Need based links
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- Regular expressions
- Creating new column with regexpr
# Apply regexpr on a string - Gives back the position of 1st occurence of a pattern in the string, in case the pattern is not found returns -1
regexpr(" ", "The Quick Brown Box")
# [1] 4
# attr(,"match.length")
# [1] 1
# attr(,"useBytes")
# [1] TRUE
regexpr(" ", "TheQuickBrownBox")
# [1] -1
# attr(,"match.length")
# [1] -1
# attr(,"useBytes")
# [1] TRUE
# To get all positions where the pattern is found
gregexpr(" ", "The Quick Brown Box")
# [[1]]
# [1] 4 10 16
# attr(,"match.length")
# [1] 1 1 1
# attr(,"useBytes")
# [1] TRUE
# creating new column - notice no indexing is done on top of regexpr formula
df$new_col <- substr(df$col,
<start_num>,
regexpr(<pattern>, df$col))
- ggplot2 cheatsheet
#Author : Rohan M. Nanaware
#Date C.: 07th Aug 2017
#Date M.: 07th Aug 2017
#Purpose: Understand the apply family, syntax, when to use, etc.
#Ref. : https://www.datacamp.com/community/tutorials/r-tutorial-apply-family#family
{
#Syntax
apply(X, MARGIN, FUN, ...)
# where: X is an array or a matrix if the dimension of the array is 2;
# MARGIN is a variable defining how the function is applied: when MARGIN=1, it applies over rows,
# whereas with MARGIN=2, it works over columns.
# Note that when you use the construct MARGIN=c(1,2), it applies to both rows and columns;
# FUN, which is the function that you want to apply to the data. It can be any R function, including a User Defined Function (UDF).
# Construct a 5x6 matrix
X <- matrix(rnorm(30), nrow=5, ncol=6)
# Sum the values of each column with `apply()`
apply(X, 2, sum)
{
# > X <- matrix(rnorm(30), nrow=5, ncol=6)
# > X
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] -1.94906932 -1.3878370 -0.1958298 -0.53938847 -0.2420091 1.8336326
# [2,] -0.06625763 -0.3724634 -0.2571309 -0.01875991 -0.7428842 -0.4180169
# [3,] 0.44975736 0.2005075 -2.3643830 -0.58094495 -1.7548130 -1.0533094
# [4,] -0.75873543 -0.9859626 0.4494635 -0.71736076 -1.1165637 -0.6567909
# [5,] -1.13292328 1.3785268 -0.1441591 -0.25306394 -1.4937538 -0.3722877
# > apply(X, 2, sum)
# [1] -3.4572283 -1.1672288 -2.5120393 -2.1095180 -5.3500238 -0.6667723
}#res
{
X = iris
head(X)
Y = apply(X, 2, unique)
Y = apply(X, 2, function(x) length(unique(x)))
}#test
}#01. apply()
{
# You want to apply a given function to every element of a list and obtain a list as result.
# When you execute ?lapply, you see that the syntax looks like the apply() function.
# The difference is that: #
# It can be used for other objects like dataframes, lists or vectors; and
# The output returned is a list (which explains the “l” in the function name),
# which has the same number of elements as the object passed to it
# Create a list of matrices
A = iris
B = iris
C = iris
MyList <- list(A,B,C)
# Extract the 2nd column from `MyList` with the selection operator `[` with `lapply()`
lapply(MyList,"[", , 2)
# Extract the 1st row from `MyList`
lapply(MyList,"[", 1, )
{
MyList <- list(X, iris, cars)
#lapply(MyList, function(x) apply(x, 1, length))
lapply(MyList, length)
lapply(iris, length)
lapply(iris[,1], length)
}#test
}#02. lapply()
{
# sapply() is a ‘wrapper’ function for lapply()
# Applying the lapply() function would give us a list, unless you pass simplify=FALSE as parameter to sapply().
# Then, a list will be returned
}#03. sapply()
{
# Initialize `Z`
Z <- sapply(MyList,"[", 1,1 )
# Return `Z`
Z
# Replicate the values of `Z`
Z <- rep(Z,c(3,1,2))
# Return `Z`
Z
# You see that the code above replicates the values of Z a number of times as established by c(3,1,2):
# three times the first, one time the second and two times the third
}#04. rep()
{
# The sweep() function is probably the closest to the apply() family.
# You use it when you want to replicate different actions on the MARGIN elements that you have chosen
# (limiting here to the matrix case).
# A typical scenario occurs in clustering, where you may need to repetitively produce normalized and centered
# or “standardized” data
dataPoints <- matrix(rnorm(25), nrow=5, ncol=5)
# Find means per column with `apply()`
dataPoints_means <- apply(dataPoints, 2, mean)
# Find standard deviation with `apply()`
dataPoints_sdev <- apply(dataPoints, 2, sd)
# Center the points
dataPoints_Trans1 <- sweep(dataPoints, 2, dataPoints_means,"-")
print(dataPoints_Trans1)
# Return the result
dataPoints_Trans1
# You produced the centered points with one call to sweep(). This function expects the following elements:
# an input array, which in this case is a matrix;
# a MARGIN, 2 to indicate the columns;
# a summary statistics (here mean); and
# a function to be applied. You use the arithmetic operator “-” for subtraction
# Normalize
dataPoints_Trans2 <- sweep(dataPoints_Trans1, 2, dataPoints_sdev, "/")
# Return the result
dataPoints_Trans2
{
sweep(dataPoints, 2, c(1,2,3,4,5), "*")
}#test
}#05. sweep()
{
aggregate()
mapply()
}#Pending
profiler_variance_data <- within(profiler_variance_data,
quantile_ <- as.integer(cut(shipment_captured_weight,
quantile(shipment_captured_weight,probs = seq(0,1,0.1)),
include.lowest = T)))profiler_variance_data <- fread('findstr /V /C:"END OF FILE" profiler_variance_data_v4.csv',
header = T,
stringsAsFactors = F,
na.strings = c("NULL","","NA"))input_data_wfld %>%
group_by(piq) %>%
summarise(pcle05 = quantile(p2pc_minutes,probs=0.05),
pcle25 = quantile(p2pc_minutes,probs=0.25),
pcle50 = quantile(p2pc_minutes,probs=0.50),
pcle75 = quantile(p2pc_minutes,probs=0.75),
pcle95 = quantile(p2pc_minutes,probs=0.95),
sq = sum(reservation_quantity),
sq_cumsum = cumsum(sq)) %>%
arrange(piq) %>%
mutate(sq_cumsum = cumsum(sq)/sum(sq)) %>%
View()