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##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
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##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
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years<-c(1980,1980,1985,1990)
scores<-c(34,44,56,83)
df<-data.frame(years,scores)
df[,2] # will access values for scores, [,x]: means return all values in column x
## [1] 34 44 56 83
df$years # to access the values for years
## [1] 1980 1980 1985 1990
df[df$scores <50,] # display all entries where score<50
##   years scores
## 1  1980     34
## 2  1980     44
df[df$year==1980,"scores"]
## [1] 34 44
df$initial=c('d','b','s','u')


subject_name<-c("John Doe", "Jahn Doe","Steve Graves")
temperature<-c(98.1,98.6,101.4)
flu_status<-c(FALSE, FALSE,TRUE)

temperature[2]
## [1] 98.6
temperature[2:3]
## [1]  98.6 101.4
temperature[-2] #to exclude the second patient's temperature data i.e. exclude second record here
## [1]  98.1 101.4
subject_name[-2]#to exclude the second patient's name data i.e. exclude second record here
## [1] "John Doe"     "Steve Graves"
gender<- factor(c("MALE","FEMALE","MALE"))
gender
## [1] MALE   FEMALE MALE  
## Levels: FEMALE MALE
#adding a level that might not be part of the factors yet but can be in future
blood<-factor(c("O","AB","A"),levels=c("A","B","AB","O"))
blood
## [1] O  AB A 
## Levels: A B AB O
#adding order into our factors
symptoms<-factor(c("SEVERE","MILD","MODERATE"), levels= c("MILD","MODERATE","SEVERE"), ordered=TRUE)
symptoms
## [1] SEVERE   MILD     MODERATE
## Levels: MILD < MODERATE < SEVERE
symptoms>"MODERATE"
## [1]  TRUE FALSE FALSE
#stringAsFactors=FALSE; do it for the data sets downloaded from internet even
pt_data<- data.frame(subject_name,temperature,flu_status,gender,blood,symptoms,stringsAsFactors = FALSE)
pt_data
##   subject_name temperature flu_status gender blood symptoms
## 1     John Doe        98.1      FALSE   MALE     O   SEVERE
## 2     Jahn Doe        98.6      FALSE FEMALE    AB     MILD
## 3 Steve Graves       101.4       TRUE   MALE     A MODERATE
pt_data$subject_name
## [1] "John Doe"     "Jahn Doe"     "Steve Graves"
pt_data[1,2] #show value of 1st row and 2nd column
## [1] 98.1
pt_data[,1] # all rows of the 1st column
## [1] "John Doe"     "Jahn Doe"     "Steve Graves"
pt_data[2,] # all the details of 2nd patient
##   subject_name temperature flu_status gender blood symptoms
## 2     Jahn Doe        98.6      FALSE FEMALE    AB     MILD
pt_data[c(1,3),c(2,4)]
##   temperature gender
## 1        98.1   MALE
## 3       101.4   MALE
pt_data[,] # extract all data
##   subject_name temperature flu_status gender blood symptoms
## 1     John Doe        98.1      FALSE   MALE     O   SEVERE
## 2     Jahn Doe        98.6      FALSE FEMALE    AB     MILD
## 3 Steve Graves       101.4       TRUE   MALE     A MODERATE
pt_data [c(1,3), c("temperature","gender")]
##   temperature gender
## 1        98.1   MALE
## 3       101.4   MALE
pt_data$temp_c<-((pt_data$temperature-32)*(5/9))
pt_data
##   subject_name temperature flu_status gender blood symptoms   temp_c
## 1     John Doe        98.1      FALSE   MALE     O   SEVERE 36.72222
## 2     Jahn Doe        98.6      FALSE FEMALE    AB     MILD 37.00000
## 3 Steve Graves       101.4       TRUE   MALE     A MODERATE 38.55556
pt_data[c("temperature","temp_c")]
##   temperature   temp_c
## 1        98.1 36.72222
## 2        98.6 37.00000
## 3       101.4 38.55556
library(ggplot2)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ lubridate 1.9.3     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplyr)

#statement to display the displacement and highway mileage
ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy))

#statement to display the displacement and city mileage
ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=cty))

#statement to display the displacement and number of cylinders
ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=cyl))

#statement to display the displacement and highway mileage, same graph with color
ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy, color=class))

#statement to display the displacement and highway mileage, same graph with size
ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy, size=class))
## Warning: Using size for a discrete variable is not advised.

#statement to display the displacement and highway mileage, same graph with shape
ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy, shape=class))
## Warning: The shape palette can deal with a maximum of 6 discrete values because more
## than 6 becomes difficult to discriminate
## ℹ you have requested 7 values. Consider specifying shapes manually if you need
##   that many have them.
## Warning: Removed 62 rows containing missing values (`geom_point()`).

#statement to display the displacement and highway mileage, same graph with blue color
ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy), color="blue")

#statement to display the displacement and highway mileage with facet wrap
ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy)) + facet_wrap(~ class,nrow=2)

#statement to display the displacement and highway mileage with facet wrap
ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy)) + facet_grid(drv~ cyl)