Subsetting, Matrices - r

Question

I am super new to R and currently playing with the "diamond" dataset.
I am trying to return the row corresponding to the lowest, mean and largest prices and put everything in a 10 by 4 matrix. Please explain an easier way of doing this if possible.
library(ggplot2)
data(diamonds)
min(diamonds$price)
mean(diamonds$price)
max(diamonds$price) # this one gives me the wrong val!
M<-matrix(1:cols, nrow = 1, ncol = cols)
colnames(M)<-c("carat","cut" , "color" , "clarity", "depth" , "table" , "price" , "x" , "y" ,"z")
# Here I need to add the rows corresponding to the min,mean,max to this matrix.
Thanks

Answer 1

If all you want to do is to select the rows in the diamonds data frame corresponding to the mean, minimum, and maximum of price, this is easily accomplished with a combination of the $ and [ forms of the extract operator in Base R.
Note that this will return a data frame with 3 rows, not 4, because there are two rows at the minimum price, no rows at the mean price, and one row at the maximum price.
library(ggplot2)
data(diamonds)
diamonds[diamonds$price %in% c(min(diamonds$price),mean(diamonds$price),max(diamonds$price)),]
...and the output:
carat cut color clarity depth table price x y z
<dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
3 2.29 Premium I VS2 60.8 60 18823 8.5 8.47 5.16
A solution with dplyr uses filter() as follows.
# dplyr solution
library(dplyr)
diamonds %>% filter(price %in% c(min(price),mean(price),max(price)))
...and the output:
# A tibble: 3 x 10
carat cut color clarity depth table price x y z
<dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
3 2.29 Premium I VS2 60.8 60 18823 8.5 8.47 5.16
>

Answer 2

matrix and dataframes are different in R. diamonds is a dataframe it is better and easy to process if you keep it as dataframe only.
summary(diamonds) gives you some nice summary stats for each column.
If you want to apply specific functions to columns using dplyr, you can do :
library(dplyr)
diamonds %>%
summarise(across(where(is.numeric),list(min = min, max = max, mean = mean))) %>%
tidyr::pivot_longer(cols = everything(),
names_to = c('col', '.value'),
names_sep = '_')
# A tibble: 7 x 4
# col min max mean
# <chr> <dbl> <dbl> <dbl>
#1 carat 0.2 5.01 0.798
#2 depth 43 79 61.7
#3 table 43 95 57.5
#4 price 326 18823 3933.
#5 x 0 10.7 5.73
#6 y 0 58.9 5.73
#7 z 0 31.8 3.54
Note that I applied these functions only to numeric columns since cut, color, clarity are factor columns.