 70% 18% 43% 70%
## 35.50 69.50 25.30 46.55 69.50col_rainbow <- rainbow(7)
boxplot(percent_1,percent_2,percent_3,percent_4,percent_5,percent_6,percent_7,col = col_rainbow,
names = c("perc_1","perc_2","perc_3","perc_4","perc_5","perc_6","perc_7"),horizontal = TRUE,
main = "Box and Whisker Plot of Quantiles",
xlab = "Quantile Values")
# Code Section -11
# Just for Fun a PIE Graph --- you always ...
# need to avoid PIE Graphs for "DESCRIPTIVE STATS"
# I personally love to use them for MARKETING
# They are usually excellent EYE CANDY :P
pie(rep(1, 7), col = rainbow(7))
MATRICE Operations - TRANSPOSE of a MATRIX
Coming back to MATRICES lets further look at some MATRIX Operations :-
# Code Section -12
m1 <- matrix(data=66:69,nrow=2,ncol=2)
m1## [,1] [,2]
## [1,] 66 68
## [2,] 67 69# Lets now TRANSPOSE this MATRIX - for more on TRANSPOSE of MATRICES
# kindly refer this Wiki Link -- https://en.wikipedia.org/wiki/Transpose
t(m1)## [,1] [,2]
## [1,] 66 67
## [2,] 68 69# As seen below - the DIAGONAL Elements remain as -is .
# 66 and 69 do not move .
# 67 and 68 switch places , thus giving us a Transpose Matrix.
# Lets look at another example of TRANSPOSE ....
m2 <- matrix(data=10:25,nrow=4,ncol=4)
m2## [,1] [,2] [,3] [,4]
## [1,] 10 14 18 22
## [2,] 11 15 19 23
## [3,] 12 16 20 24
## [4,] 13 17 21 25class(m2)## [1] "matrix"## Note in the above sequence - 10:25 - both 10 and 25 are included.
# Lets now TRANSPOSE this MATRIX - for more on TRANSPOSE of MATRICES
# kindly refer this Wiki Link -- https://en.wikipedia.org/wiki/Transpose
t(m2)## [,1] [,2] [,3] [,4]
## [1,] 10 11 12 13
## [2,] 14 15 16 17
## [3,] 18 19 20 21
## [4,] 22 23 24 25# As seen below - the DIAGONAL Elements remain as-is.
# 10, 15 , 20 , 25 -- do not move .
# Non Diagonal elements are Transposed ,giving the Transpose Matrix. The Semicolon Notation - RANGE or SEQUENCE
# Code Section -13
# Quick recap of the SEQUENCE
a_seq <- 66:69
a_seq ## [1] 66 67 68 69# In the earlier section we create a MATRIX by using a sequence within
# the COMBINE function
# We can also use the - seq - sequence function as seen below
b_seq <- seq(from=66, to=69, by=1)
b_seq## [1] 66 67 68 69#
b_seq <- seq(from=66, to=69, by=2)
b_seq## [1] 66 68#
c_seq <- seq(from=1, to=10, by=2)
c_seq## [1] 1 3 5 7 9class(c_seq)## [1] "numeric"The CBIND and RBIND Functions
We can COLUMN Bind and ROW Bind more than one data structures as seen below -
# Code Section -14
ma1 <- matrix(data=10:15,nrow=3,ncol=2)
ma1## [,1] [,2]
## [1,] 10 13
## [2,] 11 14
## [3,] 12 15#
class(ma1)## [1] "matrix"#
ma2 <- matrix(data=20:25,nrow=3,ncol=2)
ma2## [,1] [,2]
## [1,] 20 23
## [2,] 21 24
## [3,] 22 25#
class(ma2)## [1] "matrix"# ROW Bind the Matrices
ma3 <- rbind(ma1,ma2)
ma3## [,1] [,2]
## [1,] 10 13
## [2,] 11 14
## [3,] 12 15
## [4,] 20 23
## [5,] 21 24
## [6,] 22 25#
# COLUMN Bind the Matrices
ma4 <- cbind(ma1,ma2)
ma4## [,1] [,2] [,3] [,4]
## [1,] 10 13 20 23
## [2,] 11 14 21 24
## [3,] 12 15 22 25# As seen below we need to have same COLUMN Numbers to do a RBIND
#m3 <- rbind(m1,m2)
# # As seen below we need to have same ROW Numbers to do a RBIND
#m3 <- cbind(m1,m2)ROW BIND - Data Frames
# Code Section -15
df_1 <- read.csv("~/Desktop/R_Own/R_1 - Sheet1.csv",header =TRUE , sep = "," )
df_1## X. Product.Name Prod.ID Date.of.Invoice Date.of.Shipping
## 1 1 OFF-LA-10002782 MX-2014-143658 01-01-2013 02-01-2013
## 2 2 FUR-FU-10004015 MX-2012-155047 01-01-2013 02-01-2013
## 3 3 FUR-BO-10002352 MX-2012-155047 01-01-2013 02-01-2013
## 4 4 OFF-BI-10004428 MX-2012-155047 01-01-2013 02-01-2013
## 5 5 OFF-AR-10004594 MX-2012-155047 01-01-2013 02-01-2013
## 6 6 OFF-EN-10001375 MX-2012-155047 01-01-2013 02-01-2013
## 7 7 OFF-EN-10001375 MX-2013-134096 01-01-2013 02-01-2013
## 8 8 TEC-MA-10004956 MX-2013-134096 01-01-2013 02-01-2013
## 9 9 OFF-SU-10003474 MX-2013-134096 01-01-2013 02-01-2013
## 10 10 TEC-AC-10001830 MX-2013-134096 01-01-2013 02-01-2013
## 11 11 OFF-BI-10002075 MX-2013-134096 01-01-2013 02-01-2013
## 12 12 OFF-FA-10002526 MX-2013-156335 01-01-2013 02-01-2013
## 13 13 FUR-CH-10002846 MX-2013-156335 01-01-2013 02-01-2013
## 14 14 OFF-EN-10004100 MX-2014-121923 02-01-2013 04-01-2013
## 15 15 OFF-AR-10003914 MX-2014-135706 02-01-2013 03-01-2013
## 16 16 OFF-FA-10000038 MX-2014-135706 02-01-2013 03-01-2013
## 17 17 OFF-EN-10000761 US-2013-126655 02-01-2013 03-01-2013
## 18 18 FUR-FU-10003066 US-2013-126655 02-01-2013 03-01-2013
## 19 19 OFF-EN-10000075 US-2013-126655 02-01-2013 03-01-2013
## 20 20 OFF-EN-10002226 US-2013-126655 02-01-2013 03-01-2013
## 21 21 FUR-CH-10002132 MX-2013-167759 02-01-2013 04-01-2013
## 22 22 TEC-AC-10002749 MX-2013-163139 02-01-2013 02-01-2013
## 23 23 OFF-SU-10000066 MX-2013-163139 02-01-2013 02-01-2013
## 24 24 OFF-BI-10003934 US-2014-119753 02-01-2013 03-01-2013
## 25 25 OFF-BI-10003932 US-2012-133970 02-01-2013 03-01-2013
## Cost.Price Quantity Sales.Price Shipping.Index Shipping.Type
## 1 13.080 3 39.240 1 PRIORITY
## 2 252.160 8 2017.280 2 PRIORITY
## 3 193.280 2 386.560 3 PRIORITY
## 4 35.440 4 141.760 4 PRIORITY
## 5 71.600 2 143.200 5 PRIORITY
## 6 56.120 2 112.240 6 PRIORITY
## 7 56.120 2 112.240 7 STANDARD
## 8 344.640 3 1033.920 8 STANDARD
## 9 97.360 4 389.440 9 STANDARD
## 10 341.520 2 683.040 10 STANDARD
## 11 12.060 3 36.180 11 STANDARD
## 12 20.760 3 62.280 12 STANDARD
## 13 210.640 4 842.560 13 STANDARD
## 14 80.100 3 240.300 14 STANDARD
## 15 132.640 4 530.560 15 STANDARD
## 16 12.940 1 12.940 16 STANDARD
## 17 18.840 2 37.280 17 STANDARD
## 18 308.280 7 2157.560 18 STANDARD
## 19 40.176 2 79.952 19 STANDARD
## 20 8.784 3 25.952 20 PRIORITY
## 21 273.472 4 1093.688 21 PRIORITY
## 22 27.000 1 27.000 22 PRIORITY
## 23 207.000 9 1863.000 23 PRIORITY
## 24 60.660 3 181.580 24 PRIORITY
## 25 181.116 9 1629.644 25 PRIORITY
## Category
## 1 Office Supplies
## 2 Furniture
## 3 Furniture
## 4 Office Supplies
## 5 Office Supplies
## 6 Office Supplies
## 7 Office Supplies
## 8 Technology
## 9 Office Supplies
## 10 Technology
## 11 Office Supplies
## 12 Office Supplies
## 13 Furniture
## 14 Office Supplies
## 15 Office Supplies
## 16 Office Supplies
## 17 Office Supplies
## 18 Furniture
## 19 Office Supplies
## 20 Office Supplies
## 21 Furniture
## 22 Technology
## 23 Office Supplies
## 24 Office Supplies
## 25 Office Supplies# Code Section -16
df_2 <- read.csv("~/Desktop/R_Own/R_2.csv",header =TRUE , sep = "," )
df_2## X. Product.Name Prod.ID Date.of.Invoice Date.of.Shipping
## 1 1 TEC-AC-10001830 MX-2013-134096 05-01-2013 06-01-2013
## 2 2 FUR-FU-10004015 MX-2012-155047 03-01-2013 03-01-2013
## 3 3 FUR-BO-10002352 MX-2012-155047 03-01-2013 03-01-2013
## 4 4 OFF-BI-10004428 MX-2012-155047 03-01-2013 03-01-2013
## 5 5 OFF-AR-10004594 MX-2012-155047 03-01-2013 03-01-2013
## 6 6 OFF-EN-10001375 MX-2012-155047 03-01-2013 03-01-2013
## 7 7 OFF-EN-10001375 MX-2013-134096 03-01-2013 04-01-2013
## 8 8 OFF-AR-10003914 MX-2014-135706 03-01-2013 04-01-2013
## 9 9 OFF-FA-10000038 MX-2014-135706 03-01-2013 04-01-2013
## 10 10 OFF-EN-10000761 US-2013-126655 03-01-2013 04-01-2013
## 11 11 FUR-FU-10003066 US-2013-126655 03-01-2013 04-01-2013
## 12 12 OFF-EN-10000075 US-2013-126655 03-01-2013 04-01-2013
## 13 13 OFF-EN-10002226 US-2013-126655 03-01-2013 03-01-2013
## 14 14 FUR-CH-10002132 MX-2013-167759 03-01-2013 03-01-2013
## 15 15 OFF-EN-10001375 MX-2013-134096 03-01-2013 04-01-2013
## 16 16 TEC-MA-10004956 MX-2013-134096 03-01-2013 04-01-2013
## 17 17 OFF-SU-10003474 MX-2013-134096 03-01-2013 04-01-2013
## 18 18 TEC-AC-10001830 MX-2013-134096 03-01-2013 04-01-2013
## 19 19 OFF-BI-10002075 MX-2013-134096 03-01-2013 04-01-2013
## 20 20 OFF-FA-10002526 MX-2013-156335 03-01-2013 04-01-2013
## 21 21 OFF-EN-10001375 MX-2013-134096 04-01-2013 05-01-2013
## 22 22 TEC-MA-10004956 MX-2013-134096 04-01-2013 05-01-2013
## 23 23 OFF-SU-10003474 MX-2013-134096 04-01-2013 05-01-2013
## 24 24 TEC-AC-10001830 MX-2013-134096 04-01-2013 05-01-2013
## 25 25 OFF-BI-10002075 MX-2013-134096 04-01-2013 05-01-2013
## 26 26 OFF-EN-10001375 MX-2013-134096 04-01-2013 05-01-2013
## 27 27 TEC-MA-10004956 MX-2013-134096 04-01-2013 05-01-2013
## 28 28 OFF-SU-10003474 MX-2013-134096 04-01-2013 05-01-2013
## 29 29 TEC-AC-10001830 MX-2013-134096 04-01-2013 05-01-2013
## 30 30 OFF-BI-10002075 MX-2013-134096 04-01-2013 05-01-2013
## 31 31 OFF-EN-10000761 US-2013-126655 04-01-2013 05-01-2013
## 32 32 FUR-FU-10003066 US-2013-126655 04-01-2013 05-01-2013
## 33 33 OFF-EN-10000075 US-2013-126655 04-01-2013 05-01-2013
## 34 34 OFF-EN-10002226 US-2013-126655 04-01-2013 04-01-2013
## 35 35 FUR-CH-10002132 MX-2013-167759 04-01-2013 04-01-2013
## 36 36 OFF-EN-10001375 MX-2013-134096 04-01-2013 05-01-2013
## 37 37 TEC-MA-10004956 MX-2013-134096 04-01-2013 05-01-2013
## 38 38 OFF-SU-10003474 MX-2013-134096 04-01-2013 05-01-2013
## 39 39 TEC-AC-10001830 MX-2013-134096 04-01-2013 05-01-2013
## 40 40 OFF-BI-10002075 MX-2013-134096 04-01-2013 05-01-2013
## 41 41 OFF-FA-10002526 MX-2013-156335 04-01-2013 05-01-2013
## 42 42 OFF-EN-10001375 MX-2013-134096 04-01-2013 05-01-2013
## 43 43 TEC-MA-10004956 MX-2013-134096 04-01-2013 05-01-2013
## 44 44 OFF-SU-10003474 MX-2013-134096 05-01-2013 06-01-2013
## 45 45 TEC-AC-10001830 MX-2013-134096 05-01-2013 06-01-2013
## 46 46 OFF-BI-10002075 MX-2013-134096 05-01-2013 06-01-2013
## 47 47 OFF-EN-10001375 MX-2013-134096 05-01-2013 06-01-2013
## 48 48 TEC-MA-10004956 MX-2013-134096 05-01-2013 06-01-2013
## 49 49 OFF-SU-10003474 MX-2013-134096 05-01-2013 06-01-2013
## 50 50 OFF-LA-10002782 MX-2014-143658 03-01-2013 03-01-2013
## Cost.Price Quantity Sales.Price Shipping.Index Shipping.Type
## 1 341.520 2 683.040 10 STANDARD
## 2 252.160 8 2017.280 2 PRIORITY
## 3 193.280 2 386.560 3 PRIORITY
## 4 35.440 4 141.760 4 PRIORITY
## 5 71.600 2 143.200 5 PRIORITY
## 6 56.120 2 112.240 6 PRIORITY
## 7 56.120 2 112.240 7 STANDARD
## 8 132.640 4 530.560 15 STANDARD
## 9 12.940 1 12.940 16 STANDARD
## 10 18.840 2 37.280 17 STANDARD
## 11 308.280 7 2157.560 18 STANDARD
## 12 40.176 2 79.952 19 STANDARD
## 13 8.784 3 25.952 20 PRIORITY
## 14 273.472 4 1093.688 21 PRIORITY
## 15 56.120 2 112.240 7 STANDARD
## 16 344.640 3 1033.920 8 STANDARD
## 17 97.360 4 389.440 9 STANDARD
## 18 341.520 2 683.040 10 STANDARD
## 19 12.060 3 36.180 11 STANDARD
## 20 20.760 3 62.280 12 STANDARD
## 21 56.120 2 112.240 7 STANDARD
## 22 344.640 3 1033.920 8 STANDARD
## 23 97.360 4 389.440 9 STANDARD
## 24 341.520 2 683.040 10 STANDARD
## 25 12.060 3 36.180 11 STANDARD
## 26 56.120 2 112.240 7 STANDARD
## 27 344.640 3 1033.920 8 STANDARD
## 28 97.360 4 389.440 9 STANDARD
## 29 341.520 2 683.040 10 STANDARD
## 30 12.060 3 36.180 11 STANDARD
## 31 18.840 2 37.280 17 STANDARD
## 32 308.280 7 2157.560 18 STANDARD
## 33 40.176 2 79.952 19 STANDARD
## 34 8.784 3 25.952 20 PRIORITY
## 35 273.472 4 1093.688 21 PRIORITY
## 36 56.120 2 112.240 7 STANDARD
## 37 344.640 3 1033.920 8 STANDARD
## 38 97.360 4 389.440 9 STANDARD
## 39 341.520 2 683.040 10 STANDARD
## 40 12.060 3 36.180 11 STANDARD
## 41 20.760 3 62.280 12 STANDARD
## 42 56.120 2 112.240 7 STANDARD
## 43 344.640 3 1033.920 8 STANDARD
## 44 97.360 4 389.440 9 STANDARD
## 45 341.520 2 683.040 10 STANDARD
## 46 12.060 3 36.180 11 STANDARD
## 47 56.120 2 112.240 7 STANDARD
## 48 344.640 3 1033.920 8 STANDARD
## 49 97.360 4 389.440 9 STANDARD
## 50 13.080 3 39.240 1 PRIORITY
## Category
## 1 Technology
## 2 Furniture
## 3 Furniture
## 4 Office Supplies
## 5 Office Supplies
## 6 Office Supplies
## 7 Office Supplies
## 8 Office Supplies
## 9 Office Supplies
## 10 Office Supplies
## 11 Furniture
## 12 Office Supplies
## 13 Office Supplies
## 14 Furniture
## 15 Office Supplies
## 16 Technology
## 17 Office Supplies
## 18 Technology
## 19 Office Supplies
## 20 Office Supplies
## 21 Office Supplies
## 22 Technology
## 23 Office Supplies
## 24 Technology
## 25 Office Supplies
## 26 Office Supplies
## 27 Technology
## 28 Office Supplies
## 29 Technology
## 30 Office Supplies
## 31 Office Supplies
## 32 Furniture
## 33 Office Supplies
## 34 Office Supplies
## 35 Furniture
## 36 Office Supplies
## 37 Technology
## 38 Office Supplies
## 39 Technology
## 40 Office Supplies
## 41 Office Supplies
## 42 Office Supplies
## 43 Technology
## 44 Office Supplies
## 45 Technology
## 46 Office Supplies
## 47 Office Supplies
## 48 Technology
## 49 Office Supplies
## 50 Office SuppliesCOLUMN BIND- Data Frame
# Code Section -17
df_3_C <- cbind(df_1,df_2)
df_3_C## X. Product.Name Prod.ID Date.of.Invoice Date.of.Shipping
## 1 1 OFF-LA-10002782 MX-2014-143658 01-01-2013 02-01-2013
## 2 2 FUR-FU-10004015 MX-2012-155047 01-01-2013 02-01-2013
## 3 3 FUR-BO-10002352 MX-2012-155047 01-01-2013 02-01-2013
## 4 4 OFF-BI-10004428 MX-2012-155047 01-01-2013 02-01-2013
## 5 5 OFF-AR-10004594 MX-2012-155047 01-01-2013 02-01-2013
## 6 6 OFF-EN-10001375 MX-2012-155047 01-01-2013 02-01-2013
## 7 7 OFF-EN-10001375 MX-2013-134096 01-01-2013 02-01-2013
## 8 8 TEC-MA-10004956 MX-2013-134096 01-01-2013 02-01-2013
## 9 9 OFF-SU-10003474 MX-2013-134096 01-01-2013 02-01-2013
## 10 10 TEC-AC-10001830 MX-2013-134096 01-01-2013 02-01-2013
## 11 11 OFF-BI-10002075 MX-2013-134096 01-01-2013 02-01-2013
## 12 12 OFF-FA-10002526 MX-2013-156335 01-01-2013 02-01-2013
## 13 13 FUR-CH-10002846 MX-2013-156335 01-01-2013 02-01-2013
## 14 14 OFF-EN-10004100 MX-2014-121923 02-01-2013 04-01-2013
## 15 15 OFF-AR-10003914 MX-2014-135706 02-01-2013 03-01-2013
## 16 16 OFF-FA-10000038 MX-2014-135706 02-01-2013 03-01-2013
## 17 17 OFF-EN-10000761 US-2013-126655 02-01-2013 03-01-2013
## 18 18 FUR-FU-10003066 US-2013-126655 02-01-2013 03-01-2013
## 19 19 OFF-EN-10000075 US-2013-126655 02-01-2013 03-01-2013
## 20 20 OFF-EN-10002226 US-2013-126655 02-01-2013 03-01-2013
## 21 21 FUR-CH-10002132 MX-2013-167759 02-01-2013 04-01-2013
## 22 22 TEC-AC-10002749 MX-2013-163139 02-01-2013 02-01-2013
## 23 23 OFF-SU-10000066 MX-2013-163139 02-01-2013 02-01-2013
## 24 24 OFF-BI-10003934 US-2014-119753 02-01-2013 03-01-2013
## 25 25 OFF-BI-10003932 US-2012-133970 02-01-2013 03-01-2013
## 26 1 OFF-LA-10002782 MX-2014-143658 01-01-2013 02-01-2013
## 27 2 FUR-FU-10004015 MX-2012-155047 01-01-2013 02-01-2013
## 28 3 FUR-BO-10002352 MX-2012-155047 01-01-2013 02-01-2013
## 29 4 OFF-BI-10004428 MX-2012-155047 01-01-2013 02-01-2013
## 30 5 OFF-AR-10004594 MX-2012-155047 01-01-2013 02-01-2013
## 31 6 OFF-EN-10001375 MX-2012-155047 01-01-2013 02-01-2013
## 32 7 OFF-EN-10001375 MX-2013-134096 01-01-2013 02-01-2013
## 33 8 TEC-MA-10004956 MX-2013-134096 01-01-2013 02-01-2013
## 34 9 OFF-SU-10003474 MX-2013-134096 01-01-2013 02-01-2013
## 35 10 TEC-AC-10001830 MX-2013-134096 01-01-2013 02-01-2013
## 36 11 OFF-BI-10002075 MX-2013-134096 01-01-2013 02-01-2013
## 37 12 OFF-FA-10002526 MX-2013-156335 01-01-2013 02-01-2013
## 38 13 FUR-CH-10002846 MX-2013-156335 01-01-2013 02-01-2013
## 39 14 OFF-EN-10004100 MX-2014-121923 02-01-2013 04-01-2013
## 40 15 OFF-AR-10003914 MX-2014-135706 02-01-2013 03-01-2013
## 41 16 OFF-FA-10000038 MX-2014-135706 02-01-2013 03-01-2013
## 42 17 OFF-EN-10000761 US-2013-126655 02-01-2013 03-01-2013
## 43 18 FUR-FU-10003066 US-2013-126655 02-01-2013 03-01-2013
## 44 19 OFF-EN-10000075 US-2013-126655 02-01-2013 03-01-2013
## 45 20 OFF-EN-10002226 US-2013-126655 02-01-2013 03-01-2013
## 46 21 FUR-CH-10002132 MX-2013-167759 02-01-2013 04-01-2013
## 47 22 TEC-AC-10002749 MX-2013-163139 02-01-2013 02-01-2013
## 48 23 OFF-SU-10000066 MX-2013-163139 02-01-2013 02-01-2013
## 49 24 OFF-BI-10003934 US-2014-119753 02-01-2013 03-01-2013
## 50 25 OFF-BI-10003932 US-2012-133970 02-01-2013 03-01-2013
## Cost.Price Quantity Sales.Price Shipping.Index Shipping.Type
## 1 13.080 3 39.240 1 PRIORITY
## 2 252.160 8 2017.280 2 PRIORITY
## 3 193.280 2 386.560 3 PRIORITY
## 4 35.440 4 141.760 4 PRIORITY
## 5 71.600 2 143.200 5 PRIORITY
## 6 56.120 2 112.240 6 PRIORITY
## 7 56.120 2 112.240 7 STANDARD
## 8 344.640 3 1033.920 8 STANDARD
## 9 97.360 4 389.440 9 STANDARD
## 10 341.520 2 683.040 10 STANDARD
## 11 12.060 3 36.180 11 STANDARD
## 12 20.760 3 62.280 12 STANDARD
## 13 210.640 4 842.560 13 STANDARD
## 14 80.100 3 240.300 14 STANDARD
## 15 132.640 4 530.560 15 STANDARD
## 16 12.940 1 12.940 16 STANDARD
## 17 18.840 2 37.280 17 STANDARD
## 18 308.280 7 2157.560 18 STANDARD
## 19 40.176 2 79.952 19 STANDARD
## 20 8.784 3 25.952 20 PRIORITY
## 21 273.472 4 1093.688 21 PRIORITY
## 22 27.000 1 27.000 22 PRIORITY
## 23 207.000 9 1863.000 23 PRIORITY
## 24 60.660 3 181.580 24 PRIORITY
## 25 181.116 9 1629.644 25 PRIORITY
## 26 13.080 3 39.240 1 PRIORITY
## 27 252.160 8 2017.280 2 PRIORITY
## 28 193.280 2 386.560 3 PRIORITY
## 29 35.440 4 141.760 4 PRIORITY
## 30 71.600 2 143.200 5 PRIORITY
## 31 56.120 2 112.240 6 PRIORITY
## 32 56.120 2 112.240 7 STANDARD
## 33 344.640 3 1033.920 8 STANDARD
## 34 97.360 4 389.440 9 STANDARD
## 35 341.520 2 683.040 10 STANDARD
## 36 12.060 3 36.180 11 STANDARD
## 37 20.760 3 62.280 12 STANDARD
## 38 210.640 4 842.560 13 STANDARD
## 39 80.100 3 240.300 14 STANDARD
## 40 132.640 4 530.560 15 STANDARD
## 41 12.940 1 12.940 16 STANDARD
## 42 18.840 2 37.280 17 STANDARD
## 43 308.280 7 2157.560 18 STANDARD
## 44 40.176 2 79.952 19 STANDARD
## 45 8.784 3 25.952 20 PRIORITY
## 46 273.472 4 1093.688 21 PRIORITY
## 47 27.000 1 27.000 22 PRIORITY
## 48 207.000 9 1863.000 23 PRIORITY
## 49 60.660 3 181.580 24 PRIORITY
## 50 181.116 9 1629.644 25 PRIORITY
## Category X. Product.Name Prod.ID Date.of.Invoice
## 1 Office Supplies 1 TEC-AC-10001830 MX-2013-134096 05-01-2013
## 2 Furniture 2 FUR-FU-10004015 MX-2012-155047 03-01-2013
## 3 Furniture 3 FUR-BO-10002352 MX-2012-155047 03-01-2013
## 4 Office Supplies 4 OFF-BI-10004428 MX-2012-155047 03-01-2013
## 5 Office Supplies 5 OFF-AR-10004594 MX-2012-155047 03-01-2013
## 6 Office Supplies 6 OFF-EN-10001375 MX-2012-155047 03-01-2013
## 7 Office Supplies 7 OFF-EN-10001375 MX-2013-134096 03-01-2013
## 8 Technology 8 OFF-AR-10003914 MX-2014-135706 03-01-2013
## 9 Office Supplies 9 OFF-FA-10000038 MX-2014-135706 03-01-2013
## 10 Technology 10 OFF-EN-10000761 US-2013-126655 03-01-2013
## 11 Office Supplies 11 FUR-FU-10003066 US-2013-126655 03-01-2013
## 12 Office Supplies 12 OFF-EN-10000075 US-2013-126655 03-01-2013
## 13 Furniture 13 OFF-EN-10002226 US-2013-126655 03-01-2013
## 14 Office Supplies 14 FUR-CH-10002132 MX-2013-167759 03-01-2013
## 15 Office Supplies 15 OFF-EN-10001375 MX-2013-134096 03-01-2013
## 16 Office Supplies 16 TEC-MA-10004956 MX-2013-134096 03-01-2013
## 17 Office Supplies 17 OFF-SU-10003474 MX-2013-134096 03-01-2013
## 18 Furniture 18 TEC-AC-10001830 MX-2013-134096 03-01-2013
## 19 Office Supplies 19 OFF-BI-10002075 MX-2013-134096 03-01-2013
## 20 Office Supplies 20 OFF-FA-10002526 MX-2013-156335 03-01-2013
## 21 Furniture 21 OFF-EN-10001375 MX-2013-134096 04-01-2013
## 22 Technology 22 TEC-MA-10004956 MX-2013-134096 04-01-2013
## 23 Office Supplies 23 OFF-SU-10003474 MX-2013-134096 04-01-2013
## 24 Office Supplies 24 TEC-AC-10001830 MX-2013-134096 04-01-2013
## 25 Office Supplies 25 OFF-BI-10002075 MX-2013-134096 04-01-2013
## 26 Office Supplies 26 OFF-EN-10001375 MX-2013-134096 04-01-2013
## 27 Furniture 27 TEC-MA-10004956 MX-2013-134096 04-01-2013
## 28 Furniture 28 OFF-SU-10003474 MX-2013-134096 04-01-2013
## 29 Office Supplies 29 TEC-AC-10001830 MX-2013-134096 04-01-2013
## 30 Office Supplies 30 OFF-BI-10002075 MX-2013-134096 04-01-2013
## 31 Office Supplies 31 OFF-EN-10000761 US-2013-126655 04-01-2013
## 32 Office Supplies 32 FUR-FU-10003066 US-2013-126655 04-01-2013
## 33 Technology 33 OFF-EN-10000075 US-2013-126655 04-01-2013
## 34 Office Supplies 34 OFF-EN-10002226 US-2013-126655 04-01-2013
## 35 Technology 35 FUR-CH-10002132 MX-2013-167759 04-01-2013
## 36 Office Supplies 36 OFF-EN-10001375 MX-2013-134096 04-01-2013
## 37 Office Supplies 37 TEC-MA-10004956 MX-2013-134096 04-01-2013
## 38 Furniture 38 OFF-SU-10003474 MX-2013-134096 04-01-2013
## 39 Office Supplies 39 TEC-AC-10001830 MX-2013-134096 04-01-2013
## 40 Office Supplies 40 OFF-BI-10002075 MX-2013-134096 04-01-2013
## 41 Office Supplies 41 OFF-FA-10002526 MX-2013-156335 04-01-2013
## 42 Office Supplies 42 OFF-EN-10001375 MX-2013-134096 04-01-2013
## 43 Furniture 43 TEC-MA-10004956 MX-2013-134096 04-01-2013
## 44 Office Supplies 44 OFF-SU-10003474 MX-2013-134096 05-01-2013
## 45 Office Supplies 45 TEC-AC-10001830 MX-2013-134096 05-01-2013
## 46 Furniture 46 OFF-BI-10002075 MX-2013-134096 05-01-2013
## 47 Technology 47 OFF-EN-10001375 MX-2013-134096 05-01-2013
## 48 Office Supplies 48 TEC-MA-10004956 MX-2013-134096 05-01-2013
## 49 Office Supplies 49 OFF-SU-10003474 MX-2013-134096 05-01-2013
## 50 Office Supplies 50 OFF-LA-10002782 MX-2014-143658 03-01-2013
## Date.of.Shipping Cost.Price Quantity Sales.Price Shipping.Index
## 1 06-01-2013 341.520 2 683.040 10
## 2 03-01-2013 252.160 8 2017.280 2
## 3 03-01-2013 193.280 2 386.560 3
## 4 03-01-2013 35.440 4 141.760 4
## 5 03-01-2013 71.600 2 143.200 5
## 6 03-01-2013 56.120 2 112.240 6
## 7 04-01-2013 56.120 2 112.240 7
## 8 04-01-2013 132.640 4 530.560 15
## 9 04-01-2013 12.940 1 12.940 16
## 10 04-01-2013 18.840 2 37.280 17
## 11 04-01-2013 308.280 7 2157.560 18
## 12 04-01-2013 40.176 2 79.952 19
## 13 03-01-2013 8.784 3 25.952 20
## 14 03-01-2013 273.472 4 1093.688 21
## 15 04-01-2013 56.120 2 112.240 7
## 16 04-01-2013 344.640 3 1033.920 8
## 17 04-01-2013 97.360 4 389.440 9
## 18 04-01-2013 341.520 2 683.040 10
## 19 04-01-2013 12.060 3 36.180 11
## 20 04-01-2013 20.760 3 62.280 12
## 21 05-01-2013 56.120 2 112.240 7
## 22 05-01-2013 344.640 3 1033.920 8
## 23 05-01-2013 97.360 4 389.440 9
## 24 05-01-2013 341.520 2 683.040 10
## 25 05-01-2013 12.060 3 36.180 11
## 26 05-01-2013 56.120 2 112.240 7
## 27 05-01-2013 344.640 3 1033.920 8
## 28 05-01-2013 97.360 4 389.440 9
## 29 05-01-2013 341.520 2 683.040 10
## 30 05-01-2013 12.060 3 36.180 11
## 31 05-01-2013 18.840 2 37.280 17
## 32 05-01-2013 308.280 7 2157.560 18
## 33 05-01-2013 40.176 2 79.952 19
## 34 04-01-2013 8.784 3 25.952 20
## 35 04-01-2013 273.472 4 1093.688 21
## 36 05-01-2013 56.120 2 112.240 7
## 37 05-01-2013 344.640 3 1033.920 8
## 38 05-01-2013 97.360 4 389.440 9
## 39 05-01-2013 341.520 2 683.040 10
## 40 05-01-2013 12.060 3 36.180 11
## 41 05-01-2013 20.760 3 62.280 12
## 42 05-01-2013 56.120 2 112.240 7
## 43 05-01-2013 344.640 3 1033.920 8
## 44 06-01-2013 97.360 4 389.440 9
## 45 06-01-2013 341.520 2 683.040 10
## 46 06-01-2013 12.060 3 36.180 11
## 47 06-01-2013 56.120 2 112.240 7
## 48 06-01-2013 344.640 3 1033.920 8
## 49 06-01-2013 97.360 4 389.440 9
## 50 03-01-2013 13.080 3 39.240 1
## Shipping.Type Category
## 1 STANDARD Technology
## 2 PRIORITY Furniture
## 3 PRIORITY Furniture
## 4 PRIORITY Office Supplies
## 5 PRIORITY Office Supplies
## 6 PRIORITY Office Supplies
## 7 STANDARD Office Supplies
## 8 STANDARD Office Supplies
## 9 STANDARD Office Supplies
## 10 STANDARD Office Supplies
## 11 STANDARD Furniture
## 12 STANDARD Office Supplies
## 13 PRIORITY Office Supplies
## 14 PRIORITY Furniture
## 15 STANDARD Office Supplies
## 16 STANDARD Technology
## 17 STANDARD Office Supplies
## 18 STANDARD Technology
## 19 STANDARD Office Supplies
## 20 STANDARD Office Supplies
## 21 STANDARD Office Supplies
## 22 STANDARD Technology
## 23 STANDARD Office Supplies
## 24 STANDARD Technology
## 25 STANDARD Office Supplies
## 26 STANDARD Office Supplies
## 27 STANDARD Technology
## 28 STANDARD Office Supplies
## 29 STANDARD Technology
## 30 STANDARD Office Supplies
## 31 STANDARD Office Supplies
## 32 STANDARD Furniture
## 33 STANDARD Office Supplies
## 34 PRIORITY Office Supplies
## 35 PRIORITY Furniture
## 36 STANDARD Office Supplies
## 37 STANDARD Technology
## 38 STANDARD Office Supplies
## 39 STANDARD Technology
## 40 STANDARD Office Supplies
## 41 STANDARD Office Supplies
## 42 STANDARD Office Supplies
## 43 STANDARD Technology
## 44 STANDARD Office Supplies
## 45 STANDARD Technology
## 46 STANDARD Office Supplies
## 47 STANDARD Office Supplies
## 48 STANDARD Technology
## 49 STANDARD Office Supplies
## 50 PRIORITY Office SuppliesROW BIND - Data Frame
# Code Section -18
df_3_R <- rbind(df_2,df_1)
df_3_R## X. Product.Name Prod.ID Date.of.Invoice Date.of.Shipping
## 1 1 TEC-AC-10001830 MX-2013-134096 05-01-2013 06-01-2013
## 2 2 FUR-FU-10004015 MX-2012-155047 03-01-2013 03-01-2013
## 3 3 FUR-BO-10002352 MX-2012-155047 03-01-2013 03-01-2013
## 4 4 OFF-BI-10004428 MX-2012-155047 03-01-2013 03-01-2013
## 5 5 OFF-AR-10004594 MX-2012-155047 03-01-2013 03-01-2013
## 6 6 OFF-EN-10001375 MX-2012-155047 03-01-2013 03-01-2013
## 7 7 OFF-EN-10001375 MX-2013-134096 03-01-2013 04-01-2013
## 8 8 OFF-AR-10003914 MX-2014-135706 03-01-2013 04-01-2013
## 9 9 OFF-FA-10000038 MX-2014-135706 03-01-2013 04-01-2013
## 10 10 OFF-EN-10000761 US-2013-126655 03-01-2013 04-01-2013
## 11 11 FUR-FU-10003066 US-2013-126655 03-01-2013 04-01-2013
## 12 12 OFF-EN-10000075 US-2013-126655 03-01-2013 04-01-2013
## 13 13 OFF-EN-10002226 US-2013-126655 03-01-2013 03-01-2013
## 14 14 FUR-CH-10002132 MX-2013-167759 03-01-2013 03-01-2013
## 15 15 OFF-EN-10001375 MX-2013-134096 03-01-2013 04-01-2013
## 16 16 TEC-MA-10004956 MX-2013-134096 03-01-2013 04-01-2013
## 17 17 OFF-SU-10003474 MX-2013-134096 03-01-2013 04-01-2013
## 18 18 TEC-AC-10001830 MX-2013-134096 03-01-2013 04-01-2013
## 19 19 OFF-BI-10002075 MX-2013-134096 03-01-2013 04-01-2013
## 20 20 OFF-FA-10002526 MX-2013-156335 03-01-2013 04-01-2013
## 21 21 OFF-EN-10001375 MX-2013-134096 04-01-2013 05-01-2013
## 22 22 TEC-MA-10004956 MX-2013-134096 04-01-2013 05-01-2013
## 23 23 OFF-SU-10003474 MX-2013-134096 04-01-2013 05-01-2013
## 24 24 TEC-AC-10001830 MX-2013-134096 04-01-2013 05-01-2013
## 25 25 OFF-BI-10002075 MX-2013-134096 04-01-2013 05-01-2013
## 26 26 OFF-EN-10001375 MX-2013-134096 04-01-2013 05-01-2013
## 27 27 TEC-MA-10004956 MX-2013-134096 04-01-2013 05-01-2013
## 28 28 OFF-SU-10003474 MX-2013-134096 04-01-2013 05-01-2013
## 29 29 TEC-AC-10001830 MX-2013-134096 04-01-2013 05-01-2013
## 30 30 OFF-BI-10002075 MX-2013-134096 04-01-2013 05-01-2013
## 31 31 OFF-EN-10000761 US-2013-126655 04-01-2013 05-01-2013
## 32 32 FUR-FU-10003066 US-2013-126655 04-01-2013 05-01-2013
## 33 33 OFF-EN-10000075 US-2013-126655 04-01-2013 05-01-2013
## 34 34 OFF-EN-10002226 US-2013-126655 04-01-2013 04-01-2013
## 35 35 FUR-CH-10002132 MX-2013-167759 04-01-2013 04-01-2013
## 36 36 OFF-EN-10001375 MX-2013-134096 04-01-2013 05-01-2013
## 37 37 TEC-MA-10004956 MX-2013-134096 04-01-2013 05-01-2013
## 38 38 OFF-SU-10003474 MX-2013-134096 04-01-2013 05-01-2013
## 39 39 TEC-AC-10001830 MX-2013-134096 04-01-2013 05-01-2013
## 40 40 OFF-BI-10002075 MX-2013-134096 04-01-2013 05-01-2013
## 41 41 OFF-FA-10002526 MX-2013-156335 04-01-2013 05-01-2013
## 42 42 OFF-EN-10001375 MX-2013-134096 04-01-2013 05-01-2013
## 43 43 TEC-MA-10004956 MX-2013-134096 04-01-2013 05-01-2013
## 44 44 OFF-SU-10003474 MX-2013-134096 05-01-2013 06-01-2013
## 45 45 TEC-AC-10001830 MX-2013-134096 05-01-2013 06-01-2013
## 46 46 OFF-BI-10002075 MX-2013-134096 05-01-2013 06-01-2013
## 47 47 OFF-EN-10001375 MX-2013-134096 05-01-2013 06-01-2013
## 48 48 TEC-MA-10004956 MX-2013-134096 05-01-2013 06-01-2013
## 49 49 OFF-SU-10003474 MX-2013-134096 05-01-2013 06-01-2013
## 50 50 OFF-LA-10002782 MX-2014-143658 03-01-2013 03-01-2013
## 51 1 OFF-LA-10002782 MX-2014-143658 01-01-2013 02-01-2013
## 52 2 FUR-FU-10004015 MX-2012-155047 01-01-2013 02-01-2013
## 53 3 FUR-BO-10002352 MX-2012-155047 01-01-2013 02-01-2013
## 54 4 OFF-BI-10004428 MX-2012-155047 01-01-2013 02-01-2013
## 55 5 OFF-AR-10004594 MX-2012-155047 01-01-2013 02-01-2013
## 56 6 OFF-EN-10001375 MX-2012-155047 01-01-2013 02-01-2013
## 57 7 OFF-EN-10001375 MX-2013-134096 01-01-2013 02-01-2013
## 58 8 TEC-MA-10004956 MX-2013-134096 01-01-2013 02-01-2013
## 59 9 OFF-SU-10003474 MX-2013-134096 01-01-2013 02-01-2013
## 60 10 TEC-AC-10001830 MX-2013-134096 01-01-2013 02-01-2013
## 61 11 OFF-BI-10002075 MX-2013-134096 01-01-2013 02-01-2013
## 62 12 OFF-FA-10002526 MX-2013-156335 01-01-2013 02-01-2013
## 63 13 FUR-CH-10002846 MX-2013-156335 01-01-2013 02-01-2013
## 64 14 OFF-EN-10004100 MX-2014-121923 02-01-2013 04-01-2013
## 65 15 OFF-AR-10003914 MX-2014-135706 02-01-2013 03-01-2013
## 66 16 OFF-FA-10000038 MX-2014-135706 02-01-2013 03-01-2013
## 67 17 OFF-EN-10000761 US-2013-126655 02-01-2013 03-01-2013
## 68 18 FUR-FU-10003066 US-2013-126655 02-01-2013 03-01-2013
## 69 19 OFF-EN-10000075 US-2013-126655 02-01-2013 03-01-2013
## 70 20 OFF-EN-10002226 US-2013-126655 02-01-2013 03-01-2013
## 71 21 FUR-CH-10002132 MX-2013-167759 02-01-2013 04-01-2013
## 72 22 TEC-AC-10002749 MX-2013-163139 02-01-2013 02-01-2013
## 73 23 OFF-SU-10000066 MX-2013-163139 02-01-2013 02-01-2013
## 74 24 OFF-BI-10003934 US-2014-119753 02-01-2013 03-01-2013
## 75 25 OFF-BI-10003932 US-2012-133970 02-01-2013 03-01-2013
## Cost.Price Quantity Sales.Price Shipping.Index Shipping.Type
## 1 341.520 2 683.040 10 STANDARD
## 2 252.160 8 2017.280 2 PRIORITY
## 3 193.280 2 386.560 3 PRIORITY
## 4 35.440 4 141.760 4 PRIORITY
## 5 71.600 2 143.200 5 PRIORITY
## 6 56.120 2 112.240 6 PRIORITY
## 7 56.120 2 112.240 7 STANDARD
## 8 132.640 4 530.560 15 STANDARD
## 9 12.940 1 12.940 16 STANDARD
## 10 18.840 2 37.280 17 STANDARD
## 11 308.280 7 2157.560 18 STANDARD
## 12 40.176 2 79.952 19 STANDARD
## 13 8.784 3 25.952 20 PRIORITY
## 14 273.472 4 1093.688 21 PRIORITY
## 15 56.120 2 112.240 7 STANDARD
## 16 344.640 3 1033.920 8 STANDARD
## 17 97.360 4 389.440 9 STANDARD
## 18 341.520 2 683.040 10 STANDARD
## 19 12.060 3 36.180 11 STANDARD
## 20 20.760 3 62.280 12 STANDARD
## 21 56.120 2 112.240 7 STANDARD
## 22 344.640 3 1033.920 8 STANDARD
## 23 97.360 4 389.440 9 STANDARD
## 24 341.520 2 683.040 10 STANDARD
## 25 12.060 3 36.180 11 STANDARD
## 26 56.120 2 112.240 7 STANDARD
## 27 344.640 3 1033.920 8 STANDARD
## 28 97.360 4 389.440 9 STANDARD
## 29 341.520 2 683.040 10 STANDARD
## 30 12.060 3 36.180 11 STANDARD
## 31 18.840 2 37.280 17 STANDARD
## 32 308.280 7 2157.560 18 STANDARD
## 33 40.176 2 79.952 19 STANDARD
## 34 8.784 3 25.952 20 PRIORITY
## 35 273.472 4 1093.688 21 PRIORITY
## 36 56.120 2 112.240 7 STANDARD
## 37 344.640 3 1033.920 8 STANDARD
## 38 97.360 4 389.440 9 STANDARD
## 39 341.520 2 683.040 10 STANDARD
## 40 12.060 3 36.180 11 STANDARD
## 41 20.760 3 62.280 12 STANDARD
## 42 56.120 2 112.240 7 STANDARD
## 43 344.640 3 1033.920 8 STANDARD
## 44 97.360 4 389.440 9 STANDARD
## 45 341.520 2 683.040 10 STANDARD
## 46 12.060 3 36.180 11 STANDARD
## 47 56.120 2 112.240 7 STANDARD
## 48 344.640 3 1033.920 8 STANDARD
## 49 97.360 4 389.440 9 STANDARD
## 50 13.080 3 39.240 1 PRIORITY
## 51 13.080 3 39.240 1 PRIORITY
## 52 252.160 8 2017.280 2 PRIORITY
## 53 193.280 2 386.560 3 PRIORITY
## 54 35.440 4 141.760 4 PRIORITY
## 55 71.600 2 143.200 5 PRIORITY
## 56 56.120 2 112.240 6 PRIORITY
## 57 56.120 2 112.240 7 STANDARD
## 58 344.640 3 1033.920 8 STANDARD
## 59 97.360 4 389.440 9 STANDARD
## 60 341.520 2 683.040 10 STANDARD
## 61 12.060 3 36.180 11 STANDARD
## 62 20.760 3 62.280 12 STANDARD
## 63 210.640 4 842.560 13 STANDARD
## 64 80.100 3 240.300 14 STANDARD
## 65 132.640 4 530.560 15 STANDARD
## 66 12.940 1 12.940 16 STANDARD
## 67 18.840 2 37.280 17 STANDARD
## 68 308.280 7 2157.560 18 STANDARD
## 69 40.176 2 79.952 19 STANDARD
## 70 8.784 3 25.952 20 PRIORITY
## 71 273.472 4 1093.688 21 PRIORITY
## 72 27.000 1 27.000 22 PRIORITY
## 73 207.000 9 1863.000 23 PRIORITY
## 74 60.660 3 181.580 24 PRIORITY
## 75 181.116 9 1629.644 25 PRIORITY
## Category
## 1 Technology
## 2 Furniture
## 3 Furniture
## 4 Office Supplies
## 5 Office Supplies
## 6 Office Supplies
## 7 Office Supplies
## 8 Office Supplies
## 9 Office Supplies
## 10 Office Supplies
## 11 Furniture
## 12 Office Supplies
## 13 Office Supplies
## 14 Furniture
## 15 Office Supplies
## 16 Technology
## 17 Office Supplies
## 18 Technology
## 19 Office Supplies
## 20 Office Supplies
## 21 Office Supplies
## 22 Technology
## 23 Office Supplies
## 24 Technology
## 25 Office Supplies
## 26 Office Supplies
## 27 Technology
## 28 Office Supplies
## 29 Technology
## 30 Office Supplies
## 31 Office Supplies
## 32 Furniture
## 33 Office Supplies
## 34 Office Supplies
## 35 Furniture
## 36 Office Supplies
## 37 Technology
## 38 Office Supplies
## 39 Technology
## 40 Office Supplies
## 41 Office Supplies
## 42 Office Supplies
## 43 Technology
## 44 Office Supplies
## 45 Technology
## 46 Office Supplies
## 47 Office Supplies
## 48 Technology
## 49 Office Supplies
## 50 Office Supplies
## 51 Office Supplies
## 52 Furniture
## 53 Furniture
## 54 Office Supplies
## 55 Office Supplies
## 56 Office Supplies
## 57 Office Supplies
## 58 Technology
## 59 Office Supplies
## 60 Technology
## 61 Office Supplies
## 62 Office Supplies
## 63 Furniture
## 64 Office Supplies
## 65 Office Supplies
## 66 Office Supplies
## 67 Office Supplies
## 68 Furniture
## 69 Office Supplies
## 70 Office Supplies
## 71 Furniture
## 72 Technology
## 73 Office Supplies
## 74 Office Supplies
## 75 Office Supplies# We can SUBSET Data within R Data Structures with custom and inbuilt functions
#
summary(df_3_R$Cost.Price)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 8.784 23.880 71.600 137.600 273.500 344.600#
sub_df_1 <- subset(df_3_R,Cost.Price > 300)
#
summary(sub_df_1$Cost.Price) # As seen from the SUMMARY ## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 308.3 341.5 341.5 336.9 344.6 344.6# All ROWS or OBSERVATIONS with Cost.Price Values LESS THAN - 300
# have been dropped from the Sub-Set Data Frame. describeBy() with Grouping variables
# Code Section -19
library("psych", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3")
library(psych)
#?describeBy() # Seek HELP for the function - Uncomment this line.
describeBy(df_3_C,df_3_C$Date.of.Invoice) ##
## Descriptive statistics by group
## group: 01-01-2013
## vars n mean sd median trimmed mad min max range skew
## 1 1 26 7.00 3.82 7.00 7.00 4.45 1.00 13.00 12.00 0.00
## 2 2 26 12.85 7.49 14.00 12.91 10.38 1.00 24.00 23.00 -0.06
## 3 3 26 2.23 1.84 2.00 1.82 1.48 1.00 8.00 7.00 2.27
## 4 4 26 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.00 NaN
## 5 5 26 1.00 0.00 1.00 1.00 0.00 1.00 1.00 0.00 NaN
## 6 6 26 131.14 120.02 71.60 122.55 86.76 12.06 344.64 332.58 0.66
## 7 7 26 3.23 1.61 3.00 2.91 1.48 2.00 8.00 6.00 1.86
## 8 8 26 461.53 559.95 143.20 358.77 158.67 36.18 2017.28 1981.10 1.60
## 9 9 26 7.00 3.82 7.00 7.00 4.45 1.00 13.00 12.00 0.00
## 10 10 26 1.54 0.51 2.00 1.55 0.00 1.00 2.00 1.00 -0.15
## 11 11 26 1.92 0.63 2.00 1.91 0.00 1.00 3.00 2.00 0.04
## 12 12 26 19.50 13.31 19.50 19.50 18.53 1.00 38.00 37.00 0.00
## 13 13 26 10.00 5.11 10.50 10.05 5.93 1.00 18.00 17.00 -0.05
## 14 14 26 3.65 2.50 2.00 3.59 1.48 1.00 7.00 6.00 0.41
## 15 15 26 1.58 0.58 2.00 1.55 0.74 1.00 3.00 2.00 0.31
## 16 16 26 2.27 0.87 2.00 2.27 1.48 1.00 4.00 3.00 -0.17
## 17 17 26 135.66 129.31 63.86 128.20 76.15 8.78 344.64 335.86 0.61
## 18 18 26 3.15 1.76 2.50 2.91 0.74 1.00 8.00 7.00 1.43
## 19 19 26 524.05 673.79 142.48 421.43 182.41 12.94 2157.56 2144.62 1.41
## 20 20 26 11.77 6.06 10.00 11.82 8.15 2.00 21.00 19.00 0.08
## 21 21 26 1.69 0.47 2.00 1.73 0.00 1.00 2.00 1.00 -0.79
## 22 22 26 1.96 0.60 2.00 1.95 0.00 1.00 3.00 2.00 0.01
## kurtosis se
## 1 -1.35 0.75
## 2 -1.45 1.47
## 3 4.52 0.36
## 4 NaN 0.00
## 5 NaN 0.00
## 6 -1.14 23.54
## 7 3.10 0.32
## 8 1.80 109.82
## 9 -1.35 0.75
## 10 -2.05 0.10
## 11 -0.59 0.12
## 12 -1.79 2.61
## 13 -1.12 1.00
## 14 -1.68 0.49
## 15 -0.98 0.11
## 16 -1.18 0.17
## 17 -1.41 25.36
## 18 1.15 0.35
## 19 0.74 132.14
## 20 -1.55 1.19
## 21 -1.43 0.09
## 22 -0.33 0.12
## --------------------------------------------------------
## group: 02-01-2013
## vars n mean sd median trimmed mad min max range skew
## 1 1 24 19.50 3.53 19.50 19.50 4.45 14.00 25.00 11.00 0.00
## 2 2 24 12.25 6.24 12.50 12.20 5.93 2.00 23.00 21.00 -0.01
## 3 3 24 7.75 2.51 8.00 7.80 2.97 4.00 11.00 7.00 -0.27
## 4 4 24 2.00 0.00 2.00 2.00 0.00 2.00 2.00 0.00 NaN
## 5 5 24 2.00 0.59 2.00 2.00 0.00 1.00 3.00 2.00 0.00
## 6 6 24 112.58 103.84 70.38 103.39 88.24 8.78 308.28 299.50 0.65
## 7 7 24 4.00 2.77 3.00 3.80 1.48 1.00 9.00 8.00 0.80
## 8 8 24 656.62 790.46 210.94 570.90 283.91 12.94 2157.56 2144.62 0.80
## 9 9 24 19.50 3.53 19.50 19.50 4.45 14.00 25.00 11.00 0.00
## 10 10 24 1.50 0.51 1.50 1.50 0.74 1.00 2.00 1.00 0.00
## 11 11 24 1.92 0.50 2.00 1.90 0.00 1.00 3.00 2.00 -0.17
## 12 12 24 32.00 13.25 32.00 32.00 18.53 14.00 50.00 36.00 0.00
## 13 13 24 13.38 4.59 15.50 13.80 3.71 2.00 18.00 16.00 -0.82
## 14 14 24 2.33 0.92 2.00 2.10 0.00 2.00 6.00 4.00 2.91
## 15 15 24 1.92 0.78 2.00 1.90 1.48 1.00 3.00 2.00 0.13
## 16 16 24 2.83 0.92 3.00 2.90 1.48 1.00 4.00 3.00 -0.34
## 17 17 24 155.62 145.56 97.36 151.07 126.47 12.06 344.64 332.58 0.41
## 18 18 24 2.88 0.74 3.00 2.85 1.48 2.00 4.00 2.00 0.18
## 19 19 24 428.20 396.14 389.44 403.84 460.17 36.18 1093.69 1057.51 0.49
## 20 20 24 9.42 3.37 9.00 9.25 2.22 1.00 21.00 20.00 1.02
## 21 21 24 1.92 0.28 2.00 2.00 0.00 1.00 2.00 1.00 -2.83
## 22 22 24 2.29 0.55 2.00 2.30 0.00 1.00 3.00 2.00 0.08
## kurtosis se
## 1 -1.36 0.72
## 2 -1.08 1.27
## 3 -1.57 0.51
## 4 NaN 0.00
## 5 -0.24 0.12
## 6 -1.12 21.20
## 7 -0.83 0.56
## 8 -1.09 161.35
## 9 -1.36 0.72
## 10 -2.08 0.10
## 11 0.61 0.10
## 12 -1.84 2.70
## 13 -0.53 0.94
## 14 8.15 0.19
## 15 -1.40 0.16
## 16 -0.84 0.19
## 17 -1.77 29.71
## 18 -1.24 0.15
## 19 -1.40 80.86
## 20 4.49 0.69
## 21 6.27 0.06
## 22 -0.77 0.11# In this case - Grouping Variable is - Date of Invoice .
# This Grouping variable has two Values here - 01-01-2013 and 02-01-2013
# IMPORTANT NOTE --- The DOLLAR SIGN in df_3_C$Date.of.Invoice , is used
# to access a certain variable within the DATA FRAME.
# Also note that the variables within the DATA FRAME will not be stored with the Labels
# as - is Built in DATA Sets
The default R environment - comes bundled with a number of packages and data sets.
A package called DATASETS - contains a number of inbuilt data sets as seen in sections below -
# Code Section -20
library(help = "datasets")
#
# Seen below are the DataSets available within my installed version of R -
# yours may slightly differ :-
#
# AirPassengers Monthly Airline Passenger Numbers 1949-1960
# BJsales Sales Data with Leading Indicator
# BOD Biochemical Oxygen Demand
# CO2 Carbon Dioxide Uptake in Grass Plants
# ChickWeight Weight versus age of chicks on different diets
# DNase Elisa assay of DNase
# EuStockMarkets Daily Closing Prices of Major European Stock
# Indices, 1991-1998
# Formaldehyde Determination of Formaldehyde
# HairEyeColor Hair and Eye Color of Statistics Students
# Harman23.cor Harman Example 2.3
# Harman74.cor Harman Example 7.4
# Indometh Pharmacokinetics of Indomethacin
# InsectSprays Effectiveness of Insect Sprays
# JohnsonJohnson Quarterly Earnings per Johnson & Johnson Share
# LakeHuron Level of Lake Huron 1875-1972
# LifeCycleSavings Intercountry Life-Cycle Savings Data
# Loblolly Growth of Loblolly pine trees
# Nile Flow of the River Nile
# Orange Growth of Orange Trees
# OrchardSprays Potency of Orchard Sprays
# PlantGrowth Results from an Experiment on Plant Growth
# Puromycin Reaction Velocity of an Enzymatic Reaction
# Theoph Pharmacokinetics of Theophylline
# Titanic Survival of passengers on the Titanic
# ToothGrowth The Effect of Vitamin C on Tooth Growth in
# Guinea Pigs
# UCBAdmissions Student Admissions at UC Berkeley
# UKDriverDeaths Road Casualties in Great Britain 1969-84
# UKLungDeaths Monthly Deaths from Lung Diseases in the UK
# UKgas UK Quarterly Gas Consumption
# USAccDeaths Accidental Deaths in the US 1973-1978
# USArrests Violent Crime Rates by US State
# USJudgeRatings Lawyers' Ratings of State Judges in the US
# Superior Court
# USPersonalExpenditure Personal Expenditure Data
# VADeaths Death Rates in Virginia (1940)
# WWWusage Internet Usage per Minute
# WorldPhones The World's Telephones
# ability.cov Ability and Intelligence Tests
# airmiles Passenger Miles on Commercial US Airlines,
# 1937-1960
# airquality New York Air Quality Measurements
# anscombe Anscombe's Quartet of 'Identical' Simple Linear
# Regressions
# attenu The Joyner-Boore Attenuation Data
# attitude The Chatterjee-Price Attitude Data
# austres Quarterly Time Series of the Number of
# Australian Residents
# beavers Body Temperature Series of Two Beavers
# cars Speed and Stopping Distances of Cars
# chickwts Chicken Weights by Feed Type
# co2 Mauna Loa Atmospheric CO2 Concentration
# crimtab Student's 3000 Criminals Data
# datasets-package The R Datasets Package
# discoveries Yearly Numbers of Important Discoveries
# esoph Smoking, Alcohol and (O)esophageal Cancer
# euro Conversion Rates of Euro Currencies
# eurodist Distances Between European Cities and Between
# US Cities
# faithful Old Faithful Geyser Data
# freeny Freeny's Revenue Data
# infert Infertility after Spontaneous and Induced
# Abortion
# iris Edgar Anderson's Iris Data
# islands Areas of the World's Major Landmasses
# lh Luteinizing Hormone in Blood Samples
# longley Longley's Economic Regression Data
# lynx Annual Canadian Lynx trappings 1821-1934
# morley Michelson Speed of Light Data
# mtcars Motor Trend Car Road Tests
# nhtemp Average Yearly Temperatures in New Haven
# nottem Average Monthly Temperatures at Nottingham,
# 1920-1939
# npk Classical N, P, K Factorial Experiment
# occupationalStatus Occupational Status of Fathers and their Sons
# precip Annual Precipitation in US Cities
# presidents Quarterly Approval Ratings of US Presidents
# pressure Vapor Pressure of Mercury as a Function of
# Temperature
# quakes Locations of Earthquakes off Fiji
# randu Random Numbers from Congruential Generator
# RANDU
# rivers Lengths of Major North American Rivers
# rock Measurements on Petroleum Rock Samples
# sleep Student's Sleep Data
# stackloss Brownlee's Stack Loss Plant Data
# state US State Facts and Figures
# sunspot.month Monthly Sunspot Data, from 1749 to "Present"
# sunspot.year Yearly Sunspot Data, 1700-1988
# sunspots Monthly Sunspot Numbers, 1749-1983
# swiss Swiss Fertility and Socioeconomic Indicators
# (1888) Data
# treering Yearly Treering Data, -6000-1979
# trees Girth, Height and Volume for Black Cherry Trees
# uspop Populations Recorded by the US Census
# volcano Topographic Information on Auckland's Maunga
# Whau Volcano
# warpbreaks The Number of Breaks in Yarn during Weaving
# women Average Heights and Weights for American Women
# ATTACH and DETACH Datasets
To OPERATE upon an inbuilt DATASET we need to LOAD it into R .
In lay-man terms , we may equate this to our earlier - read.csv() , but
there are subtle differences.
The ATTACH will pull in a Data Set from the DataSet Package into our R Search Path.
# Code Section -21
?attach()
# # Quoting below from inbuilt HELP -
# # The database is attached to the R search path. This means that the database is
# searched by R when evaluating a variable, so objects in the database can be accessed
# by simply giving their names.
?detach()
# # Quoting below from inbuilt HELP -
# # Detach a database, i.e., remove it from the search() path of available R objects.
# Usually this is either a data.frame which has been attached or a
# package which was attached by library.
# Another Function which will help automate is - zap()
# library(epicalc)
# ?zap()
# https://artax.karlin.mff.cuni.cz/r-help/library/epicalc/html/zap.html# Code Section -22
search()## [1] ".GlobalEnv" "package:psych" "package:stats"
## [4] "package:graphics" "package:grDevices" "package:utils"
## [7] "package:datasets" "package:methods" "Autoloads"
## [10] "package:base"DATA_FRAME COLUMNS - The DF Column Vector
# Code Section -23
# Three basic methods to access a DF COLUMN - also called the
# DF Column Vector
print(df_1[[2]]) # Will Print 2nd COLUMN of the DF ## [1] OFF-LA-10002782 FUR-FU-10004015 FUR-BO-10002352 OFF-BI-10004428
## [5] OFF-AR-10004594 OFF-EN-10001375 OFF-EN-10001375 TEC-MA-10004956
## [9] OFF-SU-10003474 TEC-AC-10001830 OFF-BI-10002075 OFF-FA-10002526
## [13] FUR-CH-10002846 OFF-EN-10004100 OFF-AR-10003914 OFF-FA-10000038
## [17] OFF-EN-10000761 FUR-FU-10003066 OFF-EN-10000075 OFF-EN-10002226
## [21] FUR-CH-10002132 TEC-AC-10002749 OFF-SU-10000066 OFF-BI-10003934
## [25] OFF-BI-10003932
## 24 Levels: FUR-BO-10002352 FUR-CH-10002132 ... TEC-MA-10004956#
print(df_1[["Product.Name"]]) # Will Print the "NAMED" COLUMN of the DF ## [1] OFF-LA-10002782 FUR-FU-10004015 FUR-BO-10002352 OFF-BI-10004428
## [5] OFF-AR-10004594 OFF-EN-10001375 OFF-EN-10001375 TEC-MA-10004956
## [9] OFF-SU-10003474 TEC-AC-10001830 OFF-BI-10002075 OFF-FA-10002526
## [13] FUR-CH-10002846 OFF-EN-10004100 OFF-AR-10003914 OFF-FA-10000038
## [17] OFF-EN-10000761 FUR-FU-10003066 OFF-EN-10000075 OFF-EN-10002226
## [21] FUR-CH-10002132 TEC-AC-10002749 OFF-SU-10000066 OFF-BI-10003934
## [25] OFF-BI-10003932
## 24 Levels: FUR-BO-10002352 FUR-CH-10002132 ... TEC-MA-10004956#
print(df_1$Product.Name) # Will Print the "NAMED" COLUMN of the DF ## [1] OFF-LA-10002782 FUR-FU-10004015 FUR-BO-10002352 OFF-BI-10004428
## [5] OFF-AR-10004594 OFF-EN-10001375 OFF-EN-10001375 TEC-MA-10004956
## [9] OFF-SU-10003474 TEC-AC-10001830 OFF-BI-10002075 OFF-FA-10002526
## [13] FUR-CH-10002846 OFF-EN-10004100 OFF-AR-10003914 OFF-FA-10000038
## [17] OFF-EN-10000761 FUR-FU-10003066 OFF-EN-10000075 OFF-EN-10002226
## [21] FUR-CH-10002132 TEC-AC-10002749 OFF-SU-10000066 OFF-BI-10003934
## [25] OFF-BI-10003932
## 24 Levels: FUR-BO-10002352 FUR-CH-10002132 ... TEC-MA-10004956DATA_FRAME - Row and Column Slices
# Code Section -24
# Slicing the DF COLUMNS and ROWS
print(df_1[2]) # Will Print 2nd COLUMN - ALL ROWS - of the DF as a SLICE. ## Product.Name
## 1 OFF-LA-10002782
## 2 FUR-FU-10004015
## 3 FUR-BO-10002352
## 4 OFF-BI-10004428
## 5 OFF-AR-10004594
## 6 OFF-EN-10001375
## 7 OFF-EN-10001375
## 8 TEC-MA-10004956
## 9 OFF-SU-10003474
## 10 TEC-AC-10001830
## 11 OFF-BI-10002075
## 12 OFF-FA-10002526
## 13 FUR-CH-10002846
## 14 OFF-EN-10004100
## 15 OFF-AR-10003914
## 16 OFF-FA-10000038
## 17 OFF-EN-10000761
## 18 FUR-FU-10003066
## 19 OFF-EN-10000075
## 20 OFF-EN-10002226
## 21 FUR-CH-10002132
## 22 TEC-AC-10002749
## 23 OFF-SU-10000066
## 24 OFF-BI-10003934
## 25 OFF-BI-10003932#
print(df_1[2,]) # Will Print 2nd ROW - ALL COLUMNS - of the DF as a SLICE. ## X. Product.Name Prod.ID Date.of.Invoice Date.of.Shipping
## 2 2 FUR-FU-10004015 MX-2012-155047 01-01-2013 02-01-2013
## Cost.Price Quantity Sales.Price Shipping.Index Shipping.Type Category
## 2 252.16 8 2017.28 2 PRIORITY Furniture#
print(df_1[c(2,4,6)]) # Using an INDEX VECTOR - created with COMBINE Function. ## Product.Name Date.of.Invoice Cost.Price
## 1 OFF-LA-10002782 01-01-2013 13.080
## 2 FUR-FU-10004015 01-01-2013 252.160
## 3 FUR-BO-10002352 01-01-2013 193.280
## 4 OFF-BI-10004428 01-01-2013 35.440
## 5 OFF-AR-10004594 01-01-2013 71.600
## 6 OFF-EN-10001375 01-01-2013 56.120
## 7 OFF-EN-10001375 01-01-2013 56.120
## 8 TEC-MA-10004956 01-01-2013 344.640
## 9 OFF-SU-10003474 01-01-2013 97.360
## 10 TEC-AC-10001830 01-01-2013 341.520
## 11 OFF-BI-10002075 01-01-2013 12.060
## 12 OFF-FA-10002526 01-01-2013 20.760
## 13 FUR-CH-10002846 01-01-2013 210.640
## 14 OFF-EN-10004100 02-01-2013 80.100
## 15 OFF-AR-10003914 02-01-2013 132.640
## 16 OFF-FA-10000038 02-01-2013 12.940
## 17 OFF-EN-10000761 02-01-2013 18.840
## 18 FUR-FU-10003066 02-01-2013 308.280
## 19 OFF-EN-10000075 02-01-2013 40.176
## 20 OFF-EN-10002226 02-01-2013 8.784
## 21 FUR-CH-10002132 02-01-2013 273.472
## 22 TEC-AC-10002749 02-01-2013 27.000
## 23 OFF-SU-10000066 02-01-2013 207.000
## 24 OFF-BI-10003934 02-01-2013 60.660
## 25 OFF-BI-10003932 02-01-2013 181.116# To access MULTIPLE COLUMNS - 2,4 and 6 of the DF.Lets create a New Directory - Files Folder from within R and house our basic introductory graphs there - we use - dir.create()
# Code Section -25
#dir.create("/home/dhankar/Desktop/R_Own/Plots/Intro_1/", recursive=TRUE) # to be RUN only OncePlots and Graphs - the Basics
Over the next couple of sections we shall preview the basics of Plots and Graphs.
If we were to execute the code from the following sections , in the Console- the Graphs would not appear inline but would appear within the PLOTS pane of R Studio or in certain cases as POP Up’s .
HISTOGRAM an Introduction
# Code Section -26
# So what exactly is a HISTOGRAM -
# " estimate of the probability distribution of a continuous variable (quantitative variable)"
# Source -- Wiki -- https://en.wikipedia.org/wiki/Histogram
hist(df_1$Cost.Price,col.main="blue",col = "green")
#hist(df_1$Cost.Price,df_1$Sales.Price)
#
hist(df_1$Sales.Price,col.main="blue",col = "orange")
# Code Section -27# Code Section -28# Code Section -29# Code Section -30# Code Section -32# Code Section -33# Code Section -34# Code Section -35# Code Section -36sessionInfo()## R version 3.3.2 (2016-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.1 LTS
##
## locale:
## [1] LC_CTYPE=en_IN.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_IN.UTF-8 LC_COLLATE=en_IN.UTF-8
## [5] LC_MONETARY=en_IN.UTF-8 LC_MESSAGES=en_IN.UTF-8
## [7] LC_PAPER=en_IN.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_IN.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] psych_1.7.5
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.8 lattice_0.20-33 digest_0.6.10 rprojroot_1.1
## [5] grid_3.3.2 nlme_3.1-124 backports_1.0.4 magrittr_1.5
## [9] evaluate_0.10 stringi_1.1.2 rmarkdown_1.3 tools_3.3.2
## [13] foreign_0.8-66 stringr_1.1.0 yaml_2.1.14 parallel_3.3.2
## [17] mnormt_1.5-5 htmltools_0.3.5 knitr_1.15.1