#Initialization
data(mtcars)
View(mtcars)
Now we are going to see a summary of the data
summary(mtcars) mpg cyl disp hp drat wt qsec
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0 Min. :2.760 Min. :1.513 Min. :14.50
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89
Median :19.20 Median :6.000 Median :196.3 Median :123.0 Median :3.695 Median :3.325 Median :17.71
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7 Mean :3.597 Mean :3.217 Mean :17.85
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0 Max. :4.930 Max. :5.424 Max. :22.90
vs am gear carb
Min. :0.0000 Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :0.0000 Median :4.000 Median :2.000
Mean :0.4375 Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :1.0000 Max. :5.000 Max. :8.000 Note that the ‘mtcars’ object is of the ‘data frame’ type. Sometime we will need to convert our data into data frame type for better manipulation using the as.data.frame () function.
If I want to apply an specific function to the data frame instead of all the statistics on summary() we can use the sapply () function
sapply(mtcars, sd) mpg cyl disp hp drat wt qsec vs am gear
6.0269481 1.7859216 123.9386938 68.5628685 0.5346787 0.9784574 1.7869432 0.5040161 0.4989909 0.7378041
carb
1.6152000 As seen, here we have the standard deviation of each one of the groups (mpg, cyl, disp, hp, etc)
What if we want to run a series of statistics that are not built in R? We can create our own functions
#defining a new function, that calculates the sd and the median of x and returns it as a list
MySummary <- function(x)
{
summaryList <- list(sd(x), median(x))
names(summaryList) <- c("SD", "Median")
return(summaryList)
}
sapply(mtcars, MySummary) mpg cyl disp hp drat wt qsec vs am gear carb
SD 6.026948 1.785922 123.9387 68.56287 0.5346787 0.9784574 1.786943 0.5040161 0.4989909 0.7378041 1.6152
Median 19.2 6 196.3 123 3.695 3.325 17.71 0 0 4 2