4. For the variable condition, remap the values from numbers to text as defined in the assignment.
# Add a new column called condition2
mydata$condition2 <- as.factor(mydata$condition)
mydata
5. Replicate (question 2a) using R using any of the techniques you learned in the tutorial.
# 2a i. mean, mode, median, minimum, maximum, standard deviation for lot size
mean(mydata$lotsize)
## [1] 16766.75
Mode(mydata$lotsize)
## [1] 12500
median(mydata$lotsize)
## [1] 12500
min(mydata$lotsize)
## [1] 8000
max(mydata$lotsize)
## [1] 42025
sd(mydata$lotsize)
## [1] 10841.87
# 2a ii. mean, mode, median, minimum, maximum, standard deviation for value
mean(mydata$value)
## [1] 401.875
Mode(mydata$value)
## [1] 200 290 340 360 410 475 530 610
median(mydata$value)
## [1] 385
min(mydata$value)
## [1] 200
max(mydata$value)
## [1] 610
sd(mydata$value)
## [1] 133.0933
# 2a iii. mean, mode, median, minimum, maximum for bedrooms
mean(mydata$bedrooms)
## [1] 3.25
Mode(mydata$bedrooms)
## [1] 3
median(mydata$bedrooms)
## [1] 3
min(mydata$bedrooms)
## [1] 2
max(mydata$bedrooms)
## [1] 5
# 2a iv. mean, mode, median, minimum, maximum for height
mean(mydata$height)
## [1] 2.3125
Mode(mydata$height)
## [1] 2.5
median(mydata$height)
## [1] 2.5
min(mydata$height)
## [1] 1
max(mydata$height)
## [1] 3
# 2a v. mean, mode, median, minimum, maximum for condition
mean(mydata$condition)
## [1] 3.5
Mode(mydata$condition)
## [1] 4
median(mydata$condition)
## [1] 4
min(mydata$condition)
## [1] 1
max(mydata$condition)
## [1] 5
6. Replicate (question 2b) using R. For the variable lot size, create a new variable that converts the values from square feet to acres.
# 2b i. Convert Square feet to square acres
mydata$lotsize2 <- (mydata$lotsize)*0.00002296
mydata
# 2a v. Re-compute the summary statistics.
mean(mydata$lotsize2)
## [1] 0.3849646
Mode(mydata$lotsize2)
## [1] 0.287
median(mydata$lotsize2)
## [1] 0.287
min(mydata$lotsize2)
## [1] 0.18368
max(mydata$lotsize2)
## [1] 0.964894
7. If you’re feeling daring, try to make a summary table using stargazer. If you can’t get it to work, don’t sweat it.
# Summary table using stargazer
stargazer(mydata, summary=TRUE, header=FALSE, type = "text")
##
## ================================================================
## Statistic N Mean St. Dev. Min Pctl(25) Pctl(75) Max
## ----------------------------------------------------------------
## street 8 17.000 9.856 4 9.8 23.8 31
## lotsize 8 16,766.750 10,841.870 8,000 11,875 17,707.2 42,025
## value 8 401.875 133.093 200 327.5 488.8 610
## bedrooms 8 3.250 1.035 2 2.8 4 5
## height 8 2.312 0.594 1.000 2.375 2.500 3.000
## condition 8 3.500 1.195 1 3 4 5
## lotsize2 8 0.385 0.249 0.184 0.273 0.407 0.965
## ----------------------------------------------------------------
8. Generate a boxplot of the variables lot size and value. Make sure you label them.
boxplot(mydata$lotsize, main="Boxplot of the variables lot size", xlab="", ylab="lot size (square feets)")
boxplot(mydata$value, main="Boxplot of the variables value", xlab="", ylab="value ($1000s)")
9. Generate a histogram of the variable lot size. Overlay a red line indicating the average lot size.
hist(mydata$lotsize, breaks = 15,
main="Histogram of the lot size",
xlab="lot size (square feets)",
ylab="frequency")
# Calculate the mean and assign the result to the variable the mean
themean <- mean(mydata$lotsize, na.rm = TRUE)
# Calculate the median and assign the result to the variable themedian
themedian <- median(mydata$lotsize, na.rm= TRUE)
# Add the Mean and the Median
abline(v=themean,col="red")
abline(v=themedian,col="blue")
# Add a legend
legend(300,8, c("The Mean","The Median"), lty=c(1,1), lwd = c(2.5,2.5),col = c("red","blue"))
10. Generate a plot with the number of bedrooms on the x-axis and the value/lotsize on the y-axis.
plot(mydata$bedrooms,mydata$valuepersquarefoot,
main="Plot of number of bedrooms",
xlab="number of bedrooms",
ylab="value/lotsize")
