엑셀파일 불러들여 데이터 살펴보고 과제 수행하기
Na Hyeon Su_20153076
02 Oct 2020
엑셀파일 불러들여 데이터 살펴보고 과제 수행하기
지난 시간에 엑셀파일을 불러들여오는 명령어를 배웠는데, 이번에는 주어진 엑셀파일(mlu.xls)를 그 명령어를 이용해서 불러들인 다음 오늘 배운 내용들을 수행해봅니다. 데이터는 엑셀파일의 두번째 sheet에 있으므로 명령어를 잘 활용하여 불러들이기 바랍니다.
Q1. 데이터를 불러들여 mlu_data로 지정한 다음 카피본을 만든다.
library("readxl")
mlu <- read_excel("mlu.xlsx")
mlu_data <- as.data.frame(mlu)
summary(mlu_data)## File age utterances_mlu words_mlu
## Length:35 Length:35 Min. :323.0 Min. : 813
## Class :character Class :character 1st Qu.:561.0 1st Qu.:1368
## Mode :character Mode :character Median :621.0 Median :1716
## Mean :631.8 Mean :1710
## 3rd Qu.:716.0 3rd Qu.:2060
## Max. :890.0 Max. :2766
##
## DurationTime DurationSec Types_freq Token_freq
## Min. :1899-12-31 00:08:47 Min. : 527.0 Min. : 378.0 Min. : 832
## 1st Qu.:1899-12-31 00:15:17 1st Qu.: 940.5 1st Qu.: 567.5 1st Qu.:1446
## Median :1899-12-31 00:17:39 Median :1060.5 Median : 694.0 Median :1798
## Mean :1899-12-31 00:17:57 Mean :1088.1 Mean : 754.8 Mean :1778
## 3rd Qu.:1899-12-31 00:20:45 3rd Qu.:1245.5 3rd Qu.: 775.5 3rd Qu.:2134
## Max. :1899-12-31 00:29:22 Max. :1762.0 Max. :4014.0 Max. :2827
## NA's :1Q2. age칼럼에는 몇개의 요인이 있는가?
class(mlu_data$age)## [1] "character"length(mlu_data$age)## [1] 35table(mlu_data$age)##
## A0 A1 A2
## 12 11 12Q3. utterances_mlu를 utterances로 words_mlu를 words로 이름을 바꾸라.
library("dplyr")##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionmlu_data <- dplyr::rename(mlu_data, utterances= utterances_mlu)
mlu_data <- dplyr::rename(mlu_data, words = words_mlu)
summary(mlu_data)## File age utterances words
## Length:35 Length:35 Min. :323.0 Min. : 813
## Class :character Class :character 1st Qu.:561.0 1st Qu.:1368
## Mode :character Mode :character Median :621.0 Median :1716
## Mean :631.8 Mean :1710
## 3rd Qu.:716.0 3rd Qu.:2060
## Max. :890.0 Max. :2766
##
## DurationTime DurationSec Types_freq Token_freq
## Min. :1899-12-31 00:08:47 Min. : 527.0 Min. : 378.0 Min. : 832
## 1st Qu.:1899-12-31 00:15:17 1st Qu.: 940.5 1st Qu.: 567.5 1st Qu.:1446
## Median :1899-12-31 00:17:39 Median :1060.5 Median : 694.0 Median :1798
## Mean :1899-12-31 00:17:57 Mean :1088.1 Mean : 754.8 Mean :1778
## 3rd Qu.:1899-12-31 00:20:45 3rd Qu.:1245.5 3rd Qu.: 775.5 3rd Qu.:2134
## Max. :1899-12-31 00:29:22 Max. :1762.0 Max. :4014.0 Max. :2827
## NA's :1Q4. 각 utterance에 평균 몇개의 단어가 들어있는지를 계산해서 mlu라는 파생변수를 생성하라
mlu_data$mlu <- (mlu_data$utterances/mlu_data$words)Q5. summary 명령을 이용하여 mlu의 평균 및 quartile값을 파악하라.
summary(mlu_data$mlu)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2877 0.3429 0.3642 0.3789 0.4087 0.5781Q6. mlu값을 4개의 등급으로 나누어 가장 문장 길이가 긴 그룹을 A, 그다음 순서대로 B, C, D로 구분하여 grade칼럼을 생성하라.
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.2877 0.3429 0.3642 0.3789 0.4087 0.5781
mlu_data$grade <- ifelse(mlu_data$mlu >= 0.4087, "A",
ifelse(mlu_data$mlu >= 0.3642, "B",
ifelse(mlu_data$mlu >= 0.3429, "C","D")))
table(mlu_data$grade)##
## A B C D
## 9 9 8 9Q7. age와 mlu사이의 빈도분포를 table명령어를 이용하여 구하라.
summary(table(mlu_data$age, mlu_data$mlu))## Number of cases in table: 35
## Number of factors: 2
## Test for independence of all factors:
## Chisq = 70, df = 68, p-value = 0.4102
## Chi-squared approximation may be incorrectQ8. qplot을 이용하여 각 나이 그룹을 X축으로 하여 mlu값의 분포를 그래프로 그려라.
library("ggplot2")
table(mlu_data$age, mlu_data$mlu)##
## 0.287679425837321 0.298162729658793 0.299349240780911 0.314020344980097
## A0 0 0 0 0
## A1 0 0 0 0
## A2 1 1 1 1
##
## 0.330183106910809 0.330522765598651 0.335487077534791 0.336656891495601
## A0 0 0 0 1
## A1 1 0 0 0
## A2 0 1 1 0
##
## 0.341048653755314 0.344771241830065 0.350963914977756 0.352684144818976
## A0 0 1 0 1
## A1 0 0 0 0
## A2 1 0 1 0
##
## 0.354716981132075 0.355271565495208 0.356581017304667 0.358195623046003
## A0 1 0 0 1
## A1 0 0 1 0
## A2 0 1 0 0
##
## 0.362921348314607 0.364244741873805 0.371415566681839 0.37212449255751
## A0 0 0 0 0
## A1 0 1 1 0
## A2 1 0 0 1
##
## 0.37568058076225 0.37881679389313 0.380122950819672 0.381700054141852
## A0 0 0 1 1
## A1 1 1 0 0
## A2 0 0 0 0
##
## 0.393457117595049 0.405594405594406 0.411764705882353 0.416149068322981
## A0 0 0 0 0
## A1 1 0 1 1
## A2 0 1 0 0
##
## 0.424221453287197 0.438759689922481 0.452579034941764 0.452936444086887
## A0 0 1 0 1
## A1 1 0 1 0
## A2 0 0 0 0
##
## 0.469242902208202 0.485585585585586 0.578105781057811
## A0 1 1 1
## A1 0 0 0
## A2 0 0 0qplot(mlu_data$age, mlu_data$mlu)