R Bridge Course Week 2 Assignment

One of the challenges in working with data is wrangling. In this assignment we will use R to perform this task.

Here is a list of data sets: http://vincentarelbundock.github.io/Rdatasets/ (click on the csv index for a list)

Please select one, download it and perform the following tasks:

Load the selected dataset into R.

theURL <- "http://vincentarelbundock.github.io/Rdatasets/csv/DAAG/carprice.csv"
carprice <- read.table(file = theURL, header = TRUE, sep = ",")

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

summary(carprice)

##        X              Type      Min.Price         Price      
##  Min.   : 6.00   Compact: 7   Min.   : 6.90   Min.   : 7.40  
##  1st Qu.:17.75   Large  :11   1st Qu.:11.40   1st Qu.:13.47  
##  Median :29.50   Midsize:10   Median :14.50   Median :16.30  
##  Mean   :36.54   Small  : 7   Mean   :16.54   Mean   :18.57  
##  3rd Qu.:60.25   Sporty : 8   3rd Qu.:19.43   3rd Qu.:20.73  
##  Max.   :79.00   Van    : 5   Max.   :37.50   Max.   :40.10  
##    Max.Price      Range.Price       RoughRange         gpm100     
##  Min.   : 7.90   Min.   : 0.000   Min.   :-0.020   Min.   :2.800  
##  1st Qu.:14.97   1st Qu.: 1.700   1st Qu.: 1.705   1st Qu.:3.800  
##  Median :18.40   Median : 3.300   Median : 3.305   Median :4.200  
##  Mean   :20.63   Mean   : 4.092   Mean   : 4.089   Mean   :4.167  
##  3rd Qu.:24.50   3rd Qu.: 5.850   3rd Qu.: 5.853   3rd Qu.:4.550  
##  Max.   :42.70   Max.   :14.600   Max.   :14.600   Max.   :5.700  
##     MPG.city      MPG.highway   
##  Min.   :15.00   Min.   :20.00  
##  1st Qu.:18.00   1st Qu.:26.00  
##  Median :20.00   Median :28.00  
##  Mean   :20.96   Mean   :28.15  
##  3rd Qu.:23.00   3rd Qu.:30.00  
##  Max.   :31.00   Max.   :41.00

means <- sapply(carprice[, c("MPG.city", "MPG.highway")], mean)
medians <- sapply(carprice[, c("MPG.city", "MPG.highway")], median)
means_mediansDF <- data.frame(rbind(means, medians))
print(means_mediansDF)

##         MPG.city MPG.highway
## means   20.95833    28.14583
## medians 20.00000    28.00000

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

head(carprice)

##    X    Type Min.Price Price Max.Price Range.Price RoughRange gpm100
## 1  6 Midsize      14.2  15.7      17.3         3.1       3.09    3.8
## 2  7   Large      19.9  20.8      21.7         1.8       1.79    4.2
## 3  8   Large      22.6  23.7      24.9         2.3       2.31    4.9
## 4  9 Midsize      26.3  26.3      26.3         0.0      -0.01    4.3
## 5 10   Large      33.0  34.7      36.3         3.3       3.30    4.9
## 6 11 Midsize      37.5  40.1      42.7         5.2       5.18    4.9
##   MPG.city MPG.highway
## 1       22          31
## 2       19          28
## 3       16          25
## 4       19          27
## 5       16          25
## 6       16          25

carprice_subset <- carprice[1:12, c("X", "Type", "MPG.city", "MPG.highway")]
head(carprice_subset)

##    X    Type MPG.city MPG.highway
## 1  6 Midsize       22          31
## 2  7   Large       19          28
## 3  8   Large       16          25
## 4  9 Midsize       19          27
## 5 10   Large       16          25
## 6 11 Midsize       16          25

Create new column names for the new data frame.

names(carprice_subset)

## [1] "X"           "Type"        "MPG.city"    "MPG.highway"

names(carprice_subset) <- paste(names(carprice_subset), "_Subset", sep = "")
names(carprice_subset)

## [1] "X_Subset"           "Type_Subset"        "MPG.city_Subset"   
## [4] "MPG.highway_Subset"

Use the summary function to create an overview of your new data frame. Then print the mean and median for the same two attributes. Please compare.

summary(carprice_subset)

##     X_Subset      Type_Subset MPG.city_Subset MPG.highway_Subset
##  Min.   : 6.00   Compact:2    Min.   :15.00   Min.   :20.00     
##  1st Qu.: 8.75   Large  :3    1st Qu.:16.00   1st Qu.:25.00     
##  Median :11.50   Midsize:4    Median :19.00   Median :27.50     
##  Mean   :11.50   Small  :0    Mean   :19.25   Mean   :27.58     
##  3rd Qu.:14.25   Sporty :1    3rd Qu.:21.25   3rd Qu.:29.50     
##  Max.   :17.00   Van    :2    Max.   :25.00   Max.   :36.00

means <- sapply(carprice_subset[, c("MPG.city_Subset", "MPG.highway_Subset")], mean)
medians <- sapply(carprice_subset[, c("MPG.city_Subset", "MPG.highway_Subset")], median)
means_medians_SubsetDF <- data.frame(rbind(means, medians))
print(means_medians_SubsetDF)

##         MPG.city_Subset MPG.highway_Subset
## means             19.25           27.58333
## medians           19.00           27.50000

means_medians_compare <- means_mediansDF - means_medians_SubsetDF
names(means_medians_compare) <- paste(names(means_medians_compare), "_Diff", sep = "")
print(means_medians_compare)

##         MPG.city_Diff MPG.highway_Diff
## means        1.708333           0.5625
## medians      1.000000           0.5000

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”.

library("stringr")

vectorx <- carprice_subset[, "Type_Subset"]
vectorx <- sapply(vectorx, as.character)
unique(vectorx)

## [1] "Midsize" "Large"   "Compact" "Sporty"  "Van"

vectorx <- str_replace(string = vectorx, pattern = "Large", "Grande")
vectorx <- str_replace(string = vectorx, pattern = "Midsize", "Mid")
vectorx <- str_replace(string = vectorx, pattern = "Compact", "Tiny")
unique(vectorx)

## [1] "Mid"    "Grande" "Tiny"   "Sporty" "Van"

carprice_subset[, "Type_Subset"] <- vectorx
head(carprice_subset)

##   X_Subset Type_Subset MPG.city_Subset MPG.highway_Subset
## 1        6         Mid              22                 31
## 2        7      Grande              19                 28
## 3        8      Grande              16                 25
## 4        9         Mid              19                 27
## 5       10      Grande              16                 25
## 6       11         Mid              16                 25

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

See use of function head() above.

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.

theURL <- "https://raw.githubusercontent.com/ChadRyanBailey/BridgeRHomeworkData/master/carprice.csv"
carpriceBonus <- read.table(file = theURL, header = TRUE, sep = ",")
head(carpriceBonus)

##    X    Type Min.Price Price Max.Price Range.Price RoughRange gpm100
## 1  6 Midsize      14.2  15.7      17.3         3.1       3.09    3.8
## 2  7   Large      19.9  20.8      21.7         1.8       1.79    4.2
## 3  8   Large      22.6  23.7      24.9         2.3       2.31    4.9
## 4  9 Midsize      26.3  26.3      26.3         0.0      -0.01    4.3
## 5 10   Large      33.0  34.7      36.3         3.3       3.30    4.9
## 6 11 Midsize      37.5  40.1      42.7         5.2       5.18    4.9
##   MPG.city MPG.highway
## 1       22          31
## 2       19          28
## 3       16          25
## 4       19          27
## 5       16          25
## 6       16          25

Please submit your .rmd file and the .csv file as well as a link to your RPubs.