From the list of data sets at http://vincentarelbundock.github.io/Rdatasets/, I chose the Ice Cream Consumption data set, because I love ice cream.

# Download and read icecream.csv
directory <- getwd()
download.file(url = "https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/Ecdat/Icecream.csv", destfile = paste(directory, "icecream.csv", sep = "/"))
icecream <- read.csv("icecream.csv")
head(icecream)
##   X  cons income price temp
## 1 1 0.386     78 0.270   41
## 2 2 0.374     79 0.282   56
## 3 3 0.393     81 0.277   63
## 4 4 0.425     80 0.280   68
## 5 5 0.406     76 0.272   69
## 6 6 0.344     78 0.262   65
# Use the summary function to gain an overview of the data set
summary(icecream)
##        X              cons            income          price       
##  Min.   : 1.00   Min.   :0.2560   Min.   :76.00   Min.   :0.2600  
##  1st Qu.: 8.25   1st Qu.:0.3113   1st Qu.:79.25   1st Qu.:0.2685  
##  Median :15.50   Median :0.3515   Median :83.50   Median :0.2770  
##  Mean   :15.50   Mean   :0.3594   Mean   :84.60   Mean   :0.2753  
##  3rd Qu.:22.75   3rd Qu.:0.3912   3rd Qu.:89.25   3rd Qu.:0.2815  
##  Max.   :30.00   Max.   :0.5480   Max.   :96.00   Max.   :0.2920  
##       temp      
##  Min.   :24.00  
##  1st Qu.:32.25  
##  Median :49.50  
##  Mean   :49.10  
##  3rd Qu.:63.75  
##  Max.   :72.00
# Display the mean and median for at least two attributes
mean(icecream$price)
## [1] 0.2753
median(icecream$price)
## [1] 0.277
mean(icecream$temp)
## [1] 49.1
median(icecream$temp)
## [1] 49.5
# Create a new data frame with a subset of the columns and rows.  Rename it.
icecream2 <- data.frame(icecream$price, icecream$temp)
head(icecream2)
##   icecream.price icecream.temp
## 1          0.270            41
## 2          0.282            56
## 3          0.277            63
## 4          0.280            68
## 5          0.272            69
## 6          0.262            65
# Create new column names for the new data frame
colnames(icecream2) <- c("Price", "Temp")
head(icecream2)
##   Price Temp
## 1 0.270   41
## 2 0.282   56
## 3 0.277   63
## 4 0.280   68
## 5 0.272   69
## 6 0.262   65
# Use the summary function to create an overview of the new data frame
summary(icecream2)
##      Price             Temp      
##  Min.   :0.2600   Min.   :24.00  
##  1st Qu.:0.2685   1st Qu.:32.25  
##  Median :0.2770   Median :49.50  
##  Mean   :0.2753   Mean   :49.10  
##  3rd Qu.:0.2815   3rd Qu.:63.75  
##  Max.   :0.2920   Max.   :72.00
# Print the mean and median for the same two attributes.  Compare
mean(icecream2$Price)
## [1] 0.2753
median(icecream2$Price)
## [1] 0.277
mean(icecream2$Temp)
## [1] 49.1
median(icecream2$Temp)
## [1] 49.5
# The mean and median of the original data set and the subset are the same.
mean(icecream$price) == mean(icecream2$Price)
## [1] TRUE
median(icecream$price) == median(icecream2$Price)
## [1] TRUE
mean(icecream$temp) == mean(icecream2$Temp)
## [1] TRUE
median(icecream$temp) == median(icecream2$Temp)
## [1] TRUE
# For at least 3 values in a column, please rename so that every value in that column is renamed

# Let's do this in a copy so we can compare later
# For every value in price, let's multiply by 100
icecream3 <- icecream
icecream3$price <- icecream3$price * 100

# Compare
head(icecream$price)
## [1] 0.270 0.282 0.277 0.280 0.272 0.262
head(icecream3$price)
## [1] 27.0 28.2 27.7 28.0 27.2 26.2