xlsx package는 Excel 자료를 다루는 데 매우 유용한데, read.xlsx(filename, n)의 구조로 되어 있으며, 여기서 n은 엑셀 시트의 번호이다.
library(knitr)
# install.packages("xlsx", repos = "https://cran.rstudio.com")
library(xlsx)xlsx 패키지를 이용하여 자료를 읽어들인다.
data.usa <- read.xlsx("../data/USA-inequality.xls", 1, stringsAsFactors = FALSE)
str(data.usa)## 'data.frame': 50 obs. of 20 variables:
## $ State : chr "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ State.Abbrev : chr "AL" "AK" "AZ" "AR" ...
## $ Income.Inequality : num 0.475 0.402 0.45 0.458 0.475 ...
## $ Trust : num 23 NA 47 29 43 46 49 NA 37 38 ...
## $ Life.expectancy : num 74.6 76.7 77.5 75.1 78.3 ...
## $ Infant.mortality : num 9.1 5.5 6.4 8.3 5.5 ...
## $ Obesity : num 32 30 28.5 31 31 21.5 26.5 27 27.5 30.5 ...
## $ Mental.health : num 3.3 2.8 2.2 3.2 3.3 ...
## $ Maths.and.literacy.scores : num 258 268 263 262 259 ...
## $ Teenage.births : num 62.9 42.4 69.1 68.5 48.5 ...
## $ Homicides : num 78.9 85.6 80.4 56.1 60.5 ...
## $ Imprisonment : num 509 413 507 415 478 357 372 429 447 502 ...
## $ Index.of.health...social.problems: num 1.385 0.137 0.212 0.948 0.327 ...
## $ Overweight.children : num 35 31 30 33 30 22 27 35 32 32 ...
## $ Child.wellbeing : num 8.5 4.4 4.9 9.3 -3.4 ...
## $ Women.s.status : num -0.932 0.74 -0.147 -1.318 0.969 ...
## $ Juvenile.homicides : num 12 8 7 6 10 4 4 0 NA 8 ...
## $ High.school.drop.outs : num 24.7 11.7 19 24.7 23.2 ...
## $ Child.mental.illness : num 11.5 8.2 8.7 11.8 7.5 ...
## $ Pugnacity : num 41.8 NA 36.3 38.4 37.7 ...당장 필요한 변수들만 모아서 data frame으로 재구성한다. 변수명 설정에 유의한다.
data.usa.1 <- data.frame(Gini = data.usa$Income.Inequality, HS.index = data.usa$Index.of.health...social.problems)
str(data.usa.1)## 'data.frame': 50 obs. of 2 variables:
## $ Gini : num 0.475 0.402 0.45 0.458 0.475 ...
## $ HS.index: num 1.385 0.137 0.212 0.948 0.327 ...Gini <- data.usa.1$Gini
State <- data.usa$State
Abb <- data.usa$State.Abbrev
options(digits = 3)
kable(data.frame(State = State, State.Abb = Abb, data.usa.1))| State | State.Abb | Gini | HS.index |
|---|---|---|---|
| Alabama | AL | 0.475 | 1.385 |
| Alaska | AK | 0.402 | 0.137 |
| Arizona | AZ | 0.450 | 0.212 |
| Arkansas | AR | 0.458 | 0.948 |
| California | CA | 0.475 | 0.327 |
| Colorado | CO | 0.438 | -0.507 |
| Connecticut | CT | 0.477 | -0.660 |
| Delaware | DE | 0.429 | 0.133 |
| Florida | FL | 0.470 | 0.360 |
| Georgia | GA | 0.461 | 0.896 |
| Hawaii | HI | 0.434 | -0.388 |
| Idaho | ID | 0.427 | -0.429 |
| Illinois | IL | 0.456 | 0.206 |
| Indiana | IN | 0.424 | 0.370 |
| Iowa | IA | 0.418 | -0.895 |
| Kansas | KS | 0.435 | -0.442 |
| Kentucky | KY | 0.468 | 0.874 |
| Louisiana | LA | 0.483 | 1.595 |
| Maine | ME | 0.434 | -0.769 |
| Maryland | MD | 0.434 | 0.187 |
| Massachusetts | MA | 0.463 | -0.959 |
| Michigan | MI | 0.440 | 0.349 |
| Minnesota | MN | 0.426 | -1.216 |
| Mississippi | MS | 0.478 | 1.692 |
| Missouri | MO | 0.449 | 0.392 |
| Montana | MT | 0.436 | -0.906 |
| Nebraska | NE | 0.424 | -0.583 |
| Nevada | NV | 0.436 | 0.803 |
| New Hampshire | NH | 0.414 | -1.242 |
| New Jersey | NJ | 0.460 | -0.402 |
| New Mexico | NM | 0.460 | 0.564 |
| New York | NY | 0.499 | -0.179 |
| North Carolina | NC | 0.452 | 0.494 |
| North Dakota | ND | 0.429 | -1.145 |
| Ohio | OH | 0.441 | 0.058 |
| Oklahoma | OK | 0.455 | 0.494 |
| Oregon | OR | 0.438 | -0.346 |
| Pennsylvania | PA | 0.452 | -0.015 |
| Rhode Island | RI | 0.457 | -0.389 |
| South Carolina | SC | 0.454 | 0.899 |
| South Dakota | SD | 0.434 | -0.759 |
| Tennessee | TN | 0.465 | 0.788 |
| Texas | TX | 0.470 | 0.930 |
| Utah | UT | 0.410 | -0.709 |
| Vermont | VT | 0.423 | -1.183 |
| Virginia | VA | 0.449 | -0.055 |
| Washington | WA | 0.436 | -0.516 |
| West Virginia | WV | 0.468 | 0.482 |
| Wisconsin | WI | 0.413 | -0.473 |
| Wyoming | WY | 0.428 | -0.551 |
save.image(file = "Inequality_Index_HS_US.RData")