Use the summary function to gain an overview of the data set. Then display the mean and median for at least two attributes
knitr::opts_chunk$set(echo = TRUE)
# Use the summary function to gain an overview of the data set. Then display the mean and
# median for at least two attributes.
dts<-read.csv("C:\\data\\datasets.csv")
summary1 <- summary(dts)
summary1 ## Package Item
## Stat2Data:119 aids : 2
## Ecdat :104 channing: 2
## MASS : 85 Cigar : 2
## datasets : 72 Clothing: 2
## car : 51 CO2 : 2
## boot : 48 Crime : 2
## (Other) :448 (Other) :915
## Title
## Seven data sets showing a bifactor solution. : 8
## Individual Preferences Over Immigration Policy : 6
## John Snow's map and data on the 1854 London Cholera outbreak : 5
## Automobile Data from 'Consumer Reports' 1990 : 3
## Data from Minard's famous graphic map of Napoleon's march on Moscow: 3
## Diabetes in Pima Indian Women : 3
## (Other) :899
## csv
## https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/boot/acme.csv : 1
## https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/boot/aids.csv : 1
## https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/boot/aircondit.csv : 1
## https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/boot/aircondit7.csv: 1
## https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/boot/amis.csv : 1
## https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/boot/aml.csv : 1
## (Other) :921
## doc
## https://raw.github.com/vincentarelbundock/Rdatasets/master/doc/boot/acme.html : 1
## https://raw.github.com/vincentarelbundock/Rdatasets/master/doc/boot/aids.html : 1
## https://raw.github.com/vincentarelbundock/Rdatasets/master/doc/boot/aircondit.html : 1
## https://raw.github.com/vincentarelbundock/Rdatasets/master/doc/boot/aircondit7.html: 1
## https://raw.github.com/vincentarelbundock/Rdatasets/master/doc/boot/amis.html : 1
## https://raw.github.com/vincentarelbundock/Rdatasets/master/doc/boot/aml.html : 1
## (Other) :921Create a new data frame with a subset of the columns and rows. Make sure to rename it
knitr::opts_chunk$set(echo = TRUE)
PACKAGE <- dts[1]
ITEM <- dts[2]
DESCRIPTON <- dts[3]
subdts <- data.frame(PACKAGE,ITEM,DESCRIPTON)
subdts_car <- subset.data.frame(subdts, PACKAGE == 'car')
subdts_car## Package Item
## 186 car AMSsurvey
## 187 car Adler
## 188 car Angell
## 189 car Anscombe
## 190 car Baumann
## 191 car Bfox
## 192 car Blackmore
## 193 car Burt
## 194 car CanPop
## 195 car Chile
## 196 car Chirot
## 197 car Cowles
## 198 car Davis
## 199 car DavisThin
## 200 car Depredations
## 201 car Duncan
## 202 car Ericksen
## 203 car Florida
## 204 car Freedman
## 205 car Friendly
## 206 car Ginzberg
## 207 car Greene
## 208 car Guyer
## 209 car Hartnagel
## 210 car Highway1
## 211 car KosteckiDillon
## 212 car Leinhardt
## 213 car LoBD
## 214 car Mandel
## 215 car Migration
## 216 car Moore
## 217 car Mroz
## 218 car OBrienKaiser
## 219 car Ornstein
## 220 car Pottery
## 221 car Prestige
## 222 car Quartet
## 223 car Robey
## 224 car SLID
## 225 car Sahlins
## 226 car Salaries
## 227 car Soils
## 228 car States
## 229 car Transact
## 230 car UN
## 231 car USPop
## 232 car Vocab
## 233 car WeightLoss
## 234 car Womenlf
## 235 car Wong
## 236 car Wool
## Title
## 186 American Math Society Survey Data
## 187 Experimenter Expectations
## 188 Moral Integration of American Cities
## 189 U. S. State Public-School Expenditures
## 190 Methods of Teaching Reading Comprehension
## 191 Canadian Women's Labour-Force Participation
## 192 Exercise Histories of Eating-Disordered and Control Subjects
## 193 Fraudulent Data on IQs of Twins Raised Apart
## 194 Canadian Population Data
## 195 Voting Intentions in the 1988 Chilean Plebiscite
## 196 The 1907 Romanian Peasant Rebellion
## 197 Cowles and Davis's Data on Volunteering
## 198 Self-Reports of Height and Weight
## 199 Davis's Data on Drive for Thinness
## 200 Minnesota Wolf Depredation Data
## 201 Duncan's Occupational Prestige Data
## 202 The 1980 U.S. Census Undercount
## 203 Florida County Voting
## 204 Crowding and Crime in U. S. Metropolitan Areas
## 205 Format Effects on Recall
## 206 Data on Depression
## 207 Refugee Appeals
## 208 Anonymity and Cooperation
## 209 Canadian Crime-Rates Time Series
## 210 Highway Accidents
## 211 Treatment of Migraine Headaches
## 212 Data on Infant-Mortality
## 213 Cancer drug data use to provide an example of the use of the skew power distributions.
## 214 Contrived Collinear Data
## 215 Canadian Interprovincial Migration Data
## 216 Status, Authoritarianism, and Conformity
## 217 U.S. Women's Labor-Force Participation
## 218 O'Brien and Kaiser's Repeated-Measures Data
## 219 Interlocking Directorates Among Major Canadian Firms
## 220 Chemical Composition of Pottery
## 221 Prestige of Canadian Occupations
## 222 Four Regression Datasets
## 223 Fertility and Contraception
## 224 Survey of Labour and Income Dynamics
## 225 Agricultural Production in Mazulu Village
## 226 Salaries for Professors
## 227 Soil Compositions of Physical and Chemical Characteristics
## 228 Education and Related Statistics for the U.S. States
## 229 Transaction data
## 230 GDP and Infant Mortality
## 231 Population of the United States
## 232 Vocabulary and Education
## 233 Weight Loss Data
## 234 Canadian Women's Labour-Force Participation
## 235 Post-Coma Recovery of IQ
## 236 Wool data3. Create new column names for the new data frame
#knitr::opts_chunk$set(echo = TRUE)
#install.packages("stringr")
library(stringr)
colnames(subdts_car)<-c("PACKAGE","ITEM_NAME","DESCRIPTON")
subdts_car## PACKAGE ITEM_NAME
## 186 car AMSsurvey
## 187 car Adler
## 188 car Angell
## 189 car Anscombe
## 190 car Baumann
## 191 car Bfox
## 192 car Blackmore
## 193 car Burt
## 194 car CanPop
## 195 car Chile
## 196 car Chirot
## 197 car Cowles
## 198 car Davis
## 199 car DavisThin
## 200 car Depredations
## 201 car Duncan
## 202 car Ericksen
## 203 car Florida
## 204 car Freedman
## 205 car Friendly
## 206 car Ginzberg
## 207 car Greene
## 208 car Guyer
## 209 car Hartnagel
## 210 car Highway1
## 211 car KosteckiDillon
## 212 car Leinhardt
## 213 car LoBD
## 214 car Mandel
## 215 car Migration
## 216 car Moore
## 217 car Mroz
## 218 car OBrienKaiser
## 219 car Ornstein
## 220 car Pottery
## 221 car Prestige
## 222 car Quartet
## 223 car Robey
## 224 car SLID
## 225 car Sahlins
## 226 car Salaries
## 227 car Soils
## 228 car States
## 229 car Transact
## 230 car UN
## 231 car USPop
## 232 car Vocab
## 233 car WeightLoss
## 234 car Womenlf
## 235 car Wong
## 236 car Wool
## DESCRIPTON
## 186 American Math Society Survey Data
## 187 Experimenter Expectations
## 188 Moral Integration of American Cities
## 189 U. S. State Public-School Expenditures
## 190 Methods of Teaching Reading Comprehension
## 191 Canadian Women's Labour-Force Participation
## 192 Exercise Histories of Eating-Disordered and Control Subjects
## 193 Fraudulent Data on IQs of Twins Raised Apart
## 194 Canadian Population Data
## 195 Voting Intentions in the 1988 Chilean Plebiscite
## 196 The 1907 Romanian Peasant Rebellion
## 197 Cowles and Davis's Data on Volunteering
## 198 Self-Reports of Height and Weight
## 199 Davis's Data on Drive for Thinness
## 200 Minnesota Wolf Depredation Data
## 201 Duncan's Occupational Prestige Data
## 202 The 1980 U.S. Census Undercount
## 203 Florida County Voting
## 204 Crowding and Crime in U. S. Metropolitan Areas
## 205 Format Effects on Recall
## 206 Data on Depression
## 207 Refugee Appeals
## 208 Anonymity and Cooperation
## 209 Canadian Crime-Rates Time Series
## 210 Highway Accidents
## 211 Treatment of Migraine Headaches
## 212 Data on Infant-Mortality
## 213 Cancer drug data use to provide an example of the use of the skew power distributions.
## 214 Contrived Collinear Data
## 215 Canadian Interprovincial Migration Data
## 216 Status, Authoritarianism, and Conformity
## 217 U.S. Women's Labor-Force Participation
## 218 O'Brien and Kaiser's Repeated-Measures Data
## 219 Interlocking Directorates Among Major Canadian Firms
## 220 Chemical Composition of Pottery
## 221 Prestige of Canadian Occupations
## 222 Four Regression Datasets
## 223 Fertility and Contraception
## 224 Survey of Labour and Income Dynamics
## 225 Agricultural Production in Mazulu Village
## 226 Salaries for Professors
## 227 Soil Compositions of Physical and Chemical Characteristics
## 228 Education and Related Statistics for the U.S. States
## 229 Transaction data
## 230 GDP and Infant Mortality
## 231 Population of the United States
## 232 Vocabulary and Education
## 233 Weight Loss Data
## 234 Canadian Women's Labour-Force Participation
## 235 Post-Coma Recovery of IQ
## 236 Wool data4. Use the summary function to create an overview of your new data frame. The print the mean and median for the same two attributes. Please compare
summary2 <- summary(subdts_car)
summary2## PACKAGE ITEM_NAME
## car :51 Adler : 1
## boot : 0 AMSsurvey: 1
## cluster : 0 Angell : 1
## COUNT : 0 Anscombe : 1
## datasets: 0 Baumann : 1
## Ecdat : 0 Bfox : 1
## (Other) : 0 (Other) :45
## DESCRIPTON
## Canadian Women's Labour-Force Participation: 2
## Agricultural Production in Mazulu Village : 1
## American Math Society Survey Data : 1
## Anonymity and Cooperation : 1
## Canadian Crime-Rates Time Series : 1
## Canadian Interprovincial Migration Data : 1
## (Other) :44BONUS - place the original .csv in a github file and have R read from the link. This will be a very useful skill as you progress in your data science education and career.
latent.growth.data <- read.csv("https://github.com/jameskuruvilla/R-Assignments_MSDA/blob/master/R-Assignments/datasets.csv")
#latent.growth.data