Untidy data is sorted to answer following questions:
1) Where in the US has there been the most interest in yoga?
2) When people were most interested in yoga in the last 10 years?
3) Interactive map showing the popularity of Yoga by state.
library(stringr)
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
library(ggplot2)
library(ggmap)
## Google Maps API Terms of Service: http://developers.google.com/maps/terms.
## Please cite ggmap if you use it: see citation("ggmap") for details.
library(leaflet)
data <- read.csv("yoga.csv")
data <- data[-1,]
head(data)
## X Alabama..us.al. Alaska..us.ak. Arizona..us.az. Arkansas..us.ar.
## 2 2004-01 20 23 21 24
## 3 2004-02 8 26 25 16
## 4 2004-03 10 26 22 26
## 5 2004-04 15 34 24 18
## 6 2004-05 15 14 24 11
## 7 2004-06 12 21 23 14
## California..us.ca. Colorado..us.co. Connecticut..us.ct. Delaware..us.de.
## 2 32 33 27 47
## 3 27 30 26 28
## 4 28 29 30 51
## 5 25 27 22 27
## 6 25 24 25 20
## 7 27 27 26 22
## District.of.Columbia..us.dc. Florida..us.fl. Georgia..us.ga.
## 2 32 21 21
## 3 36 17 20
## 4 29 17 20
## 5 29 19 15
## 6 24 20 16
## 7 26 18 18
## Hawaii..us.hi. Idaho..us.id. Illinois..us.il. Indiana..us.in.
## 2 36 21 25 24
## 3 24 22 23 14
## 4 36 21 25 17
## 5 30 18 17 14
## 6 39 14 17 9
## 7 44 17 20 18
## Iowa..us.ia. Kansas..us.ks. Kentucky..us.ky. Louisiana..us.la.
## 2 14 20 17 20
## 3 16 12 19 15
## 4 18 13 18 17
## 5 19 19 12 20
## 6 14 15 15 14
## 7 17 18 17 16
## Maine..us.me. Maryland..us.md. Massachusetts..us.ma. Michigan..us.mi.
## 2 29 26 41 19
## 3 29 23 33 18
## 4 26 22 32 17
## 5 21 17 31 15
## 6 25 21 27 15
## 7 22 23 29 17
## Minnesota..us.mn. Mississippi..us.ms. Missouri..us.mo. Montana..us.mt.
## 2 26 16 19 44
## 3 22 20 18 26
## 4 21 18 15 41
## 5 17 18 12 25
## 6 22 13 15 24
## 7 18 18 20 21
## Nebraska..us.ne. Nevada..us.nv. New.Hampshire..us.nh. New.Jersey..us.nj.
## 2 15 20 45 27
## 3 21 25 20 22
## 4 16 24 22 23
## 5 16 17 26 20
## 6 14 25 23 22
## 7 15 23 27 21
## New.Mexico..us.nm. New.York..us.ny. North.Carolina..us.nc.
## 2 33 35 23
## 3 25 28 22
## 4 26 29 20
## 5 18 26 18
## 6 21 28 16
## 7 32 28 22
## North.Dakota..us.nd. Ohio..us.oh. Oklahoma..us.ok. Oregon..us.or.
## 2 52 19 22 34
## 3 45 16 19 30
## 4 45 15 12 28
## 5 45 13 11 22
## 6 45 16 17 25
## 7 45 16 17 27
## Pennsylvania..us.pa. Rhode.Island..us.ri. South.Carolina..us.sc.
## 2 19 44 24
## 3 18 26 19
## 4 20 27 18
## 5 19 31 14
## 6 16 26 11
## 7 20 33 19
## South.Dakota..us.sd. Tennessee..us.tn. Texas..us.tx. Utah..us.ut.
## 2 25 21 24 26
## 3 22 18 16 20
## 4 21 16 17 10
## 5 21 21 16 20
## 6 28 15 17 19
## 7 28 16 17 16
## Vermont..us.vt. Virginia..us.va. Washington..us.wa.
## 2 42 22 30
## 3 39 16 29
## 4 41 19 27
## 5 37 17 25
## 6 31 18 25
## 7 34 22 27
## West.Virginia..us.wv. Wisconsin..us.wi. Wyoming..us.wy.
## 2 23 18 0
## 3 17 17 37
## 4 27 20 35
## 5 26 17 37
## 6 21 18 35
## 7 28 19 35
#str(data)
colnames(data) <- c("Year","Alabama","Alaska","Arizona","Arkansas","California","Colorado","Connecticut","Delaware","DC","Florida","Georgia","Hawaii","Idaho","Illinois","Indiana","Iowa","Kansus","Kentucky","Louisiana","Maine","Maryland","Masachusetts","Michigan","Minnesota","Mississippi","Missouri","Montana","Nebraska","Nevada","NH","New_Jersey","New_Mexico","New_York","North_Carolina","North_Dakota","Ohio","Oklahoma","Oregon","Pennsylvania","Rhode_Island","South_Carolina","South_Dakota","Tennessse","Texas","Utah","Vermont","Virginia","Washington","West_Virginia","Wisconsin","Wyoming")
data1 <-data %>%
separate(Year,into = c("Year","month"),sep = "-")
data1 <- data1[,-2]
data2 <- data1 %>%
group_by(Year)%>%
summarise_all(funs(sum))
data2 <- data.frame(data2)
data3 <- setNames(data.frame(t(data2[,-1])),data2[,1])
for(i in 1:51) {
data3$total[i] <- rowSums(data3[i,1:13],na.rm = TRUE)
}
head(data3)
## 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## Alabama 151 153 166 136 127 134 162 144 163 181 211 236
## Alaska 267 276 252 280 273 306 427 416 433 481 512 582
## Arizona 271 270 255 245 263 271 300 264 283 288 307 323
## Arkansas 204 185 176 163 139 159 209 168 189 240 259 256
## California 321 309 301 287 266 257 234 291 301 320 329 329
## Colorado 337 353 369 347 332 328 367 360 374 410 409 437
## 2016 total
## Alabama 78 2042
## Alaska 221 4726
## Arizona 113 3453
## Arkansas 77 2424
## California 114 3659
## Colorado 155 4578
max(data3$total)
## [1] 7529
Year 2015 recorded highest number of yoga practisioners.
data3$Regions <- c("Alabama","Alaska","Arizona","Arkansas","California","Colorado","Connecticut","Delaware","DC","Florida","Georgia","Hawaii","Idaho","Illinois","Indiana","Iowa","Kansus","Kentucky","Louisiana","Maine","Maryland","Masachusetts","Michigan","Minnesota","Mississippi","Missouri","Montana","Nebraska","Nevada","NH","New_Jersey","New_Mexico","New_York","North_Carolina","North_Dakota","Ohio","Oklahoma","Oregon","Pennsylvania","Rhode_Island","South_Carolina","South_Dakota","Tennessse","Texas","Utah","Vermont","Virginia","Washington","West_Virginia","Wisconsin","Wyoming","Total1")
data3$total <- as.numeric(data3$total)
data3$Regions <- as.character(data3$Regions)
Getting latitudes and longitudes of all states in data frame.
for (i in 1:nrow(data3)) {
latlon = geocode(data3[i,15])
data3$lon[i] = as.numeric(latlon[1])
data3$lat[i] = as.numeric(latlon[2])
}
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Alabama
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Alaska
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Arizona
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Arkansas
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=California
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Colorado
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Connecticut
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Delaware
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=DC
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Florida
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Georgia
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Hawaii
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Idaho
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Illinois
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Indiana
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Iowa
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Kansus
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Kentucky
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Louisiana
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Maine
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Maryland
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Masachusetts
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Michigan
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Minnesota
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Mississippi
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Missouri
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Montana
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Nebraska
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Nevada
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=NH
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=New_Jersey
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=New_Mexico
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=New_York
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=North_Carolina
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=North_Dakota
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Ohio
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Oklahoma
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Oregon
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Pennsylvania
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Rhode_Island
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=South_Carolina
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=South_Dakota
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Tennessse
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Texas
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Utah
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Vermont
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Virginia
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Washington
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=West_Virginia
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Wisconsin
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Wyoming
## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Total1