library(lubridate)
## Loading required package: timechange
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
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(reshape)
##
## Attaching package: 'reshape'
## The following object is masked from 'package:dplyr':
##
## rename
## The following object is masked from 'package:lubridate':
##
## stamp
rename=dplyr::rename
#part3 09
df<-read.csv("disease.csv")
glimpse(df)
## Rows: 4
## Columns: 194
## $ year <int> 1999, 2000, 2001, 2002
## $ Afghanistan <int> 0, 0, 0, 0
## $ Albania <dbl> 89.0, 132.0, 54.0, 4.9
## $ Algeria <dbl> 25.0, 0.0, 14.0, 0.7
## $ Andorra <dbl> 245.0, 138.0, 312.0, 12.4
## $ Angola <dbl> 217.0, 57.0, 45.0, 5.9
## $ Antigua...Barbuda <dbl> 102.0, 128.0, 45.0, 4.9
## $ Argentina <dbl> 193.0, 25.0, 221.0, 8.3
## $ Armenia <dbl> 21.0, 179.0, 11.0, 3.8
## $ Australia <dbl> 261.0, 72.0, 212.0, 10.4
## $ Austria <dbl> 279.0, 75.0, 191.0, 9.7
## $ Azerbaijan <dbl> 21.0, 46.0, 5.0, 1.3
## $ Bahamas <dbl> 122.0, 176.0, 51.0, 6.3
## $ Bahrain <int> 42, 63, 7, 2
## $ Bangladesh <int> 0, 0, 0, 0
## $ Barbados <dbl> 143.0, 173.0, 36.0, 6.3
## $ Belarus <dbl> 142.0, 373.0, 42.0, 14.4
## $ Belgium <dbl> 295.0, 84.0, 212.0, 10.5
## $ Belize <dbl> 263.0, 114.0, 8.0, 6.8
## $ Benin <dbl> 34.0, 4.0, 13.0, 1.1
## $ Bhutan <dbl> 23.0, 0.0, 0.0, 0.4
## $ Bolivia <dbl> 167.0, 41.0, 8.0, 3.8
## $ Bosnia.Herzegovina <dbl> 76.0, 173.0, 8.0, 4.6
## $ Botswana <dbl> 173.0, 35.0, 35.0, 5.4
## $ Brazil <dbl> 245.0, 145.0, 16.0, 7.2
## $ Brunei <dbl> 31.0, 2.0, 1.0, 0.6
## $ Bulgaria <dbl> 231.0, 252.0, 94.0, 10.3
## $ Burkina.Faso <dbl> 25.0, 7.0, 7.0, 4.3
## $ Burundi <dbl> 88.0, 0.0, 0.0, 6.3
## $ Cote.d.Ivoire <int> 37, 1, 7, 4
## $ Cabo.Verde <int> 144, 56, 16, 4
## $ Cambodia <dbl> 57.0, 65.0, 1.0, 2.2
## $ Cameroon <dbl> 147.0, 1.0, 4.0, 5.8
## $ Canada <dbl> 240.0, 122.0, 100.0, 8.2
## $ Central.African.Republic <dbl> 17.0, 2.0, 1.0, 1.8
## $ Chad <dbl> 15.0, 1.0, 1.0, 0.4
## $ Chile <dbl> 130.0, 124.0, 172.0, 7.6
## $ China <int> 79, 192, 8, 5
## $ Colombia <dbl> 159.0, 76.0, 3.0, 4.2
## $ Comoros <dbl> 1.0, 3.0, 1.0, 0.1
## $ Congo <dbl> 76.0, 1.0, 9.0, 1.7
## $ Cook.Islands <dbl> 0.0, 254.0, 74.0, 5.9
## $ Costa.Rica <dbl> 149.0, 87.0, 11.0, 4.4
## $ Croatia <dbl> 230.0, 87.0, 254.0, 10.2
## $ Cuba <dbl> 93.0, 137.0, 5.0, 4.2
## $ Cyprus <dbl> 192.0, 154.0, 113.0, 8.2
## $ Czech.Republic <dbl> 361.0, 170.0, 134.0, 11.8
## $ North.Korea <int> 0, 0, 0, 0
## $ DR.Congo <dbl> 32.0, 3.0, 1.0, 2.3
## $ Denmark <dbl> 224.0, 81.0, 278.0, 10.4
## $ Djibouti <dbl> 15.0, 44.0, 3.0, 1.1
## $ Dominica <dbl> 52.0, 286.0, 26.0, 6.6
## $ Dominican.Republic <dbl> 193.0, 147.0, 9.0, 6.2
## $ Ecuador <dbl> 162.0, 74.0, 3.0, 4.2
## $ Egypt <dbl> 6.0, 4.0, 1.0, 0.2
## $ El.Salvador <dbl> 52.0, 69.0, 2.0, 2.2
## $ Equatorial.Guinea <dbl> 92.0, 0.0, 233.0, 5.8
## $ Eritrea <dbl> 18.0, 0.0, 0.0, 0.5
## $ Estonia <dbl> 224.0, 194.0, 59.0, 9.5
## $ Ethiopia <dbl> 20.0, 3.0, 0.0, 0.7
## $ Fiji <int> 77, 35, 1, 2
## $ Finland <int> 263, 133, 97, 10
## $ France <dbl> 127.0, 151.0, 370.0, 11.8
## $ Gabon <dbl> 347.0, 98.0, 59.0, 8.9
## $ Gambia <dbl> 8.0, 0.0, 1.0, 2.4
## $ Georgia <dbl> 52.0, 100.0, 149.0, 5.4
## $ Germany <dbl> 346.0, 117.0, 175.0, 11.3
## $ Ghana <dbl> 31.0, 3.0, 10.0, 1.8
## $ Greece <dbl> 133.0, 112.0, 218.0, 8.3
## $ Grenada <dbl> 199.0, 438.0, 28.0, 11.9
## $ Guatemala <dbl> 53.0, 69.0, 2.0, 2.2
## $ Guinea <dbl> 9.0, 0.0, 2.0, 0.2
## $ Guinea.Bissau <dbl> 28.0, 31.0, 21.0, 2.5
## $ Guyana <dbl> 93.0, 302.0, 1.0, 7.1
## $ Haiti <dbl> 1.0, 326.0, 1.0, 5.9
## $ Honduras <int> 69, 98, 2, 3
## $ Hungary <dbl> 234.0, 215.0, 185.0, 11.3
## $ Iceland <dbl> 233.0, 61.0, 78.0, 6.6
## $ India <dbl> 9.0, 114.0, 0.0, 2.2
## $ Indonesia <dbl> 5.0, 1.0, 0.0, 0.1
## $ Iran <int> 0, 0, 0, 0
## $ Iraq <dbl> 9.0, 3.0, 0.0, 0.2
## $ Ireland <dbl> 313.0, 118.0, 165.0, 11.4
## $ Israel <dbl> 63.0, 69.0, 9.0, 2.5
## $ Italy <dbl> 85.0, 42.0, 237.0, 6.5
## $ Jamaica <dbl> 82.0, 88.0, 9.0, 3.4
## $ Japan <int> 77, 202, 16, 7
## $ Jordan <dbl> 6.0, 21.0, 1.0, 0.5
## $ Kazakhstan <dbl> 124.0, 246.0, 12.0, 6.8
## $ Kenya <dbl> 58.0, 22.0, 2.0, 1.8
## $ Kiribati <int> 21, 34, 1, 1
## $ Kuwait <int> 0, 0, 0, 0
## $ Kyrgyzstan <dbl> 31.0, 88.0, 6.0, 2.4
## $ Laos <dbl> 62.0, 0.0, 123.0, 6.2
## $ Latvia <dbl> 281.0, 216.0, 62.0, 10.5
## $ Lebanon <dbl> 20.0, 55.0, 31.0, 1.9
## $ Lesotho <dbl> 82.0, 50.0, 0.0, 2.8
## $ Liberia <dbl> 19.0, 152.0, 2.0, 3.1
## $ Libya <int> 0, 0, 0, 0
## $ Lithuania <dbl> 343.0, 244.0, 56.0, 12.9
## $ Luxembourg <dbl> 236.0, 133.0, 271.0, 11.4
## $ Madagascar <dbl> 26.0, 15.0, 4.0, 0.8
## $ Malawi <dbl> 8.0, 11.0, 1.0, 1.5
## $ Malaysia <dbl> 13.0, 4.0, 0.0, 0.3
## $ Maldives <int> 0, 0, 0, 0
## $ Mali <dbl> 5.0, 1.0, 1.0, 0.6
## $ Malta <dbl> 149.0, 100.0, 120.0, 6.6
## $ Marshall.Islands <int> 0, 0, 0, 0
## $ Mauritania <int> 0, 0, 0, 0
## $ Mauritius <dbl> 98.0, 31.0, 18.0, 2.6
## $ Mexico <dbl> 238.0, 68.0, 5.0, 5.5
## $ Micronesia <dbl> 62.0, 50.0, 18.0, 2.3
## $ Monaco <int> 0, 0, 0, 0
## $ Mongolia <dbl> 77.0, 189.0, 8.0, 4.9
## $ Montenegro <dbl> 31.0, 114.0, 128.0, 4.9
## $ Morocco <dbl> 12.0, 6.0, 10.0, 0.5
## $ Mozambique <dbl> 47.0, 18.0, 5.0, 1.3
## $ Myanmar <dbl> 5.0, 1.0, 0.0, 0.1
## $ Namibia <dbl> 376.0, 3.0, 1.0, 6.8
## $ Nauru <int> 49, 0, 8, 1
## $ Nepal <dbl> 5.0, 6.0, 0.0, 0.2
## $ Netherlands <dbl> 251.0, 88.0, 190.0, 9.4
## $ New.Zealand <dbl> 203.0, 79.0, 175.0, 9.3
## $ Nicaragua <dbl> 78.0, 118.0, 1.0, 3.5
## $ Niger <dbl> 3.0, 2.0, 1.0, 0.1
## $ Nigeria <dbl> 42.0, 5.0, 2.0, 9.1
## $ Niue <int> 188, 200, 7, 7
## $ Norway <dbl> 169.0, 71.0, 129.0, 6.7
## $ Oman <dbl> 22.0, 16.0, 1.0, 0.7
## $ Pakistan <int> 0, 0, 0, 0
## $ Palau <dbl> 306.0, 63.0, 23.0, 6.9
## $ Panama <dbl> 285.0, 104.0, 18.0, 7.2
## $ Papua.New.Guinea <dbl> 44.0, 39.0, 1.0, 1.5
## $ Paraguay <dbl> 213.0, 117.0, 74.0, 7.3
## $ Peru <dbl> 163.0, 160.0, 21.0, 6.1
## $ Philippines <dbl> 71.0, 186.0, 1.0, 4.6
## $ Poland <dbl> 343.0, 215.0, 56.0, 10.9
## $ Portugal <int> 194, 67, 339, 11
## $ Qatar <dbl> 1.0, 42.0, 7.0, 0.9
## $ South.Korea <dbl> 140.0, 16.0, 9.0, 9.8
## $ Moldova <dbl> 109.0, 226.0, 18.0, 6.3
## $ Romania <dbl> 297.0, 122.0, 167.0, 10.4
## $ Russian.Federation <dbl> 247.0, 326.0, 73.0, 11.5
## $ Rwanda <dbl> 43.0, 2.0, 0.0, 6.8
## $ St..Kitts...Nevis <dbl> 194.0, 205.0, 32.0, 7.7
## $ St..Lucia <dbl> 171.0, 315.0, 71.0, 10.1
## $ St..Vincent...the.Grenadines <dbl> 120.0, 221.0, 11.0, 6.3
## $ Samoa <dbl> 105.0, 18.0, 24.0, 2.6
## $ San.Marino <int> 0, 0, 0, 0
## $ Sao.Tome...Principe <dbl> 56.0, 38.0, 140.0, 4.2
## $ Saudi.Arabia <dbl> 0.0, 5.0, 0.0, 0.1
## $ Senegal <dbl> 9.0, 1.0, 7.0, 0.3
## $ Serbia <dbl> 283.0, 131.0, 127.0, 9.6
## $ Seychelles <dbl> 157.0, 25.0, 51.0, 4.1
## $ Sierra.Leone <dbl> 25.0, 3.0, 2.0, 6.7
## $ Singapore <dbl> 60.0, 12.0, 11.0, 1.5
## $ Slovakia <dbl> 196.0, 293.0, 116.0, 11.4
## $ Slovenia <dbl> 270.0, 51.0, 276.0, 10.6
## $ Solomon.Islands <dbl> 56.0, 11.0, 1.0, 1.2
## $ Somalia <int> 0, 0, 0, 0
## $ South.Africa <dbl> 225.0, 76.0, 81.0, 8.2
## $ Spain <int> 284, 157, 112, 10
## $ Sri.Lanka <dbl> 16.0, 104.0, 0.0, 2.2
## $ Sudan <dbl> 8.0, 13.0, 0.0, 1.7
## $ Suriname <dbl> 128.0, 178.0, 7.0, 5.6
## $ Swaziland <dbl> 90.0, 2.0, 2.0, 4.7
## $ Sweden <dbl> 152.0, 60.0, 186.0, 7.2
## $ Switzerland <dbl> 185.0, 100.0, 280.0, 10.2
## $ Syria <int> 5, 35, 16, 1
## $ Tajikistan <dbl> 2.0, 15.0, 0.0, 0.3
## $ Thailand <dbl> 99.0, 258.0, 1.0, 6.4
## $ Macedonia <dbl> 106.0, 27.0, 86.0, 3.9
## $ Timor.Leste <dbl> 1.0, 1.0, 4.0, 0.1
## $ Togo <dbl> 36.0, 2.0, 19.0, 1.3
## $ Tonga <dbl> 36.0, 21.0, 5.0, 1.1
## $ Trinidad...Tobago <dbl> 197.0, 156.0, 7.0, 6.4
## $ Tunisia <dbl> 51.0, 3.0, 20.0, 1.3
## $ Turkey <dbl> 51.0, 22.0, 7.0, 1.4
## $ Turkmenistan <dbl> 19.0, 71.0, 32.0, 2.2
## $ Tuvalu <int> 6, 41, 9, 1
## $ Uganda <dbl> 45.0, 9.0, 0.0, 8.3
## $ Ukraine <dbl> 206.0, 237.0, 45.0, 8.9
## $ United.Arab.Emirates <dbl> 16.0, 135.0, 5.0, 2.8
## $ United.Kingdom <dbl> 219.0, 126.0, 195.0, 10.4
## $ Tanzania <dbl> 36.0, 6.0, 1.0, 5.7
## $ USA <dbl> 249.0, 158.0, 84.0, 8.7
## $ Uruguay <dbl> 115.0, 35.0, 220.0, 6.6
## $ Uzbekistan <dbl> 25.0, 101.0, 8.0, 2.4
## $ Vanuatu <dbl> 21.0, 18.0, 11.0, 0.9
## $ Venezuela <dbl> 333.0, 100.0, 3.0, 7.7
## $ Vietnam <int> 111, 2, 1, 2
## $ Yemen <dbl> 6.0, 0.0, 0.0, 0.1
## $ Zambia <dbl> 32.0, 19.0, 4.0, 2.5
## $ Zimbabwe <dbl> 64.0, 18.0, 4.0, 4.7
df1<-melt(df,id='year')
glimpse(df1)
## Rows: 772
## Columns: 3
## $ year <int> 1999, 2000, 2001, 2002, 1999, 2000, 2001, 2002, 1999, 2000, 2…
## $ variable <fct> Afghanistan, Afghanistan, Afghanistan, Afghanistan, Albania, …
## $ value <dbl> 0.0, 0.0, 0.0, 0.0, 89.0, 132.0, 54.0, 4.9, 25.0, 0.0, 14.0, …
names(df1)[2:3]<-c('country','disease')
df1 %>% filter(year==2000) %>% summarize(m=mean(disease))
## m
## 1 81.01036
df1 %>% filter(year==2000) %>% filter(disease>81.01036) %>% nrow->result
print(result)
## [1] 76
#part3 10
x<-sample(1:20,10)
y<-c(1,2,3,4,5,37,41,42,44,10)
a<-data.frame(x,y)
quantile(a$y)
## 0% 25% 50% 75% 100%
## 1.00 3.25 7.50 40.00 44.00
df<-quantile(a$y)
df1<-abs(df[[2]]-df[[4]]) #abs()<- 절대값 함수
df1<-floor(df1) # floor()<- 소수점 버림 함수
cat(df1)
## 36
#part3 11
df<-read.csv("facebook.csv")
glimpse(df)
## Rows: 6,997
## Columns: 16
## $ status_id <chr> "246675545449582_1649696485147474", "24667554544958…
## $ status_type <chr> "video", "photo", "video", "photo", "photo", "photo…
## $ status_published <chr> "4/22/2018 6:00", "4/21/2018 22:45", "4/21/2018 6:1…
## $ num_reactions <int> 529, 150, 227, 111, 213, 217, 503, 295, 203, 170, 2…
## $ num_comments <int> 512, 0, 236, 0, 0, 6, 614, 453, 1, 9, 2, 4, 4, 4, 1…
## $ num_shares <int> 262, 0, 57, 0, 0, 0, 72, 53, 0, 1, 3, 0, 2, 0, 0, 3…
## $ num_likes <int> 432, 150, 204, 111, 204, 211, 418, 260, 198, 167, 2…
## $ num_loves <int> 92, 0, 21, 0, 9, 5, 70, 32, 5, 3, 7, 5, 6, 8, 10, 2…
## $ num_wows <int> 3, 0, 1, 0, 0, 1, 10, 1, 0, 0, 1, 4, 2, 1, 1, 1, 0,…
## $ num_hahas <int> 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 5, 0, …
## $ num_sads <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ num_angrys <int> 0, 0, 0, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ react_comment_r <dbl> 1.0332031, 0.0000000, 0.9618644, 0.0000000, 0.00000…
## $ react_share_r <dbl> 2.019084, 0.000000, 3.982456, 0.000000, 0.000000, 0…
## $ postive_reactions <int> 527, 150, 226, 111, 213, 217, 498, 293, 203, 170, 2…
## $ negative_reactions <int> 2, 0, 1, 0, 0, 0, 5, 2, 0, 0, 0, 0, 0, 0, 0, 5, 0, …
colSums(is.na(df))
## status_id status_type status_published num_reactions
## 0 0 0 0
## num_comments num_shares num_likes num_loves
## 0 0 0 0
## num_wows num_hahas num_sads num_angrys
## 0 0 0 0
## react_comment_r react_share_r postive_reactions negative_reactions
## 120 117 0 0
df1<- df %>% mutate(ratio=(num_loves+num_wows)/(num_reactions)) %>% filter(ratio<0.5&ratio>0.4) %>%
filter(status_type=='video') %>% nrow
cat(df1)
## 90
print(df1)
## [1] 90
#part3 12
df<- read.csv("netflix.csv")
glimpse(df)
## Rows: 8,807
## Columns: 11
## $ show_id <chr> "s1", "s2", "s3", "s4", "s5", "s6", "s7", "s8", "s9", "s1…
## $ type <chr> "Movie", "TV Show", "TV Show", "TV Show", "TV Show", "TV …
## $ title <chr> "Dick Johnson Is Dead", "Blood & Water", "Ganglands", "Ja…
## $ director <chr> "Kirsten Johnson", "", "Julien Leclercq", "", "", "Mike F…
## $ cast <chr> "", "Ama Qamata, Khosi Ngema, Gail Mabalane, Thabang Mola…
## $ country <chr> "United States", "South Africa", "", "", "India", "", "",…
## $ date_added <chr> "25-Sep-21", "24-Sep-21", "24-Sep-21", "24-Sep-21", "24-S…
## $ release_year <int> 2020, 2021, 2021, 2021, 2021, 2021, 2021, 1993, 2021, 202…
## $ rating <chr> "PG-13", "TV-MA", "TV-MA", "TV-MA", "TV-MA", "TV-MA", "PG…
## $ duration <chr> "90 min", "2 Seasons", "1 Season", "1 Season", "2 Seasons…
## $ listed_in <chr> "Documentaries", "International TV Shows, TV Dramas, TV M…
df %>% filter(country=='United Kingdom') %>% mutate(ymd=dmy(date_added)) %>% select(ymd) %>%
filter(ymd>='2018-01-01'&ymd<='2018-01-30')
## Warning: 15 failed to parse.
## ymd
## 1 2018-01-30
## 2 2018-01-18
## 3 2018-01-01
## 4 2018-01-15
## 5 2018-01-01
df %>% filter(country=='United Kingdom') %>% count(date_added)
## date_added n
## 1 1
## 2 April 4, 2017 1
## 3 December 1, 2018 1
## 4 December 15, 2017 1
## 5 December 15, 2018 1
## 6 December 2, 2017 1
## 7 February 1, 2019 1
## 8 January 1, 2018 1
## 9 July 26, 2019 1
## 10 March 16, 2016 1
## 11 March 31, 2017 1
## 12 March 31, 2018 2
## 13 October 1, 2019 2
## 14 September 1, 2019 1
## 15 01-Apr-17 2
## 16 01-Apr-18 2
## 17 01-Apr-19 1
## 18 01-Apr-20 3
## 19 01-Apr-21 2
## 20 01-Aug-16 6
## 21 01-Aug-17 11
## 22 01-Aug-18 1
## 23 01-Aug-19 4
## 24 01-Aug-20 1
## 25 01-Dec-17 2
## 26 01-Dec-18 2
## 27 01-Dec-19 1
## 28 01-Dec-20 2
## 29 01-Feb-18 2
## 30 01-Feb-19 11
## 31 01-Feb-20 1
## 32 01-Feb-21 1
## 33 01-Jan-18 2
## 34 01-Jan-19 2
## 35 01-Jan-20 2
## 36 01-Jan-21 1
## 37 01-Jul-17 1
## 38 01-Jul-20 1
## 39 01-Jul-21 1
## 40 01-Jun-16 1
## 41 01-Jun-17 4
## 42 01-Mar-17 7
## 43 01-Mar-18 2
## 44 01-Mar-19 2
## 45 01-May-17 1
## 46 01-May-18 2
## 47 01-May-19 3
## 48 01-May-21 1
## 49 01-Nov-16 1
## 50 01-Nov-18 1
## 51 01-Nov-19 3
## 52 01-Oct-16 1
## 53 01-Oct-17 4
## 54 01-Oct-18 2
## 55 01-Oct-19 1
## 56 01-Sep-16 5
## 57 01-Sep-17 1
## 58 01-Sep-19 1
## 59 01-Sep-20 1
## 60 02-Dec-20 1
## 61 02-Feb-19 1
## 62 02-Jan-19 1
## 63 02-Jun-17 1
## 64 02-Jun-21 3
## 65 02-Nov-16 1
## 66 02-Oct-18 10
## 67 02-Oct-19 3
## 68 03-Dec-18 1
## 69 03-Dec-19 1
## 70 03-Mar-20 1
## 71 03-May-19 1
## 72 03-Sep-20 1
## 73 04-Jan-20 1
## 74 04-Jun-21 1
## 75 04-Nov-17 1
## 76 04-Nov-19 1
## 77 04-Oct-19 1
## 78 05-Apr-19 2
## 79 05-Aug-21 1
## 80 05-Feb-19 1
## 81 05-Jan-19 1
## 82 05-Jun-19 1
## 83 05-Mar-20 1
## 84 05-Nov-19 1
## 85 05-Oct-18 1
## 86 05-Sep-20 1
## 87 06-Jul-21 5
## 88 06-Oct-20 1
## 89 06-Sep-16 1
## 90 06-Sep-17 1
## 91 07-Sep-21 1
## 92 08-Aug-21 1
## 93 08-Feb-19 1
## 94 08-Jan-19 1
## 95 08-Jan-21 1
## 96 08-May-18 1
## 97 08-Nov-19 1
## 98 09-Dec-16 1
## 99 09-Jan-20 1
## 100 09-Mar-17 1
## 101 09-Nov-16 1
## 102 09-Sep-16 1
## 103 10-Apr-18 1
## 104 10-Jan-20 2
## 105 10-Jul-18 1
## 106 10-Oct-15 1
## 107 10-Sep-19 2
## 108 11-Jan-19 1
## 109 11-Sep-17 1
## 110 11-Sep-20 1
## 111 12-Dec-17 1
## 112 12-Dec-19 1
## 113 12-Feb-21 1
## 114 12-Jan-21 1
## 115 12-Jul-19 4
## 116 12-Mar-21 1
## 117 12-May-17 1
## 118 13-Aug-17 1
## 119 13-Feb-21 1
## 120 13-Jul-17 1
## 121 13-Mar-18 1
## 122 13-May-21 1
## 123 13-Oct-17 1
## 124 13-Oct-20 1
## 125 14-Aug-20 1
## 126 14-Feb-17 1
## 127 14-May-17 1
## 128 14-Sep-21 1
## 129 15-Apr-17 1
## 130 15-Apr-20 1
## 131 15-Aug-16 3
## 132 15-Dec-16 2
## 133 15-Dec-18 2
## 134 15-Feb-19 1
## 135 15-Feb-21 1
## 136 15-Jan-18 1
## 137 15-Jun-15 1
## 138 15-Jun-17 1
## 139 15-Mar-17 1
## 140 15-Mar-19 2
## 141 15-Mar-21 1
## 142 15-May-16 1
## 143 15-May-17 1
## 144 15-May-19 2
## 145 15-May-20 2
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a=6
cat(a)
## 6
#part4 01
library(caret)
## Loading required package: ggplot2
## Loading required package: lattice
library(recipes)
##
## Attaching package: 'recipes'
## The following object is masked from 'package:stats':
##
## step
library(pROC)
## Type 'citation("pROC")' for a citation.
##
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
##
## cov, smooth, var
x_test<-read.csv('X_test.csv', fileEncoding = 'euc-kr')
x_train<-read.csv('X_train.csv', fileEncoding = 'euc-kr')
y_train<-read.csv('Y_train.csv', fileEncoding = 'euc-kr')
glimpse(x_train)
## Rows: 3,500
## Columns: 10
## $ cust_id <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1…
## $ 총구매액 <dbl> 68282840, 2136000, 3197000, 16077620, 29050000, 1137900…
## $ 최대구매액 <int> 11264000, 2136000, 1639000, 4935000, 24000000, 9552000,…
## $ 환불금액 <int> 6860000, 300000, NA, NA, NA, 462000, 4582000, 29524000,…
## $ 주구매상품 <chr> "기타", "스포츠", "남성 캐주얼", "기타", "보석", "디자…
## $ 주구매지점 <chr> "강남점", "잠실점", "관악점", "광주점", "본 점", "일산…
## $ 내점일수 <int> 19, 2, 2, 18, 2, 3, 5, 63, 18, 1, 25, 3, 2, 27, 84, 152…
## $ 내점당구매건수 <dbl> 3.894737, 1.500000, 2.000000, 2.444444, 1.500000, 1.666…
## $ 주말방문비율 <dbl> 0.52702703, 0.00000000, 0.00000000, 0.31818182, 0.00000…
## $ 구매주기 <int> 17, 1, 1, 16, 85, 42, 42, 5, 15, 0, 13, 89, 16, 10, 4, …
glimpse(y_train)
## Rows: 3,500
## Columns: 2
## $ cust_id <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
## $ gender <int> 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1,…
left_join(x_train,y_train,by='cust_id') %>% mutate(index='train')->train #열결합
glimpse(train)
## Rows: 3,500
## Columns: 12
## $ cust_id <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1…
## $ 총구매액 <dbl> 68282840, 2136000, 3197000, 16077620, 29050000, 1137900…
## $ 최대구매액 <int> 11264000, 2136000, 1639000, 4935000, 24000000, 9552000,…
## $ 환불금액 <int> 6860000, 300000, NA, NA, NA, 462000, 4582000, 29524000,…
## $ 주구매상품 <chr> "기타", "스포츠", "남성 캐주얼", "기타", "보석", "디자…
## $ 주구매지점 <chr> "강남점", "잠실점", "관악점", "광주점", "본 점", "일산…
## $ 내점일수 <int> 19, 2, 2, 18, 2, 3, 5, 63, 18, 1, 25, 3, 2, 27, 84, 152…
## $ 내점당구매건수 <dbl> 3.894737, 1.500000, 2.000000, 2.444444, 1.500000, 1.666…
## $ 주말방문비율 <dbl> 0.52702703, 0.00000000, 0.00000000, 0.31818182, 0.00000…
## $ 구매주기 <int> 17, 1, 1, 16, 85, 42, 42, 5, 15, 0, 13, 89, 16, 10, 4, …
## $ gender <int> 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0…
## $ index <chr> "train", "train", "train", "train", "train", "train", "…
x_test %>% mutate(index='test')->test
glimpse(test)
## Rows: 2,482
## Columns: 11
## $ cust_id <int> 3500, 3501, 3502, 3503, 3504, 3505, 3506, 3507, 3508, 3…
## $ 총구매액 <dbl> 70900400, 310533100, 305264140, 7594080, 1795790, 13000…
## $ 최대구매액 <int> 22000000, 38558000, 14825000, 5225000, 1411200, 2160000…
## $ 환불금액 <int> 4050000, 48034700, 30521000, NA, NA, NA, 39566000, NA, …
## $ 주구매상품 <chr> "골프", "농산물", "가공식품", "주방용품", "수산품", "화…
## $ 주구매지점 <chr> "부산본점", "잠실점", "본 점", "부산본점", "청량리점",…
## $ 내점일수 <int> 13, 90, 101, 5, 3, 5, 144, 1, 1, 28, 21, 3, 23, 30, 3, …
## $ 내점당구매건수 <dbl> 1.461538, 2.433333, 14.623762, 2.000000, 2.666667, 2.20…
## $ 주말방문비율 <dbl> 0.78947368, 0.36986301, 0.08327691, 0.00000000, 0.12500…
## $ 구매주기 <int> 26, 3, 3, 47, 8, 61, 2, 0, 0, 12, 14, 2, 15, 11, 112, 2…
## $ index <chr> "test", "test", "test", "test", "test", "test", "test",…
bind_rows(train,test)->full# 두 데이터셋을 행결합
full$gender<- ifelse(full$gender==0,'남성','여성')
full$gender<-as.factor(full$gender)
full$index<- as.factor(full$index)
names(full)
## [1] "cust_id" "총구매액" "최대구매액" "환불금액"
## [5] "주구매상품" "주구매지점" "내점일수" "내점당구매건수"
## [9] "주말방문비율" "구매주기" "gender" "index"
data<-full %>% rename(total='총구매액',
max='최대구매액',
refund='환불금액',
product='주구매상품',
store='주구매지점',
day='내점일수',
count='내점당구매건수',
week='주말방문비율',
cycle='구매주기') %>%
select(cust_id,index,gender,total,max,refund,product,store,day,count,week,cycle)
colSums(is.na(data))
## cust_id index gender total max refund product store day count
## 0 0 2482 0 0 3906 0 0 0 0
## week cycle
## 0 0
data$refund<- ifelse(is.na(data$refund),0,data$refund)
recipe(gender~.,data=data) %>%
step_YeoJohnson(total,max,refund,day,count,week,cycle) %>%
step_scale(total,max,refund,day,count,week,cycle) %>%
step_center(total,max,refund,day,count,week,cycle) %>% prep() %>% juice()->data1
data1 %>% filter(index=='train') %>% select(-index)->train
data1 %>% filter(index=='test') %>% select(-index)->test
ctrl<-trainControl(method='cv',number=10,
summaryFunction=twoClassSummary,
classProbs = TRUE)
train(gender~.,data=train,
method='rpart',
metric="ROC",
trControl=ctrl)->rffit
train(gender~.,data=train,
method='glm', family=binomial,
metric="ROC",
trControl=ctrl)->rffit1
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
rffit
## CART
##
## 3500 samples
## 10 predictor
## 2 classes: '남성', '여성'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 3150, 3150, 3150, 3151, 3150, 3150, ...
## Resampling results across tuning parameters:
##
## cp ROC Sens Spec
## 0.005319149 0.6287350 0.8062796 0.3471837
## 0.006838906 0.6225665 0.8090696 0.3313960
## 0.007598784 0.6224032 0.8058586 0.3366991
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was cp = 0.005319149.
rffit1
## Generalized Linear Model
##
## 3500 samples
## 10 predictor
## 2 classes: '남성', '여성'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 3150, 3150, 3149, 3150, 3150, 3150, ...
## Resampling results:
##
## ROC Sens Spec
## 0.6819856 0.8461564 0.3761682
predict(rffit,test,type='prob')->pred_fit1
head(pred_fit1)
## 남성 여성
## 1 0.7364290 0.2635710
## 2 0.7364290 0.2635710
## 3 0.7364290 0.2635710
## 4 0.4471698 0.5528302
## 5 0.4471698 0.5528302
## 6 0.6927711 0.3072289
names(pred_fit1)[1]<-'gender'
head(pred_fit1)
## gender 여성
## 1 0.7364290 0.2635710
## 2 0.7364290 0.2635710
## 3 0.7364290 0.2635710
## 4 0.4471698 0.5528302
## 5 0.4471698 0.5528302
## 6 0.6927711 0.3072289
bind_cols(x_test,pred_fit1) %>% select(cust_id,gender)->df
df
## cust_id gender
## 1 3500 0.7364290
## 2 3501 0.7364290
## 3 3502 0.7364290
## 4 3503 0.4471698
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## 2375 5874 0.7364290
## 2376 5875 0.7364290
## 2377 5876 0.2173913
## 2378 5877 0.3913043
## 2379 5878 0.7010309
## 2380 5879 0.3913043
## 2381 5880 0.4471698
## 2382 5881 0.4471698
## 2383 5882 0.6927711
## 2384 5883 0.7364290
## 2385 5884 0.7364290
## 2386 5885 0.7364290
## 2387 5886 0.7364290
## 2388 5887 0.4471698
## 2389 5888 0.7364290
## 2390 5889 0.4471698
## 2391 5890 0.6666667
## 2392 5891 0.5934066
## 2393 5892 0.6666667
## 2394 5893 0.7364290
## 2395 5894 0.5934066
## 2396 5895 0.4471698
## 2397 5896 0.5934066
## 2398 5897 0.5934066
## 2399 5898 0.7364290
## 2400 5899 0.7364290
## 2401 5900 0.7364290
## 2402 5901 0.7364290
## 2403 5902 0.7364290
## 2404 5903 0.7364290
## 2405 5904 0.7364290
## 2406 5905 0.3913043
## 2407 5906 0.3913043
## 2408 5907 0.3913043
## 2409 5908 0.6666667
## 2410 5909 0.7364290
## 2411 5910 0.8281250
## 2412 5911 0.7364290
## 2413 5912 0.7364290
## 2414 5913 0.4471698
## 2415 5914 0.7364290
## 2416 5915 0.4471698
## 2417 5916 0.3913043
## 2418 5917 0.3913043
## 2419 5918 0.4471698
## 2420 5919 0.4471698
## 2421 5920 0.7364290
## 2422 5921 0.7364290
## 2423 5922 0.7724138
## 2424 5923 0.3913043
## 2425 5924 0.4471698
## 2426 5925 0.7364290
## 2427 5926 0.5934066
## 2428 5927 0.6619718
## 2429 5928 0.7364290
## 2430 5929 0.7364290
## 2431 5930 0.6666667
## 2432 5931 0.7364290
## 2433 5932 0.4471698
## 2434 5933 0.7364290
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## 2436 5935 0.7364290
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## 2438 5937 0.5934066
## 2439 5938 0.7010309
## 2440 5939 0.5934066
## 2441 5940 0.6927711
## 2442 5941 0.7364290
## 2443 5942 0.7364290
## 2444 5943 0.4471698
## 2445 5944 0.7364290
## 2446 5945 0.7364290
## 2447 5946 0.7364290
## 2448 5947 0.7364290
## 2449 5948 0.3913043
## 2450 5949 0.7364290
## 2451 5950 0.5934066
## 2452 5951 0.3913043
## 2453 5952 0.7364290
## 2454 5953 0.7364290
## 2455 5954 0.8281250
## 2456 5955 0.4471698
## 2457 5956 0.7364290
## 2458 5957 0.4471698
## 2459 5958 0.7364290
## 2460 5959 0.8281250
## 2461 5960 0.3913043
## 2462 5961 0.7364290
## 2463 5962 0.4471698
## 2464 5963 0.7364290
## 2465 5964 0.4471698
## 2466 5965 0.7364290
## 2467 5966 0.1333333
## 2468 5967 0.4471698
## 2469 5968 0.4471698
## 2470 5969 0.3913043
## 2471 5970 0.7364290
## 2472 5971 0.3913043
## 2473 5972 0.7364290
## 2474 5973 0.6927711
## 2475 5974 0.4471698
## 2476 5975 0.1333333
## 2477 5976 0.7364290
## 2478 5977 0.5934066
## 2479 5978 0.4471698
## 2480 5979 0.7364290
## 2481 5980 0.3913043
## 2482 5981 0.4471698
#part4 02
train<-read.csv("insurance_train_10.csv")
test<-read.csv("insurance_test_10.csv")
glimpse(train)
## Rows: 6,969
## Columns: 9
## $ Gender <chr> "Male", "Female", "Male", "Male", "Male", "Female", "F…
## $ Ever_Married <chr> "No", "Yes", "Yes", "Yes", "No", "No", "Yes", "Yes", "…
## $ Age <int> 22, 67, 67, 56, 32, 33, 61, 55, 26, 19, 58, 41, 32, 31…
## $ Graduated <chr> "No", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "Yes", …
## $ Profession <chr> "Healthcare", "Engineer", "Lawyer", "Artist", "Healthc…
## $ Work_Experience <int> 1, 1, 0, 0, 1, 1, 0, 1, 1, 4, 0, 1, 9, 1, 1, 0, 12, 3,…
## $ Spending_Score <chr> "Low", "Low", "High", "Average", "Low", "Low", "Low", …
## $ Family_Size <int> 4, 1, 2, 2, 3, 3, 3, 4, 3, 4, 1, 2, 5, 6, 4, 1, 1, 4, …
## $ Segmentation <int> 4, 2, 2, 3, 3, 4, 4, 3, 1, 4, 2, 3, 4, 2, 2, 3, 1, 4, …
glimpse(test)
## Rows: 2,267
## Columns: 9
## $ X <int> 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17…
## $ Gender <chr> "Female", "Male", "Female", "Male", "Male", "Male", "F…
## $ Ever_Married <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes"…
## $ Age <int> 36, 37, 69, 59, 47, 61, 47, 50, 19, 22, 22, 50, 27, 18…
## $ Graduated <chr> "Yes", "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", …
## $ Profession <chr> "Engineer", "Healthcare", "", "Executive", "Doctor", "…
## $ Work_Experience <int> 0, 8, 0, 11, 0, 5, 1, 2, 0, 0, 0, 1, 8, 0, 0, 1, 1, 8,…
## $ Spending_Score <chr> "Low", "Average", "Low", "High", "High", "Low", "Avera…
## $ Family_Size <int> 1, 4, 1, 2, 5, 3, 3, 4, 4, 3, 6, 5, 3, 3, 1, 3, 2, 1, …
colSums(is.na(train))
## Gender Ever_Married Age Graduated Profession
## 0 0 0 0 0
## Work_Experience Spending_Score Family_Size Segmentation
## 0 0 0 0
colSums(is.na(test))
## X Gender Ever_Married Age Graduated
## 0 0 0 0 0
## Profession Work_Experience Spending_Score Family_Size
## 0 0 0 0
train$Segmentation<-as.factor(train$Segmentation)
library(caret)
ctrl<-trainControl(method='cv',number=10)
train(Segmentation~.,data=train,
method='knn', trControl=ctrl,
preProcess=c('center','scale'))->knn_fit
knn_fit
## k-Nearest Neighbors
##
## 6969 samples
## 8 predictor
## 4 classes: '1', '2', '3', '4'
##
## Pre-processing: centered (19), scaled (19)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 6273, 6270, 6274, 6271, 6273, 6272, ...
## Resampling results across tuning parameters:
##
## k Accuracy Kappa
## 5 0.4838596 0.3105291
## 7 0.4887413 0.3169519
## 9 0.4927565 0.3220579
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was k = 9.
confusionMatrix(knn_fit)
## Cross-Validated (10 fold) Confusion Matrix
##
## (entries are percentual average cell counts across resamples)
##
## Reference
## Prediction 1 2 3 4
## 1 9.7 5.4 2.8 5.3
## 2 5.5 7.3 5.4 2.0
## 3 3.8 8.0 14.1 1.3
## 4 5.3 2.8 3.2 18.2
##
## Accuracy (average) : 0.4928
predict(knn_fit,test)->pred_fit
head(pred_fit)
## [1] 2 1 2 3 3 1
## Levels: 1 2 3 4
NROW(pred_fit)
## [1] 2267
glimpse(test)
## Rows: 2,267
## Columns: 9
## $ X <int> 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17…
## $ Gender <chr> "Female", "Male", "Female", "Male", "Male", "Male", "F…
## $ Ever_Married <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes"…
## $ Age <int> 36, 37, 69, 59, 47, 61, 47, 50, 19, 22, 22, 50, 27, 18…
## $ Graduated <chr> "Yes", "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", …
## $ Profession <chr> "Engineer", "Healthcare", "", "Executive", "Doctor", "…
## $ Work_Experience <int> 0, 8, 0, 11, 0, 5, 1, 2, 0, 0, 0, 1, 8, 0, 0, 1, 1, 8,…
## $ Spending_Score <chr> "Low", "Average", "Low", "High", "High", "Low", "Avera…
## $ Family_Size <int> 1, 4, 1, 2, 5, 3, 3, 4, 4, 3, 6, 5, 3, 3, 1, 3, 2, 1, …
bind_cols(test,pred_fit)->df
## New names:
## • `` -> `...10`
names(df)[9]<-"Segmentation_pred"
df %>% select(9)->df1
write.csv(df1,"수험번호.csv",row.names=FALSE)
head(df1)
## Segmentation_pred
## 1 1
## 2 4
## 3 1
## 4 2
## 5 5
## 6 3