my_table <-
tibble(val = runif(n = 10, min = -2, max = 2)) |>
arrange(val)
my_table# A tibble: 10 × 1
val
<dbl>
1 -1.46
2 -0.855
3 0.0764
4 0.567
5 0.628
6 0.820
7 0.946
8 1.32
9 1.66
10 1.75 Numeric functions
There are many numeric functions in R that you might find beneficial based on your specific use cases and the nature of your data. Here are some of them categorized based on their functionality.
In order to demonstrate these functions, we will create a small table:
Basic Arithmetic Operations
abs(x): Returns the absolute value of x.sqrt(x): Returns the square root of x.ceiling(x): Returns the smallest integer not less than x.floor(x): Returns the largest integer not greater than x.trunc(x): Returns the truncated value of x.round(x, digits): Rounds the values of x to the specified number of decimal places.
my_table |>
mutate(
abs_val = abs(val),
sqrt_val = sqrt(val),
ceiling_val = ceiling(val),
floor_val = floor(val),
trunc_val = trunc(val),
round_val = round(val, 1)
)Warning: There was 1 warning in `mutate()`.
ℹ In argument: `sqrt_val = sqrt(val)`.
Caused by warning in `sqrt()`:
! NaNs produced# A tibble: 10 × 7
val abs_val sqrt_val ceiling_val floor_val trunc_val round_val
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 -1.46 1.46 NaN -1 -2 -1 -1.5
2 -0.855 0.855 NaN 0 -1 0 -0.9
3 0.0764 0.0764 0.276 1 0 0 0.1
4 0.567 0.567 0.753 1 0 0 0.6
5 0.628 0.628 0.792 1 0 0 0.6
6 0.820 0.820 0.906 1 0 0 0.8
7 0.946 0.946 0.973 1 0 0 0.9
8 1.32 1.32 1.15 2 1 1 1.3
9 1.66 1.66 1.29 2 1 1 1.7
10 1.75 1.75 1.32 2 1 1 1.7my_table |>
mutate(abs_val = abs(val),
sqrt_val = sqrt(val),
ceiling_val = ceiling(val),
floor_val = floor(val),
trunc_val = trunc(val),
round_val = round(val, 1)
) |>
pivot_longer(cols = ends_with("_val"),
names_to = "operation",
values_to = "output") |>
mutate(operation = str_remove(operation, "_val"))Warning: There was 1 warning in `mutate()`.
ℹ In argument: `sqrt_val = sqrt(val)`.
Caused by warning in `sqrt()`:
! NaNs produced# A tibble: 60 × 3
val operation output
<dbl> <chr> <dbl>
1 -1.46 abs 1.46
2 -1.46 sqrt NaN
3 -1.46 ceiling -1
4 -1.46 floor -2
5 -1.46 trunc -1
6 -1.46 round -1.5
7 -0.855 abs 0.855
8 -0.855 sqrt NaN
9 -0.855 ceiling 0
10 -0.855 floor -1
# ℹ 50 more rowsTrigonometric Functions
sin(x),cos(x),tan(x): Return the sine, cosine, and tangent of x, respectively.asin(x),acos(x),atan(x): Return the inverse sine, cosine, and tangent of x, respectively.
my_table |>
mutate(sin_val = sin(val),
cos_val = cos(val),
tan_val = tan(val),
asin_val = asin(val),
acos_val = acos(val),
atan_val = atan(val))Warning: There were 2 warnings in `mutate()`.
The first warning was:
ℹ In argument: `asin_val = asin(val)`.
Caused by warning in `asin()`:
! NaNs produced
ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.# A tibble: 10 × 7
val sin_val cos_val tan_val asin_val acos_val atan_val
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 -1.46 -0.994 0.109 -9.10 NaN NaN -0.971
2 -0.855 -0.755 0.656 -1.15 -1.03 2.60 -0.708
3 0.0764 0.0763 0.997 0.0765 0.0765 1.49 0.0762
4 0.567 0.537 0.844 0.637 0.603 0.968 0.516
5 0.628 0.588 0.809 0.726 0.679 0.892 0.561
6 0.820 0.731 0.682 1.07 0.962 0.609 0.687
7 0.946 0.811 0.585 1.39 1.24 0.329 0.758
8 1.32 0.969 0.246 3.93 NaN NaN 0.923
9 1.66 0.996 -0.0883 -11.3 NaN NaN 1.03
10 1.75 0.984 -0.177 -5.57 NaN NaN 1.05 Exponential and Logarithmic Functions
exp(x): Calculates the exponential of x.log(x, base): Computes logarithms with base base. Default is the natural logarithm.log10(x): Computes base 10 logarithms.
my_table |>
mutate(exp_val = exp(val),
loge_val = log(val),
log2_val = log(val, 2),
log10_val = log10(val))Warning: There were 3 warnings in `mutate()`.
The first warning was:
ℹ In argument: `loge_val = log(val)`.
Caused by warning in `log()`:
! NaNs produced
ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.# A tibble: 10 × 5
val exp_val loge_val log2_val log10_val
<dbl> <dbl> <dbl> <dbl> <dbl>
1 -1.46 0.232 NaN NaN NaN
2 -0.855 0.425 NaN NaN NaN
3 0.0764 1.08 -2.57 -3.71 -1.12
4 0.567 1.76 -0.567 -0.819 -0.246
5 0.628 1.87 -0.465 -0.671 -0.202
6 0.820 2.27 -0.198 -0.286 -0.0860
7 0.946 2.58 -0.0551 -0.0795 -0.0239
8 1.32 3.75 0.279 0.402 0.121
9 1.66 5.26 0.506 0.731 0.220
10 1.75 5.74 0.559 0.806 0.243 Statistical Functions
scale(x): Centers and scales a numeric vector.quantile(x, probs): Calculates quantiles from a numeric vector.rank(x): Returns the sample ranks of the values in a vector.ecdf(x): Computes empirical cumulative distribution functions.
my_table |>
mutate(scale_val = scale(val))# A tibble: 10 × 2
val scale_val[,1]
<dbl> <dbl>
1 -1.46 -1.93
2 -0.855 -1.35
3 0.0764 -0.450
4 0.567 0.0211
5 0.628 0.0797
6 0.820 0.265
7 0.946 0.386
8 1.32 0.747
9 1.66 1.07
10 1.75 1.16 quantile(my_table$val) 0% 25% 50% 75% 100%
-1.4613336 0.1990334 0.7241141 1.2279312 1.7483017 rank(my_table$val) [1] 1 2 3 4 5 6 7 8 9 10ecdf(my_table$val)Empirical CDF
Call: ecdf(my_table$val)
x[1:10] = -1.4613, -0.85544, 0.076384, ..., 1.6592, 1.7483Random Number Generation
runif(n): Generates n uniform random numbers.rnorm(n): Generates n normal random numbers.sample(x, size): Randomly samples elements from a vector.
runif(10, min = 3, max = 7) [1] 4.830967 5.876449 6.738689 4.021715 4.849171 6.760058 6.912906 3.469949
[9] 4.899988 5.241331rnorm(10, mean = 100, sd = 10) [1] 113.04870 122.86645 86.11139 97.21211 98.66679 106.35950 97.15747
[8] 73.43545 75.59533 113.20113xvals = rnorm(1000, mean = 1000, sd = 300)
sample(xvals, 10) [1] 1305.0618 987.4085 1159.8462 1363.2729 1016.6461 312.1086 1371.7452
[8] 573.2997 1317.9478 1259.4334Miscellaneous
cumsum(x): Computes the cumulative sum of elements.cumprod(x): Computes the cumulative product of elements.diff(x): Computes differences between subsequent elements.pmin(x, y),pmax(x, y): Element-wise minima and maxima of two vectors.
vals1 <- rnorm(3, mean = 100, sd = 10)
print(vals1)[1] 103.88439 91.55107 107.37990vals2 <- rnorm(3, mean = 100, sd = 10)
print(vals2)[1] 89.20240 89.73526 102.88793cumsum(vals1)[1] 103.8844 195.4355 302.8154cumprod(vals1)[1] 103.8844 9510.7269 1021260.9342diff(vals1)[1] -12.33332 15.82884pmin(vals1, vals2)[1] 89.20240 89.73526 102.88793pmax(vals1, vals2)[1] 103.88439 91.55107 107.37990Working with NA Values
is.na(x): Checks for missing values.na.omit(x): Returns the object with incomplete cases removed.
vals <- rnorm(5, mean = 0, sd = 10)
print(vals)[1] 0.9081132 2.6262318 0.6933491 -5.2864382 -1.3020216vals <- sqrt(vals)Warning in sqrt(vals): NaNs producedprint(vals)[1] 0.9529497 1.6205653 0.8326759 NaN NaNis.na(vals)[1] FALSE FALSE FALSE TRUE TRUEas.numeric(na.omit(vals))[1] 0.9529497 1.6205653 0.8326759Vectorized if-else
ifelse(test, yes, no): Returns a value with the same shape as test where each element is either yes or no based on the condition.
vals <- rnorm(10, mean = 0, sd = 10)
print(vals) [1] 16.2015874 -0.1789512 -13.1848940 -8.4456016 -11.0181487 -9.0009014
[7] -12.6108392 -26.2584890 6.6906575 6.6004151ifelse(vals > 0, 3, -1) [1] 3 -1 -1 -1 -1 -1 -1 -1 3 3Each of these functions has its role and application, and using them effectively can enhance your data manipulation and analytical capabilities in R.