도움말 > 활성 데이터셋에 대한 도움말 (만약 이용가능하다면)
Help > Help on active data set (if available)
R에는 많은 패키지가 있으며, 그 패키지에는 많은 경우 데이터셋을 포함하고 있다. 그 데이터셋에 대한 도움말을 볼 수 있는 기능이다. 생각해보라. 예제 데이터셋을 통하여 함수를 연습하는데, 데이터셋의 특성을 알지 못한다면 분석과 시각화에 필요한 통찰력을 얻을 수 있겠는가.
위의 화면은 MASS 패키지가 적재되었고, 또 그 안에 포함된 housing 이라는 데이터셋이 활성화된 상태에서 '도움말 > 활성 데이터셋에 관한 도움말 (만약 이용가능하다면)' 메뉴 기능을 이용할 수 있다는 것이다. 해당 메뉴를 선택하면, 새로운 웹 브라우저 창이 등장하면서, 활성 데이터셋의 도움말이 제공될 것이다.
help("housing") # housing 데이터셋 도움말 보기
?housing # housing 데이터셋 도움말 보기 (? 활용)datasets::warpbreaks()
data(warpbreaks, package="datasets") # 데이터셋 불러오기
help("warpbreaks") # 데이터셋 도움말 보기
summary(warpbreaks) # 데이터셋 통계 요약 보기
R Commander 화면 상단 우측에 있는 <데이터셋 보기> 버튼을 누른다. 아래와 같이 warpbreaks 데이터셋의 내부 구성을 볼 수 있다.
| warpbreaks {datasets} | R Documentation |
This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.
warpbreaksA data frame with 54 observations on 3 variables.
| [,1] | breaks | numeric | The number of breaks |
| [,2] | wool | factor | The type of wool (A or B) |
| [,3] | tension | factor | The level of tension (L, M, H) |
There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).
Tippett, L. H. C. (1950) Technological Applications of Statistics. Wiley. Page 106.
Tukey, J. W. (1977) Exploratory Data Analysis. Addison-Wesley.
McNeil, D. R. (1977) Interactive Data Analysis. Wiley.
xtabs for ways to display these data as a table.
require(stats); require(graphics)
summary(warpbreaks)
opar <- par(mfrow = c(1, 2), oma = c(0, 0, 1.1, 0))
plot(breaks ~ tension, data = warpbreaks, col = "lightgray",
varwidth = TRUE, subset = wool == "A", main = "Wool A")
plot(breaks ~ tension, data = warpbreaks, col = "lightgray",
varwidth = TRUE, subset = wool == "B", main = "Wool B")
mtext("warpbreaks data", side = 3, outer = TRUE)
par(opar)
summary(fm1 <- lm(breaks ~ wool*tension, data = warpbreaks))
anova(fm1)
| warpbreaks 데이터셋 예제 (0) | 2022.06.25 |
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carData::Adler
데이터 > 패키지에 있는 데이터 > 첨부된 패키지에서 데이터셋 읽기... 기능을 선택하면, 위와 같은 메뉴 창을 보게된다.
carData를 선택하여 두번 클릭하면, 오른쪽에 carData 패키지에 내장된 데이터셋 목록이 등장한다. Adler 데이터셋을 선택한다.
data(Adler, package="carData") # Adler 데이터셋 활성화시키기
help("Adler", package="carData")# 도움말파일 열기
| Adler {carData} | R Documentation |
The Adler data frame has 108 rows and 3 columns.
The “experimenters” were the actual subjects of the study. They collected ratings of the apparent success of people in pictures who were pre-selected for their average appearance of success. The experimenters were told prior to collecting data that particular subjects were either high or low in their tendency to rate appearance of success, and were instructed to get good data, scientific data, or were given no such instruction. Each experimenter collected ratings from 18 randomly assigned subjects. This version of the Adler data is taken from Erickson and Nosanchuk (1977). The data described in the original source, Adler (1973), have a more complex structure.
Adler
This data frame contains the following columns:
instruction
a factor with levels: good, good data; none, no stress; scientific, scientific data.
expectation
a factor with levels: high, expect high ratings; low, expect low ratings.
rating
The average rating obtained.
Erickson, B. H., and Nosanchuk, T. A. (1977) Understanding Data. McGraw-Hill Ryerson.
Adler, N. E. (1973) Impact of prior sets given experimenters and subjects on the experimenter expectancy effect. Sociometry 36, 113–126.
carData::Cowles
data(Cowles, package="carData")
help("Cowles")
| Cowles {carData} | R Documentation |
The Cowles data frame has 1421 rows and 4 columns. These data come from a study of the personality determinants of volunteering for psychological research.
CowlesThis data frame contains the following columns:
neuroticism
scale from Eysenck personality inventory
extraversion
scale from Eysenck personality inventory
sex
a factor with levels: female; male
volunteer
volunteeing, a factor with levels: no; yes
Cowles, M. and C. Davis (1987) The subject matter of psychology: Volunteers. British Journal of Social Psychology 26, 97–102.
carData::Friendly()
data(Friendly, package="carData")
help("Friendly")| Friendly {carData} | R Documentation |
The Friendly data frame has 30 rows and 2 columns. The data are from an experiment on subjects' ability to remember words based on the presentation format.
Friendly
This data frame contains the following columns:
condition
A factor with levels: Before, Recalled words presented before others; Meshed, Recalled words meshed with others; SFR, Standard free recall.
correct
Number of words correctly recalled, out of 40 on final trial of the experiment.
Friendly, M. and Franklin, P. (1980) Interactive presentation in multitrial free recall. Memory and Cognition 8 265–270 [Personal communication from M. Friendly].
Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.
MASS::birthwt
data(birthwt, package="MASS")birthwt 데이터셋이 활성화된 후, <데이터셋 보기> 버튼을 누르면 아래와 같이 내부 구성을 볼 수 있다:
help("birthwt")
| birthwt {MASS} | R Documentation |
The birthwt data frame has 189 rows and 10 columns. The data were collected at Baystate Medical Center, Springfield, Mass during 1986.
birthwt
This data frame contains the following columns:
low
indicator of birth weight less than 2.5 kg.
age
mother's age in years.
lwt
mother's weight in pounds at last menstrual period.
race
mother's race (1 = white, 2 = black, 3 = other).
smoke
smoking status during pregnancy.
ptl
number of previous premature labours.
ht
history of hypertension.
ui
presence of uterine irritability.
ftv
number of physician visits during the first trimester.
bwt
birth weight in grams.
Hosmer, D.W. and Lemeshow, S. (1989) Applied Logistic Regression. New York: Wiley
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
bwt <- with(birthwt, {
race <- factor(race, labels = c("white", "black", "other"))
ptd <- factor(ptl > 0)
ftv <- factor(ftv)
levels(ftv)[-(1:2)] <- "2+"
data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0),
ptd, ht = (ht > 0), ui = (ui > 0), ftv)
})
options(contrasts = c("contr.treatment", "contr.poly"))
glm(low ~ ., binomial, bwt)
| bwt 데이터셋 (0) | 2022.06.24 |
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datasets::USArrests()
data(USArrests, package="datasets")R Commander 화면 상단에서 <데이터셋 보기> 버튼을 누르면 아래와 같은 내부 구성을 확인할 수 있다.
help("USArrests")| USArrests {datasets} | R Documentation |
This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.
USArrestsA data frame with 50 observations on 4 variables.
| [,1] | Murder | numeric | Murder arrests (per 100,000) |
| [,2] | Assault | numeric | Assault arrests (per 100,000) |
| [,3] | UrbanPop | numeric | Percent urban population |
| [,4] | Rape | numeric | Rape arrests (per 100,000) |
USArrests contains the data as in McNeil's monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule that R's round() uses), as found by Daniel S Coven (Arizona).
See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.
World Almanac and Book of facts 1975. (Crime rates).
Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.
McNeil, D. R. (1977) Interactive Data Analysis. New York: Wiley.
The state data sets.
summary(USArrests)
require(graphics)
pairs(USArrests, panel = panel.smooth, main = "USArrests data")
## Difference between 'USArrests' and its correction
USArrests["Maryland", "UrbanPop"] # 67 -- the transcription error
UA.C <- USArrests
UA.C["Maryland", "UrbanPop"] <- 76.6
## also +/- 0.5 to restore the original <n>.5 percentages
s5u <- c("Colorado", "Florida", "Mississippi", "Wyoming")
s5d <- c("Nebraska", "Pennsylvania")
UA.C[s5u, "UrbanPop"] <- UA.C[s5u, "UrbanPop"] + 0.5
UA.C[s5d, "UrbanPop"] <- UA.C[s5d, "UrbanPop"] - 0.5
## ==> UA.C is now a *C*orrected version of USArrests
| USArrests 데이터셋 예제 (0) | 2022.06.25 |
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carData > Prestige
data(Prestige, package="carData")
help("Prestige")| Prestige {carData} | R Documentation |
The Prestige data frame has 102 rows and 6 columns. The observations are occupations.
Prestige
This data frame contains the following columns:
education
Average education of occupational incumbents, years, in 1971.
income
Average income of incumbents, dollars, in 1971.
women
Percentage of incumbents who are women.
prestige
Pineo-Porter prestige score for occupation, from a social survey conducted in the mid-1960s.
census
Canadian Census occupational code.
type
Type of occupation. A factor with levels (note: out of order): bc, Blue Collar; prof, Professional, Managerial, and Technical; wc, White Collar.
Canada (1971) Census of Canada. Vol. 3, Part 6. Statistics Canada [pp. 19-1–19-21].
Personal communication from B. Blishen, W. Carroll, and C. Moore, Departments of Sociology, York University and University of Victoria.
Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.
| Prestige.csv (0) | 2022.02.22 |
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carData > Moore
data(Moore, package="carData")
help("Moore")| Moore {carData} | R Documentation |
The Moore data frame has 45 rows and 4 columns. The data are for subjects in a social-psychological experiment, who were faced with manipulated disagreement from a partner of either of low or high status. The subjects could either conform to the partner's judgment or stick with their own judgment.
Moore
This data frame contains the following columns:
partner.status
Partner's status. A factor with levels: high, low.
conformity
Number of conforming responses in 40 critical trials.
fcategory
F-Scale Categorized. A factor with levels (note levels out of order): high, low, medium.
fscore
Authoritarianism: F-Scale score.
Moore, J. C., Jr. and Krupat, E. (1971) Relationship between source status, authoritarianism and conformity in a social setting. Sociometry 34, 122–134.
Personal communication from J. Moore, Department of Sociology, York University.
Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.
carData::DavisThin()
help("DavisThin")| DavisThin {carData} | R Documentation |
The DavisThin data frame has 191 rows and 7 columns. This is part of a larger dataset for a study of eating disorders. The seven variables in the data frame comprise a "drive for thinness" scale, to be formed by summing the items.
DavisThinThis data frame contains the following columns:
DT1
a numeric vector
DT2
a numeric vector
DT3
a numeric vector
DT4
a numeric vector
DT5
a numeric vector
DT6
a numeric vector
DT7
a numeric vector
Davis, C., G. Claridge, and D. Cerullo (1997) Personality factors predisposing to weight preoccupation: A continuum approach to the association between eating disorders and personality disorders. Journal of Psychiatric Research 31, 467–480. [personal communication from the authors.]
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.