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Basic Functions in R, in this tutorial, we are going to discuss basic statistical or user-defined functions. Functions are very useful in R for faster and safe execution.

Some will be inbuilt functions and others may be user-defined, here we are going to discuss very useful functions in our day-to-day life.

Basic Functions in R

Getting Data

```library(datasets)
data<-iris\$Sepal.Length```

Statistical Functions

The following commands can be used to get the mean, median, quantiles, minimum, maximum, variance, and standard deviation.

```mean(data)
median(data)
sd(data)
var(data)
max(data)
min(data)
quantile(data)
summary(data)```

the summary function will provide the output based on the type of the dataset.

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Control Structures

The following are basic control structures.

if statements

```if (condition) {
# code executed when condition is TRUE
} else {
# code executed when condition is FALSE
}```

for statements

```for(i in 1:x)
{ code }```

while statements

```while (test_expression)
{
statement
}```

repeat statements

```repeat {
statement
}```

break and next statements

```if (test_expression) {
break
}```

switch statements

`switch(expression, case1, case2, case3....)`

scan statements

```scan(file = "", what = double(), nmax = -1, n = -1, sep = "",
quote = if(identical(sep, "\n")) "" else "'\"", dec = ".",
skip = 0, nlines = 0, na.strings = "NA",
flush = FALSE, fill = FALSE, strip.white = FALSE,
quiet = FALSE, blank.lines.skip = TRUE, multi.line = TRUE,
comment.char = "", allowEscapes = FALSE,
fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)```

User-defined function

Just consider one example, the following function takes a single input value and computes its square

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The variable x, is a formal parameter of the function.

When the function is called it is passed an argument that provides a value for the formal parameter

```Square<-function(x){x*x}
Square(5)
25
Square(10:20)
100 121 144 169 196 225 256 289 324 361 400```

Another one example is rep function, you can use for different purposes based on your requirements.

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```rep("A",4)
rep(1:5,2)
rep(1:5,rep(2,5))```

Sorting

```order(data)
[1]  14   9  39  43  42   4   7  23  48   3  30  12  13
[14]  25  31  46   2  10  35  38  58 107   5   8  26  27
[27]  36  41  44  50  61  94   1  18  20  22  24  40  45
[40]  47  99  28  29  33  60  49   6  11  17  21  32  85
[53]  34  37  54  81  82  90  91  65  67  70  89  95 122
[66]  16  19  56  80  96  97 100 114  15  68  83  93 102
[79] 115 143  62  71 150  63  79  84  86 120 139  64  72
[92]  74  92 128 135  69  98 127 149  57  73  88 101 104
[105] 124 134 137 147  52  75 112 116 129 133 138  55 105
[118] 111 117 148  59  76  66  78  87 109 125 141 145 146
[131]  77 113 144  53 121 140 142  51 103 110 126 130 108
[144] 131 106 118 119 123 136 132```

You try for reverse sorting also based on rev function.

```rev(order(data))
[1] 132 136 123 119 118 106 131 108 130 126 110 103  51
[14] 142 140 121  53 144 113  77 146 145 141 125 109  87
[27]  78  66  76  59 148 117 111 105  55 138 133 129 116
[40] 112  75  52 147 137 134 124 104 101  88  73  57 149
[53] 127  98  69 135 128  92  74  72  64 139 120  86  84
[66]  79  63 150  71  62 143 115 102  93  83  68  15 114
[79] 100  97  96  80  56  19  16 122  95  89  70  67  65
[92]  91  90  82  81  54  37  34  85  32  21  17  11   6
[105]  49  60  33  29  28  99  47  45  40  24  22  20  18
[118]   1  94  61  50  44  41  36  27  26   8   5 107  58
[131]  38  35  10   2  46  31  25  13  12  30   3  48  23
[144]   7   4  42  43  39   9  14```

cbind function

Suppose if you want to combine the data frame, matrix or vectors, you can use cbind functions.

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```a<-c(1,2,3)
b<-c(4,5,6)
cbind(a,b)
a b
[1,] 1 4
[2,] 2 5
[3,] 3 6```

rbind function

Suppose if you want to do row binding then rbind function will be handy.

```a<-c(1,2,3)
b<-c(4,5,6)
rbind(a,b)
[,1] [,2] [,3]
a    1    2    3
b    4    5    6```

Conclusion

There are a lot of functions available, above mentioned functions will be useful for solving day-to-day data arrangement problems.

Some useful functions are here

```identical()
sum()
paste()
is.na()
is.logical()
stopifnot()
length()```

You can comment if any other useful functions in the comment section.

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