Giga thoughts...

Monday, March 2, 2015

R incantations for the uninitiated

Here are some basic R incantations that will get you started with R

A) Scalars & Vectors:
Chant 1 - Now repeat after me, with your right hand forward at shoulder height "In R there are no scalars. There are only vectors of length 1".
Just kidding:-)

To create an integer variable x with a value 5 we write

x <- 5="" br="" or="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">x = 5

While the former notation may seem odd, it is actually more logical considering that the RHS is assigned to LHS. Anyway both seem to work
Vectors can be created as follows

a <- 2:10="" br="" c="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">b <- c="" code="" his="" is="" language="">

B) Sequences:
There are several ways of creating sequences of numbers which you intend to use for your computation
<- 25="" 5="" code="" from="" seq="" sequence="" to="">

Other ways to create sequences
Increment by 2

> seq(5, 25, by=2)
[1]  5  7  9 11 13 15 17 19 21 23 25

>seq(5,25,length=18) # Create sequence from 5 to 25 with a total length of 18
[1]  5.000000  6.176471  7.352941  8.529412  9.705882 10.882353 12.058824 13.235294
[9] 14.411765 15.588235 16.764706 17.941176 19.117647 20.294118 21.470588 22.647059
[17] 23.823529 25.000000

C) Conditions and loops
An if-else if-else construct goes like this

if(condition) {
do something
} else if (condition) {
do something
} else {
do something
}

Note: Make sure the statements appear as above with the else if and else appearing on the same line as the closing braces, otherwise R complains about ‘unexpected else’ in else statement

D) Loops
I would like to mention 2 ways of doing 'for' loops in R.
a)

for (i in 1:10) {
statement
}

> a <- length="10)<br" seq="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">> a
[1]  5.000000  7.222222  9.444444 11.666667 13.888889 16.111111 18.333333
[8] 20.555556 22.777778 25.000000

b) Sequence along the vector sequence. Note: This is useful as we don't have to know the length of the vector/sequence

for (i in seq_along(a)){
+   print(a[i])
+ }

[1] 5
[1] 7.222222
[1] 9.444444
[1] 11.66667
…

There are others ways of looping with ‘while’ and ‘repeat’ which I have not included in this post.

R makes manipulation of matrices and data frames really easy. All the elements in a matrix are numeric while data frames can have different types for each of the element

E) Matrix

> rnorm(12,5,2)
[1] 2.699961 3.160208 5.087478 3.969129 3.317840 4.551565 2.585758 2.397780
[9] 5.297535 6.574757 7.468268 2.440835

a) Create a vector of 12 random numbers with a mean of 5 and SD of 2

> a <-rnorm br="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">b) Convert the vector to a matrix with 4 rows and 3 columns
> mat <- a="" br="" matrix="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">> mat[,1]     [,2]     [,3]
[1,] 5.197010 3.839281 9.022818
[2,] 4.053590 5.321399 5.587495
[3,] 4.225763 4.873768 6.648151
[4,] 4.709784 4.129093 2.575523

c) Subset rows 1 & 2 from the matrix

> mat[1:2,]
[,1]     [,2]     [,3]
[1,] 5.19701 3.839281 9.022818
[2,] 4.05359 5.321399 5.587495

d) Subset matrix a rows 1& 2 and with columns 2 & 3

> mat[1:2,2:3]
[,1]     [,2]
[1,] 3.839281 9.022818
[2,] 5.321399 5.587495

e) Subset matrix a for all row elements for the column 3

> mat[,3]
[1] 9.022818 5.587495 6.648151 2.575523

e) Add row names and column names for the matrix as follows
> names <- br="" c="" jim="" joe="" pat="" style="font-style: inherit; font-weight: inherit; line-height: 1.7;" tim="">> v <- br="" data.frame="" mat="" names="" style="font-style: inherit; font-weight: inherit; line-height: 1.7;">

> v
names       X1       X2       X3
1   tim 5.197010 3.839281 9.022818
2   pat 4.053590 5.321399 5.587495
3   joe 4.225763 4.873768 6.648151
4   jim 4.709784 4.129093 2.575523

> colnames(v) <- a="" b="" br="" c="" names="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">> v
names        a        b        c
1   tim 5.197010 3.839281 9.022818
2   pat 4.053590 5.321399 5.587495
3   joe 4.225763 4.873768 6.648151
4   jim 4.709784 4.129093 2.575523

F) Data Frames
In R data frames are the most important method to manipulate large amounts of data. One can read data in .csv format into data frame using

df <- code="" mydata.csv="" read.csv="">
To get a feel of data frames it is useful to play around with the numerous data sets that are available with the installation of R
To check the available dataframes do
>data()
AirPassengers                    Monthly Airline Passenger Numbers 1949-1960
BJsales                          Sales Data with Leading Indicator
BJsales.lead (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
...
...

I will be using the mtcars data frame. Here are some of the most important commands on data frames
a) load data from mtcars
data(mtcars)
b)

> head(mtcars,3) # Display the top 3 rows of the data frame
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

> tail(mtcars,4) # Display the boittom 4 rows of the data frame
mpg cyl disp  hp drat   wt qsec vs am gear carb
Ford Pantera L 15.8   8  351 264 4.22 3.17 14.5  0  1    5    4
Ferrari Dino   19.7   6  145 175 3.62 2.77 15.5  0  1    5    6
Maserati Bora  15.0   8  301 335 3.54 3.57 14.6  0  1    5    8
Volvo 142E     21.4   4  121 109 4.11 2.78 18.6  1  1    4    2

> names(mtcars)  # Display the names of the columns of the data frame
[1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear" "carb"

> summary(mtcars) # Display the summary of the data frame
mpg             cyl             disp             hp             drat             wt
Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0   Min.   :2.760   Min.   :1.513
1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5   1st Qu.:3.080   1st Qu.:2.581
Median :19.20   Median :6.000   Median :196.3   Median :123.0   Median :3.695   Median :3.325
Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7   Mean   :3.597   Mean   :3.217
3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0   3rd Qu.:3.920   3rd Qu.:3.610
Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0   Max.   :4.930   Max.   :5.424
qsec             vs               am              gear            carb
Min.   :14.50   Min.   :0.0000   Min.   :0.0000   Min.   :3.000   Min.   :1.000
1st Qu.:16.89   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000
Median :17.71   Median :0.0000   Median :0.0000   Median :4.000   Median :2.000
Mean   :17.85   Mean   :0.4375   Mean   :0.4062   Mean   :3.688   Mean   :2.812
3rd Qu.:18.90   3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000
Max.   :22.90   Max.   :1.0000   Max.   :1.0000   Max.   :5.000   Max.   :8.000

> str(mtcars) # Generate a concise description of the data frame - values in each column, factors
'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 ...

> mtcars[mtcars$mpg == 10.4,] #Select all rows in mtcars where the mpg column has a value 10.4
mpg cyl disp  hp drat    wt  qsec vs am gear carb
Cadillac Fleetwood  10.4   8  472 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8  460 215 3.00 5.424 17.82  0  0    3    4

> mtcars[(mtcars$mpg >20) & (mtcars$mpg <24 all="" in="" mpg="" mtcars="" rows="" select="" the="" where=""> 20 and mpg < 24
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
Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

> myset <- 4="" 6="" all="" are="" br="" calls="" cyl="=" cylinder="" either="" get="" mtcars="" or="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;" which="">> myset
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
Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2…
…
…

> mean(myset$mpg) # Determine the mean of the set created above
[1] 23.97222

k) > table(mtcars$cyl) #Create a table of cars which have 4,6, or 8 cylinders

4 6 8
11 7 14

G) lapply,sapply,tapply
I use the iris data set for these commands
a) > data(iris) #Load iris data set

 > names(iris)  #Show the column names of the data set
[1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"
c) > lapply(iris,class) #Show the class of all the columns in iris
$Sepal.Length
[1] "numeric"
$Sepal.Width
[1] "numeric"
$Petal.Length
[1] "numeric"
$Petal.Width
[1] "numeric"
$Species
[1] "factor"

> sapply(iris,class) # Display a summary of the class of the iris data set
Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species
"numeric"    "numeric"    "numeric"    "numeric"     "factor"

tapply: Instead of getting the mean for each of the species as below we can use tapply
> a <-iris br="" iris="" pecies="=" setosa="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">> mean(a$Sepal.Length)
[1] 5.006
> b <-iris br="" iris="" pecies="=" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;" versicolor="">> mean(b$Sepal.Length)
[1] 5.936
> c <-iris br="" iris="" pecies="=" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;" virginica="">> mean(c$Sepal.Length)
[1] 6.588

> tapply(iris$Sepal.Length,iris$Species,mean)
setosa versicolor virginica
5.006 5.936 6.588

Hopefully this highly condensed version of R will set you on a R-oll.

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Programming Zen and now – Some essential tips-2

This post is a follow-up to my earlier post – How to program – Some essential tips. In this post I expand on some of the ideas of my earlier post.

Programming means different things to different people. To some programming is a drudgery almost akin to manual labor, to others programming is an insurmountable mountain full of frustrations and disappointments while to others it is an intense problem solving and a creative activity. In my opinion programming can mean anything to you. It is your attitude towards coding that make it a chore, a daunting task or something really creative.

Here are some my insights on how to go about learning to code

Eyes wide open: People generally get frustrated when a piece of code that they wrote does not do what they intended it to do. In some cases the code snippet will do nothing when they were expecting final result, sometimes the code will crash or it will go into an infinite loop and drive the person nuts. (Let me assure you - I have been there, done that!) The usual reaction when this happens is anger and frustration where we generally tinker around with the code only to get the same result. Soon the emotions will progress from anger to hopelessness.

The first thing that one needs to while coding is to keep your ‘eyes wide open’. We tend to be guilty of ignoring the error messages that show up. Here one way to attack coding

a) Fully understand the ‘what’ of the problem. If there is an infinite loop or a core dump check after which point does it happen? If there is an execution error, what is the error trying to tell us?
b) Next look into ‘why’ the error occurred. You could either use debugger or insert appropriate print statements to take the offending code apart.
c) Thirdly think 'how' you can address the situation. Make appropriate changes and re-run the code
d) Did it solve the issue.If yes, move forward. Otherwise go to step a)

Remember that we learn more from our programming mistakes more than when our code just ‘happens’ to work! Mistakes in our code make us to explain every part of the program

Changing times:

Times have changed. Programming Zen and programming now are worlds apart. In many ways, IDEs, Git, Google etc. have made the programmer’s life a lot easier

‘Git’ing from here to there: Here is a trick that I learnt fairly recently, though it should have occurred to me more than 2 years back. This is using Git judiciously for all programming tasks (Note: I am saying nothing new here!). I find it really useful in writing code with incremental changes. I create my initial code on the master and then test out incremental changes on a ‘new branch’ even for personal projects. Once I have proved a small increment works, I merge it with the ‘main’ branch. I again start working on the ‘new’ for the next incremental change followed by a merge to the master

The steps are

Make initial changes

1. git add .
2. git commit –m “ Initial changes’

Create a new branch
3. git checkout –b ‘new

Make incremental changes. Test.
4.git add .
5. git commit –m "Change 1"

Merge with the master
6.git checkout master
7. git merge new

Continue to work with ‘new’.
8 . git checkout new
9. Go to step 4)

This process can be continued till you get your final product. I find this extremely useful instead of just using an IDE to make code changes. Invariably you can run into a situation where you had something working some time back and in the next instant it is broken and you can’t figure out all the changes you made to the working code. This can be extremely frustrating. With Git you have a history of changes and you can switch to an earlier version of working code and start from there.

Rarely do I find a reason to have more than 1 branch

Here is a pictorial version of this

Taking help from Dr. Google: For most questions and errors that you encounter you will find others who have hit similar bugs. Just google it. You will more than surprised that others went down the exact same path that you are treading. Besides the internet is full of tutorials, blogs and articles on key aspects of programming

Explore the cave of Stack overflow: Spend time exploring Stack overflow. Stack overflow is replete with code snippets and questions that you wanted to ask. There is so much information out there. If you really don’t find an answer to your problem, post it in Stack overflow and you are bound to get an answer or a link to a similar question asked previously

Finally programming requires dollops of patience. Develop patience along with your skill in coding and soon programming will much more enjoyable to you.

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A crime map of India in R – Crimes against women

In this post I take a look at the gory crime scene across India to determine which states are the heavy weights in crimes. Who is the undisputed champion of rapes in a year? Which state excels in cruelty by husbands and the relatives to wives? Which state leads in dowry deaths? To get the answers to these questions I perform analysis of the state-wise crime data against women with the data from Open Government Data (OGD) Platform India. The dataset for this analysis was taken for the Crime against Women from OGD.

The data in OGD is available for crimes against women in different states under different ‘crime heads’ like rape, dowry deaths, kidnapping & abduction etc. The data is available for years from 2001 to 2012. This data is plotted as a scatter plot and a linear regression line is then fit on the available data. Based on this linear model, the projected incidence of crimes likes rapes, dowry deaths, abduction & kidnapping is performed for each of the states. This is then used to build a table of different crime heads for all the states predicting the number of crimes till the year 2018. Fortunately, R crunches through the data sets quite easily. The overall projections of crimes against as women is shown below based on the linear regression for each of these states

Projections over the next couple of years
The tables below are based on the projected incidence of crimes under various categories assuming that these states maintain their torrid crime rate. A cursory look at the tables below clearly indicate the Uttar Pradesh is the undisputed heavy weight champion in 4 of 5 categories shown. Maharashtra and Andhra Pradesh take 2nd and 3rd ranks in the total crimes against women and are significant contenders in other categories too.

A) Projected rapes in India
The top 3 heavy weights in projected rapes over the next 5 years are 1) Madhya Pradesh 2) Uttar Pradesh 3) Maharashtra

Full table: Rape.csv
B) Projected Dowry deaths in India

Full table: Dowry Deaths.csv
C) Kidnapping & Abduction

Full table: Kidnapping&Abduction.csv
D) Cruelty by husband & relatives

Full table: Cruelty by husbands_relatives.csv
E) Total crimes against women

Full table: Total crimes.csv
Here is a beautiful visualization of 'Total crimes against women' created as a choropleth map by Philip Predruco.

The implementation for this analysis was done using the R language. The R code, dataset, output and the crime charts can be accessed at GitHub at crime-against-women

Directory structure
- R code
- dataset used
- output
- statewise-crime-charts

The analysis has been completely parametrized. A quick look at the implementation is shown below. A function state crime was created as given below

statecrime.R
This function (statecrime.R) does the following
a) Creates a scatter plot for the state for the crime head
b) Computes a best linear regression fir and draws this line
c) Uses the model parameters (coefficients) to compute the projected crime in the years to come
d) Writes the projected values to a text file
c) Creates a directory with the name of the state if it does not exist and stores the jpeg of the plot there.

statecrime <- br="" crime="" function="" indiacrime="" row="" state="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">year <- br="" c="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;"># Make seperate folders for each state
if(!file.exists(state)) {
dir.create(state)
}
setwd(state)
crimeplot <- br="" crime="" jpg="" paste="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">jpeg(crimeplot)

# Plot the details of the crime

plot(year,thecrime ,pch= 15, col="red", xlab = "Year", ylab= crime, main = atitle,
,xlim=c(2001,2018),ylim=c(ymin,ymax), axes=FALSE)

A linear regression line is fit using 'lm'

# Fit a linear regression model
lmfit <-lm br="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;" thecrime="" year=""># Draw the lmfit line
abline(lmfit)

The model parameters are then used to draw the line and also project for the next 5 years from 2013 to 2018

nyears <-c br="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">nthecrime <- br="" length="" nyears="" rep="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;"># Projected crime incidents from 2013 to 2018 using a linear regression model
for (i in seq_along(nyears)) {
nthecrime[i] <- br="" coefficients="" i="" lmfit="" nyears="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">}

The projected data for each state is appended into an appropriate file which is then used to display the tables at the top of this post

# Write the projected crime rate in a file

nthecrime <- br="" nthecrime="" round="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">nthecrime <- br="" c="" n="" nthecrime="" state="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">print(nthecrime)
#write(nthecrime,file=fileconn, ncolumns=9, append=TRUE,sep="\t")
filename <- br="" crime="" paste="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;" txt=""># Write the output in the ./output directory
setwd("./output")
cat(nthecrime, file=filename, sep=",",append=TRUE)

The above function is then repeatedly called for each state for the different crime heads. (Note: It is possible to check the read both the states and crime heads with R and perform the computation repeatedly. However, I have done this the manual way!)

crimereport.R

# 1. Andhra Pradesh
i <- 1="" br="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">statecrime(indiacrime, i, "Andhra Pradesh","Rape")
i <- br="" i="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">statecrime(indiacrime, i, "Andhra Pradesh","Kidnapping& Abduction")
i <- br="" i="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">statecrime(indiacrime, i, "Andhra Pradesh","Dowry Deaths")
i <- br="" i="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">statecrime(indiacrime, i, "Andhra Pradesh","Assault on Women")
i <- br="" i="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">statecrime(indiacrime, i, "Andhra Pradesh","Insult to modesty")
i <- br="" i="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">statecrime(indiacrime, i, "Andhra Pradesh","Cruelty by husband_relatives")
i <- br="" i="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">statecrime(indiacrime, i, "Andhra Pradesh","Imporation of girls from foreign country")
i <- br="" i="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">statecrime(indiacrime, i, "Andhra Pradesh","Immoral traffic act")
i <- br="" i="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">statecrime(indiacrime, i, "Andhra Pradesh","Dowry prohibition act")
i <- br="" i="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">statecrime(indiacrime, i, "Andhra Pradesh","Indecent representation of Women Act")
i <- br="" i="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">statecrime(indiacrime, i, "Andhra Pradesh","Commission of Sati Act")
i <- br="" i="" style="font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: inherit; font-weight: inherit; line-height: 1.7;">statecrime(indiacrime, i, "Andhra Pradesh","Total crimes against women")
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