From ae3913bf3db0887986ddfa612ba1364e4662f27f Mon Sep 17 00:00:00 2001
From: DH K <kmensch0002@gmail.com>
Date: Fri, 20 Mar 2026 17:04:54 +0900
Subject: [PATCH] study 03/20

---
 .Rhistory                                    | 575 ++-----------------
 .Rproj.user/F3FE0F57/pcs/workbench-pane.pper |   2 +-
 .Rproj.user/F3FE0F57/sources/prop/0CB164D0   |   4 +-
 .Rproj.user/F3FE0F57/sources/prop/INDEX      |   1 +
 .Rproj.user/shared/notebooks/paths           |   2 +-
 기초 명령어 실습.R                           |  14 +
 6 files changed, 82 insertions(+), 516 deletions(-)

diff --git a/.Rhistory b/.Rhistory
index 102aefb..6c3e94d 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,512 +1,63 @@
-str(x3)
-str(x7)
-x7 = 'FLASE'
-str(x7)
-x6 = 'false'            # character
-str(x7)
-x8 = 8.8
-x8 = 8.8
-str(x8)
-xx1 <- c(1:100)
-xx1
-xx1 <- c(1:100)
-xx1
-is.na()
-is.na(xx1)
-table(is.na(xx1))
-x1 = c(1,'love',TRUE,2+3i)
-x1
-install.packages('ggplot2')
-install.packages('dplyr')
-v1 = c(27,35,47,41)
-mode(v1)
-is.numeric(v1)
-is.na(v1)
-table(is.na(v1))
-xx1 <- c(1:100)
-v2 = c(27,35,47,NA,55)
-mode(v2)
-is.numeric(v2)
-is.na(v2)
-table(is.na(v1))
-table(is.na(v1))
-table(is.na(v1))
-table(is.na(v2))
-length(vw)
-length(v1)
-length(v2)
-names(v1)
-names(v1) = c('kim','lee','park','choi')
-names(v1)
-v1
-v1[0]
-## 기초 명령어 실습 ##
-1+1
-1:10
-dd = c(1:10)
-## c()    -> 같은 종류의 요소(numeric, int, float등)만 있을때
-## list() -> 다양한 종류의 요소가 들어올때
-dd[-10]
-## 기본유형 ##
-x1 = 3                  # numeric
-x2 = "Love is choice"   # character
-x3 = FALSE              # logical
-x4 = 3-2i               # complex
-x5 = '123'              # character
-x6 = 'false'            # character
-x7 = 'FLASE'            # character
-str(x1)
-### 문자 - 정수 - 논리
-### 결측값은 95%까지
-### 정규분포는 30번이상한것으로 해야한다
-xx1 <- c(1:100)
-xx1
-is.na(xx1)
-table(is.na(xx1))
-x1 = c(1,'love',TRUE,2+3i)
-x1
-v1 = c(27,35,47,41)
-mode(v1)
-is.numeric(v1)
-is.na(v1)
-table(is.na(v1))
-v2 = c(27,35,47,NA,55)
-mode(v2)
-is.numeric(v2)
-is.na(v2)
-table(is.na(v2))
-length(v1)
-length(v2)
-names(v1)
-names(v1) = c('kim','lee','park','choi')
-names(v1)
-## indexing ## 대괄호 == [ ] => 추출해라
-v1
-v1[1]
-v1[1:3]
-v1[-1]
-v1[:-1]
-v1[1:-1]
-v1[-1]
-v1[1:3]
-v1[-4]
-v1[1,3]
-v1[3:-1]
-v1[c(2,4)]
-v1[-4]
-v1 = 1:3
-v2 = 4:6
-v3 = v1+v2
-v3 = v1+v2
-v1 = 1:3
-v2 = 1:6
-v3 = v1+v2
-v3
-gender = c('m','f','f','m','f','f')
-gender_facotr = factor(gender)
-levels(gender_facotr)
-gender = c('m','f','f','m','f','f')
-gender_facotr = factor(gender)
-levels(gender_facotr)
-gender_factor2 = factor(gender, levels = c('m','f'), labels = c('남자','여자'))
-gender_factor2
-install.packages(c("cluster", "lattice", "viridisLite"))
-gender_factor3
-gender_factor3 = factor(gender,ordered = TRUE)
-gender_factor3
-v2 = 4:6
-m1 = rbind(v1,v2)
-m2 = m2 cbind(v1,v2)
-v1 = 1:3
-v2 = 4:6
-m1 = rbind(v1,v2)
-m2 = m2 cbind(v1,v2)
-1:3
-v2 = 4:6
-m1 = rbind(v1,v2)
-m2 =
-v1 = 1:3
-v2 = 4:6
-m1 = rbind(v1,v2)
-m2 = cbind(v1,v2)
-m2
-v1 = 1:3
-v2 = 4:6
-m1 = rbind(v1,v2)
-m2 = cbind(v1,v2)
-m1
-m2
-v1 = 1:3
-v2 = 4:6
-m1 = rbind(v1,v2)
-m2 = cbind(v1,v2)
-v1
-v2
-m1
-m2
-m1[2,1]
-m1[2,1] == 8
-m1
-[2,1] == 8
-m1
-m1
-m1[2,1] == 8
-m1[2,1] = 8
-m1
-m1[,3]
-is.na(m1)
-table(is.na(m1))
-m1[1,1] = NA
-m1
-table(is.na(m1))
-m1[2,3] = NA
-table(is.na(m1))
-str(m1)
-m3 = matrix(1:4, nrow = 2,ncol = 2)
-m3
-m4 = matrix(1:4, nrow = 2,ncol = 2,byrow = TRUE)
-m4
-m3
-m4
-m4[2,1] = NA
-a1 = array(1:10, dim=10)
-a2 = array(1:10, dim=c(2, 5))
-a3 = array(1:10, dim=c(3, 3, 4))
-a1
-a2
-a3
-a3[,,2][3,3]
-a3[,,2][3,3] = 100
-a3
-id = 1:5
-age = c(29, 32, 47, 35, 23)
-gender = c('f','m','m','f','f')
-height = c(163, 177, 172, 157, 169)
-DF1 = data.frame(id, age, gender, height)
-DF2 = data.frame(id, age, gender, height, stringsAsFactors=FALSE)
-DF1
-DF2
-View(DF1)
-str(DF1)
-DF3 = data.frame(id, age, gender, height, stringsAsFactors=TRUE)
-DF3
-View(DF2)
-View(DF3)
-str(DF2)
-str(DF3)
-DF4 = DF3[-1]
-DF4
-View(DF4)
-str(DF4)
-install.packages('psych')
-library(psych)
-describe(DF4)
-## 기초 명령어 실습 ##
-1+1
-1:10
-dd = c(1:10)
-## c()    -> 같은 종류의 요소(numeric, int, float등)만 있을때
-## list() -> 다양한 종류의 요소가 들어올때
-dd[-10]
-## 기본유형 ##
-x1 = 3                  # numeric
-x2 = "Love is choice"   # character
-x3 = FALSE              # logical
-x4 = 3-2i               # complex
-x5 = '123'              # character
-x6 = 'false'            # character
-x7 = 'FLASE'            # character
-str(x1)
-### 문자 - 정수 - 논리
-### 결측값은 95%까지
-### 정규분포는 30번이상한것으로 해야한다
-xx1 <- c(1:100)
-xx1
-is.na(xx1)
-table(is.na(xx1))
-x1 = c(1,'love',TRUE,2+3i)
-x1
-v1 = c(27,35,47,41)
-mode(v1)
-is.numeric(v1)
-is.na(v1)
-table(is.na(v1))
-v2 = c(27,35,47,NA,55)
-mode(v2)
-is.numeric(v2)
-is.na(v2)
-table(is.na(v2))
-length(v1)
-length(v2)
-names(v1)
-names(v1) = c('kim','lee','park','choi')
-names(v1)
-## Indexing ## 대괄호 == [ ] => 추출해라
-v1
-v1[1]
-v1[1:3]
-v1[-4]
-v1[c(2,4)]
-v1 = 1:3
-v2 = 4:6
-v3 = v1+v2 # can add list
-v1 = 1:3
-v2 = 1:6
-v3 = v1+v2 # reusable add
-v3
-### 데이터거래사
-## Factor, ordered Factor (등급을 정할 수 있는)
-gender = c('m','f','f','m','f','f')
-gender_factor = factor(gender)
-levels(gender_factor)
-gender_factor2 = factor(gender, levels = c('m','f'), labels = c('남자','여자'))
-gender_factor2
-gender_factor3 = factor(gender,ordered = TRUE)
-gender_factor3
-## Matrix ##
-v1 = 1:3
-v2 = 4:6
-m1 = rbind(v1,v2)
-m2 = cbind(v1,v2)
-v1
-v2
-m1
-m2
-m1[2,1] == 8
-m1[2,1] = 8
-m1[,3]
-is.na(m1)
-table(is.na(m1))
-m1[1,1] = NA
-m1[2,3] = NA
-table(is.na(m1))
-str(m1)
-m3 = matrix(1:4, nrow = 2, ncol = 2)
-m4 = matrix(1:4, nrow = 2, ncol = 2, byrow = TRUE)
-m3
-m4[2,1] = NA
-## array ##
-a1 = array(1:10, dim=10)
-a2 = array(1:10, dim=c(2, 5))
-a3 = array(1:10, dim=c(3, 3, 4))
-a1
-a2
-a3[,,2][3,3] = 100
-a3
-## DataFrame ##
-id = 1:5
-age = c(29, 32, 47, 35, 23)
-gender = c('f','m','m','f','f')
-height = c(163, 177, 172, 157, 169)
-DF1 = data.frame(id, age, gender, height)
-DF2 = data.frame(id, age, gender, height, stringsAsFactors=FALSE)
-DF3 = data.frame(id, age, gender, height, stringsAsFactors=TRUE)
-DF1
-DF2
-DF3
-View(DF3)
-str(DF3)
-DF4 = DF3[-1]
-DF4
-View(DF4)
-str(DF4)
-## 기술통계량 ##
-library('psych')
-describe(DF4)
-rownames(DF1)
-colnames(DF1)
-rownames(DF1) = paste('R',1:5,sep='')
-rownames(DF1)
-rownames(DF1) = paste('Edge',1:5,sep='')
-rownames(DF1)
-rownames(DF1) = paste('Edge',1:5,sep='_')
-rownames(DF1)
-View(DF1)
-colnames(DF1) = paste(c('id','나이','성별','키'))
-colnames(DF1)
-colnames(DF1) = paste('id','나이','성별','키')
-colnames(DF1)
-colnames(DF1) = paste(c('id','나이','성별','키'))
-colnames(DF1) = paste(c('아이디','나이','성별','키'))
-colnames(DF1)
-str(DF1)
-str(DF4)
-dim(DF1)
-install.packages("caret")
-## 기술통계량 ##
-library(psych)
-describe(DF4)
-search()
-searchpaths()
-## read text data ##
-k200 = read.csv(file = './data/k100.csv',header = TRUE)
-k200
-## 기초 명령어 실습 ##
-1+1
-1:10
-dd = c(1:10)
-## c()    -> 같은 종류의 요소(numeric, int, float등)만 있을때
-## list() -> 다양한 종류의 요소가 들어올때
-dd[-10]
-## 기본유형 ##
-x1 = 3                  # numeric
-x2 = "Love is choice"   # character
-x3 = FALSE              # logical
-x4 = 3-2i               # complex
-x5 = '123'              # character
-x6 = 'false'            # character
-x7 = 'FLASE'            # character
-str(x1)
-### 문자 - 정수 - 논리
-### 결측값은 95%까지
-### 정규분포는 30번이상한것으로 해야한다
-xx1 <- c(1:100)
-xx1
-is.na(xx1)
-table(is.na(xx1))
-x1 = c(1,'love',TRUE,2+3i)
-x1
-v1 = c(27,35,47,41)
-mode(v1)
-is.numeric(v1)
-is.na(v1)
-table(is.na(v1))
-v2 = c(27,35,47,NA,55)
-mode(v2)
-is.numeric(v2)
-is.na(v2)
-table(is.na(v2))
-length(v1)
-length(v2)
-names(v1)
-names(v1) = c('kim','lee','park','choi')
-names(v1)
-## Indexing ## 대괄호 == [ ] => 추출해라
-v1
-v1[1]
-v1[1:3]
-v1[-4]
-v1[c(2,4)]
-v1 = 1:3
-v2 = 4:6
-v3 = v1+v2 # can add list
-v1 = 1:3
-v2 = 1:6
-v3 = v1+v2 # reusable add
-v3
-### 데이터거래사
-## Factor, ordered Factor (등급을 정할 수 있는)
-gender = c('m','f','f','m','f','f')
-gender_factor = factor(gender)
-levels(gender_factor)
-gender_factor2 = factor(gender, levels = c('m','f'), labels = c('남자','여자'))
-gender_factor2
-gender_factor3 = factor(gender,ordered = TRUE)
-gender_factor3
-## Matrix ##
-v1 = 1:3
-v2 = 4:6
-m1 = rbind(v1,v2)
-m2 = cbind(v1,v2)
-v1
-v2
-m1
-m2
-m1[2,1] == 8
-m1[2,1] = 8
-m1[,3]
-is.na(m1)
-table(is.na(m1))
-m1[1,1] = NA
-m1[2,3] = NA
-table(is.na(m1))
-str(m1)
-m3 = matrix(1:4, nrow = 2, ncol = 2)
-m4 = matrix(1:4, nrow = 2, ncol = 2, byrow = TRUE)
-m3
-m4[2,1] = NA
-## array ##
-a1 = array(1:10, dim=10)
-a2 = array(1:10, dim=c(2, 5))
-a3 = array(1:10, dim=c(3, 3, 4))
-a1
-a2
-a3[,,2][3,3] = 100
-a3
-## DataFrame ##
-id = 1:5
-age = c(29, 32, 47, 35, 23)
-gender = c('f','m','m','f','f')
-height = c(163, 177, 172, 157, 169)
-DF1 = data.frame(id, age, gender, height)
-DF2 = data.frame(id, age, gender, height, stringsAsFactors=FALSE)
-DF3 = data.frame(id, age, gender, height, stringsAsFactors=TRUE)
-DF1
-DF2
-DF3
-View(DF3)
-str(DF3)
-DF4 = DF3[-1]
-DF4
-View(DF4)
-str(DF4)
-## 기술통계량 ##
-library(psych)
-describe(DF4)
-rownames(DF1) = paste('Edge',1:5,sep='_')
-rownames(DF1)
-colnames(DF1) = paste(c('아이디','나이','성별','키'))
-colnames(DF1)
-dim(DF1)
-searchpaths()
-## read text data ##
-k200 = read.csv(file = './data/k100.csv',header = TRUE)
-k200
-View(k200)
-k300 = read.csv(file = './data/ebook.csv',header = TRUE)
-View(k300)
-tsv100 = read.table(file = './data/survey_tab.txt', header = TRUE, encoding = 'CP949')
-tsv100 = read.table(file = './data/survey_tab.txt', header = TRUE, fileEncoding = 'CP949')
-View(tsv100)
-## data handling ##
-library(ggplot2)
-head(diamonds)
-head(Titanic)
-View(diamonds)
-View(Titanic)
-## data structure ##
-str(diamonds)
-str(Titanic)
-View(diamonds)
-head(diamonds)
-## indexing ##
-diamonds[ , 2]
-diamonds[ , 2, drop=FALSE]
-diamonds[ , c(2, 3, 7)]
-diamonds[ , 7:10]
-diamonds[ , seq(from=2, to=10, by=2)]
-xx=diamonds[ , c(2, 3, 7)]
-View(xx)
-xx
-ss = diamonds[,7]
-describe(ss)
-describe(diamonds)
-diamonds[ , c('cut', 'price'
-diamonds[ , 'cut']
-diamonds[ , 'cut']
-diamonds[ , c('cut', 'price')]
-diamonds[diamonds$cut == 'Fair', ]
-diamonds[diamonds$price >= 18000, ]
-a
-a = diamonds[diamonds$cut == 'Fair', ]
-a
-table(a)
-= diamonds[diamonds$price >= 18000, ]
-b
-b = diamonds[diamonds$price >= 18000, ]
-b
-'Fair') & (diamonds$price >= 18000), ]
-c = diamonds[(diamonds$cut == 'Fair') & (diamonds$price >= 18000), ]
-d = diamonds[(diamonds$cut == 'Fair') | (diamonds$price >= 18000), ]
-View(c)
-View(d)
+View(BMI)
+str(BMI)
+BMI$religion <- factor(BMI$religion,
+levels=c("Bu", "C1", "C2","No"),
+labels=c("불교", "개신교", "가톨릭", "없음"))
+# 범주형 관측항목 추가
+BMI$종교 <- factor(BMI$religion,
+levels=c("Bu", "C1", "C2","No"),
+labels=c("불교", "개신교", "가톨릭", "없음"))
+# 범주형 관측항목 추가
+BMI$종교 <- factor(BMI$religion,
+levels=c("Bu", "C1", "C2","No"),
+labels=c("불교", "개신교", "가톨릭", "없음"))
+BMI$종교 <- factor(BMI$religion,
+levels=c("Bu", "C1", "C2","No"),
+labels=c("불교", "개신교", "가톨릭", "없음"))
+# 범주형 관측항목 추가
+BMI$종교_1 <- factor(BMI$종교,
+levels=c("Bu", "C1", "C2","No"),
+labels=c("불교", "개신교", "가톨릭", "없음"))
+str(BMI)
+# 범주형 관측항목 추가
+BMI$종교_1 <- factor(BMI$종교,
+levels=c("Bu", "C1", "C2","No"),
+labels=c("불교", "개신교", "가톨릭", "없음"))
+str(BMI)
+# 범주형 관측항목 추가
+BMI$종교_1 <- factor(BMI$종교,
+levels=c("Bu", "C1", "C2","No"),
+labels=c("불교", "개신교", "가톨릭", "없음"))
+str(BMI)
+# 범주형 관측항목 추가
+BMI$종교_1 <- factor(BMI$종교,
+levels=c("Bu", "C1", "C2","No"),
+labels=c("불교", "개신교", "가톨릭", "없음"))
+str(BMI)
+# 범주형 관측항목 추가
+BMI$종교_1 <- factor(BMI$종교,
+levels=c("Bu", "C1", "C2","No"),
+labels=c("불교", "개신교", "가톨릭", "없음"))
+str(BMI)
+# 범주형 관측항목 추가
+BMI$종교_1 <- factor(BMI$종교,
+levels=c("Bu", "C1", "C2","No"),
+labels=c("불교", "개신교", "가톨릭", "없음"))
+str(BMI)
+table(BMI$종교)
+table(BMI$종교_1)
+barplot(table(BMI$종교_1), col=1:4)
+barplot(table(BMI$종교_1), col=1:4, main='종교대이터의 인원수')
+barplot(table(BMI$종교_1), col=1:4, main='종교대이터의 인원수',ylab = ;'명',xlab='종교', sub='본그래프는 종교의 인원수를 나타내는 그래프_전북대학교_2026_평생교육유ㅗㄴ')
+barplot(table(BMI$종교_1), col=1:4, main='종교대이터의 인원수',ylab = '명',xlab='종교', sub='본그래프는 종교의 인원수를 나타내는 그래프_전북대학교_2026_평생교육원')
+View(m3)
+# 시각화
+par(family = "AppleGothic")
+barplot(table(BMI$종교_1), col=1:4, main='종교대이터의 인원수',ylab = '명',xlab='종교', sub='본그래프는 종교의 인원수를 나타내는 그래프_전북대학교_2026_평생교육원')
+BMI$판정 <- ordered(BMI$등급,
+levels=seq(1,5),
+labels=c("저체중", "정상", "과체중", "초기비만", "비만"))
+table(BMI$판정)
+barplot(table(BMI$판정), col=1:5,
+main='BMI 판정',ylab = '명',xlab='등급',
+sub='본그래프는 BMI의 인원수를 나타내는 그래프_전북대학교_2026_평생교육원')
diff --git a/.Rproj.user/F3FE0F57/pcs/workbench-pane.pper b/.Rproj.user/F3FE0F57/pcs/workbench-pane.pper
index 43f45f7..db76c53 100644
--- a/.Rproj.user/F3FE0F57/pcs/workbench-pane.pper
+++ b/.Rproj.user/F3FE0F57/pcs/workbench-pane.pper
@@ -1,6 +1,6 @@
 {
     "TabSet1": 0,
-    "TabSet2": 2,
+    "TabSet2": 1,
     "Sidebar": -1,
     "TabZoom": {}
 }
\ No newline at end of file
diff --git a/.Rproj.user/F3FE0F57/sources/prop/0CB164D0 b/.Rproj.user/F3FE0F57/sources/prop/0CB164D0
index 54de194..c73f03e 100644
--- a/.Rproj.user/F3FE0F57/sources/prop/0CB164D0
+++ b/.Rproj.user/F3FE0F57/sources/prop/0CB164D0
@@ -2,6 +2,6 @@
     "tempName": "Untitled1",
     "source_window_id": "",
     "Source": "Source",
-    "cursorPosition": "185,54",
-    "scrollLine": "172"
+    "cursorPosition": "215,0",
+    "scrollLine": "201"
 }
\ No newline at end of file
diff --git a/.Rproj.user/F3FE0F57/sources/prop/INDEX b/.Rproj.user/F3FE0F57/sources/prop/INDEX
index 6c05954..27874dc 100644
--- a/.Rproj.user/F3FE0F57/sources/prop/INDEX
+++ b/.Rproj.user/F3FE0F57/sources/prop/INDEX
@@ -3,6 +3,7 @@
 ~%2FR%20project%2Fr_study%2F%EA%B8%B0%EC%B4%88%20%EB%AA%85%EB%A0%B9%EC%96%B4%20%EC%8B%A4%EC%8A%B5.R="0CB164D0"
 ~%2FR%20project%2Fr_study%2Fadvance.R="11E47905"
 ~%2FR%20project%2Fr_study%2Fbasic.R="B0F755B1"
+~%2FR%20project%2Fr_study%2Fbmi.R="23FFD201"
 ~%2FR%20project%2Fr_study%2Fdata%2F%E1%84%8B%E1%85%B5%E1%86%AB%E1%84%80%E1%85%AE%E1%84%8C%E1%85%AE%E1%84%90%E1%85%A2%E1%86%A8%E1%84%8E%E1%85%A9%E1%86%BC%E1%84%8C%E1%85%A9%E1%84%89%E1%85%A12015.csv="F4F2A450"
 ~%2FR%20project%2Fr_study%2Fdata%2Febook.csv="B032A455"
 ~%2FR%20project%2Fr_study%2Fdata%2Fsurvey_blank.txt="36BC293B"
diff --git a/.Rproj.user/shared/notebooks/paths b/.Rproj.user/shared/notebooks/paths
index 7089d7b..2d61cef 100644
--- a/.Rproj.user/shared/notebooks/paths
+++ b/.Rproj.user/shared/notebooks/paths
@@ -1,8 +1,8 @@
 /Users/dh/R project/r_study/basic.R="38F7B9A1"
+/Users/dh/R project/r_study/bmi.R="FEED5E2B"
 /Users/dh/R project/r_study/data/ebook.csv="D6CC5A14"
 /Users/dh/R project/r_study/data/survey_blank.txt="38B1159C"
 /Users/dh/R project/r_study/data/survey_comma.txt="F11DD8B3"
 /Users/dh/R project/r_study/data/survey_tab.txt="2B3FFD48"
-/Users/dh/R project/r_study/data/인구주택총조사2015.csv="034CDF16"
 /Users/dh/R project/r_study/기초 실습 II.R="83772E57"
 /Users/dh/R project/r_study/기초 명령어 실습.R="CF8199EA"
diff --git a/기초 명령어 실습.R b/기초 명령어 실습.R
index 15b84e7..d90a620 100644
--- a/기초 명령어 실습.R	
+++ b/기초 명령어 실습.R	
@@ -173,8 +173,10 @@ library(ggplot2)
 
 head(diamonds)
 View(diamonds)
+tail(diamonds)
 head(Titanic)
 View(Titanic)
+tail(Titanic)
 
 ## data structure ##
 str(diamonds)
@@ -199,3 +201,15 @@ b = diamonds[diamonds$price >= 18000, ]  # 다이아몬드의 가격을 기준
 b
 c = diamonds[(diamonds$cut == 'Fair') & (diamonds$price >= 18000), ]
 d = diamonds[(diamonds$cut == 'Fair') | (diamonds$price >= 18000), ]
+
+diamonds$xyz = diamonds$x + diamonds$y + diamonds$z
+View(diamonds)
+
+diamonds$means = diamonds$xyz/3
+
+kkk = diamonds[-c(10,20,30),]
+str(kkk)
+kkk1 = diamonds[-c(100:200),]
+str(kkk1)
+kkk2 = diamonds[-seq(from=1, to=length(diamonds), by=10),]
+str(kkk2)