# Create a data frame
df <- tibble::tibble(
a = rnorm(10),
b = rnorm(10),
c = rnorm(10),
d = rnorm(10)
)
# Re-scale each column
df$a <- (df$a - min(df$a, na.rm = TRUE)) /
(max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE))
df$b <- (df$b - min(df$b, na.rm = TRUE)) /
(max(df$b, na.rm = TRUE) - min(df$a, na.rm = TRUE))
df$c <- (df$c - min(df$c, na.rm = TRUE)) /
(max(df$c, na.rm = TRUE) - min(df$c, na.rm = TRUE))
df$d <- (df$d - min(df$d, na.rm = TRUE)) /
(max(df$d, na.rm = TRUE) - min(df$d, na.rm = TRUE))
df
## # A tibble: 10 × 4
## a b c d
## <dbl> <dbl> <dbl> <dbl>
## 1 0.904 3.46 1 0.890
## 2 0.389 2.41 0.395 0.505
## 3 0.432 1.72 0.463 0.0754
## 4 0.675 1.73 0.783 0.703
## 5 0 3.34 0.950 0.0998
## 6 0.525 0 0 1
## 7 0.138 0.976 0.756 0.101
## 8 1 2.35 0.455 0.553
## 9 0.456 3.16 0.576 0
## 10 0.764 2.21 0.382 0.604
rescale <- function(x) {
# body
x <- (x - min(x, na.rm = TRUE)) /
(max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
# return value
return(x)
}
df$a <- rescale(df$a)
df$b <- rescale(df$b)
df$c <- rescale(df$c)
df$d <- rescale(df$d)
df
## # A tibble: 10 × 4
## a b c d
## <dbl> <dbl> <dbl> <dbl>
## 1 0.904 1 1 0.890
## 2 0.389 0.698 0.395 0.505
## 3 0.432 0.499 0.463 0.0754
## 4 0.675 0.501 0.783 0.703
## 5 0 0.965 0.950 0.0998
## 6 0.525 0 0 1
## 7 0.138 0.282 0.756 0.101
## 8 1 0.680 0.455 0.553
## 9 0.456 0.915 0.576 0
## 10 0.764 0.639 0.382 0.604
When creating functions and name, it is better that the prefix of the names are the same and the last part after the _ is what differs.
|| = or && = and
detect_sign <- function(x) {
if(x > 0) {
message("Value is positive")
print(x)
} else if(x == 0) {
warning("Value is not positive, but it can be accepted")
print(x)
} else{
stop("Value is negative, the function must stop")
print(x)
}
}
3 %>% detect_sign
## Value is positive
## [1] 3
0 %>% detect_sign
## Warning in detect_sign(.): Value is not positive, but it can be accepted
## [1] 0
#-1 %>% detect_sign
# When the if statement can fit in one line we can drop the {}, otherwise it has to be there
x <- c(1:10, 100, NA)
x
## [1] 1 2 3 4 5 6 7 8 9 10 100 NA
x %>% mean()
## [1] NA
x %>% mean(na.rm = TRUE)
## [1] 14.09091
x %>% mean(na.rm = TRUE, trim = 0.1)
## [1] 6
mean_remove_na <- function(x, na.rm = TRUE, ...) {
avg <- mean(x, na.rm = na.rm, ...)
return(avg)
}
x %>% mean_remove_na()
## [1] 14.09091
x %>% mean_remove_na(na.rm = FALSE)
## [1] NA
x %>% mean_remove_na(trim = 0.1)
## [1] 6
two types of functions