DATA607Project2
jim lung
03-11-2017
library(dplyr)
library(tidyr)
library(knitr)
library(ggplot2)
library(plotly)Wide data set 1 - New_York_City_Leading_Causes_of_Death
The source: https://data.cityofnewyork.us/Health/New-York-City-Leading-Causes-of-Death/jb7j-dtam/data
causeofdeath<-read.csv("~/New_York_City_Leading_Causes_of_Death.csv")
glimpse(causeofdeath)## Observations: 1,094
## Variables: 7
## $ Year <int> 2014, 2011, 2008, 2010, 2012, 2007, 20...
## $ Leading.Cause <fctr> Diabetes Mellitus (E10-E14), Cerebrov...
## $ Sex <fctr> F, M, M, F, M, M, F, F, F, M, M, F, F...
## $ Race.Ethnicity <fctr> Other Race/ Ethnicity, White Non-Hisp...
## $ Deaths <fctr> 11, 290, 60, 1045, 170, ., 14, 163, 1...
## $ Death.Rate <fctr> ., 21.7, ., 85.9, 19.9, ., ., 15.5, 8...
## $ Age.Adjusted.Death.Rate <fctr> ., 18.2, ., 98.5, 23.3, ., ., 14.8, 9...Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.
library(stringr)
causedeath <- causeofdeath %>% rename("Leading_Cause"=Leading.Cause, "Race_Ethnicity"=Race.Ethnicity, "Death_Rate"=Death.Rate, "Age_Adjusted_Death_Rate"=Age.Adjusted.Death.Rate)
head(causedeath)## Year Leading_Cause Sex
## 1 2014 Diabetes Mellitus (E10-E14) F
## 2 2011 Cerebrovascular Disease (Stroke: I60-I69) M
## 3 2008 Malignant Neoplasms (Cancer: C00-C97) M
## 4 2010 Malignant Neoplasms (Cancer: C00-C97) F
## 5 2012 Cerebrovascular Disease (Stroke: I60-I69) M
## 6 2007 Mental and Behavioral Disorders due to Use of Alcohol (F10) M
## Race_Ethnicity Deaths Death_Rate Age_Adjusted_Death_Rate
## 1 Other Race/ Ethnicity 11 . .
## 2 White Non-Hispanic 290 21.7 18.2
## 3 Not Stated/Unknown 60 . .
## 4 Hispanic 1045 85.9 98.5
## 5 Black Non-Hispanic 170 19.9 23.3
## 6 Not Stated/Unknown . . .cause2012<- causedeath %>%
filter(Year == "2012") %>%
group_by(Year, Race_Ethnicity) %>%
select(Year, Sex, Leading_Cause, Race_Ethnicity, Deaths,Death_Rate, Age_Adjusted_Death_Rate)
head(cause2012)## Source: local data frame [6 x 7]
## Groups: Year, Race_Ethnicity [3]
##
## Year Sex
## <int> <fctr>
## 1 2012 M
## 2 2012 F
## 3 2012 F
## 4 2012 M
## 5 2012 F
## 6 2012 M
## # ... with 5 more variables: Leading_Cause <fctr>, Race_Ethnicity <fctr>,
## # Deaths <fctr>, Death_Rate <fctr>, Age_Adjusted_Death_Rate <fctr>kable(causedeath %>%
select(Year, Sex, Leading_Cause, Death_Rate) %>%
group_by(Year, Sex) %>%
select(Year, Sex, Leading_Cause, Death_Rate) %>%
arrange(Death_Rate) %>%
top_n(1))## Selecting by Death_Rate| Year | Sex | Leading_Cause | Death_Rate |
|---|---|---|---|
| 2011 | F | Malignant Neoplasms (Cancer: C00-C97) | 88.6 |
| 2010 | F | Diabetes Mellitus (E10-E14) | 9.1 |
| 2008 | F | Human Immunodeficiency Virus Disease (HIV: B20-B24) | 9.6 |
| 2007 | F | Chronic Lower Respiratory Diseases (J40-J47) | 9.9 |
| 2012 | F | Malignant Neoplasms (Cancer: C00-C97) | 90.8 |
| 2009 | F | Malignant Neoplasms (Cancer: C00-C97) | 91.6 |
| 2013 | F | Malignant Neoplasms (Cancer: C00-C97) | 92.1 |
| 2010 | M | Malignant Neoplasms (Cancer: C00-C97) | 92.5 |
| 2007 | M | Malignant Neoplasms (Cancer: C00-C97) | 92.9 |
| 2012 | M | Malignant Neoplasms (Cancer: C00-C97) | 96.4 |
| 2014 | M | Diseases of Heart (I00-I09, I11, I13, I20-I51) | 96.5 |
| 2014 | F | Diseases of Heart (I00-I09, I11, I13, I20-I51) | 97.1 |
| 2013 | M | Malignant Neoplasms (Cancer: C00-C97) | 97.1 |
| 2008 | M | Malignant Neoplasms (Cancer: C00-C97) | 97.7 |
| 2009 | M | Malignant Neoplasms (Cancer: C00-C97) | 99.1 |
| 2011 | M | Malignant Neoplasms (Cancer: C00-C97) | 99.2 |
library(plotly)
ggplotly(ggplot(cause2012,aes(x=Race_Ethnicity,y=Age_Adjusted_Death_Rate))+geom_bar(aes(fill=Sex),stat="identity",position="dodge")+ylab("Death Rate")+ggtitle("Race_Ethnicity"))Wide data set 2 - Total world population
The source: http://data.worldbank.org/indicator/SP.POP.TOTL
population<-read.csv("~/population.csv", skip = 4)
glimpse(population)## Observations: 264
## Variables: 62
## $ Country.Name <fctr> Aruba, Andorra, Afghanistan, Angola, Albania, ...
## $ Country.Code <fctr> ABW, AND, AFG, AGO, ALB, ARB, ARE, ARG, ARM, A...
## $ Indicator.Name <fctr> Population, total, Population, total, Populati...
## $ Indicator.Code <fctr> SP.POP.TOTL, SP.POP.TOTL, SP.POP.TOTL, SP.POP....
## $ X1960 <dbl> 54208, 13414, 8994793, 5270844, 1608800, 925405...
## $ X1961 <dbl> 55435, 14376, 9164945, 5367287, 1659800, 950779...
## $ X1962 <dbl> 56226, 15376, 9343772, 5465905, 1711319, 977111...
## $ X1963 <dbl> 56697, 16410, 9531555, 5565808, 1762621, 100439...
## $ X1964 <dbl> 57029, 17470, 9728645, 5665701, 1814135, 103263...
## $ X1965 <dbl> 57360, 18551, 9935358, 5765025, 1864791, 106184...
## $ X1966 <dbl> 57712, 19646, 10148841, 5863568, 1914573, 10921...
## $ X1967 <dbl> 58049, 20755, 10368600, 5962831, 1965598, 11234...
## $ X1968 <dbl> 58385, 21888, 10599790, 6066094, 2022272, 11555...
## $ X1969 <dbl> 58724, 23061, 10849510, 6177703, 2081695, 11882...
## $ X1970 <dbl> 59065, 24279, 11121097, 6300969, 2135479, 12212...
## $ X1971 <dbl> 59438, 25560, 11412821, 6437645, 2187853, 12543...
## $ X1972 <dbl> 59849, 26892, 11716896, 6587647, 2243126, 12880...
## $ X1973 <dbl> 60239, 28231, 12022514, 6750215, 2296752, 13229...
## $ X1974 <dbl> 60525, 29514, 12315553, 6923749, 2350124, 13604...
## $ X1975 <dbl> 60655, 30706, 12582954, 7107334, 2404831, 14011...
## $ X1976 <dbl> 60589, 31781, 12831361, 7299508, 2458526, 14454...
## $ X1977 <dbl> 60366, 32769, 13056499, 7501320, 2513546, 14929...
## $ X1978 <dbl> 60106, 33746, 13222547, 7717139, 2566266, 15430...
## $ X1979 <dbl> 59978, 34819, 13283279, 7952882, 2617832, 15945...
## $ X1980 <dbl> 60096, 36063, 13211412, 8211950, 2671997, 16467...
## $ X1981 <dbl> 60567, 37502, 12996923, 8497950, 2726056, 16992...
## $ X1982 <dbl> 61344, 39112, 12667001, 8807511, 2784278, 17523...
## $ X1983 <dbl> 62204, 40862, 12279095, 9128655, 2843960, 18060...
## $ X1984 <dbl> 62831, 42704, 11912510, 9444918, 2904429, 18600...
## $ X1985 <dbl> 63028, 44597, 11630498, 9745209, 2964762, 19156...
## $ X1986 <dbl> 62644, 46515, 11438949, 10023700, 3022635, 1972...
## $ X1987 <dbl> 61835, 48458, 11337932, 10285712, 3083605, 2028...
## $ X1988 <dbl> 61077, 50431, 11375768, 10544904, 3142336, 2085...
## $ X1989 <dbl> 61032, 52449, 11608351, 10820992, 3227943, 2142...
## $ X1990 <dbl> 62148, 54511, 12067570, 11127870, 3286542, 2221...
## $ X1991 <dbl> 64623, 56674, 12789374, 11472173, 3266790, 2281...
## $ X1992 <dbl> 68235, 58904, 13745630, 11848971, 3247039, 2324...
## $ X1993 <dbl> 72498, 61003, 14824371, 12246786, 3227287, 2386...
## $ X1994 <dbl> 76700, 62707, 15869967, 12648483, 3207536, 2448...
## $ X1995 <dbl> 80326, 63854, 16772522, 13042666, 3187784, 2525...
## $ X1996 <dbl> 83195, 64291, 17481800, 13424813, 3168033, 2584...
## $ X1997 <dbl> 85447, 64147, 18034130, 13801868, 3148281, 2641...
## $ X1998 <dbl> 87276, 63888, 18511480, 14187710, 3128530, 2697...
## $ X1999 <dbl> 89004, 64161, 19038420, 14601983, 3108778, 2755...
## $ X2000 <dbl> 90858, 65399, 19701940, 15058638, 3089027, 2813...
## $ X2001 <dbl> 92894, 67770, 20531160, 15562791, 3060173, 2872...
## $ X2002 <dbl> 94995, 71046, 21487079, 16109696, 3051010, 2934...
## $ X2003 <dbl> 97015, 74783, 22507368, 16691395, 3039616, 2997...
## $ X2004 <dbl> 98742, 78337, 23499850, 17295500, 3026939, 3063...
## $ X2005 <dbl> 100031, 81223, 24399948, 17912942, 3011487, 313...
## $ X2006 <dbl> 100830, 83373, 25183615, 18541467, 2992547, 320...
## $ X2007 <dbl> 101218, 84878, 25877544, 19183907, 2970017, 328...
## $ X2008 <dbl> 101342, 85616, 26528741, 19842251, 2947314, 336...
## $ X2009 <dbl> 101416, 85474, 27207291, 20520103, 2927519, 345...
## $ X2010 <dbl> 101597, 84419, 27962207, 21219954, 2913021, 353...
## $ X2011 <dbl> 101936, 82326, 28809167, 21942296, 2904780, 361...
## $ X2012 <dbl> 102393, 79316, 29726803, 22685632, 2900247, 368...
## $ X2013 <dbl> 102921, 75902, 30682500, 23448202, 2896652, 376...
## $ X2014 <dbl> 103441, 72786, 31627506, 24227524, 2893654, 384...
## $ X2015 <dbl> 103889, 70473, 32526562, 25021974, 2889167, 392...
## $ X2016 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ X <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...population$Indicator.Name<-NULL
population$Indicator.Code<-NULL
population$X<-NULL
population$X2016<-NULL
population <- population[complete.cases(population), ]
head(population)## Country.Name Country.Code X1960 X1961 X1962 X1963 X1964
## 1 Aruba ABW 54208 55435 56226 56697 57029
## 2 Andorra AND 13414 14376 15376 16410 17470
## 3 Afghanistan AFG 8994793 9164945 9343772 9531555 9728645
## 4 Angola AGO 5270844 5367287 5465905 5565808 5665701
## 5 Albania ALB 1608800 1659800 1711319 1762621 1814135
## 6 Arab World ARB 92540534 95077992 97711191 100439395 103263656
## X1965 X1966 X1967 X1968 X1969 X1970 X1971
## 1 57360 57712 58049 58385 58724 59065 59438
## 2 18551 19646 20755 21888 23061 24279 25560
## 3 9935358 10148841 10368600 10599790 10849510 11121097 11412821
## 4 5765025 5863568 5962831 6066094 6177703 6300969 6437645
## 5 1864791 1914573 1965598 2022272 2081695 2135479 2187853
## 6 106184090 109210743 112342573 115557094 118823872 122128175 125439089
## X1972 X1973 X1974 X1975 X1976 X1977 X1978
## 1 59849 60239 60525 60655 60589 60366 60106
## 2 26892 28231 29514 30706 31781 32769 33746
## 3 11716896 12022514 12315553 12582954 12831361 13056499 13222547
## 4 6587647 6750215 6923749 7107334 7299508 7501320 7717139
## 5 2243126 2296752 2350124 2404831 2458526 2513546 2566266
## 6 128801209 132298955 136044481 140114424 144542039 149296257 154303887
## X1979 X1980 X1981 X1982 X1983 X1984 X1985
## 1 59978 60096 60567 61344 62204 62831 63028
## 2 34819 36063 37502 39112 40862 42704 44597
## 3 13283279 13211412 12996923 12667001 12279095 11912510 11630498
## 4 7952882 8211950 8497950 8807511 9128655 9444918 9745209
## 5 2617832 2671997 2726056 2784278 2843960 2904429 2964762
## 6 159455867 164670713 169926632 175237742 180606318 186005735 191564251
## X1986 X1987 X1988 X1989 X1990 X1991 X1992
## 1 62644 61835 61077 61032 62148 64623 68235
## 2 46515 48458 50431 52449 54511 56674 58904
## 3 11438949 11337932 11375768 11608351 12067570 12789374 13745630
## 4 10023700 10285712 10544904 10820992 11127870 11472173 11848971
## 5 3022635 3083605 3142336 3227943 3286542 3266790 3247039
## 6 197202142 202851923 208546282 214250542 222117305 228171034 232417246
## X1993 X1994 X1995 X1996 X1997 X1998 X1999
## 1 72498 76700 80326 83195 85447 87276 89004
## 2 61003 62707 63854 64291 64147 63888 64161
## 3 14824371 15869967 16772522 17481800 18034130 18511480 19038420
## 4 12246786 12648483 13042666 13424813 13801868 14187710 14601983
## 5 3227287 3207536 3187784 3168033 3148281 3128530 3108778
## 6 238628852 244893420 252591048 258423164 264153948 269795252 275503565
## X2000 X2001 X2002 X2003 X2004 X2005 X2006
## 1 90858 92894 94995 97015 98742 100031 100830
## 2 65399 67770 71046 74783 78337 81223 83373
## 3 19701940 20531160 21487079 22507368 23499850 24399948 25183615
## 4 15058638 15562791 16109696 16691395 17295500 17912942 18541467
## 5 3089027 3060173 3051010 3039616 3026939 3011487 2992547
## 6 281326250 287291826 293402563 299731956 306387415 313430911 320906736
## X2007 X2008 X2009 X2010 X2011 X2012 X2013
## 1 101218 101342 101416 101597 101936 102393 102921
## 2 84878 85616 85474 84419 82326 79316 75902
## 3 25877544 26528741 27207291 27962207 28809167 29726803 30682500
## 4 19183907 19842251 20520103 21219954 21942296 22685632 23448202
## 5 2970017 2947314 2927519 2913021 2904780 2900247 2896652
## 6 328766559 336886468 345054176 353112237 361031820 368802611 376504253
## X2014 X2015
## 1 103441 103889
## 2 72786 70473
## 3 31627506 32526562
## 4 24227524 25021974
## 5 2893654 2889167
## 6 384222592 392022276Using Tidyr function:
tpopulation <- population %>%
gather(group, "population", -Country.Code, -Country.Name) %>%
separate(group, c("X", "Year"), sep="X") %>%
select(-X) %>%
arrange(Country.Code, Year)
tpopulation$Year <- as.numeric(tpopulation$Year)
tpopulation <- as.data.frame(tpopulation)
tpopulation## Country.Name Country.Code
## 1 Aruba ABW
## 2 Aruba ABW
## 3 Aruba ABW
## 4 Aruba ABW
## 5 Aruba ABW
## 6 Aruba ABW
## 7 Aruba ABW
## 8 Aruba ABW
## 9 Aruba ABW
## 10 Aruba ABW
## 11 Aruba ABW
## 12 Aruba ABW
## 13 Aruba ABW
## 14 Aruba ABW
## 15 Aruba ABW
## 16 Aruba ABW
## 17 Aruba ABW
## 18 Aruba ABW
## 19 Aruba ABW
## 20 Aruba ABW
## 21 Aruba ABW
## 22 Aruba ABW
## 23 Aruba ABW
## 24 Aruba ABW
## 25 Aruba ABW
## 26 Aruba ABW
## 27 Aruba ABW
## 28 Aruba ABW
## 29 Aruba ABW
## 30 Aruba ABW
## 31 Aruba ABW
## 32 Aruba ABW
## 33 Aruba ABW
## 34 Aruba ABW
## 35 Aruba ABW
## 36 Aruba ABW
## 37 Aruba ABW
## 38 Aruba ABW
## 39 Aruba ABW
## 40 Aruba ABW
## 41 Aruba ABW
## 42 Aruba ABW
## 43 Aruba ABW
## 44 Aruba ABW
## 45 Aruba ABW
## 46 Aruba ABW
## 47 Aruba ABW
## 48 Aruba ABW
## 49 Aruba ABW
## 50 Aruba ABW
## 51 Aruba ABW
## 52 Aruba ABW
## 53 Aruba ABW
## 54 Aruba ABW
## 55 Aruba ABW
## 56 Aruba ABW
## 57 Afghanistan AFG
## 58 Afghanistan AFG
## 59 Afghanistan AFG
## 60 Afghanistan AFG
## 61 Afghanistan AFG
## 62 Afghanistan AFG
## 63 Afghanistan AFG
## 64 Afghanistan AFG
## 65 Afghanistan AFG
## 66 Afghanistan AFG
## 67 Afghanistan AFG
## 68 Afghanistan AFG
## 69 Afghanistan AFG
## 70 Afghanistan AFG
## 71 Afghanistan AFG
## 72 Afghanistan AFG
## 73 Afghanistan AFG
## 74 Afghanistan AFG
## 75 Afghanistan AFG
## 76 Afghanistan AFG
## 77 Afghanistan AFG
## 78 Afghanistan AFG
## 79 Afghanistan AFG
## 80 Afghanistan AFG
## 81 Afghanistan AFG
## 82 Afghanistan AFG
## 83 Afghanistan AFG
## 84 Afghanistan AFG
## 85 Afghanistan AFG
## 86 Afghanistan AFG
## 87 Afghanistan AFG
## 88 Afghanistan AFG
## 89 Afghanistan AFG
## 90 Afghanistan AFG
## 91 Afghanistan AFG
## 92 Afghanistan AFG
## 93 Afghanistan AFG
## 94 Afghanistan AFG
## 95 Afghanistan AFG
## 96 Afghanistan AFG
## 97 Afghanistan AFG
## 98 Afghanistan AFG
## 99 Afghanistan AFG
## 100 Afghanistan AFG
## 101 Afghanistan AFG
## 102 Afghanistan AFG
## 103 Afghanistan AFG
## 104 Afghanistan AFG
## 105 Afghanistan AFG
## 106 Afghanistan AFG
## 107 Afghanistan AFG
## 108 Afghanistan AFG
## 109 Afghanistan AFG
## 110 Afghanistan AFG
## 111 Afghanistan AFG
## 112 Afghanistan AFG
## 113 Angola AGO
## 114 Angola AGO
## 115 Angola AGO
## 116 Angola AGO
## 117 Angola AGO
## 118 Angola AGO
## 119 Angola AGO
## 120 Angola AGO
## 121 Angola AGO
## 122 Angola AGO
## 123 Angola AGO
## 124 Angola AGO
## 125 Angola AGO
## 126 Angola AGO
## 127 Angola AGO
## 128 Angola AGO
## 129 Angola AGO
## 130 Angola AGO
## 131 Angola AGO
## 132 Angola AGO
## 133 Angola AGO
## 134 Angola AGO
## 135 Angola AGO
## 136 Angola AGO
## 137 Angola AGO
## 138 Angola AGO
## 139 Angola AGO
## 140 Angola AGO
## 141 Angola AGO
## 142 Angola AGO
## 143 Angola AGO
## 144 Angola AGO
## 145 Angola AGO
## 146 Angola AGO
## 147 Angola AGO
## 148 Angola AGO
## 149 Angola AGO
## 150 Angola AGO
## 151 Angola AGO
## 152 Angola AGO
## 153 Angola AGO
## 154 Angola AGO
## 155 Angola AGO
## 156 Angola AGO
## 157 Angola AGO
## 158 Angola AGO
## 159 Angola AGO
## 160 Angola AGO
## 161 Angola AGO
## 162 Angola AGO
## 163 Angola AGO
## 164 Angola AGO
## 165 Angola AGO
## 166 Angola AGO
## 167 Angola AGO
## 168 Angola AGO
## 169 Albania ALB
## 170 Albania ALB
## 171 Albania ALB
## 172 Albania ALB
## 173 Albania ALB
## 174 Albania ALB
## 175 Albania ALB
## 176 Albania ALB
## 177 Albania ALB
## 178 Albania ALB
## 179 Albania ALB
## 180 Albania ALB
## 181 Albania ALB
## 182 Albania ALB
## 183 Albania ALB
## 184 Albania ALB
## 185 Albania ALB
## 186 Albania ALB
## 187 Albania ALB
## 188 Albania ALB
## 189 Albania ALB
## 190 Albania ALB
## 191 Albania ALB
## 192 Albania ALB
## 193 Albania ALB
## 194 Albania ALB
## 195 Albania ALB
## 196 Albania ALB
## 197 Albania ALB
## 198 Albania ALB
## 199 Albania ALB
## 200 Albania ALB
## 201 Albania ALB
## 202 Albania ALB
## 203 Albania ALB
## 204 Albania ALB
## 205 Albania ALB
## 206 Albania ALB
## 207 Albania ALB
## 208 Albania ALB
## 209 Albania ALB
## 210 Albania ALB
## 211 Albania ALB
## 212 Albania ALB
## 213 Albania ALB
## 214 Albania ALB
## 215 Albania ALB
## 216 Albania ALB
## 217 Albania ALB
## 218 Albania ALB
## 219 Albania ALB
## 220 Albania ALB
## 221 Albania ALB
## 222 Albania ALB
## 223 Albania ALB
## 224 Albania ALB
## 225 Andorra AND
## 226 Andorra AND
## 227 Andorra AND
## 228 Andorra AND
## 229 Andorra AND
## 230 Andorra AND
## 231 Andorra AND
## 232 Andorra AND
## 233 Andorra AND
## 234 Andorra AND
## 235 Andorra AND
## 236 Andorra AND
## 237 Andorra AND
## 238 Andorra AND
## 239 Andorra AND
## 240 Andorra AND
## 241 Andorra AND
## 242 Andorra AND
## 243 Andorra AND
## 244 Andorra AND
## 245 Andorra AND
## 246 Andorra AND
## 247 Andorra AND
## 248 Andorra AND
## 249 Andorra AND
## 250 Andorra AND
## Year population
## 1 1960 54208
## 2 1961 55435
## 3 1962 56226
## 4 1963 56697
## 5 1964 57029
## 6 1965 57360
## 7 1966 57712
## 8 1967 58049
## 9 1968 58385
## 10 1969 58724
## 11 1970 59065
## 12 1971 59438
## 13 1972 59849
## 14 1973 60239
## 15 1974 60525
## 16 1975 60655
## 17 1976 60589
## 18 1977 60366
## 19 1978 60106
## 20 1979 59978
## 21 1980 60096
## 22 1981 60567
## 23 1982 61344
## 24 1983 62204
## 25 1984 62831
## 26 1985 63028
## 27 1986 62644
## 28 1987 61835
## 29 1988 61077
## 30 1989 61032
## 31 1990 62148
## 32 1991 64623
## 33 1992 68235
## 34 1993 72498
## 35 1994 76700
## 36 1995 80326
## 37 1996 83195
## 38 1997 85447
## 39 1998 87276
## 40 1999 89004
## 41 2000 90858
## 42 2001 92894
## 43 2002 94995
## 44 2003 97015
## 45 2004 98742
## 46 2005 100031
## 47 2006 100830
## 48 2007 101218
## 49 2008 101342
## 50 2009 101416
## 51 2010 101597
## 52 2011 101936
## 53 2012 102393
## 54 2013 102921
## 55 2014 103441
## 56 2015 103889
## 57 1960 8994793
## 58 1961 9164945
## 59 1962 9343772
## 60 1963 9531555
## 61 1964 9728645
## 62 1965 9935358
## 63 1966 10148841
## 64 1967 10368600
## 65 1968 10599790
## 66 1969 10849510
## 67 1970 11121097
## 68 1971 11412821
## 69 1972 11716896
## 70 1973 12022514
## 71 1974 12315553
## 72 1975 12582954
## 73 1976 12831361
## 74 1977 13056499
## 75 1978 13222547
## 76 1979 13283279
## 77 1980 13211412
## 78 1981 12996923
## 79 1982 12667001
## 80 1983 12279095
## 81 1984 11912510
## 82 1985 11630498
## 83 1986 11438949
## 84 1987 11337932
## 85 1988 11375768
## 86 1989 11608351
## 87 1990 12067570
## 88 1991 12789374
## 89 1992 13745630
## 90 1993 14824371
## 91 1994 15869967
## 92 1995 16772522
## 93 1996 17481800
## 94 1997 18034130
## 95 1998 18511480
## 96 1999 19038420
## 97 2000 19701940
## 98 2001 20531160
## 99 2002 21487079
## 100 2003 22507368
## 101 2004 23499850
## 102 2005 24399948
## 103 2006 25183615
## 104 2007 25877544
## 105 2008 26528741
## 106 2009 27207291
## 107 2010 27962207
## 108 2011 28809167
## 109 2012 29726803
## 110 2013 30682500
## 111 2014 31627506
## 112 2015 32526562
## 113 1960 5270844
## 114 1961 5367287
## 115 1962 5465905
## 116 1963 5565808
## 117 1964 5665701
## 118 1965 5765025
## 119 1966 5863568
## 120 1967 5962831
## 121 1968 6066094
## 122 1969 6177703
## 123 1970 6300969
## 124 1971 6437645
## 125 1972 6587647
## 126 1973 6750215
## 127 1974 6923749
## 128 1975 7107334
## 129 1976 7299508
## 130 1977 7501320
## 131 1978 7717139
## 132 1979 7952882
## 133 1980 8211950
## 134 1981 8497950
## 135 1982 8807511
## 136 1983 9128655
## 137 1984 9444918
## 138 1985 9745209
## 139 1986 10023700
## 140 1987 10285712
## 141 1988 10544904
## 142 1989 10820992
## 143 1990 11127870
## 144 1991 11472173
## 145 1992 11848971
## 146 1993 12246786
## 147 1994 12648483
## 148 1995 13042666
## 149 1996 13424813
## 150 1997 13801868
## 151 1998 14187710
## 152 1999 14601983
## 153 2000 15058638
## 154 2001 15562791
## 155 2002 16109696
## 156 2003 16691395
## 157 2004 17295500
## 158 2005 17912942
## 159 2006 18541467
## 160 2007 19183907
## 161 2008 19842251
## 162 2009 20520103
## 163 2010 21219954
## 164 2011 21942296
## 165 2012 22685632
## 166 2013 23448202
## 167 2014 24227524
## 168 2015 25021974
## 169 1960 1608800
## 170 1961 1659800
## 171 1962 1711319
## 172 1963 1762621
## 173 1964 1814135
## 174 1965 1864791
## 175 1966 1914573
## 176 1967 1965598
## 177 1968 2022272
## 178 1969 2081695
## 179 1970 2135479
## 180 1971 2187853
## 181 1972 2243126
## 182 1973 2296752
## 183 1974 2350124
## 184 1975 2404831
## 185 1976 2458526
## 186 1977 2513546
## 187 1978 2566266
## 188 1979 2617832
## 189 1980 2671997
## 190 1981 2726056
## 191 1982 2784278
## 192 1983 2843960
## 193 1984 2904429
## 194 1985 2964762
## 195 1986 3022635
## 196 1987 3083605
## 197 1988 3142336
## 198 1989 3227943
## 199 1990 3286542
## 200 1991 3266790
## 201 1992 3247039
## 202 1993 3227287
## 203 1994 3207536
## 204 1995 3187784
## 205 1996 3168033
## 206 1997 3148281
## 207 1998 3128530
## 208 1999 3108778
## 209 2000 3089027
## 210 2001 3060173
## 211 2002 3051010
## 212 2003 3039616
## 213 2004 3026939
## 214 2005 3011487
## 215 2006 2992547
## 216 2007 2970017
## 217 2008 2947314
## 218 2009 2927519
## 219 2010 2913021
## 220 2011 2904780
## 221 2012 2900247
## 222 2013 2896652
## 223 2014 2893654
## 224 2015 2889167
## 225 1960 13414
## 226 1961 14376
## 227 1962 15376
## 228 1963 16410
## 229 1964 17470
## 230 1965 18551
## 231 1966 19646
## 232 1967 20755
## 233 1968 21888
## 234 1969 23061
## 235 1970 24279
## 236 1971 25560
## 237 1972 26892
## 238 1973 28231
## 239 1974 29514
## 240 1975 30706
## 241 1976 31781
## 242 1977 32769
## 243 1978 33746
## 244 1979 34819
## 245 1980 36063
## 246 1981 37502
## 247 1982 39112
## 248 1983 40862
## 249 1984 42704
## 250 1985 44597
## [ reached getOption("max.print") -- omitted 14198 rows ]Analysis for max and min of population:
population_percent <- tpopulation %>%
na.omit() %>% #omit the na value
group_by(Country.Code) %>% #Group country for comparing
mutate(min_year = min(Year), max_year = max(Year)) %>% #calculate min and max number
filter(Year == min_year | Year == max_year) %>% #Year can be "min" or "max"
mutate(rn = str_c("Y", row_number())) %>% # labelling M1 and M2 for min and max
select(Country.Name, Country.Code, population, rn) %>%
spread(rn, population) %>% # Separate M1 and M2 for min and max
rename(Min.Year=Y1, Max.Year=Y2) %>%
mutate(Percent.Change = round(((Max.Year - Min.Year)/Min.Year) * 100, 2))
population_percent <- as.data.frame(population_percent)
population_percent## Country.Name Country.Code
## 1 Korea, Dem. People\xe2\u0080檚 Rep. PRK
## 2 Afghanistan AFG
## 3 Albania ALB
## 4 Algeria DZA
## 5 American Samoa ASM
## 6 Andorra AND
## 7 Angola AGO
## 8 Antigua and Barbuda ATG
## 9 Arab World ARB
## 10 Argentina ARG
## 11 Armenia ARM
## 12 Aruba ABW
## 13 Australia AUS
## 14 Austria AUT
## 15 Azerbaijan AZE
## 16 Bahamas, The BHS
## 17 Bahrain BHR
## 18 Bangladesh BGD
## 19 Barbados BRB
## 20 Belarus BLR
## 21 Belgium BEL
## 22 Belize BLZ
## 23 Benin BEN
## 24 Bermuda BMU
## 25 Bhutan BTN
## 26 Bolivia BOL
## 27 Bosnia and Herzegovina BIH
## 28 Botswana BWA
## 29 Brazil BRA
## 30 British Virgin Islands VGB
## 31 Brunei Darussalam BRN
## 32 Bulgaria BGR
## 33 Burkina Faso BFA
## 34 Burundi BDI
## 35 Cabo Verde CPV
## 36 Cambodia KHM
## 37 Cameroon CMR
## 38 Canada CAN
## 39 Caribbean small states CSS
## 40 Cayman Islands CYM
## 41 Central African Republic CAF
## 42 Central Europe and the Baltics CEB
## 43 Chad TCD
## 44 Channel Islands CHI
## 45 Chile CHL
## 46 China CHN
## 47 Colombia COL
## 48 Comoros COM
## 49 Congo, Dem. Rep. COD
## 50 Congo, Rep. COG
## 51 Costa Rica CRI
## 52 Cote d'Ivoire CIV
## 53 Croatia HRV
## 54 Cuba CUB
## 55 Curacao CUW
## 56 Cyprus CYP
## 57 Czech Republic CZE
## 58 Denmark DNK
## 59 Djibouti DJI
## 60 Dominica DMA
## 61 Dominican Republic DOM
## 62 Early-demographic dividend EAR
## 63 East Asia & Pacific EAS
## 64 East Asia & Pacific (excluding high income) EAP
## 65 East Asia & Pacific (IDA & IBRD countries) TEA
## 66 Ecuador ECU
## 67 Egypt, Arab Rep. EGY
## 68 El Salvador SLV
## 69 Equatorial Guinea GNQ
## 70 Estonia EST
## 71 Ethiopia ETH
## 72 Euro area EMU
## 73 Europe & Central Asia ECS
## 74 Europe & Central Asia (excluding high income) ECA
## 75 Europe & Central Asia (IDA & IBRD countries) TEC
## 76 European Union EUU
## 77 Faroe Islands FRO
## 78 Fiji FJI
## 79 Finland FIN
## 80 Fragile and conflict affected situations FCS
## 81 France FRA
## 82 French Polynesia PYF
## 83 Gabon GAB
## 84 Gambia, The GMB
## 85 Georgia GEO
## 86 Germany DEU
## 87 Ghana GHA
## 88 Gibraltar GIB
## 89 Greece GRC
## 90 Greenland GRL
## 91 Grenada GRD
## 92 Guam GUM
## 93 Guatemala GTM
## 94 Guinea GIN
## 95 Guinea-Bissau GNB
## 96 Guyana GUY
## 97 Haiti HTI
## 98 Heavily indebted poor countries (HIPC) HPC
## 99 High income HIC
## 100 Honduras HND
## 101 Hong Kong SAR, China HKG
## 102 Hungary HUN
## 103 IBRD only IBD
## 104 Iceland ISL
## 105 IDA & IBRD total IBT
## 106 IDA blend IDB
## 107 IDA only IDX
## 108 IDA total IDA
## 109 India IND
## 110 Indonesia IDN
## 111 Iran, Islamic Rep. IRN
## 112 Iraq IRQ
## 113 Ireland IRL
## 114 Isle of Man IMN
## 115 Israel ISR
## 116 Italy ITA
## 117 Jamaica JAM
## 118 Japan JPN
## 119 Jordan JOR
## 120 Kazakhstan KAZ
## 121 Kenya KEN
## 122 Kiribati KIR
## 123 Korea, Rep. KOR
## 124 Kosovo KSV
## 125 Kyrgyz Republic KGZ
## 126 Lao PDR LAO
## 127 Late-demographic dividend LTE
## 128 Latin America & Caribbean LCN
## 129 Latin America & Caribbean (excluding high income) LAC
## 130 Latin America & the Caribbean (IDA & IBRD countries) TLA
## 131 Latvia LVA
## 132 Least developed countries: UN classification LDC
## 133 Lebanon LBN
## 134 Lesotho LSO
## 135 Liberia LBR
## 136 Libya LBY
## 137 Liechtenstein LIE
## 138 Lithuania LTU
## 139 Low & middle income LMY
## 140 Low income LIC
## 141 Lower middle income LMC
## 142 Luxembourg LUX
## 143 Macao SAR, China MAC
## 144 Macedonia, FYR MKD
## 145 Madagascar MDG
## 146 Malawi MWI
## 147 Malaysia MYS
## 148 Maldives MDV
## 149 Mali MLI
## 150 Malta MLT
## 151 Marshall Islands MHL
## 152 Mauritania MRT
## 153 Mauritius MUS
## 154 Mexico MEX
## 155 Micronesia, Fed. Sts. FSM
## 156 Middle East & North Africa MEA
## 157 Middle East & North Africa (excluding high income) MNA
## 158 Middle East & North Africa (IDA & IBRD countries) TMN
## 159 Middle income MIC
## 160 Moldova MDA
## 161 Monaco MCO
## 162 Mongolia MNG
## 163 Montenegro MNE
## 164 Morocco MAR
## 165 Mozambique MOZ
## 166 Myanmar MMR
## 167 Namibia NAM
## 168 Nauru NRU
## 169 Nepal NPL
## 170 Netherlands NLD
## 171 New Caledonia NCL
## 172 New Zealand NZL
## 173 Nicaragua NIC
## 174 Niger NER
## 175 Nigeria NGA
## 176 North America NAC
## 177 Northern Mariana Islands MNP
## 178 Norway NOR
## 179 OECD members OED
## 180 Oman OMN
## 181 Other small states OSS
## 182 Pacific island small states PSS
## 183 Pakistan PAK
## 184 Palau PLW
## 185 Panama PAN
## 186 Papua New Guinea PNG
## 187 Paraguay PRY
## 188 Peru PER
## 189 Philippines PHL
## 190 Poland POL
## 191 Portugal PRT
## 192 Post-demographic dividend PST
## 193 Pre-demographic dividend PRE
## 194 Puerto Rico PRI
## 195 Qatar QAT
## 196 Romania ROU
## 197 Russian Federation RUS
## 198 Rwanda RWA
## 199 Samoa WSM
## 200 San Marino SMR
## Min.Year Max.Year Percent.Change
## 1 11424179 25155317 120.19
## 2 8994793 32526562 261.62
## 3 1608800 2889167 79.59
## 4 11124892 39666519 256.56
## 5 20012 55538 177.52
## 6 13414 70473 425.37
## 7 5270844 25021974 374.72
## 8 54681 91818 67.92
## 9 92540534 392022276 323.62
## 10 20619075 43416755 110.57
## 11 1867396 3017712 61.60
## 12 54208 103889 91.65
## 13 10276477 23781169 131.41
## 14 7047539 8611088 22.19
## 15 3897889 9651349 147.60
## 16 109526 388019 254.27
## 17 162501 1377237 747.53
## 18 48200702 160995642 234.01
## 19 230934 284215 23.07
## 20 8198000 9513000 16.04
## 21 9153489 11285721 23.29
## 22 92068 359287 290.24
## 23 2431620 10879829 347.43
## 24 44400 65235 46.93
## 25 224108 774830 245.74
## 26 3693451 10724705 190.37
## 27 3214520 3810416 18.54
## 28 524029 2262485 331.75
## 29 72493585 207847528 186.71
## 30 8036 30117 274.78
## 31 81825 423188 417.19
## 32 7867374 7177991 -8.76
## 33 4829291 18105570 274.91
## 34 2786740 11178921 301.15
## 35 202316 520502 157.27
## 36 5722370 15577899 172.23
## 37 5361367 23344179 335.41
## 38 17909009 35851774 100.19
## 39 4190810 7048966 68.20
## 40 7867 59967 662.26
## 41 1503501 4900274 225.92
## 42 91401583 103318638 13.04
## 43 3002596 14037472 367.51
## 44 109419 163692 49.60
## 45 7695692 17948141 133.22
## 46 667070000 1371220000 105.56
## 47 16480384 48228704 192.64
## 48 188732 788474 317.77
## 49 15248246 77266814 406.73
## 50 1013581 4620330 355.84
## 51 1333042 4807850 260.67
## 52 3474724 22701556 553.33
## 53 4140000 4224404 2.04
## 54 7141129 11389562 59.49
## 55 124826 158040 26.61
## 56 572929 1165300 103.39
## 57 9602006 10551219 9.89
## 58 4579603 5676002 23.94
## 59 83636 887861 961.58
## 60 60016 72680 21.10
## 61 3294039 10528391 219.62
## 62 980067595 3122703317 218.62
## 63 1042479827 2279186469 118.63
## 64 896492991 2035129646 127.01
## 65 885053233 2009929013 127.10
## 66 4545548 16144363 255.17
## 67 27072397 91508084 238.01
## 68 2762897 6126583 121.74
## 69 252115 845060 235.19
## 70 1211537 1311998 8.29
## 71 22151218 99390750 348.69
## 72 265396501 339425073 27.89
## 73 667516938 907944124 36.02
## 74 275220745 411338238 49.46
## 75 308998195 453562136 46.78
## 76 409498462 509668361 24.46
## 77 34266 48199 40.66
## 78 393383 892145 126.79
## 79 4429634 5482013 23.76
## 80 118691130 485609230 309.14
## 81 46814237 66808385 42.71
## 82 78083 282764 262.13
## 83 499189 1725292 245.62
## 84 367929 1990924 441.12
## 85 3645600 3679000 0.92
## 86 72814900 81413145 11.81
## 87 6652285 27409893 312.04
## 88 21529 32217 49.64
## 89 8331725 10823732 29.91
## 90 32500 56114 72.66
## 91 89861 106825 18.88
## 92 66741 169885 154.54
## 93 4127555 16342897 295.95
## 94 3577413 12608590 252.45
## 95 616407 1844325 199.21
## 96 564222 767085 35.95
## 97 3866160 10711067 177.05
## 98 162491185 721104105 343.78
## 99 758716470 1187189841 56.47
## 100 2002333 8075060 303.28
## 101 3075605 7305700 137.54
## 102 9983967 9844686 -1.40
## 103 1871184856 4524078275 141.78
## 104 175574 330823 88.42
## 105 2302766065 6183634830 168.53
## 106 163861743 585760189 257.47
## 107 267719466 1073796366 301.09
## 108 431581209 1659556555 284.53
## 109 449661874 1311050527 191.56
## 110 87792512 257563815 193.38
## 111 21906905 79109272 261.12
## 112 7289759 36423395 399.65
## 113 2828600 4640703 64.06
## 114 48441 87780 81.21
## 115 2114020 8380400 296.42
## 116 50199700 60802085 21.12
## 117 1629003 2725941 67.34
## 118 92500572 126958472 37.25
## 119 888632 7594547 754.63
## 120 9714260 17544126 80.60
## 121 8105440 46050302 468.14
## 122 41234 112423 172.65
## 123 25012374 50617045 102.37
## 124 947000 1797151 89.77
## 125 2172300 5957000 174.23
## 126 2119944 6802023 220.86
## 127 1099608911 2249154750 104.54
## 128 220572260 632959079 186.96
## 129 206448158 605364992 193.23
## 130 210495731 616862604 193.05
## 131 2120979 1978440 -6.72
## 132 241072814 954218054 295.82
## 133 1804927 5850743 224.15
## 134 851412 2135022 150.76
## 135 1120314 4503438 301.98
## 136 1434576 6278438 337.65
## 137 16504 37531 127.41
## 138 2778550 2910199 4.74
## 139 2276339100 6159443196 170.59
## 140 154643623 638286288 312.75
## 141 946484445 2927414098 209.29
## 142 313970 569676 81.44
## 143 171456 587606 242.72
## 144 1488664 2078453 39.62
## 145 5099371 24235390 375.26
## 146 3618604 17215232 375.74
## 147 8160975 30331007 271.66
## 148 89875 409163 355.26
## 149 5263730 17599694 234.36
## 150 326550 431333 32.09
## 151 14665 52993 261.36
## 152 858170 4067564 373.98
## 153 659351 1262605 91.49
## 154 38174114 127017224 232.73
## 155 44539 104460 134.54
## 156 105557277 424065257 301.74
## 157 97914047 362560941 270.28
## 158 97914047 358138798 265.77
## 159 2121695477 5521156908 160.22
## 160 2544000 3554150 39.71
## 161 22454 37731 68.04
## 162 955514 2959134 209.69
## 163 480579 622388 29.51
## 164 12328534 34377511 178.85
## 165 7493278 27977863 273.37
## 166 21486424 53897154 150.84
## 167 602545 2458830 308.07
## 168 4433 10222 130.59
## 169 10056945 28513700 183.52
## 170 11486631 16936520 47.45
## 171 79000 273000 245.57
## 172 2371800 4595700 93.76
## 173 1774696 6082032 242.71
## 174 3395212 19899120 486.09
## 175 45211614 182201962 303.00
## 176 198624409 357335829 79.91
## 177 10070 55070 446.87
## 178 3581239 5195921 45.09
## 179 788769132 1282975040 62.66
## 180 551737 4490541 713.89
## 181 9190554 28650005 211.73
## 182 865811 2351091 171.55
## 183 44911810 188924874 320.66
## 184 9638 21291 120.91
## 185 1132924 3929141 246.81
## 186 1966957 7619321 287.37
## 187 1902871 6639123 248.90
## 188 10061519 31376670 211.85
## 189 26273023 100699395 283.28
## 190 29637450 37999494 28.21
## 191 8857716 10348648 16.83
## 192 754697140 1097737383 45.45
## 193 188373482 850271023 351.38
## 194 2358000 3474182 47.34
## 195 47309 2235355 4625.01
## 196 18406905 19832389 7.74
## 197 119897000 144096812 20.18
## 198 2933424 11609666 295.77
## 199 108645 193228 77.85
## 200 15393 31781 106.46
## [ reached getOption("max.print") -- omitted 58 rows ]Summary of max and min percentage change:
comparecountry <- filter(population_percent, Percent.Change==min(Percent.Change) |Percent.Change==max(Percent.Change))
kable(comparecountry)| Country.Name | Country.Code | Min.Year | Max.Year | Percent.Change |
|---|---|---|---|---|
| Bulgaria | BGR | 7867374 | 7177991 | -8.76 |
| United Arab Emirates | ARE | 92612 | 9156963 | 9787.45 |
Wide data set 3 - Lottery-Cash-4-Life-Winning-Numbers
The source: https://data.ny.gov/Government-Finance/Lottery-Cash-4-Life-Winning-Numbers-Beginning-2014/kwxv-fwze
lottery<-read.csv("~/lottery.csv")
glimpse(lottery)## Observations: 286
## Variables: 3
## $ Draw.Date <fctr> 06/16/2014, 06/19/2014, 06/23/2014, 06/26/201...
## $ Winning.Numbers <fctr> 09 36 44 53 59, 08 13 43 56 60, 05 16 21 33 4...
## $ Cash.Ball <int> 3, 2, 4, 3, 2, 2, 2, 3, 2, 1, 3, 3, 2, 1, 3, 2...dlottery <- lottery %>%
separate(Draw.Date, c("Month","Day","Year"), sep="/") %>%
separate(Winning.Numbers, c("N1","N2","N3","N4","N5")) %>%
gather(lottery,"Number", 4:9) %>%
arrange(Year,Month,Day)
dlottery$Month <- as.numeric(dlottery$Month)
dlottery$Day <- as.numeric(dlottery$Day)
dlottery$Year <- as.numeric(dlottery$Year)
dlottery <- as.data.frame(dlottery)
dlottery## Month Day Year lottery Number
## 1 6 16 2014 N1 09
## 2 6 16 2014 N2 36
## 3 6 16 2014 N3 44
## 4 6 16 2014 N4 53
## 5 6 16 2014 N5 59
## 6 6 16 2014 Cash.Ball 3
## 7 6 19 2014 N1 08
## 8 6 19 2014 N2 13
## 9 6 19 2014 N3 43
## 10 6 19 2014 N4 56
## 11 6 19 2014 N5 60
## 12 6 19 2014 Cash.Ball 2
## 13 6 23 2014 N1 05
## 14 6 23 2014 N2 16
## 15 6 23 2014 N3 21
## 16 6 23 2014 N4 33
## 17 6 23 2014 N5 47
## 18 6 23 2014 Cash.Ball 4
## 19 6 26 2014 N1 15
## 20 6 26 2014 N2 22
## 21 6 26 2014 N3 51
## 22 6 26 2014 N4 52
## 23 6 26 2014 N5 58
## 24 6 26 2014 Cash.Ball 3
## 25 6 30 2014 N1 01
## 26 6 30 2014 N2 04
## 27 6 30 2014 N3 10
## 28 6 30 2014 N4 28
## 29 6 30 2014 N5 33
## 30 6 30 2014 Cash.Ball 2
## 31 7 3 2014 N1 08
## 32 7 3 2014 N2 10
## 33 7 3 2014 N3 25
## 34 7 3 2014 N4 28
## 35 7 3 2014 N5 31
## 36 7 3 2014 Cash.Ball 2
## 37 7 7 2014 N1 11
## 38 7 7 2014 N2 13
## 39 7 7 2014 N3 23
## 40 7 7 2014 N4 54
## 41 7 7 2014 N5 55
## 42 7 7 2014 Cash.Ball 2
## 43 7 10 2014 N1 11
## 44 7 10 2014 N2 12
## 45 7 10 2014 N3 31
## 46 7 10 2014 N4 54
## 47 7 10 2014 N5 59
## 48 7 10 2014 Cash.Ball 3
## 49 7 14 2014 N1 09
## 50 7 14 2014 N2 19
## 51 7 14 2014 N3 34
## 52 7 14 2014 N4 37
## 53 7 14 2014 N5 49
## 54 7 14 2014 Cash.Ball 2
## 55 7 17 2014 N1 08
## 56 7 17 2014 N2 09
## 57 7 17 2014 N3 22
## 58 7 17 2014 N4 46
## 59 7 17 2014 N5 51
## 60 7 17 2014 Cash.Ball 1
## 61 7 21 2014 N1 04
## 62 7 21 2014 N2 06
## 63 7 21 2014 N3 11
## 64 7 21 2014 N4 24
## 65 7 21 2014 N5 31
## 66 7 21 2014 Cash.Ball 3
## 67 7 24 2014 N1 05
## 68 7 24 2014 N2 20
## 69 7 24 2014 N3 35
## 70 7 24 2014 N4 43
## 71 7 24 2014 N5 48
## 72 7 24 2014 Cash.Ball 3
## 73 7 28 2014 N1 06
## 74 7 28 2014 N2 15
## 75 7 28 2014 N3 31
## 76 7 28 2014 N4 51
## 77 7 28 2014 N5 53
## 78 7 28 2014 Cash.Ball 2
## 79 7 31 2014 N1 13
## 80 7 31 2014 N2 25
## 81 7 31 2014 N3 26
## 82 7 31 2014 N4 32
## 83 7 31 2014 N5 58
## 84 7 31 2014 Cash.Ball 1
## 85 8 4 2014 N1 17
## 86 8 4 2014 N2 21
## 87 8 4 2014 N3 36
## 88 8 4 2014 N4 48
## 89 8 4 2014 N5 60
## 90 8 4 2014 Cash.Ball 3
## 91 8 7 2014 N1 03
## 92 8 7 2014 N2 18
## 93 8 7 2014 N3 38
## 94 8 7 2014 N4 40
## 95 8 7 2014 N5 49
## 96 8 7 2014 Cash.Ball 2
## 97 8 11 2014 N1 05
## 98 8 11 2014 N2 10
## 99 8 11 2014 N3 33
## 100 8 11 2014 N4 48
## 101 8 11 2014 N5 57
## 102 8 11 2014 Cash.Ball 3
## 103 8 14 2014 N1 03
## 104 8 14 2014 N2 11
## 105 8 14 2014 N3 34
## 106 8 14 2014 N4 46
## 107 8 14 2014 N5 60
## 108 8 14 2014 Cash.Ball 2
## 109 8 18 2014 N1 06
## 110 8 18 2014 N2 10
## 111 8 18 2014 N3 29
## 112 8 18 2014 N4 44
## 113 8 18 2014 N5 57
## 114 8 18 2014 Cash.Ball 2
## 115 8 21 2014 N1 11
## 116 8 21 2014 N2 24
## 117 8 21 2014 N3 40
## 118 8 21 2014 N4 43
## 119 8 21 2014 N5 47
## 120 8 21 2014 Cash.Ball 2
## 121 8 25 2014 N1 06
## 122 8 25 2014 N2 08
## 123 8 25 2014 N3 34
## 124 8 25 2014 N4 40
## 125 8 25 2014 N5 59
## 126 8 25 2014 Cash.Ball 4
## 127 8 28 2014 N1 12
## 128 8 28 2014 N2 24
## 129 8 28 2014 N3 27
## 130 8 28 2014 N4 38
## 131 8 28 2014 N5 49
## 132 8 28 2014 Cash.Ball 2
## 133 9 1 2014 N1 09
## 134 9 1 2014 N2 21
## 135 9 1 2014 N3 27
## 136 9 1 2014 N4 45
## 137 9 1 2014 N5 53
## 138 9 1 2014 Cash.Ball 2
## 139 9 4 2014 N1 03
## 140 9 4 2014 N2 21
## 141 9 4 2014 N3 28
## 142 9 4 2014 N4 32
## 143 9 4 2014 N5 56
## 144 9 4 2014 Cash.Ball 3
## 145 9 8 2014 N1 01
## 146 9 8 2014 N2 17
## 147 9 8 2014 N3 23
## 148 9 8 2014 N4 44
## 149 9 8 2014 N5 45
## 150 9 8 2014 Cash.Ball 2
## 151 9 11 2014 N1 12
## 152 9 11 2014 N2 44
## 153 9 11 2014 N3 56
## 154 9 11 2014 N4 58
## 155 9 11 2014 N5 59
## 156 9 11 2014 Cash.Ball 2
## 157 9 15 2014 N1 03
## 158 9 15 2014 N2 05
## 159 9 15 2014 N3 09
## 160 9 15 2014 N4 32
## 161 9 15 2014 N5 56
## 162 9 15 2014 Cash.Ball 1
## 163 9 18 2014 N1 05
## 164 9 18 2014 N2 10
## 165 9 18 2014 N3 11
## 166 9 18 2014 N4 24
## 167 9 18 2014 N5 28
## 168 9 18 2014 Cash.Ball 3
## 169 9 22 2014 N1 28
## 170 9 22 2014 N2 39
## 171 9 22 2014 N3 40
## 172 9 22 2014 N4 53
## 173 9 22 2014 N5 54
## 174 9 22 2014 Cash.Ball 1
## 175 9 25 2014 N1 09
## 176 9 25 2014 N2 11
## 177 9 25 2014 N3 28
## 178 9 25 2014 N4 30
## 179 9 25 2014 N5 41
## 180 9 25 2014 Cash.Ball 4
## 181 9 29 2014 N1 19
## 182 9 29 2014 N2 36
## 183 9 29 2014 N3 37
## 184 9 29 2014 N4 43
## 185 9 29 2014 N5 50
## 186 9 29 2014 Cash.Ball 1
## 187 10 2 2014 N1 23
## 188 10 2 2014 N2 34
## 189 10 2 2014 N3 35
## 190 10 2 2014 N4 44
## 191 10 2 2014 N5 52
## 192 10 2 2014 Cash.Ball 1
## 193 10 6 2014 N1 16
## 194 10 6 2014 N2 30
## 195 10 6 2014 N3 37
## 196 10 6 2014 N4 52
## 197 10 6 2014 N5 55
## 198 10 6 2014 Cash.Ball 3
## 199 10 9 2014 N1 23
## 200 10 9 2014 N2 26
## [ reached getOption("max.print") -- omitted 1516 rows ]library(plotly)
ggplot(dlottery, aes(x = Year, y=Number)) + coord_flip() + geom_bar(stat="identity") + xlab("Year") + ylab("Number") 