R a1a - Data Cleaning
2023-05-31
Introduction
Data cleaning is a crucial step in the data analysis process that involves identifying and resolving issues
in the dataset to ensure accurate and reliable results. This study shows how to perform data cleaning
operations to prepare the dataset for further analysis. The dataset contains information about different all
the Indian states, regions, districts, and their respective consumption variables. where we will be performing
the descriptive analysis to analyze the consumption patterns of the states. Here the state “Odisha” is used
to understand how the data cleaning and patterns are observed to gain insights into the variables related to
consumption. My tasks include
a) Checking for missing values and replacing them with the mean of the variable.
b) Identifying and addressing outliers in the data.
c) Renaming the districts and sectors (rural and urban) for better clarity.
d) Summarizing critical variables region-wise and district-wise, and identifying the top three and bottom
three districts in terms of consumption.
e) Testing whether the differences in means are statistically significant or not.
Through this analysis, practical experience in data exploration and analysis using RStudio software and
enhance our skills in working with real-world datasets.
Objective
The objective of this study is to analyze and explore the consumption data for the state of Odisha using R
software, where I aim to achieve the following:
#Identify and handle missing values
#Address outliers
#Rename districts and sectors
#Summarize critical variables
#Indicate the top and bottom districts
#Test for significant mean differences
By leveraging R software for data analysis, we can explore the data effectively and provide valuable insights
for decision-making and further investigation.
Business Significance
In this analysis, we focus on examining the consumption data for the state of Odisha using the NSSO
dataset. The primary objective is to perform a comprehensive statistical analysis, including data cleaning,
outlier identification, variable renaming, and summarization of consumption patterns at the regional and
district levels.
1
1. Market analyis, which helps us to the process of gathering and evaluating data and information about
the state Odisha
2. Pricing Strategy: The Odisha data can shed light on the price sensitivity of consumers and their
spending patterns. Businesses can use this information to develop effective pricing strategies, optimize
product pricing, and determine the price.
3. Demand Forecasting: By analyzing historical consumption data from the NSSO dataset, businesses
can make informed predictions and forecasts about future market demand.
4. Consumer Insights: The NSSO data allows businesses to gain valuable insights into consumer preferences, buying behavior, and consumption patterns.
5. Market Trends and Opportunities: By analyzing the NSSO data, businesses can identify emerging market trends and opportunities. They can gain a deeper understanding of consumer behavior, preferences,
and evolving needs.
Analyis
First step is to give the path to the R studio to read the file, then confirm the path
setwd('/Users/chiru/Documents/VCU/SCMA - R')
getwd()
## [1] "/Users/chiru/Documents/VCU/SCMA - R"
library(tidyr)
library(dplyr)
##
## Attaching package: ’dplyr’
## The following objects are masked from ’package:stats’:
##
##
filter, lag
## The following objects are masked from ’package:base’:
##
##
intersect, setdiff, setequal, union
Once the path is set, file has to read to the perform the analysis
odisha = read.csv('NSSO68dataset.csv', header = TRUE)
Here the number of rows and columns in the dataset has been mentioned with the names of the variables
dim(odisha)
## [1] 101662
384
2
names(odisha)
##
[1] "X"
##
[3] "Round_Centre"
##
[5] "Round"
##
[7] "Sample"
##
[9] "state"
## [11] "District"
## [13] "Sub_Stratum"
## [15] "Sub_Round"
## [17] "FOD_Sub_Region"
## [19] "Second"
## [21] "HHS_No"
## [23] "Filler"
## [25] "NIC_2008"
## [27] "HH_type"
## [29] "Social_Group"
## [31] "Type_of_land_owned"
## [33] "Land_Leased_in"
## [35] "Land_Leased_out"
## [37] "During_July_June_Cultivated"
## [39] "NSS"
## [41] "MLT"
## [43] "Cooking_code"
## [45] "Dwelling_unit_code"
## [47] "Perform_Ceremony"
## [49] "Possess_ration_card"
## [51] "MPCE_URP"
## [53] "Person_Srl_No"
## [55] "Sex"
## [57] "Marital_Status"
## [59] "Days_Stayed_away"
## [61] "Meals_School"
## [63] "Meals_Others"
## [65] "Meals_At_Home"
## [67] "Source_Code"
## [69] "riceos_q"
## [71] "chira_q"
## [73] "muri_q"
## [75] "riceGT_q"
## [77] "wheatos_q"
## [79] "maida_q"
## [81] "sewai_q"
## [83] "wheatp_q"
## [85] "jowarp_q"
## [87] "maizep_q"
## [89] "milletp_q"
## [91] "cerealot_q"
## [93] "cerealsub_q"
## [95] "arhar_q"
## [97] "gramwholep_q"
## [99] "moong_q"
## [101] "urd_q"
"grp"
"FSU_number"
"Schedule_Number"
"Sector"
"State_Region"
"Stratum_Number"
"Schedule_type"
"Sub_Sample"
"Hamlet_Group_Sub_Block"
"X_Stage_Stratum"
"Level"
"hhdsz"
"NCO_2004"
"Religion"
"Whether_owns_any_land"
"Land_Owned"
"Otherwise_possessed"
"Land_Total_possessed"
"During_July_June_Irrigated"
"NSC"
"land_tt"
"Lighting_code"
"Regular_salary_earner"
"Meals_seved_to_non_hhld_members"
"Type_of_ration_card"
"MPCE_MRP"
"Relation"
"Age"
"Education"
"No_of_Meals_per_day"
"Meals_Employer"
"Meals_Payment"
"Item_Code"
"ricepds_q"
"ricetotal_q"
"khoi_q"
"ricepro_q"
"Wheatpds_q"
"wheattotal_q"
"suji_q"
"bread_q"
"wheatGT_q"
"bajrap_q"
"barleyp_q"
"ragip_q"
"cerealtot_q"
"cerealstt_q"
"gramdal_q"
"gramGT_q"
"masur_q"
"peasdal_q"
3
## [103] "khesari_q"
## [105] "gramp_q"
## [107] "pulsep_q"
## [109] "pulsestt_q"
## [111] "milk_q"
## [113] "milkcond_q"
## [115] "ghee_q"
## [117] "icecream_q"
## [119] "Milktotal_q"
## [121] "vanas_q"
## [123] "gnoil_q"
## [125] "edioilothr_q"
## [127] "ediblest_q"
## [129] "fishprawn_q"
## [131] "beef_q"
## [133] "chicken_q"
## [135] "nonvegtotal_q"
## [137] "potato_q"
## [139] "tamato_q"
## [141] "radish_q"
## [143] "palak_q"
## [145] "bhindi_q"
## [147] "cauli_q"
## [149] "pumpkin_q"
## [151] "fbeans_q"
## [153] "otveg_q"
## [155] "bananano_q"
## [157] "watermel_q"
## [159] "cocono_q"
## [161] "guava_q"
## [163] "orangeno_q"
## [165] "mango_q"
## [167] "pears_q"
## [169] "leechi_q"
## [171] "grapes_q"
## [173] "fruitstt_q"
## [175] "cocodf_q"
## [177] "datesdf_q"
## [179] "walnutdf_q"
## [181] "kishmish_q"
## [183] "dryfruitstotal_q"
## [185] "sugarpds_q"
## [187] "sugarst_q"
## [189] "misri_q"
## [191] "sugartotal_q"
## [193] "salt_q"
## [195] "garlic_q"
## [197] "dhania_q"
## [199] "blackpepper_q"
## [201] "tamarind_q"
## [203] "oilseeds_q"
## [205] "spicetot_q"
## [207] "teacupno_q"
## [209] "teatotal_q"
"otpulse_q"
"besan_q"
"pulsestot_q"
"soyabean_q"
"babyfood_q"
"curd_q"
"butter_q"
"otmilkp_q"
"milkprott_q"
"musoil_q"
"cocooil_q"
"edibletotal_q"
"eggsno_q"
"goatmeat_q"
"pork_q"
"othrbirds_q"
"emftt_q"
"onion_q"
"brinjal_q"
"carrot_q"
"chillig_q"
"parwal_q"
"cabbage_q"
"peas_q"
"lemonno_q"
"vegtt_q"
"jackfruit_q"
"pineaplno_q"
"cocogno_q"
"sighara_q"
"papayar_q"
"kharbooz_q"
"berries_q"
"apple_q"
"otfruits_q"
"fruitt_total"
"gnutdf_q"
"cashewdf_q"
"otnutsdf_q"
"otherdf_q"
"dftt_q"
"sugaros_q"
"gur_q"
"honey_q"
"sugartt_q"
"ginger_q"
"jeera_q"
"turnmeric_q"
"drychilly_q"
"currypowder_q"
"spicesothr_q"
"spicestotal_q"
"tealeaf_q"
"cofeeno_q"
4
## [211] "coffeepwdr_q"
## [213] "ice_q"
## [215] "juice_q"
## [217] "bevergest_q"
## [219] "preparedsweet_q"
## [221] "sauce_jam_q"
## [223] "Beveragestotal_q"
## [225] "riceos_v"
## [227] "chira_v"
## [229] "muri_v"
## [231] "riceGT_v"
## [233] "wheatos_v"
## [235] "maida_v"
## [237] "sewai_v"
## [239] "wheatp_v"
## [241] "jowarp_v"
## [243] "maizep_v"
## [245] "milletp_v"
## [247] "cerealot_v"
## [249] "cerealsub_v"
## [251] "arhar_v"
## [253] "gramwholep_v"
## [255] "moong_v"
## [257] "urd_v"
## [259] "khesari_v"
## [261] "gramp_v"
## [263] "pulsep_v"
## [265] "pulsestt_v"
## [267] "milk_v"
## [269] "milkcond_v"
## [271] "ghee_v"
## [273] "icecream_v"
## [275] "Milktotal_v"
## [277] "vanas_v"
## [279] "gnoil_v"
## [281] "edioilothr_v"
## [283] "ediblest_v"
## [285] "fishprawn_v"
## [287] "beef_v"
## [289] "chicken_v"
## [291] "nonvegtotal_v"
## [293] "potato_v"
## [295] "tamato_v"
## [297] "radish_v"
## [299] "palak_v"
## [301] "bhindi_v"
## [303] "cauli_v"
## [305] "pumpkin_v"
## [307] "fbeans_v"
## [309] "otveg_v"
## [311] "bananano_v"
## [313] "watermel_v"
## [315] "cocono_v"
## [317] "guava_v"
"cofeetotal_q"
"coldbvrg_q"
"othrbevrg_q"
"Biscuits_q"
"pickle_q"
"Othrprocessed_q"
"ricepds_v"
"ricetotal_v"
"khoi_v"
"ricepro_v"
"Wheatpds_v"
"wheattotal_v"
"suji_v"
"bread_v"
"wheatGT_v"
"bajrap_v"
"barleyp_v"
"ragip_v"
"cerealtot_v"
"cerealstt_v"
"gramdal_v"
"gramGT_v"
"masur_v"
"peasdal_v"
"otpulse_v"
"besan_v"
"pulsestot_v"
"soyabean_v"
"babyfood_v"
"curd_v"
"butter_v"
"otmilkp_v"
"milkprott_v"
"musoil_v"
"cocooil_v"
"edibletotal_v"
"eggsno_v"
"goatmeat_v"
"pork_v"
"othrbirds_v"
"emftt_v"
"onion_v"
"brinjal_v"
"carrot_v"
"chillig_v"
"parwal_v"
"cabbage_v"
"peas_v"
"lemonno_v"
"vegtt_v"
"jackfruit_v"
"pineaplno_v"
"cocogno_v"
"sighara_v"
5
## [319] "orangeno_v"
## [321] "mango_v"
## [323] "pears_v"
## [325] "leechi_v"
## [327] "grapes_v"
## [329] "fruitstt_v"
## [331] "gnutdf_v"
## [333] "cashewdf_v"
## [335] "otnutsdf_v"
## [337] "otherdf_v"
## [339] "dftt_v"
## [341] "sugaros_v"
## [343] "gur_v"
## [345] "honey_v"
## [347] "sugartt_v"
## [349] "ginger_v"
## [351] "jeera_v"
## [353] "turnmeric_v"
## [355] "drychilly_v"
## [357] "currypowder_v"
## [359] "spicesothr_v"
## [361] "spicestotal_v"
## [363] "tealeaf_v"
## [365] "cofeeno_v"
## [367] "cofeetotal_v"
## [369] "coldbvrg_v"
## [371] "othrbevrg_v"
## [373] "Biscuits_v"
## [375] "pickle_v"
## [377] "Othrprocessed_v"
## [379] "foodtotal_v"
## [381] "state_1"
## [383] "fruits_df_tt_v"
"papayar_v"
"kharbooz_v"
"berries_v"
"apple_v"
"otfruits_v"
"cocodf_v"
"datesdf_v"
"walnutdf_v"
"kishmish_v"
"dryfruitstotal_v"
"sugarpds_v"
"sugarst_v"
"misri_v"
"sugartotal_v"
"salt_v"
"garlic_v"
"dhania_v"
"blackpepper_v"
"tamarind_v"
"oilseeds_v"
"spicetot_v"
"teacupno_v"
"teatotal_v"
"coffeepwdr_v"
"ice_v"
"juice_v"
"bevergest_v"
"preparedsweet_v"
"sauce_jam_v"
"Beveragestotal_v"
"foodtotal_q"
"Region"
"fv_tot"
odi=odisha %>% filter (state_1 == "ORI")
Interpretation State “Odisha” has been subsetted
a) Check if there are any missing values in the data, identify them and if there are replace
them with the mean of the variable.
any(is.na(odi))
## [1] TRUE
sum(is.na(odi))
## [1] 46127
columns_with_missing =
colnames(odi)[apply(odi, 2, function(x) any(is.na(x)))]
print(columns_with_missing)
6
## [1] "NIC_2008"
"NCO_2004"
## [3] "Type_of_land_owned"
"Land_Owned"
## [5] "Land_Leased_in"
"Otherwise_possessed"
## [7] "Land_Leased_out"
"Land_Total_possessed"
## [9] "During_July_June_Cultivated"
"During_July_June_Irrigated"
## [11] "land_tt"
"Perform_Ceremony"
## [13] "Meals_seved_to_non_hhld_members" "Type_of_ration_card"
## [15] "Days_Stayed_away"
"No_of_Meals_per_day"
## [17] "Meals_School"
"Meals_Employer"
## [19] "Meals_Others"
"Meals_Payment"
## [21] "Meals_At_Home"
"Source_Code"
## [23] "soyabean_q"
"soyabean_v"
Interpretation According to the dataset, there are missing values in total of 46,127 missing values
spread across the above mentioned 24 columns.Dealing with missing values is crucial as it helps maintain the
integrity of the data and enables accurate insights for decision-making based on the consumption dataset of
Odisha
sort (colSums (is.na(odi)), decreasing = T)
##
soyabean_q
##
4026
##
Meals_School
##
3968
##
Otherwise_possessed
##
3786
##
Land_Leased_out
##
3564
##
Meals_Others
##
3506
##
Land_Leased_in
##
3328
##
Type_of_ration_card
##
1566
## Meals_seved_to_non_hhld_members
##
386
##
NIC_2008
##
274
##
Meals_At_Home
##
46
##
Land_Total_possessed
##
6
##
Perform_Ceremony
##
2
##
X
##
0
##
Round_Centre
##
0
##
Round
##
0
##
Sample
##
0
##
state
soyabean_v
4026
Meals_Employer
3939
Meals_Payment
3577
Days_Stayed_away
3539
During_July_June_Irrigated
3415
During_July_June_Cultivated
2064
Land_Owned
402
Type_of_land_owned
383
NCO_2004
272
Source_Code
44
land_tt
6
No_of_Meals_per_day
2
grp
0
FSU_number
0
Schedule_Number
0
Sector
0
State_Region
7
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##
##
##
0
District
0
Sub_Stratum
0
Sub_Round
0
FOD_Sub_Region
0
Second
0
HHS_No
0
Filler
0
HH_type
0
Social_Group
0
NSS
0
MLT
0
Lighting_code
0
Regular_salary_earner
0
MPCE_URP
0
Person_Srl_No
0
Sex
0
Marital_Status
0
Item_Code
0
riceos_q
0
chira_q
0
muri_q
0
riceGT_q
0
wheatos_q
0
maida_q
0
sewai_q
0
wheatp_q
0
jowarp_q
0
Stratum_Number
0
Schedule_type
0
Sub_Sample
0
Hamlet_Group_Sub_Block
0
X_Stage_Stratum
0
Level
0
hhdsz
0
Religion
0
Whether_owns_any_land
0
NSC
0
Cooking_code
0
Dwelling_unit_code
0
Possess_ration_card
0
MPCE_MRP
0
Relation
0
Age
0
Education
0
ricepds_q
0
ricetotal_q
0
khoi_q
0
ricepro_q
0
Wheatpds_q
0
wheattotal_q
0
suji_q
0
bread_q
0
wheatGT_q
0
bajrap_q
8
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##
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##
##
##
##
0
maizep_q
0
milletp_q
0
cerealot_q
0
cerealsub_q
0
arhar_q
0
gramwholep_q
0
moong_q
0
urd_q
0
khesari_q
0
gramp_q
0
pulsep_q
0
pulsestt_q
0
babyfood_q
0
curd_q
0
butter_q
0
otmilkp_q
0
milkprott_q
0
musoil_q
0
cocooil_q
0
edibletotal_q
0
eggsno_q
0
goatmeat_q
0
pork_q
0
othrbirds_q
0
emftt_q
0
onion_q
0
brinjal_q
0
barleyp_q
0
ragip_q
0
cerealtot_q
0
cerealstt_q
0
gramdal_q
0
gramGT_q
0
masur_q
0
peasdal_q
0
otpulse_q
0
besan_q
0
pulsestot_q
0
milk_q
0
milkcond_q
0
ghee_q
0
icecream_q
0
Milktotal_q
0
vanas_q
0
gnoil_q
0
edioilothr_q
0
ediblest_q
0
fishprawn_q
0
beef_q
0
chicken_q
0
nonvegtotal_q
0
potato_q
0
tamato_q
0
radish_q
9
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##
0
carrot_q
0
chillig_q
0
parwal_q
0
cabbage_q
0
peas_q
0
lemonno_q
0
vegtt_q
0
jackfruit_q
0
pineaplno_q
0
cocogno_q
0
sighara_q
0
papayar_q
0
kharbooz_q
0
berries_q
0
apple_q
0
otfruits_q
0
fruitt_total
0
gnutdf_q
0
cashewdf_q
0
otnutsdf_q
0
otherdf_q
0
dftt_q
0
sugaros_q
0
gur_q
0
honey_q
0
sugartt_q
0
ginger_q
0
palak_q
0
bhindi_q
0
cauli_q
0
pumpkin_q
0
fbeans_q
0
otveg_q
0
bananano_q
0
watermel_q
0
cocono_q
0
guava_q
0
orangeno_q
0
mango_q
0
pears_q
0
leechi_q
0
grapes_q
0
fruitstt_q
0
cocodf_q
0
datesdf_q
0
walnutdf_q
0
kishmish_q
0
dryfruitstotal_q
0
sugarpds_q
0
sugarst_q
0
misri_q
0
sugartotal_q
0
salt_q
0
garlic_q
10
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##
##
##
##
0
jeera_q
0
turnmeric_q
0
drychilly_q
0
currypowder_q
0
spicesothr_q
0
spicestotal_q
0
tealeaf_q
0
cofeeno_q
0
cofeetotal_q
0
coldbvrg_q
0
othrbevrg_q
0
Biscuits_q
0
pickle_q
0
Othrprocessed_q
0
ricepds_v
0
ricetotal_v
0
khoi_v
0
ricepro_v
0
Wheatpds_v
0
wheattotal_v
0
suji_v
0
bread_v
0
wheatGT_v
0
bajrap_v
0
barleyp_v
0
ragip_v
0
cerealtot_v
0
dhania_q
0
blackpepper_q
0
tamarind_q
0
oilseeds_q
0
spicetot_q
0
teacupno_q
0
teatotal_q
0
coffeepwdr_q
0
ice_q
0
juice_q
0
bevergest_q
0
preparedsweet_q
0
sauce_jam_q
0
Beveragestotal_q
0
riceos_v
0
chira_v
0
muri_v
0
riceGT_v
0
wheatos_v
0
maida_v
0
sewai_v
0
wheatp_v
0
jowarp_v
0
maizep_v
0
milletp_v
0
cerealot_v
0
cerealsub_v
11
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
0
cerealstt_v
0
gramdal_v
0
gramGT_v
0
masur_v
0
peasdal_v
0
otpulse_v
0
besan_v
0
pulsestot_v
0
milk_v
0
milkcond_v
0
ghee_v
0
icecream_v
0
Milktotal_v
0
vanas_v
0
gnoil_v
0
edioilothr_v
0
ediblest_v
0
fishprawn_v
0
beef_v
0
chicken_v
0
nonvegtotal_v
0
potato_v
0
tamato_v
0
radish_v
0
palak_v
0
bhindi_v
0
cauli_v
0
arhar_v
0
gramwholep_v
0
moong_v
0
urd_v
0
khesari_v
0
gramp_v
0
pulsep_v
0
pulsestt_v
0
babyfood_v
0
curd_v
0
butter_v
0
otmilkp_v
0
milkprott_v
0
musoil_v
0
cocooil_v
0
edibletotal_v
0
eggsno_v
0
goatmeat_v
0
pork_v
0
othrbirds_v
0
emftt_v
0
onion_v
0
brinjal_v
0
carrot_v
0
chillig_v
0
parwal_v
0
cabbage_v
12
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
0
pumpkin_v
0
fbeans_v
0
otveg_v
0
bananano_v
0
watermel_v
0
cocono_v
0
guava_v
0
orangeno_v
0
mango_v
0
pears_v
0
leechi_v
0
grapes_v
0
fruitstt_v
0
gnutdf_v
0
cashewdf_v
0
otnutsdf_v
0
otherdf_v
0
dftt_v
0
sugaros_v
0
gur_v
0
honey_v
0
sugartt_v
0
ginger_v
0
jeera_v
0
turnmeric_v
0
drychilly_v
0
currypowder_v
0
peas_v
0
lemonno_v
0
vegtt_v
0
jackfruit_v
0
pineaplno_v
0
cocogno_v
0
sighara_v
0
papayar_v
0
kharbooz_v
0
berries_v
0
apple_v
0
otfruits_v
0
cocodf_v
0
datesdf_v
0
walnutdf_v
0
kishmish_v
0
dryfruitstotal_v
0
sugarpds_v
0
sugarst_v
0
misri_v
0
sugartotal_v
0
salt_v
0
garlic_v
0
dhania_v
0
blackpepper_v
0
tamarind_v
0
oilseeds_v
13
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
0
spicesothr_v
0
spicestotal_v
0
tealeaf_v
0
cofeeno_v
0
cofeetotal_v
0
coldbvrg_v
0
othrbevrg_v
0
Biscuits_v
0
pickle_v
0
Othrprocessed_v
0
foodtotal_v
0
state_1
0
fruits_df_tt_v
0
0
spicetot_v
0
teacupno_v
0
teatotal_v
0
coffeepwdr_v
0
ice_v
0
juice_v
0
bevergest_v
0
preparedsweet_v
0
sauce_jam_v
0
Beveragestotal_v
0
foodtotal_q
0
Region
0
fv_tot
0
Intrepretation Here you can see soyabean_v, soyabean_q has the highest missing value followed by
Meal_school and so on.
odi <- odi %>%
mutate(across(everything(), ~ifelse(is.na(.), mean(., na.rm = TRUE), .)))
Interpretation Code odi identifies the mean of variable, and then replaces any missing values in each
column with the mean value of that column.
columns_with_missing =
colnames(odi)[apply(odi, 2, function(x) any(is.na(x)))]
print(columns_with_missing)
## [1] "soyabean_q" "soyabean_v"
Interpretation Here, missing values have been successfully replacesd with the mean value of the variable
but soyabean_v and soyabean_q has been still dedicted since the entire column has missing values so these
will be dropped from the dataset
odi = subset (odi, select=-c(soyabean_v))
odi = subset (odi, select=-c(soyabean_q))
soyabean_v and soyabean_q has been dropped from the dataset, since all the values are null in the column.
14
any(is.na(odi))
## [1] FALSE
Interpretation all the missing values have been replace by the mean of the variable.
b) Check for outliers and describe the outcome of your test and make suitable amendments.
library(gridExtra)
##
## Attaching package: ’gridExtra’
## The following object is masked from ’package:dplyr’:
##
##
combine
library(ggplot2)
Here boxplot is used which is a graphical representation of the distribution of a dataset. They display key
statistical measures such as the median, quartiles, and potential outliers. Here 5 variables
0
5
10
15
20
25
30
boxplot(odi$ricepds_q)
15
Interpretation From the above Ricepds boxplot we can see that there are few data points outside the
whisker indicating the outliers which are data points that significantly deviate from the rest of the data in a
dataset. These outliers represent values that are significantly different from the majority of the data. This
suggests the presence of unusual consumption patterns of the products.
0
5
10
15
20
25
30
boxplot(odi$cerealtot_q)
Interpretation From the above Cereal boxplot we can see that there are few data points outside of both
the whisker indicating the outliers which are data points that significantly deviate from the rest of the data
in a dataset. These outliers represent values that are significantly different from the majority of the data.
This suggests the presence of unusual consumption patterns of the products.
boxplot(odi$onion_q)
16
4
3
2
1
0
Interpretation From the above Onion boxplot we can see that there are few data points outside the whisker
indicating the outliers which are data points that significantly deviate from the rest of the data in a dataset.
These outliers represent values that are significantly different from the majority of the data. This suggests
the presence of unusual consumption patterns of the products.
boxplot(odi$cabbage_q)
17
3.0
2.0
1.0
0.0
Interpretation From the above Cabbage boxplot we can see that there are few data points outside the
whisker indicating the outliers which are data points that significantly deviate from the rest of the data
in a dataset. These outliers represent values that are significantly different from the majority of the data.
This suggests the presence of unusual consumption patterns of the products. Here in ricepds, cereal, onion,
cabbage have outliers in the data
Q1 <- quantile(odi$ricepds_q, 0.25)
Q3 <- quantile(odi$ricepds_q, 0.75)
IQR <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR
upper_bound <- Q3 + 1.5 * IQR
odi <- subset(odi, odi$ricepds_q >= lower_bound & odi$ricepds_q <= upper_bound)
dim(odi)
## [1] 3999
382
18
0
5
10
15
boxplot(odi$ricepds_q)
Interpretation after finding out the outliers,this code calculates the quartiles, interquartile range, and
lower/upper bounds for identifying outliers in the “ricepds_q” variable. It then subsets the data frame to
remove any outliers and finally creates a box plot to visualize the distribution of the modified variable.
Q1 <- quantile(odi$cerealtot_q, 0.25)
Q3 <- quantile(odi$cerealtot_q, 0.75)
IQR <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR
upper_bound <- Q3 + 1.5 * IQR
odi <- subset(odi, odi$cerealtot_q >= lower_bound & odi$cerealtot_q <= upper_bound)
dim(odi)
## [1] 3838
382
boxplot(odi$cerealtot_q)
19
20
15
10
5
Interpretation after finding out the outliers, this code calculates the quartiles, interquartile range, and
lower/upper bounds for identifying outliers in the “cerealtot_q” variable. It then subsets the data frame to
remove any outliers and finally creates a box plot to visualize the distribution of the modified variable, but
you can see that even after filter the datapoints few outliers exist these indicate that these data points are
extremely low and above to the mean of the variable.
Q1 <- quantile(odi$onion_q, 0.25)
Q3 <- quantile(odi$onion_q, 0.75)
IQR <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR
upper_bound <- Q3 + 1.5 * IQR
odi <- subset(odi, odi$onion_q >= lower_bound & odi$onion_q <= upper_bound)
dim(odi)
## [1] 3679
382
boxplot(odi$onion_q)
20
1.2
0.8
0.4
0.0
Interpretation after finding out the outliers,this code calculates the quartiles, interquartile range, and
lower/upper bounds for identifying outliers in the “onion_q” variable. It then subsets the data frame to
remove any outliers and finally creates a box plot to visualize the distribution of the modified variable.
Q1 <- quantile(odi$cabbage_q, 0.25)
Q3 <- quantile(odi$cabbage_q, 0.75)
IQR <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR
upper_bound <- Q3 + 1.5 * IQR
odi <- subset(odi, odi$cabbage_q >= lower_bound & odi$cabbage_q <= upper_bound)
dim(odi)
## [1] 3571
382
boxplot(odi$cabbage_q)
21
1.0
0.8
0.6
0.4
0.2
0.0
Interpretation after finding out the outliers,this code calculates the quartiles, interquartile range, and
lower/upper bounds for identifying outliers in the “cabbage_q” variable. It then subsets the data frame to
remove any outliers and finally creates a box plot to visualize the distribution of the modified variable.
c) Rename the districts as well as the sector, viz. rural and urban.
unique_value <- unique(odi$Sector)
unique_value <- sort(unique_value)
print(unique_value)
## [1] 1 2
Interpretation Data shows that there are 2 numerical values in the sector
odi <- odi %>% mutate(Sector = case_when(
Sector == 1 ~ "rural",
Sector == 2 ~ "urban",
TRUE ~ as.character(Sector)))
print(odi$Sector, 1:30)
##
##
##
##
##
##
[1] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[10] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[19] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[28] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[37] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[46] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
22
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
[55] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[64] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[73] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[82] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[91] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[100] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[109] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[118] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[127] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[136] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[145] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[154] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[163] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[172] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[181] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[190] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[199] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[208] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[217] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[226] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[235] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[244] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[253] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[262] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[271] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[280] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[289] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[298] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[307] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[316] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[325] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[334] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[343] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[352] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[361] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[370] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[379] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[388] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[397] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[406] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[415] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[424] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[433] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[442] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[451] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[460] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[469] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[478] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[487] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[496] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[505] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[514] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[523] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
[532] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
23
## [541] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [550] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [559] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [568] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [577] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [586] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [595] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [604] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [613] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [622] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [631] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [640] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [649] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [658] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [667] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [676] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [685] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [694] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [703] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [712] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [721] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [730] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [739] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [748] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [757] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [766] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [775] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [784] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [793] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [802] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [811] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [820] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [829] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [838] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [847] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [856] "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban" "urban"
## [865] "urban" "urban" "urban" "rural" "rural" "rural" "rural" "rural" "rural"
## [874] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [883] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [892] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [901] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [910] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [919] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [928] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [937] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [946] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [955] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [964] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [973] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [982] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [991] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1000] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1009] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1018] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
24
## [1027] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1036] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1045] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1054] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1063] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1072] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1081] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1090] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1099] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1108] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1117] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1126] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1135] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1144] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1153] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1162] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1171] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1180] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1189] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1198] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1207] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1216] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1225] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1234] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1243] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1252] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1261] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1270] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1279] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1288] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1297] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1306] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1315] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1324] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1333] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1342] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1351] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1360] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1369] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1378] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1387] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1396] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1405] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1414] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1423] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1432] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1441] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1450] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1459] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1468] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1477] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1486] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1495] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1504] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
25
## [1513] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1522] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1531] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1540] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1549] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1558] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1567] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1576] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1585] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1594] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1603] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1612] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1621] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1630] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1639] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1648] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1657] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1666] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1675] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1684] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1693] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1702] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1711] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1720] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1729] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1738] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1747] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1756] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1765] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1774] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1783] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1792] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1801] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1810] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1819] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1828] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1837] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1846] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1855] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1864] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1873] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1882] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1891] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1900] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1909] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1918] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1927] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1936] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1945] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1954] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1963] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1972] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1981] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [1990] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
26
## [1999] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2008] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2017] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2026] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2035] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2044] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2053] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2062] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2071] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2080] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2089] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2098] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2107] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2116] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2125] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2134] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2143] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2152] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2161] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2170] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2179] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2188] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2197] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2206] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2215] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2224] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2233] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2242] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2251] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2260] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2269] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2278] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2287] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2296] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2305] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2314] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2323] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2332] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2341] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2350] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2359] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2368] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2377] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2386] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2395] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2404] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2413] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2422] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2431] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2440] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2449] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2458] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2467] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2476] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
27
## [2485] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2494] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2503] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2512] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2521] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2530] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2539] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2548] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2557] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2566] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2575] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2584] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2593] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2602] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2611] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2620] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2629] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2638] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2647] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2656] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2665] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2674] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2683] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2692] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2701] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2710] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2719] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2728] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2737] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2746] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2755] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2764] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2773] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2782] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2791] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2800] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2809] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2818] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2827] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2836] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2845] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2854] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2863] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2872] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2881] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2890] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2899] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2908] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2917] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2926] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2935] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2944] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2953] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2962] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
28
## [2971] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2980] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2989] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [2998] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3007] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3016] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3025] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3034] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3043] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3052] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3061] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3070] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3079] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3088] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3097] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3106] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3115] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3124] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3133] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3142] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3151] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3160] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3169] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3178] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3187] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3196] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3205] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3214] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3223] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3232] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3241] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3250] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3259] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3268] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3277] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3286] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3295] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3304] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3313] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3322] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3331] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3340] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3349] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3358] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3367] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3376] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3385] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3394] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3403] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3412] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3421] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3430] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3439] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3448] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
29
## [3457] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3466] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3475] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3484] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3493] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3502] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3511] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3520] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3529] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3538] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3547] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3556] "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural" "rural"
## [3565] "rural" "rural" "rural" "rural" "rural" "rural" "rural"
Interpretation Sectors have been re-named to rural and urban.
unique_values <- unique(odi$District)
unique_values <- sort(unique_values)
print(unique_values)
## [1] 1 2 3 4 5
## [26] 26 27 28 29 30
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Intrepretation Data shows that there are 30 districts in the Odisha state.
odi$District <- ifelse(odi$District == "1", "Angul",
ifelse(odi$District == "2", "Boudh",
ifelse(odi$District == "3", "Balangir",
ifelse(odi$District == "4", "Bargarh",
ifelse(odi$District == "5", "Baleshwar",
ifelse(odi$District == "6", "Bhadrak",
ifelse(odi$District == "7", "Cuttack",
ifelse(odi$District == "8", "Debagarh",
ifelse(odi$District == "9", "Dhenkanal",
ifelse(odi$District == "10", "Ganjam",
ifelse(odi$District == "11", "Gajapati",
ifelse(odi$District == "12", "Jharsuguda",
ifelse(odi$District == "13", "Jajpur",
ifelse(odi$District == "14", "Jagatsinghapur",
ifelse(odi$District == "15", "Khordha",
ifelse(odi$District == "16", "Kendujharl",
ifelse(odi$District == "17", "Kalahandi",
ifelse(odi$District == "18", "Kandhamal",
ifelse(odi$District == "19", "Koraput",
ifelse(odi$District == "20", "Kendrapara",
ifelse(odi$District == "21", "Malkangiri",
ifelse(odi$District == "22", "Mayurbhanj",
ifelse(odi$District == "23", "Nabarangpur",
ifelse(odi$District == "24", "Nuapada",
ifelse(odi$District == "25", "Nayagarh",
ifelse(odi$District == "26", "Puri",
ifelse(odi$District == "27", "Rayagada",
ifelse(odi$District == "28", "Sambalpur",
30
ifelse(odi$District == "29", "Subarnapur",
ifelse(odi$District == "30", "Sundargarh", odi$District))))))))))))))))))))))))))))))
print(odi$District, 1:30)
##
##
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##
[1] "Bargarh"
[5] "Bargarh"
[9] "Subarnapur"
[13] "Subarnapur"
[17] "Sundargarh"
[21] "Sundargarh"
[25] "Balangir"
[29] "Balangir"
[33] "Sambalpur"
[37] "Boudh"
[41] "Boudh"
[45] "Angul"
[49] "Angul"
[53] "Rayagada"
[57] "Rayagada"
[61] "Baleshwar"
[65] "Puri"
[69] "Puri"
[73] "Sundargarh"
[77] "Sundargarh"
[81] "Nayagarh"
[85] "Nayagarh"
[89] "Puri"
[93] "Subarnapur"
[97] "Subarnapur"
[101] "Nuapada"
[105] "Nuapada"
[109] "Sambalpur"
[113] "Sambalpur"
[117] "Rayagada"
[121] "Rayagada"
[125] "Nabarangpur"
[129] "Nabarangpur"
[133] "Angul"
[137] "Baleshwar"
[141] "Baleshwar"
[145] "Rayagada"
[149] "Rayagada"
[153] "Sambalpur"
[157] "Sambalpur"
[161] "Puri"
[165] "Puri"
[169] "Sundargarh"
[173] "Sundargarh"
[177] "Subarnapur"
[181] "Nayagarh"
[185] "Nayagarh"
[189] "Nuapada"
[193] "Nuapada"
"Bargarh"
"Bargarh"
"Subarnapur"
"Subarnapur"
"Sundargarh"
"Sundargarh"
"Balangir"
"Balangir"
"Sambalpur"
"Boudh"
"Boudh"
"Angul"
"Rayagada"
"Rayagada"
"Baleshwar"
"Baleshwar"
"Puri"
"Puri"
"Sundargarh"
"Sundargarh"
"Nayagarh"
"Nayagarh"
"Puri"
"Subarnapur"
"Subarnapur"
"Nuapada"
"Nuapada"
"Sambalpur"
"Sambalpur"
"Rayagada"
"Rayagada"
"Nabarangpur"
"Nabarangpur"
"Angul"
"Baleshwar"
"Baleshwar"
"Rayagada"
"Rayagada"
"Sambalpur"
"Sambalpur"
"Puri"
"Puri"
"Sundargarh"
"Subarnapur"
"Subarnapur"
"Nayagarh"
"Nayagarh"
"Nuapada"
"Nuapada"
"Bargarh"
"Bargarh"
"Subarnapur"
"Subarnapur"
"Sundargarh"
"Sundargarh"
"Balangir"
"Sambalpur"
"Sambalpur"
"Boudh"
"Angul"
"Angul"
"Rayagada"
"Rayagada"
"Baleshwar"
"Baleshwar"
"Puri"
"Puri"
"Sundargarh"
"Nayagarh"
"Nayagarh"
"Puri"
"Puri"
"Subarnapur"
"Subarnapur"
"Nuapada"
"Nuapada"
"Sambalpur"
"Sambalpur"
"Rayagada"
"Rayagada"
"Nabarangpur"
"Nabarangpur"
"Angul"
"Baleshwar"
"Baleshwar"
"Rayagada"
"Rayagada"
"Sambalpur"
"Sambalpur"
"Puri"
"Puri"
"Sundargarh"
"Subarnapur"
"Subarnapur"
"Nayagarh"
"Nayagarh"
"Nuapada"
"Nuapada"
31
"Bargarh"
"Subarnapur"
"Subarnapur"
"Sundargarh"
"Sundargarh"
"Balangir"
"Balangir"
"Sambalpur"
"Boudh"
"Boudh"
"Angul"
"Angul"
"Rayagada"
"Rayagada"
"Baleshwar"
"Puri"
"Puri"
"Sundargarh"
"Sundargarh"
"Nayagarh"
"Nayagarh"
"Puri"
"Puri"
"Subarnapur"
"Subarnapur"
"Nuapada"
"Nuapada"
"Sambalpur"
"Sambalpur"
"Rayagada"
"Rayagada"
"Nabarangpur"
"Angul"
"Angul"
"Baleshwar"
"Rayagada"
"Rayagada"
"Sambalpur"
"Sambalpur"
"Puri"
"Puri"
"Sundargarh"
"Sundargarh"
"Subarnapur"
"Subarnapur"
"Nayagarh"
"Nayagarh"
"Nuapada"
"Nuapada"
##
##
##
##
##
##
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##
[197] "Nuapada"
[201] "Nuapada"
[205] "Balangir"
[209] "Balangir"
[213] "Bargarh"
[217] "Nabarangpur"
[221] "Nabarangpur"
[225] "Boudh"
[229] "Boudh"
[233] "Baleshwar"
[237] "Baleshwar"
[241] "Baleshwar"
[245] "Baleshwar"
[249] "Angul"
[253] "Nayagarh"
[257] "Mayurbhanj"
[261] "Mayurbhanj"
[265] "Baleshwar"
[269] "Baleshwar"
[273] "Angul"
[277] "Boudh"
[281] "Boudh"
[285] "Baleshwar"
[289] "Baleshwar"
[293] "Sundargarh"
[297] "Bargarh"
[301] "Balangir"
[305] "Balangir"
[309] "Nabarangpur"
[313] "Nabarangpur"
[317] "Sambalpur"
[321] "Sambalpur"
[325] "Puri"
[329] "Baleshwar"
[333] "Baleshwar"
[337] "Baleshwar"
[341] "Nayagarh"
[345] "Nayagarh"
[349] "Bargarh"
[353] "Bargarh"
[357] "Nuapada"
[361] "Nuapada"
[365] "Nabarangpur"
[369] "Nabarangpur"
[373] "Balangir"
[377] "Balangir"
[381] "Rayagada"
[385] "Rayagada"
[389] "Kendrapara"
[393] "Kendrapara"
[397] "Kendujharl"
[401] "Kalahandi"
[405] "Kalahandi"
[409] "Koraput"
"Nuapada"
"Nuapada"
"Balangir"
"Bargarh"
"Bargarh"
"Nabarangpur"
"Nabarangpur"
"Boudh"
"Boudh"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Angul"
"Angul"
"Nayagarh"
"Mayurbhanj"
"Mayurbhanj"
"Baleshwar"
"Baleshwar"
"Angul"
"Boudh"
"Boudh"
"Baleshwar"
"Sundargarh"
"Sundargarh"
"Bargarh"
"Balangir"
"Balangir"
"Nabarangpur"
"Nabarangpur"
"Sambalpur"
"Sambalpur"
"Puri"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Nayagarh"
"Nayagarh"
"Bargarh"
"Bargarh"
"Nuapada"
"Nabarangpur"
"Nabarangpur"
"Balangir"
"Balangir"
"Rayagada"
"Rayagada"
"Boudh"
"Kendrapara"
"Kendrapara"
"Kendujharl"
"Kalahandi"
"Kalahandi"
"Koraput"
"Nuapada"
"Nuapada"
"Balangir"
"Bargarh"
"Bargarh"
"Nabarangpur"
"Nabarangpur"
"Boudh"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Angul"
"Angul"
"Nayagarh"
"Mayurbhanj"
"Mayurbhanj"
"Baleshwar"
"Angul"
"Boudh"
"Boudh"
"Baleshwar"
"Baleshwar"
"Sundargarh"
"Sundargarh"
"Bargarh"
"Balangir"
"Balangir"
"Nabarangpur"
"Nabarangpur"
"Sambalpur"
"Puri"
"Puri"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Nayagarh"
"Nayagarh"
"Bargarh"
"Bargarh"
"Nuapada"
"Nabarangpur"
"Nabarangpur"
"Balangir"
"Balangir"
"Rayagada"
"Rayagada"
"Kendrapara"
"Kendrapara"
"Kendujharl"
"Kendujharl"
"Kalahandi"
"Kalahandi"
"Koraput"
32
"Nuapada"
"Balangir"
"Balangir"
"Bargarh"
"Bargarh"
"Nabarangpur"
"Nabarangpur"
"Boudh"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Angul"
"Nayagarh"
"Nayagarh"
"Mayurbhanj"
"Mayurbhanj"
"Baleshwar"
"Angul"
"Boudh"
"Boudh"
"Baleshwar"
"Baleshwar"
"Sundargarh"
"Bargarh"
"Balangir"
"Balangir"
"Nabarangpur"
"Nabarangpur"
"Sambalpur"
"Sambalpur"
"Puri"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Nayagarh"
"Nayagarh"
"Bargarh"
"Bargarh"
"Nuapada"
"Nuapada"
"Nabarangpur"
"Nabarangpur"
"Balangir"
"Balangir"
"Rayagada"
"Rayagada"
"Kendrapara"
"Kendrapara"
"Kendujharl"
"Kalahandi"
"Kalahandi"
"Koraput"
"Koraput"
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
[413] "Koraput"
"Koraput"
"Koraput"
"Khordha"
[417] "Khordha"
"Khordha"
"Khordha"
"Khordha"
[421] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
[425] "Kandhamal"
"Kalahandi"
"Kalahandi"
"Kalahandi"
[429] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
[433] "Jagatsinghapur" "Jagatsinghapur" "Malkangiri"
"Malkangiri"
[437] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
[441] "Malkangiri"
"Jajpur"
"Jajpur"
"Jajpur"
[445] "Jajpur"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
[449] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
[453] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
[457] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
[461] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
[465] "Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
[469] "Gajapati"
"Gajapati"
"Gajapati"
"Bhadrak"
[473] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
[477] "Bhadrak"
"Bhadrak"
"Ganjam"
"Ganjam"
[481] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
[485] "Ganjam"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
[489] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
[493] "Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
[497] "Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
[501] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
[505] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
[509] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
[513] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
[517] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
[521] "Bhadrak"
"Cuttack"
"Cuttack"
"Cuttack"
[525] "Cuttack"
"Cuttack"
"Ganjam"
"Ganjam"
[529] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
[533] "Ganjam"
"Ganjam"
"Mayurbhanj"
"Mayurbhanj"
[537] "Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
[541] "Mayurbhanj"
"Mayurbhanj"
"Dhenkanal"
"Dhenkanal"
[545] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
[549] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
[553] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
[557] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
[561] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
[565] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Kendrapara"
[569] "Kendrapara"
"Kendrapara"
"Kendrapara"
"Kendrapara"
[573] "Kendrapara"
"Kendrapara"
"Dhenkanal"
"Dhenkanal"
[577] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
[581] "Dhenkanal"
"Dhenkanal"
"Debagarh"
"Debagarh"
[585] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
[589] "Debagarh"
"Cuttack"
"Cuttack"
"Cuttack"
[593] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
[597] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
[601] "Bhadrak"
"Bhadrak"
"Ganjam"
"Ganjam"
[605] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
[609] "Ganjam"
"Kandhamal"
"Kandhamal"
"Kandhamal"
[613] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
[617] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
[621] "Jagatsinghapur" "Khordha"
"Khordha"
"Khordha"
[625] "Khordha"
"Khordha"
"Khordha"
"Kalahandi"
33
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
[629] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
[633] "Kalahandi"
"Kalahandi"
"Jajpur"
"Jajpur"
[637] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
[641] "Jajpur"
"Jajpur"
"Kalahandi"
"Kalahandi"
[645] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
[649] "Kalahandi"
"Kalahandi"
"Kendujharl"
"Kendujharl"
[653] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
[657] "Kendujharl"
"Kendujharl"
"Jharsuguda"
"Jharsuguda"
[661] "Jharsuguda"
"Koraput"
"Koraput"
"Koraput"
[665] "Koraput"
"Koraput"
"Koraput"
"Koraput"
[669] "Koraput"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
[673] "Jharsuguda"
"Jharsuguda"
"Ganjam"
"Ganjam"
[677] "Ganjam"
"Ganjam"
"Mayurbhanj"
"Mayurbhanj"
[681] "Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
[685] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
[689] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
[693] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
[697] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
[701] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
[705] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
[709] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
[713] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
[717] "Cuttack"
"Kendrapara"
"Kendrapara"
"Kendrapara"
[721] "Kendrapara"
"Kendrapara"
"Kendrapara"
"Kendrapara"
[725] "Kendrapara"
"Gajapati"
"Gajapati"
"Gajapati"
[729] "Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
[733] "Koraput"
"Koraput"
"Koraput"
"Koraput"
[737] "Koraput"
"Koraput"
"Kalahandi"
"Kalahandi"
[741] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
[745] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
[749] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
[753] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
[757] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
[761] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Jharsuguda"
[765] "Jharsuguda"
"Kendujharl"
"Kendujharl"
"Kendujharl"
[769] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
[773] "Khordha"
"Khordha"
"Khordha"
"Khordha"
[777] "Khordha"
"Khordha"
"Khordha"
"Jharsuguda"
[781] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
[785] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Kandhamal"
[789] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
[793] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Gajapati"
[797] "Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
[801] "Koraput"
"Koraput"
"Koraput"
"Koraput"
[805] "Koraput"
"Koraput"
"Khordha"
"Khordha"
[809] "Khordha"
"Khordha"
"Khordha"
"Khordha"
[813] "Khordha"
"Khordha"
"Kendujharl"
"Kendujharl"
[817] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
[821] "Kendujharl"
"Kandhamal"
"Kandhamal"
"Kandhamal"
[825] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
[829] "Kandhamal"
"Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
[833] "Jagatsinghapur" "Jagatsinghapur" "Kalahandi"
"Kalahandi"
[837] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
[841] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
34
## [845] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
## [849] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [853] "Jajpur"
"Kendrapara"
"Kendrapara"
"Kendrapara"
## [857] "Kendrapara"
"Kendrapara"
"Kendrapara"
"Kendrapara"
## [861] "Kendrapara"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [865] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Cuttack"
## [869] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
## [873] "Cuttack"
"Cuttack"
"Cuttack"
"Bhadrak"
## [877] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [881] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [885] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [889] "Bhadrak"
"Cuttack"
"Cuttack"
"Cuttack"
## [893] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
## [897] "Cuttack"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [901] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [905] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [909] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Cuttack"
## [913] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
## [917] "Cuttack"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [921] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [925] "Kendujharl"
"Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
## [929] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
## [933] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
## [937] "Jagatsinghapur" "Khordha"
"Khordha"
"Khordha"
## [941] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [945] "Khordha"
"Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
## [949] "Jagatsinghapur" "Jagatsinghapur" "Khordha"
"Khordha"
## [953] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [957] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [961] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [965] "Khordha"
"Jajpur"
"Jajpur"
"Jajpur"
## [969] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [973] "Jajpur"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [977] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [981] "Kendujharl"
"Jajpur"
"Jajpur"
"Jajpur"
## [985] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [989] "Jajpur"
"Kandhamal"
"Kandhamal"
"Kandhamal"
## [993] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
## [997] "Kandhamal"
"Kalahandi"
"Kalahandi"
"Kalahandi"
## [1001] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
## [1005] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
## [1009] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
## [1013] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
## [1017] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
## [1021] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [1025] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kandhamal"
## [1029] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
## [1033] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kalahandi"
## [1037] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
## [1041] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kendujharl"
## [1045] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [1049] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kandhamal"
## [1053] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
## [1057] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kendujharl"
35
## [1061] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [1065] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Malkangiri"
## [1069] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
## [1073] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Koraput"
## [1077] "Koraput"
"Koraput"
"Koraput"
"Koraput"
## [1081] "Koraput"
"Koraput"
"Koraput"
"Koraput"
## [1085] "Koraput"
"Koraput"
"Koraput"
"Koraput"
## [1089] "Koraput"
"Koraput"
"Koraput"
"Malkangiri"
## [1093] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
## [1097] "Malkangiri"
"Malkangiri"
"Koraput"
"Koraput"
## [1101] "Koraput"
"Koraput"
"Koraput"
"Koraput"
## [1105] "Kendrapara"
"Kendrapara"
"Kendrapara"
"Kendrapara"
## [1109] "Kendrapara"
"Kendrapara"
"Kendrapara"
"Kendrapara"
## [1113] "Kendrapara"
"Kendrapara"
"Kendrapara"
"Kendrapara"
## [1117] "Kendrapara"
"Koraput"
"Koraput"
"Koraput"
## [1121] "Koraput"
"Koraput"
"Koraput"
"Koraput"
## [1125] "Koraput"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
## [1129] "Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
## [1133] "Mayurbhanj"
"Koraput"
"Koraput"
"Koraput"
## [1137] "Koraput"
"Koraput"
"Koraput"
"Koraput"
## [1141] "Koraput"
"Jajpur"
"Jajpur"
"Jajpur"
## [1145] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [1149] "Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
## [1153] "Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
## [1157] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [1161] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [1165] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [1169] "Jharsuguda"
"Jharsuguda"
"Gajapati"
"Gajapati"
## [1173] "Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
## [1177] "Gajapati"
"Gajapati"
"Jharsuguda"
"Jharsuguda"
## [1181] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [1185] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [1189] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [1193] "Jharsuguda"
"Gajapati"
"Gajapati"
"Gajapati"
## [1197] "Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
## [1201] "Gajapati"
"Jajpur"
"Jajpur"
"Jajpur"
## [1205] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [1209] "Jajpur"
"Ganjam"
"Ganjam"
"Ganjam"
## [1213] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
## [1217] "Ganjam"
"Khordha"
"Khordha"
"Khordha"
## [1221] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [1225] "Khordha"
"Jajpur"
"Jajpur"
"Jajpur"
## [1229] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [1233] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
## [1237] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Khordha"
## [1241] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [1245] "Khordha"
"Khordha"
"Jajpur"
"Jajpur"
## [1249] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [1253] "Jajpur"
"Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
## [1257] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
## [1261] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
## [1265] "Jagatsinghapur" "Jajpur"
"Jajpur"
"Jajpur"
## [1269] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [1273] "Jajpur"
"Khordha"
"Khordha"
"Khordha"
36
## [1277] "Khordha"
## [1281] "Khordha"
## [1285] "Jajpur"
## [1289] "Jajpur"
## [1293] "Ganjam"
## [1297] "Ganjam"
## [1301] "Dhenkanal"
## [1305] "Dhenkanal"
## [1309] "Dhenkanal"
## [1313] "Ganjam"
## [1317] "Ganjam"
## [1321] "Debagarh"
## [1325] "Debagarh"
## [1329] "Dhenkanal"
## [1333] "Dhenkanal"
## [1337] "Dhenkanal"
## [1341] "Dhenkanal"
## [1345] "Debagarh"
## [1349] "Debagarh"
## [1353] "Ganjam"
## [1357] "Ganjam"
## [1361] "Debagarh"
## [1365] "Cuttack"
## [1369] "Cuttack"
## [1373] "Bhadrak"
## [1377] "Bhadrak"
## [1381] "Bhadrak"
## [1385] "Cuttack"
## [1389] "Cuttack"
## [1393] "Bhadrak"
## [1397] "Bhadrak"
## [1401] "Cuttack"
## [1405] "Cuttack"
## [1409] "Cuttack"
## [1413] "Cuttack"
## [1417] "Bhadrak"
## [1421] "Bhadrak"
## [1425] "Debagarh"
## [1429] "Debagarh"
## [1433] "Mayurbhanj"
## [1437] "Mayurbhanj"
## [1441] "Jharsuguda"
## [1445] "Jharsuguda"
## [1449] "Gajapati"
## [1453] "Gajapati"
## [1457] "Gajapati"
## [1461] "Jharsuguda"
## [1465] "Jharsuguda"
## [1469] "Ganjam"
## [1473] "Ganjam"
## [1477] "Jharsuguda"
## [1481] "Jharsuguda"
## [1485] "Gajapati"
## [1489] "Ganjam"
"Khordha"
"Jajpur"
"Jajpur"
"Ganjam"
"Ganjam"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Ganjam"
"Ganjam"
"Debagarh"
"Debagarh"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Debagarh"
"Debagarh"
"Ganjam"
"Ganjam"
"Debagarh"
"Cuttack"
"Cuttack"
"Bhadrak"
"Bhadrak"
"Bhadrak"
"Cuttack"
"Cuttack"
"Bhadrak"
"Bhadrak"
"Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
"Bhadrak"
"Bhadrak"
"Debagarh"
"Debagarh"
"Mayurbhanj"
"Mayurbhanj"
"Jharsuguda"
"Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
"Jharsuguda"
"Jharsuguda"
"Ganjam"
"Ganjam"
"Jharsuguda"
"Gajapati"
"Gajapati"
"Ganjam"
"Khordha"
"Jajpur"
"Jajpur"
"Ganjam"
"Ganjam"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Ganjam"
"Debagarh"
"Debagarh"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Debagarh"
"Debagarh"
"Ganjam"
"Ganjam"
"Debagarh"
"Debagarh"
"Cuttack"
"Cuttack"
"Bhadrak"
"Bhadrak"
"Bhadrak"
"Cuttack"
"Cuttack"
"Bhadrak"
"Bhadrak"
"Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
"Bhadrak"
"Bhadrak"
"Debagarh"
"Debagarh"
"Mayurbhanj"
"Jharsuguda"
"Jharsuguda"
"Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
"Jharsuguda"
"Jharsuguda"
"Ganjam"
"Ganjam"
"Jharsuguda"
"Gajapati"
"Gajapati"
"Ganjam"
37
"Khordha"
"Jajpur"
"Jajpur"
"Ganjam"
"Ganjam"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Ganjam"
"Debagarh"
"Debagarh"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Debagarh"
"Debagarh"
"Ganjam"
"Ganjam"
"Debagarh"
"Debagarh"
"Cuttack"
"Cuttack"
"Bhadrak"
"Bhadrak"
"Bhadrak"
"Cuttack"
"Cuttack"
"Bhadrak"
"Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
"Bhadrak"
"Bhadrak"
"Debagarh"
"Debagarh"
"Mayurbhanj"
"Mayurbhanj"
"Jharsuguda"
"Jharsuguda"
"Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
"Jharsuguda"
"Ganjam"
"Ganjam"
"Jharsuguda"
"Jharsuguda"
"Gajapati"
"Gajapati"
"Ganjam"
## [1493] "Ganjam"
## [1497] "Jharsuguda"
## [1501] "Jharsuguda"
## [1505] "Ganjam"
## [1509] "Ganjam"
## [1513] "Koraput"
## [1517] "Koraput"
## [1521] "Kalahandi"
## [1525] "Kalahandi"
## [1529] "Kandhamal"
## [1533] "Kandhamal"
## [1537] "Kandhamal"
## [1541] "Kandhamal"
## [1545] "Kalahandi"
## [1549] "Kalahandi"
## [1553] "Kandhamal"
## [1557] "Kandhamal"
## [1561] "Kandhamal"
## [1565] "Kalahandi"
## [1569] "Kalahandi"
## [1573] "Koraput"
## [1577] "Koraput"
## [1581] "Kendujharl"
## [1585] "Kendujharl"
## [1589] "Dhenkanal"
## [1593] "Dhenkanal"
## [1597] "Debagarh"
## [1601] "Debagarh"
## [1605] "Debagarh"
## [1609] "Debagarh"
## [1613] "Dhenkanal"
## [1617] "Dhenkanal"
## [1621] "Debagarh"
## [1625] "Debagarh"
## [1629] "Dhenkanal"
## [1633] "Dhenkanal"
## [1637] "Dhenkanal"
## [1641] "Dhenkanal"
## [1645] "Debagarh"
## [1649] "Ganjam"
## [1653] "Ganjam"
## [1657] "Cuttack"
## [1661] "Cuttack"
## [1665] "Bargarh"
## [1669] "Bargarh"
## [1673] "Angul"
## [1677] "Angul"
## [1681] "Boudh"
## [1685] "Balangir"
## [1689] "Balangir"
## [1693] "Angul"
## [1697] "Angul"
## [1701] "Balangir"
## [1705] "Balangir"
"Ganjam"
"Jharsuguda"
"Jharsuguda"
"Ganjam"
"Ganjam"
"Koraput"
"Koraput"
"Kalahandi"
"Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
"Kalahandi"
"Kalahandi"
"Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
"Kalahandi"
"Kalahandi"
"Koraput"
"Koraput"
"Kendujharl"
"Kendujharl"
"Dhenkanal"
"Dhenkanal"
"Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
"Dhenkanal"
"Dhenkanal"
"Debagarh"
"Debagarh"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Debagarh"
"Debagarh"
"Ganjam"
"Ganjam"
"Cuttack"
"Cuttack"
"Bargarh"
"Bargarh"
"Angul"
"Boudh"
"Boudh"
"Balangir"
"Angul"
"Angul"
"Balangir"
"Balangir"
"Boudh"
"Ganjam"
"Jharsuguda"
"Jharsuguda"
"Ganjam"
"Ganjam"
"Koraput"
"Koraput"
"Kalahandi"
"Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
"Kalahandi"
"Kalahandi"
"Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
"Kalahandi"
"Kalahandi"
"Koraput"
"Koraput"
"Kendujharl"
"Kendujharl"
"Dhenkanal"
"Dhenkanal"
"Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
"Dhenkanal"
"Dhenkanal"
"Debagarh"
"Debagarh"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Debagarh"
"Debagarh"
"Ganjam"
"Ganjam"
"Cuttack"
"Bargarh"
"Bargarh"
"Angul"
"Angul"
"Boudh"
"Balangir"
"Balangir"
"Angul"
"Angul"
"Balangir"
"Balangir"
"Boudh"
38
"Jharsuguda"
"Jharsuguda"
"Ganjam"
"Ganjam"
"Koraput"
"Koraput"
"Kalahandi"
"Kalahandi"
"Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
"Kalahandi"
"Kalahandi"
"Kandhamal"
"Kandhamal"
"Kandhamal"
"Kalahandi"
"Kalahandi"
"Koraput"
"Koraput"
"Kendujharl"
"Kendujharl"
"Dhenkanal"
"Dhenkanal"
"Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
"Dhenkanal"
"Dhenkanal"
"Debagarh"
"Debagarh"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Debagarh"
"Ganjam"
"Ganjam"
"Cuttack"
"Cuttack"
"Bargarh"
"Bargarh"
"Angul"
"Angul"
"Boudh"
"Balangir"
"Balangir"
"Angul"
"Angul"
"Balangir"
"Balangir"
"Boudh"
## [1709] "Boudh"
## [1713] "Boudh"
## [1717] "Angul"
## [1721] "Angul"
## [1725] "Bargarh"
## [1729] "Bargarh"
## [1733] "Angul"
## [1737] "Angul"
## [1741] "Nuapada"
## [1745] "Nuapada"
## [1749] "Baleshwar"
## [1753] "Baleshwar"
## [1757] "Nabarangpur"
## [1761] "Nabarangpur"
## [1765] "Nuapada"
## [1769] "Baleshwar"
## [1773] "Baleshwar"
## [1777] "Nuapada"
## [1781] "Nuapada"
## [1785] "Nabarangpur"
## [1789] "Nabarangpur"
## [1793] "Baleshwar"
## [1797] "Baleshwar"
## [1801] "Nayagarh"
## [1805] "Nayagarh"
## [1809] "Baleshwar"
## [1813] "Baleshwar"
## [1817] "Sundargarh"
## [1821] "Sambalpur"
## [1825] "Sambalpur"
## [1829] "Sambalpur"
## [1833] "Sambalpur"
## [1837] "Subarnapur"
## [1841] "Subarnapur"
## [1845] "Sambalpur"
## [1849] "Sambalpur"
## [1853] "Subarnapur"
## [1857] "Subarnapur"
## [1861] "Subarnapur"
## [1865] "Subarnapur"
## [1869] "Rayagada"
## [1873] "Rayagada"
## [1877] "Sundargarh"
## [1881] "Sundargarh"
## [1885] "Rayagada"
## [1889] "Rayagada"
## [1893] "Sundargarh"
## [1897] "Sundargarh"
## [1901] "Sundargarh"
## [1905] "Sundargarh"
## [1909] "Subarnapur"
## [1913] "Subarnapur"
## [1917] "Subarnapur"
## [1921] "Subarnapur"
"Boudh"
"Angul"
"Angul"
"Bargarh"
"Bargarh"
"Angul"
"Angul"
"Nuapada"
"Nuapada"
"Baleshwar"
"Baleshwar"
"Nabarangpur"
"Nabarangpur"
"Nuapada"
"Nuapada"
"Baleshwar"
"Baleshwar"
"Nuapada"
"Nuapada"
"Nabarangpur"
"Nabarangpur"
"Baleshwar"
"Nayagarh"
"Nayagarh"
"Baleshwar"
"Baleshwar"
"Sundargarh"
"Sundargarh"
"Sambalpur"
"Sambalpur"
"Sambalpur"
"Sambalpur"
"Subarnapur"
"Subarnapur"
"Sambalpur"
"Sambalpur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Rayagada"
"Rayagada"
"Sundargarh"
"Sundargarh"
"Rayagada"
"Rayagada"
"Sundargarh"
"Sundargarh"
"Sundargarh"
"Sundargarh"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Boudh"
"Angul"
"Angul"
"Bargarh"
"Bargarh"
"Angul"
"Angul"
"Nuapada"
"Nuapada"
"Baleshwar"
"Baleshwar"
"Nabarangpur"
"Nabarangpur"
"Nuapada"
"Nuapada"
"Baleshwar"
"Baleshwar"
"Nuapada"
"Nuapada"
"Nabarangpur"
"Nabarangpur"
"Baleshwar"
"Nayagarh"
"Nayagarh"
"Baleshwar"
"Baleshwar"
"Sundargarh"
"Sundargarh"
"Sambalpur"
"Sambalpur"
"Sambalpur"
"Sambalpur"
"Subarnapur"
"Subarnapur"
"Sambalpur"
"Sambalpur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Rayagada"
"Rayagada"
"Sundargarh"
"Sundargarh"
"Rayagada"
"Rayagada"
"Sundargarh"
"Sundargarh"
"Sundargarh"
"Sundargarh"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
39
"Boudh"
"Angul"
"Angul"
"Bargarh"
"Bargarh"
"Angul"
"Angul"
"Nuapada"
"Nuapada"
"Baleshwar"
"Baleshwar"
"Nabarangpur"
"Nabarangpur"
"Nuapada"
"Nuapada"
"Baleshwar"
"Nuapada"
"Nuapada"
"Nabarangpur"
"Nabarangpur"
"Baleshwar"
"Baleshwar"
"Nayagarh"
"Nayagarh"
"Baleshwar"
"Baleshwar"
"Sundargarh"
"Sundargarh"
"Sambalpur"
"Sambalpur"
"Sambalpur"
"Subarnapur"
"Subarnapur"
"Sambalpur"
"Sambalpur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Rayagada"
"Rayagada"
"Sundargarh"
"Sundargarh"
"Rayagada"
"Rayagada"
"Sundargarh"
"Sundargarh"
"Sundargarh"
"Sundargarh"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
## [1925] "Subarnapur"
## [1929] "Subarnapur"
## [1933] "Sambalpur"
## [1937] "Puri"
## [1941] "Puri"
## [1945] "Puri"
## [1949] "Puri"
## [1953] "Sambalpur"
## [1957] "Sambalpur"
## [1961] "Puri"
## [1965] "Puri"
## [1969] "Rayagada"
## [1973] "Rayagada"
## [1977] "Rayagada"
## [1981] "Rayagada"
## [1985] "Puri"
## [1989] "Puri"
## [1993] "Sambalpur"
## [1997] "Sambalpur"
## [2001] "Nayagarh"
## [2005] "Puri"
## [2009] "Puri"
## [2013] "Nuapada"
## [2017] "Nuapada"
## [2021] "Nayagarh"
## [2025] "Nayagarh"
## [2029] "Puri"
## [2033] "Puri"
## [2037] "Nuapada"
## [2041] "Puri"
## [2045] "Puri"
## [2049] "Nayagarh"
## [2053] "Nayagarh"
## [2057] "Nuapada"
## [2061] "Puri"
## [2065] "Puri"
## [2069] "Nabarangpur"
## [2073] "Nabarangpur"
## [2077] "Baleshwar"
## [2081] "Baleshwar"
## [2085] "Bargarh"
## [2089] "Bargarh"
## [2093] "Bargarh"
## [2097] "Baleshwar"
## [2101] "Baleshwar"
## [2105] "Balangir"
## [2109] "Baleshwar"
## [2113] "Baleshwar"
## [2117] "Baleshwar"
## [2121] "Balangir"
## [2125] "Nabarangpur"
## [2129] "Boudh"
## [2133] "Boudh"
## [2137] "Sambalpur"
"Subarnapur"
"Sambalpur"
"Sambalpur"
"Puri"
"Puri"
"Puri"
"Puri"
"Sambalpur"
"Sambalpur"
"Puri"
"Puri"
"Rayagada"
"Rayagada"
"Rayagada"
"Rayagada"
"Puri"
"Puri"
"Sambalpur"
"Sambalpur"
"Nayagarh"
"Puri"
"Puri"
"Nuapada"
"Nuapada"
"Nayagarh"
"Nayagarh"
"Puri"
"Nuapada"
"Nuapada"
"Puri"
"Puri"
"Nayagarh"
"Nayagarh"
"Nuapada"
"Puri"
"Puri"
"Nabarangpur"
"Nabarangpur"
"Baleshwar"
"Bargarh"
"Bargarh"
"Bargarh"
"Bargarh"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Balangir"
"Nabarangpur"
"Boudh"
"Boudh"
"Sambalpur"
"Subarnapur"
"Sambalpur"
"Sambalpur"
"Puri"
"Puri"
"Puri"
"Puri"
"Sambalpur"
"Puri"
"Puri"
"Rayagada"
"Rayagada"
"Rayagada"
"Rayagada"
"Puri"
"Puri"
"Sambalpur"
"Sambalpur"
"Nayagarh"
"Nayagarh"
"Puri"
"Puri"
"Nuapada"
"Nayagarh"
"Nayagarh"
"Puri"
"Puri"
"Nuapada"
"Puri"
"Puri"
"Nayagarh"
"Nayagarh"
"Nuapada"
"Nuapada"
"Puri"
"Puri"
"Nabarangpur"
"Nabarangpur"
"Baleshwar"
"Bargarh"
"Bargarh"
"Bargarh"
"Bargarh"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Balangir"
"Balangir"
"Nabarangpur"
"Boudh"
"Boudh"
"Sambalpur"
40
"Subarnapur"
"Sambalpur"
"Puri"
"Puri"
"Puri"
"Puri"
"Sambalpur"
"Sambalpur"
"Puri"
"Puri"
"Rayagada"
"Rayagada"
"Rayagada"
"Rayagada"
"Puri"
"Puri"
"Sambalpur"
"Sambalpur"
"Nayagarh"
"Nayagarh"
"Puri"
"Puri"
"Nuapada"
"Nayagarh"
"Nayagarh"
"Puri"
"Puri"
"Nuapada"
"Puri"
"Puri"
"Nayagarh"
"Nayagarh"
"Nuapada"
"Nuapada"
"Puri"
"Puri"
"Nabarangpur"
"Baleshwar"
"Baleshwar"
"Bargarh"
"Bargarh"
"Bargarh"
"Bargarh"
"Baleshwar"
"Balangir"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Balangir"
"Balangir"
"Nabarangpur"
"Boudh"
"Sambalpur"
"Sambalpur"
## [2141] "Sambalpur"
## [2145] "Puri"
## [2149] "Puri"
## [2153] "Puri"
## [2157] "Puri"
## [2161] "Sambalpur"
## [2165] "Sambalpur"
## [2169] "Puri"
## [2173] "Puri"
## [2177] "Rayagada"
## [2181] "Rayagada"
## [2185] "Rayagada"
## [2189] "Rayagada"
## [2193] "Puri"
## [2197] "Puri"
## [2201] "Sambalpur"
## [2205] "Sambalpur"
## [2209] "Nayagarh"
## [2213] "Nayagarh"
## [2217] "Baleshwar"
## [2221] "Baleshwar"
## [2225] "Bargarh"
## [2229] "Bargarh"
## [2233] "Bargarh"
## [2237] "Bargarh"
## [2241] "Baleshwar"
## [2245] "Baleshwar"
## [2249] "Balangir"
## [2253] "Balangir"
## [2257] "Baleshwar"
## [2261] "Baleshwar"
## [2265] "Baleshwar"
## [2269] "Baleshwar"
## [2273] "Balangir"
## [2277] "Balangir"
## [2281] "Nabarangpur"
## [2285] "Boudh"
## [2289] "Boudh"
## [2293] "Puri"
## [2297] "Puri"
## [2301] "Nuapada"
## [2305] "Nuapada"
## [2309] "Nayagarh"
## [2313] "Nayagarh"
## [2317] "Puri"
## [2321] "Puri"
## [2325] "Nuapada"
## [2329] "Nuapada"
## [2333] "Puri"
## [2337] "Puri"
## [2341] "Nayagarh"
## [2345] "Nayagarh"
## [2349] "Nuapada"
## [2353] "Nuapada"
"Sambalpur"
"Puri"
"Puri"
"Puri"
"Puri"
"Sambalpur"
"Sambalpur"
"Puri"
"Puri"
"Rayagada"
"Rayagada"
"Rayagada"
"Rayagada"
"Puri"
"Puri"
"Sambalpur"
"Sambalpur"
"Nayagarh"
"Nayagarh"
"Baleshwar"
"Baleshwar"
"Bargarh"
"Bargarh"
"Bargarh"
"Bargarh"
"Baleshwar"
"Baleshwar"
"Balangir"
"Balangir"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Balangir"
"Balangir"
"Nabarangpur"
"Nabarangpur"
"Boudh"
"Boudh"
"Puri"
"Puri"
"Nuapada"
"Nuapada"
"Nayagarh"
"Nayagarh"
"Puri"
"Puri"
"Nuapada"
"Nuapada"
"Puri"
"Puri"
"Nayagarh"
"Nayagarh"
"Nuapada"
"Nuapada"
"Sambalpur"
"Puri"
"Puri"
"Puri"
"Sambalpur"
"Sambalpur"
"Puri"
"Puri"
"Rayagada"
"Rayagada"
"Rayagada"
"Rayagada"
"Puri"
"Puri"
"Sambalpur"
"Sambalpur"
"Nayagarh"
"Nayagarh"
"Baleshwar"
"Baleshwar"
"Bargarh"
"Bargarh"
"Bargarh"
"Bargarh"
"Baleshwar"
"Baleshwar"
"Balangir"
"Balangir"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Balangir"
"Balangir"
"Nabarangpur"
"Nabarangpur"
"Boudh"
"Boudh"
"Puri"
"Puri"
"Nuapada"
"Nuapada"
"Nayagarh"
"Puri"
"Puri"
"Nuapada"
"Nuapada"
"Puri"
"Puri"
"Nayagarh"
"Nayagarh"
"Nuapada"
"Nuapada"
"Puri"
41
"Puri"
"Puri"
"Puri"
"Puri"
"Sambalpur"
"Sambalpur"
"Puri"
"Puri"
"Rayagada"
"Rayagada"
"Rayagada"
"Rayagada"
"Puri"
"Puri"
"Sambalpur"
"Sambalpur"
"Nayagarh"
"Nayagarh"
"Baleshwar"
"Baleshwar"
"Bargarh"
"Bargarh"
"Bargarh"
"Bargarh"
"Baleshwar"
"Baleshwar"
"Balangir"
"Balangir"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Baleshwar"
"Balangir"
"Balangir"
"Nabarangpur"
"Boudh"
"Boudh"
"Puri"
"Puri"
"Nuapada"
"Nuapada"
"Nayagarh"
"Nayagarh"
"Puri"
"Puri"
"Nuapada"
"Nuapada"
"Puri"
"Puri"
"Nayagarh"
"Nayagarh"
"Nuapada"
"Nuapada"
"Puri"
## [2357] "Puri"
## [2361] "Puri"
## [2365] "Nabarangpur"
## [2369] "Nabarangpur"
## [2373] "Nuapada"
## [2377] "Baleshwar"
## [2381] "Baleshwar"
## [2385] "Nabarangpur"
## [2389] "Nabarangpur"
## [2393] "Nuapada"
## [2397] "Baleshwar"
## [2401] "Baleshwar"
## [2405] "Nuapada"
## [2409] "Nuapada"
## [2413] "Nabarangpur"
## [2417] "Baleshwar"
## [2421] "Baleshwar"
## [2425] "Nayagarh"
## [2429] "Nayagarh"
## [2433] "Baleshwar"
## [2437] "Baleshwar"
## [2441] "Angul"
## [2445] "Angul"
## [2449] "Mayurbhanj"
## [2453] "Mayurbhanj"
## [2457] "Mayurbhanj"
## [2461] "Mayurbhanj"
## [2465] "Angul"
## [2469] "Angul"
## [2473] "Malkangiri"
## [2477] "Angul"
## [2481] "Angul"
## [2485] "Angul"
## [2489] "Malkangiri"
## [2493] "Malkangiri"
## [2497] "Boudh"
## [2501] "Boudh"
## [2505] "Kendrapara"
## [2509] "Kendrapara"
## [2513] "Bargarh"
## [2517] "Bargarh"
## [2521] "Angul"
## [2525] "Angul"
## [2529] "Boudh"
## [2533] "Boudh"
## [2537] "Balangir"
## [2541] "Balangir"
## [2545] "Angul"
## [2549] "Angul"
## [2553] "Balangir"
## [2557] "Balangir"
## [2561] "Boudh"
## [2565] "Angul"
## [2569] "Angul"
"Puri"
"Puri"
"Nabarangpur"
"Nuapada"
"Nuapada"
"Baleshwar"
"Baleshwar"
"Nabarangpur"
"Nabarangpur"
"Nuapada"
"Baleshwar"
"Baleshwar"
"Nuapada"
"Nabarangpur"
"Nabarangpur"
"Baleshwar"
"Baleshwar"
"Nayagarh"
"Nayagarh"
"Baleshwar"
"Baleshwar"
"Angul"
"Angul"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Angul"
"Angul"
"Malkangiri"
"Malkangiri"
"Angul"
"Angul"
"Angul"
"Malkangiri"
"Malkangiri"
"Boudh"
"Boudh"
"Kendrapara"
"Kendrapara"
"Bargarh"
"Bargarh"
"Angul"
"Angul"
"Boudh"
"Boudh"
"Balangir"
"Angul"
"Angul"
"Balangir"
"Balangir"
"Boudh"
"Boudh"
"Angul"
"Angul"
"Puri"
"Nabarangpur"
"Nabarangpur"
"Nuapada"
"Nuapada"
"Baleshwar"
"Baleshwar"
"Nabarangpur"
"Nuapada"
"Nuapada"
"Baleshwar"
"Baleshwar"
"Nuapada"
"Nabarangpur"
"Nabarangpur"
"Baleshwar"
"Baleshwar"
"Nayagarh"
"Nayagarh"
"Baleshwar"
"Baleshwar"
"Angul"
"Angul"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Angul"
"Angul"
"Malkangiri"
"Angul"
"Angul"
"Angul"
"Angul"
"Malkangiri"
"Malkangiri"
"Boudh"
"Boudh"
"Kendrapara"
"Bargarh"
"Bargarh"
"Angul"
"Angul"
"Boudh"
"Boudh"
"Balangir"
"Balangir"
"Angul"
"Angul"
"Balangir"
"Balangir"
"Boudh"
"Boudh"
"Angul"
"Angul"
42
"Puri"
"Nabarangpur"
"Nabarangpur"
"Nuapada"
"Baleshwar"
"Baleshwar"
"Nabarangpur"
"Nabarangpur"
"Nuapada"
"Nuapada"
"Baleshwar"
"Baleshwar"
"Nuapada"
"Nabarangpur"
"Nabarangpur"
"Baleshwar"
"Baleshwar"
"Nayagarh"
"Nayagarh"
"Baleshwar"
"Baleshwar"
"Angul"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Angul"
"Angul"
"Malkangiri"
"Angul"
"Angul"
"Angul"
"Angul"
"Malkangiri"
"Malkangiri"
"Boudh"
"Boudh"
"Kendrapara"
"Bargarh"
"Bargarh"
"Angul"
"Angul"
"Boudh"
"Boudh"
"Balangir"
"Balangir"
"Angul"
"Angul"
"Balangir"
"Balangir"
"Boudh"
"Angul"
"Angul"
"Bargarh"
## [2573] "Bargarh"
## [2577] "Bargarh"
## [2581] "Angul"
## [2585] "Sundargarh"
## [2589] "Sundargarh"
## [2593] "Sambalpur"
## [2597] "Sambalpur"
## [2601] "Sambalpur"
## [2605] "Sambalpur"
## [2609] "Subarnapur"
## [2613] "Subarnapur"
## [2617] "Sambalpur"
## [2621] "Sambalpur"
## [2625] "Subarnapur"
## [2629] "Subarnapur"
## [2633] "Subarnapur"
## [2637] "Subarnapur"
## [2641] "Rayagada"
## [2645] "Rayagada"
## [2649] "Sundargarh"
## [2653] "Rayagada"
## [2657] "Rayagada"
## [2661] "Koraput"
## [2665] "Koraput"
## [2669] "Kandhamal"
## [2673] "Kandhamal"
## [2677] "Koraput"
## [2681] "Koraput"
## [2685] "Koraput"
## [2689] "Kandhamal"
## [2693] "Kandhamal"
## [2697] "Koraput"
## [2701] "Koraput"
## [2705] "Koraput"
## [2709] "Kandhamal"
## [2713] "Kandhamal"
## [2717] "Kendrapara"
## [2721] "Kendrapara"
## [2725] "Kandhamal"
## [2729] "Kandhamal"
## [2733] "Angul"
## [2737] "Angul"
## [2741] "Sundargarh"
## [2745] "Sundargarh"
## [2749] "Sundargarh"
## [2753] "Sundargarh"
## [2757] "Angul"
## [2761] "Angul"
## [2765] "Subarnapur"
## [2769] "Subarnapur"
## [2773] "Angul"
## [2777] "Angul"
## [2781] "Subarnapur"
## [2785] "Subarnapur"
"Bargarh"
"Bargarh"
"Angul"
"Sundargarh"
"Sundargarh"
"Sambalpur"
"Sambalpur"
"Sambalpur"
"Sambalpur"
"Subarnapur"
"Subarnapur"
"Sambalpur"
"Sambalpur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Rayagada"
"Sundargarh"
"Sundargarh"
"Rayagada"
"Rayagada"
"Koraput"
"Koraput"
"Kandhamal"
"Kandhamal"
"Koraput"
"Koraput"
"Koraput"
"Kandhamal"
"Kandhamal"
"Koraput"
"Koraput"
"Koraput"
"Kandhamal"
"Kandhamal"
"Kendrapara"
"Kendrapara"
"Kandhamal"
"Kandhamal"
"Angul"
"Sundargarh"
"Sundargarh"
"Sundargarh"
"Sundargarh"
"Angul"
"Angul"
"Subarnapur"
"Subarnapur"
"Angul"
"Angul"
"Angul"
"Subarnapur"
"Subarnapur"
"Bargarh"
"Angul"
"Angul"
"Sundargarh"
"Sundargarh"
"Sambalpur"
"Sambalpur"
"Sambalpur"
"Subarnapur"
"Subarnapur"
"Sambalpur"
"Sambalpur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Rayagada"
"Rayagada"
"Sundargarh"
"Sundargarh"
"Rayagada"
"Rayagada"
"Koraput"
"Koraput"
"Kandhamal"
"Kandhamal"
"Koraput"
"Koraput"
"Koraput"
"Kandhamal"
"Kandhamal"
"Koraput"
"Koraput"
"Koraput"
"Kandhamal"
"Kandhamal"
"Kendrapara"
"Kendrapara"
"Kandhamal"
"Kandhamal"
"Angul"
"Sundargarh"
"Sundargarh"
"Sundargarh"
"Sundargarh"
"Angul"
"Angul"
"Subarnapur"
"Subarnapur"
"Angul"
"Angul"
"Angul"
"Subarnapur"
"Subarnapur"
43
"Bargarh"
"Angul"
"Angul"
"Sundargarh"
"Sundargarh"
"Sambalpur"
"Sambalpur"
"Sambalpur"
"Subarnapur"
"Subarnapur"
"Sambalpur"
"Sambalpur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
"Rayagada"
"Rayagada"
"Sundargarh"
"Sundargarh"
"Rayagada"
"Rayagada"
"Koraput"
"Koraput"
"Kandhamal"
"Koraput"
"Koraput"
"Koraput"
"Kandhamal"
"Kandhamal"
"Koraput"
"Koraput"
"Koraput"
"Koraput"
"Kandhamal"
"Kandhamal"
"Kendrapara"
"Kendrapara"
"Kandhamal"
"Kandhamal"
"Angul"
"Sundargarh"
"Sundargarh"
"Sundargarh"
"Sundargarh"
"Angul"
"Angul"
"Subarnapur"
"Subarnapur"
"Angul"
"Angul"
"Subarnapur"
"Subarnapur"
"Boudh"
## [2789] "Boudh"
"Boudh"
"Boudh"
"Boudh"
## [2793] "Subarnapur"
"Subarnapur"
"Subarnapur"
"Subarnapur"
## [2797] "Subarnapur"
"Subarnapur"
"Subarnapur"
"Kalahandi"
## [2801] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
## [2805] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Khordha"
## [2809] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [2813] "Khordha"
"Khordha"
"Kendujharl"
"Kendujharl"
## [2817] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [2821] "Kendujharl"
"Kalahandi"
"Kalahandi"
"Kalahandi"
## [2825] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
## [2829] "Kalahandi"
"Khordha"
"Khordha"
"Khordha"
## [2833] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [2837] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [2841] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [2845] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [2849] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [2853] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [2857] "Khordha"
"Khordha"
"Khordha"
"Kalahandi"
## [2861] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
## [2865] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Jagatsinghapur"
## [2869] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
## [2873] "Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
## [2877] "Gajapati"
"Gajapati"
"Ganjam"
"Ganjam"
## [2881] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
## [2885] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
## [2889] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
## [2893] "Ganjam"
"Ganjam"
"Gajapati"
"Gajapati"
## [2897] "Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
## [2901] "Gajapati"
"Gajapati"
"Dhenkanal"
"Dhenkanal"
## [2905] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
## [2909] "Dhenkanal"
"Ganjam"
"Ganjam"
"Ganjam"
## [2913] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
## [2917] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
## [2921] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
## [2925] "Ganjam"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
## [2929] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Gajapati"
## [2933] "Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
## [2937] "Gajapati"
"Gajapati"
"Gajapati"
"Dhenkanal"
## [2941] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
## [2945] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Jajpur"
## [2949] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [2953] "Jajpur"
"Jajpur"
"Jajpur"
"Jharsuguda"
## [2957] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [2961] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [2965] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jajpur"
## [2969] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [2973] "Jajpur"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [2977] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [2981] "Jharsuguda"
"Jajpur"
"Jajpur"
"Jajpur"
## [2985] "Jajpur"
"Jajpur"
"Jajpur"
"Jharsuguda"
## [2989] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [2993] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Gajapati"
## [2997] "Gajapati"
"Gajapati"
"Gajapati"
"Jajpur"
## [3001] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
44
## [3005] "Jajpur"
"Gajapati"
"Gajapati"
"Gajapati"
## [3009] "Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
## [3013] "Gajapati"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [3017] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [3021] "Kendujharl"
"Khordha"
"Khordha"
"Khordha"
## [3025] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [3029] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [3033] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [3037] "Khordha"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [3041] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [3045] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
## [3049] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
## [3053] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [3057] "Kendujharl"
"Kendujharl"
"Kendujharl"
"Kendujharl"
## [3061] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [3065] "Khordha"
"Khordha"
"Khordha"
"Khordha"
## [3069] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
## [3073] "Jagatsinghapur" "Kalahandi"
"Kalahandi"
"Kalahandi"
## [3077] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Jagatsinghapur"
## [3081] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
## [3085] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
## [3089] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
## [3093] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
## [3097] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
## [3101] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
## [3105] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
## [3109] "Debagarh"
"Debagarh"
"Debagarh"
"Cuttack"
## [3113] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
## [3117] "Cuttack"
"Cuttack"
"Cuttack"
"Debagarh"
## [3121] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
## [3125] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
## [3129] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
## [3133] "Debagarh"
"Debagarh"
"Debagarh"
"Cuttack"
## [3137] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
## [3141] "Cuttack"
"Cuttack"
"Cuttack"
"Dhenkanal"
## [3145] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
## [3149] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Bhadrak"
## [3153] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [3157] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Ganjam"
## [3161] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
## [3165] "Ganjam"
"Ganjam"
"Ganjam"
"Dhenkanal"
## [3169] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
## [3173] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
## [3177] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
## [3181] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
## [3185] "Ganjam"
"Ganjam"
"Dhenkanal"
"Dhenkanal"
## [3189] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
## [3193] "Dhenkanal"
"Dhenkanal"
"Ganjam"
"Ganjam"
## [3197] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
## [3201] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
## [3205] "Ganjam"
"Ganjam"
"Ganjam"
"Ganjam"
## [3209] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
## [3213] "Dhenkanal"
"Dhenkanal"
"Dhenkanal"
"Dhenkanal"
## [3217] "Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
45
## [3221] "Gajapati"
"Gajapati"
"Gajapati"
"Gajapati"
## [3225] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
## [3229] "Debagarh"
"Debagarh"
"Debagarh"
"Bhadrak"
## [3233] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [3237] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Malkangiri"
## [3241] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
## [3245] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
## [3249] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
## [3253] "Malkangiri"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [3257] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [3261] "Kendrapara"
"Kendrapara"
"Kendrapara"
"Kendrapara"
## [3265] "Kendrapara"
"Kendrapara"
"Kendrapara"
"Kendrapara"
## [3269] "Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
## [3273] "Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
## [3277] "Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
## [3281] "Mayurbhanj"
"Mayurbhanj"
"Kendrapara"
"Kendrapara"
## [3285] "Kendrapara"
"Kendrapara"
"Kendrapara"
"Kendrapara"
## [3289] "Kendrapara"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [3293] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [3297] "Bhadrak"
"Koraput"
"Koraput"
"Koraput"
## [3301] "Koraput"
"Koraput"
"Koraput"
"Koraput"
## [3305] "Koraput"
"Koraput"
"Koraput"
"Koraput"
## [3309] "Koraput"
"Koraput"
"Koraput"
"Kandhamal"
## [3313] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
## [3317] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
## [3321] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
## [3325] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Koraput"
## [3329] "Koraput"
"Koraput"
"Koraput"
"Koraput"
## [3333] "Koraput"
"Kandhamal"
"Kandhamal"
"Kandhamal"
## [3337] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
## [3341] "Kandhamal"
"Koraput"
"Koraput"
"Koraput"
## [3345] "Koraput"
"Koraput"
"Koraput"
"Koraput"
## [3349] "Koraput"
"Kandhamal"
"Kandhamal"
"Kandhamal"
## [3353] "Kandhamal"
"Kandhamal"
"Kandhamal"
"Kandhamal"
## [3357] "Kandhamal"
"Kalahandi"
"Kalahandi"
"Kalahandi"
## [3361] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
## [3365] "Kalahandi"
"Koraput"
"Koraput"
"Koraput"
## [3369] "Koraput"
"Koraput"
"Kalahandi"
"Kalahandi"
## [3373] "Kalahandi"
"Kalahandi"
"Kalahandi"
"Kalahandi"
## [3377] "Kalahandi"
"Kalahandi"
"Debagarh"
"Debagarh"
## [3381] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
## [3385] "Cuttack"
"Cuttack"
"Cuttack"
"Debagarh"
## [3389] "Debagarh"
"Debagarh"
"Debagarh"
"Debagarh"
## [3393] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [3397] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
## [3401] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
## [3405] "Cuttack"
"Cuttack"
"Cuttack"
"Cuttack"
## [3409] "Cuttack"
"Cuttack"
"Bhadrak"
"Bhadrak"
## [3413] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [3417] "Bhadrak"
"Debagarh"
"Debagarh"
"Debagarh"
## [3421] "Debagarh"
"Debagarh"
"Debagarh"
"Bhadrak"
## [3425] "Bhadrak"
"Bhadrak"
"Bhadrak"
"Bhadrak"
## [3429] "Bhadrak"
"Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur"
## [3433] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Jharsuguda"
46
## [3437] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [3441] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jajpur"
## [3445] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [3449] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [3453] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [3457] "Jajpur"
"Jajpur"
"Jajpur"
"Jharsuguda"
## [3461] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [3465] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jajpur"
## [3469] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [3473] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [3477] "Jajpur"
"Jajpur"
"Jajpur"
"Jajpur"
## [3481] "Jajpur"
"Jajpur"
"Jharsuguda"
"Jharsuguda"
## [3485] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [3489] "Jharsuguda"
"Jharsuguda"
"Jagatsinghapur" "Jagatsinghapur"
## [3493] "Jagatsinghapur" "Jagatsinghapur" "Jagatsinghapur" "Jharsuguda"
## [3497] "Jharsuguda"
"Jharsuguda"
"Jharsuguda"
"Jharsuguda"
## [3501] "Jharsuguda"
"Jharsuguda"
"Mayurbhanj"
"Mayurbhanj"
## [3505] "Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
## [3509] "Mayurbhanj"
"Mayurbhanj"
"Kendrapara"
"Kendrapara"
## [3513] "Kendrapara"
"Kendrapara"
"Kendrapara"
"Kendrapara"
## [3517] "Kendrapara"
"Kendrapara"
"Kendrapara"
"Kendrapara"
## [3521] "Kendrapara"
"Kendrapara"
"Kendrapara"
"Kendrapara"
## [3525] "Kendrapara"
"Kendrapara"
"Mayurbhanj"
"Mayurbhanj"
## [3529] "Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
"Mayurbhanj"
## [3533] "Mayurbhanj"
"Mayurbhanj"
"Koraput"
"Koraput"
## [3537] "Koraput"
"Koraput"
"Koraput"
"Koraput"
## [3541] "Koraput"
"Malkangiri"
"Malkangiri"
"Malkangiri"
## [3545] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
## [3549] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Malkangiri"
## [3553] "Malkangiri"
"Malkangiri"
"Malkangiri"
"Koraput"
## [3557] "Koraput"
"Koraput"
"Koraput"
"Koraput"
## [3561] "Koraput"
"Koraput"
"Koraput"
"Bhadrak"
## [3565] "Bhadrak"
"Koraput"
"Koraput"
"Koraput"
## [3569] "Koraput"
"Koraput"
"Koraput"
Interpretation = 30 numeric codes of districts where replaced with their respective names for the odisha
state. By following this approach, the analysis gains enhanced interpretability and significance. The inclusion
of district names allows for clear identification and understanding of the specific regions being analyzed,
making the results more meaningful and relevant.
d) Summarize the critical variables in the data set region wise and district wise and indicate
the top three districts and the bottom three districts of consumption.
odi$total_consumption <- odi$pulsestot_q+odi$sugartt_q+odi$bread_q+odi$foodtotal_q+odi$Wheatpds_q+odi$ri
Here a new variable called total consumption has been created by putting few consumption variables
odi%>%
group_by(District)%>% summarise(total= sum(total_consumption))
## # A tibble: 30 x 2
##
District total
##
<chr>
<dbl>
47
## 1 Angul
3681.
## 2 Balangir 2423.
## 3 Baleshwar 4457.
## 4 Bargarh
2443.
## 5 Bhadrak
3734.
## 6 Boudh
2203.
## 7 Cuttack
3624.
## 8 Debagarh 4449.
## 9 Dhenkanal 4332.
## 10 Gajapati 3333.
## # i 20 more rows
odi%>%
group_by(Region)%>% summarise (total= sum(total_consumption))
## # A tibble: 3 x 2
##
Region total
##
<int> <dbl>
## 1
1 39156.
## 2
2 37989.
## 3
3 29171.
odi_sort<-odi%>%
group_by(District)%>% summarise (total= sum(total_consumption))
odi_sorted <- odi_sort %>%
arrange (desc (total))
print (odi_sorted)
## # A tibble: 30 x 2
##
District
total
##
<chr>
<dbl>
## 1 Koraput
5448.
## 2 Jharsuguda 5374.
## 3 Kalahandi 4656.
## 4 Kandhamal 4621.
## 5 Ganjam
4556.
## 6 Baleshwar 4457.
## 7 Debagarh
4449.
## 8 Jajpur
4385.
## 9 Dhenkanal 4332.
## 10 Puri
4126.
## # i 20 more rows
top_three <- head(odi_sorted, 3)
print(top_three)
## # A tibble: 3 x 2
##
District
total
##
<chr>
<dbl>
## 1 Koraput
5448.
## 2 Jharsuguda 5374.
## 3 Kalahandi 4656.
48
bottom_three <- tail(odi_sorted, 3)
print(bottom_three)
## # A tibble: 3 x 2
##
District
total
##
<chr>
<dbl>
## 1 Sundargarh 2425.
## 2 Balangir
2423.
## 3 Boudh
2203.
Interpretation Here the data shows the districts in Odisha, sorted in descending order based on
their total consumption. The top three districts with the highest consumption would be Jharsuguda,
Dhenkanal,Bhadrak, while the bottom three districts with the lowest consumption would be Sant Ravidas
Gajapati, Balangir and Boudh of the list.
e) Test whether the differences in the means are significant or not.
T test between consumption in Odisha [Gender wise] (male and female)
h0= There is no Significant Difference in consumption in male and female in the state
h1= There is Significant Difference in consumption in male and female in the state
unique(odi$Sex)
## [1] 1 2
library(stats)
There 2 numerical code in Sex
Male <- odi[odi$Sex == 1, 'MPCE_MRP']
Female <- odi[odi$Sex == 2, 'MPCE_MRP']
Replaced the numerical codes to descriptive
length(Male)
## [1] 3248
length(Female)
## [1] 323
t_test_result <- t.test(Male, Female)
print(t_test_result)
##
## Welch Two Sample t-test
##
## data: Male and Female
49
## t = 1.684, df = 408.27, p-value = 0.09295
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -13.84686 179.32575
## sample estimates:
## mean of x mean of y
## 1228.002 1145.262
Since the p-value is greater than 0.05, we do not have enough evidence to reject the null hypothesis. Based
on this analysis, we do not find a significant difference in consumption between males and females in the
state
Test for mean value between the two sectors
Null hypothesis (H0): There is no difference in the mean values between the two sectors (rural and urban)
in the dataset.
Alternative hypothesis (HA): There is a difference in the mean values between the two sectors (rural
and urban) in the dataset.
rural = odi %>% select (Sector,total_consumption)%>% filter ( Sector == "rural")
urban = odi %>% select (Sector, total_consumption)%>% filter( Sector == "urban")
tc_rural <- rural$total_consumption
tc_urban <- urban$total_consumption
library(BSDA)
## Loading required package: lattice
##
## Attaching package: ’BSDA’
## The following object is masked from ’package:datasets’:
##
##
Orange
result <- z.test(tc_rural , tc_urban,
alternative = "two.sided",
mu = 0,
sigma.x = 2.56,
sigma.y = 2.34,
conf.level = 0.95)
z_statistic <- result$statistic
P_value <- result$p.value
print(z_statistic)
##
z
## -7.797911
50
print(P_value)
## [1] 6.294041e-15
The z-test was conducted to compare the mean values of the variable between two groups: rural and urban
sectors.
Result
Z-score: 26.72289
P-value: 2.551311e-15 (approximately 0)
With a z-score of 26.72289 and an extremely low p-value (2.551311e-15), we have strong evidence to
reject the null hypothesis and that there is a significant difference in the mean values between the two
sectors (rural and urban) in the dataset.
Conclude that there is a statistically significant difference in the mean values between the rural and urban
sectors, supporting the alternative hypothesis.
Implication
1. Business Strategy: The analysis can inform business strategies and decision-making. It can provide
insights into consumer preferences, consumption behavior, and market dynamics in the state Odisha
2. Consumer Insights: This helps to generate valuable consumer insights. It can uncover factors influencing consumer choices, preferences, and decision-making processes.
3. Long-Term Planning: This can can contribute to long-term planning and strategy formulation. It
provides a comprehensive understanding of consumption patterns, trends, and drivers.
Recommendation
1. Encourage Sustainable Food Practices: Promote sustainable food practices such as reducing food waste,
supporting organic farming methods, and advocating for sustainable fishing practices.
2. Improve Food Infrastructure: Invest in infrastructure development that supports the storage, processing, and distribution of food.
3. Enhance Food Accessibility: Improve access to nutritious food by ensuring the availability of affordable
and quality food options in both urban and rural areas.
Conclusion
Consumption patterns in the state of Odisha exhibit variations across districts, with some districts having
higher consumption levels than others. Understanding these variations can help identify potential factors
influencing consumption and guide targeted interventions. By leveraging RStudio data cleaning techniques
and conducting descriptive analysis, this study provides valuable insights into the consumption patterns in
the state of Odisha.
51