Write a function of one variable that for an input numeric vector, computes the mean of the samples above the median. Check that the input is a numeric vector and if it isn’t, return an appropriate message about the wrong function argument.

mean_median_function <- function(x){
  if (is.numeric(x) == FALSE) {
    stop("Input is not numeric vector.")
  }
  m <- c(x[x>median(x)])
  mean_median <- mean(m)
  mean_median
}

To test the function, we can use the function on the following simple example:

x_test <-c(1,2,3,4,5)
mean_median_function(x_test)
## [1] 4.5

As we can see, the function returns the expected values of 4.5.

Write a function that given a data.frame, reports the numbers of columns of each possible class.

We want to write a function that returns the number of columns of each possible class - numeric, integer, character, logical, factor.

The following function takes advantage of sum counting the number of True’s. A logical vector is created for each of the possible classes.

df_col <- function(df){
  #Define logical vectors of length ncol(df)
    num <- vector(mode = "logical", length = ncol(df))
    int <- vector(mode = "logical", length = ncol(df))
    char <- vector(mode = "logical", length = ncol(df))
    log <- vector(mode = "logical", length = ncol(df))
    fac <- vector(mode = "logical", length = ncol(df))
  #Fill the vectors via sapply function 
    num <- sapply(df, is.numeric)
    int <- sapply(df, is.integer)
    char <- sapply(df, is.character)
    log <- sapply(df, is.logical)
    fac <- sapply(df, is.factor)
  #Use the sum function to count the number of "TRUE" 
    num_sum <- sum(num)
    num_int <- sum(int)
    num_char <- sum(char)
    num_log <- sum(log)
    num_fac <- sum(fac)
  #Output results
    labels_out <- c("Numeric", "Integer", "Character", "Logistic", "Factor")
    nums_out <- c(num_sum, num_int, num_char, num_log, num_fac) 
    output <- list(labels_out, nums_out)
    output
}

To test, df_col we can use the previously explored data.frame “mtcars”, which we know is comprised of 11 numeric columns.

data.frame(mtcars)
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
df_col(mtcars)
## [[1]]
## [1] "Numeric"   "Integer"   "Character" "Logistic"  "Factor"   
## 
## [[2]]
## [1] 11  0  0  0  0