Week 2 Homework

#install.packages("Stat2Data")
#install.packages("tidyverse")
library (Stat2Data)
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, union

library(tidyverse)

## -- Attaching packages -------------------------------------------------------------------------------- tidyverse 1.3.0 --

## v ggplot2 3.3.2     v purrr   0.3.4
## v tibble  3.0.3     v stringr 1.4.0
## v tidyr   1.1.0     v forcats 0.5.0
## v readr   1.3.1

## -- Conflicts ----------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

urlfile <- "https://raw.githubusercontent.com/okhaimova/CerealR/master/Cereal.csv"
Cereal <- read_csv(urlfile)

## Warning: Missing column names filled in: 'X1' [1]

## Parsed with column specification:
## cols(
##   X1 = col_double(),
##   Cereal = col_character(),
##   Calories = col_double(),
##   Sugar = col_double(),
##   Fiber = col_double()
## )

1. Use the summary function to gain an overview of the data set. Then display the mean and median for at least two attributes

#require(Stat2Data), to be used if loading data from package
#data(Cereal)

summary(Cereal)

##        X1           Cereal             Calories         Sugar       
##  Min.   : 1.00   Length:36          Min.   : 50.0   Min.   : 0.000  
##  1st Qu.: 9.75   Class :character   1st Qu.: 90.0   1st Qu.: 1.750  
##  Median :18.50   Mode  :character   Median :104.0   Median : 5.000  
##  Mean   :18.50                      Mean   :101.6   Mean   : 5.714  
##  3rd Qu.:27.25                      3rd Qu.:110.0   3rd Qu.: 9.075  
##  Max.   :36.00                      Max.   :160.0   Max.   :15.000  
##      Fiber       
##  Min.   : 0.000  
##  1st Qu.: 1.000  
##  Median : 3.000  
##  Mean   : 3.592  
##  3rd Qu.: 4.250  
##  Max.   :14.000

mean(Cereal$Calories)

## [1] 101.6028

median(Cereal$Calories)

## [1] 104

mean(Cereal$Sugar)

## [1] 5.713889

median(Cereal$Sugar)

## [1] 5

mean(Cereal$Fiber)

## [1] 3.591667

median(Cereal$Fiber)

## [1] 3

# to store as a vector to compare later

x <- c(mean(Cereal$Calories), median(Cereal$Calories), mean(Cereal$Sugar), median(Cereal$Sugar))

2. Create a new data frame with a subset of the columns and rows. Make sure to rename it.

cerealDF <- subset(Cereal, Calories >= 100, c(Cereal, Calories, Sugar))

3. Create new column names for the new data frame.

colnames(cerealDF) <- c("Brand", "Cal", "SugarContent")

4. 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.

summary(cerealDF)

##     Brand                Cal         SugarContent  
##  Length:25          Min.   :100.0   Min.   : 0.00  
##  Class :character   1st Qu.:100.0   1st Qu.: 3.00  
##  Mode  :character   Median :110.0   Median : 6.00  
##                     Mean   :112.3   Mean   : 6.52  
##                     3rd Qu.:110.0   3rd Qu.:11.00  
##                     Max.   :160.0   Max.   :15.00

mean(cerealDF$Cal)

## [1] 112.32

median(cerealDF$Cal)

## [1] 110

mean(cerealDF$SugarContent)

## [1] 6.52

median(cerealDF$SugarContent)

## [1] 6

# to store as a vector to compare

y <- c(mean(cerealDF$Cal), median(cerealDF$Cal), mean(cerealDF$SugarContent), median(cerealDF$SugarContent))


#create a data frame of the vectors we created and compare

CerealStat <- data.frame(x,y,x-y)
rownames(CerealStat) <- c("Average Calories", "Median Calories", "Average Sugar", "Median Sugar")
colnames(CerealStat) <- c("Cereal", "cerealDF", "Difference")

CerealStat

##                      Cereal cerealDF  Difference
## Average Calories 101.602778   112.32 -10.7172222
## Median Calories  104.000000   110.00  -6.0000000
## Average Sugar      5.713889     6.52  -0.8061111
## Median Sugar       5.000000     6.00  -1.0000000

5. For at least 3 values in a column please rename so that every value in that column is renamed. For example, suppose I have 20 values of the letter “e” in one column. Rename those values so that all 20 would show as “excellent”.

cerealDF$SugarLevel <- (cerealDF$SugarContent)
cerealDF$SugarLevel[cerealDF$SugarContent <= 5] <- "Good"
cerealDF$SugarLevel[cerealDF$SugarContent > 5 & cerealDF$SugarContent < 9 ] <- "Fair"
cerealDF$SugarLevel[cerealDF$SugarContent >= 9] <- "Bad"

6. Display enough rows to see examples of all of steps 1-5 above.

head(cerealDF,5)

## # A tibble: 5 x 4
##   Brand                   Cal SugarContent SugarLevel
##   <chr>                 <dbl>        <dbl> <chr>     
## 1 Common Sense Oat Bran   100            6 Fair      
## 2 Product 19              100            3 Good      
## 3 Just Right              140            9 Bad       
## 4 Special K               110            3 Good      
## 5 Oatbake Raisin Nut      110            8 Fair

BONUS – 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.