Additional Course
- Data Frame
- List
- Factor
- Table
- Structures and Attributes
- Netcdf Structure
- Which
- apply Group
- Functions
- String Manipulation
Data Frame
nonhomogeneous, number of rows for each clomns are the same
v1 <- c("ahmet","mehmet","hüseyin")
v2 <- c( TRUE, FALSE, FALSE)
m1 <- matrix(1:6 , 3, 2)
df1 <- data.frame(v1,v2,m1)
df1## v1 v2 X1 X2
## 1 ahmet TRUE 1 4
## 2 mehmet FALSE 2 5
## 3 hüseyin FALSE 3 6df1[1]
df1[[1]]
str(df1)
df1 <- data.frame(v1,v2,m1,stringsAsFactors = F)
df1[1]
df1[[1]]
str(df1)
df1$v1
df1## v1 v2 X1 X2
## 1 ahmet TRUE 1 4
## 2 mehmet FALSE 2 5
## 3 hüseyin FALSE 3 6df1[1:4]
df1[1:4,3]
df1[1:6,3]
df1[3,1:4]df1$v2## [1] TRUE FALSE FALSEas.character(df1$v2)
df1$v2
df1$v2 <- as.character(df1$v2)
as.numeric(df1$v2)attributes(df1)## $names
## [1] "v1" "v2" "X1" "X2"
##
## $class
## [1] "data.frame"
##
## $row.names
## [1] 1 2 3attributes(df1)$names
attr(df1,"names")
attr(df1,"names") <- c("isim","logic","sayi1","sayi2")
df1
attr(df1,"$row.names") <- c("a","b","c")
df1
read.csv("https://web.itu.edu.tr/~tokerem/18397_Cekmekoy_Omerli_15dk.txt")## sta_no.year.month.day.hour.minutes.temp.precipitation.pressure.relative_humidity
## 1 18397;2017;7;26;18;0;23.9;0;1003;94
## 2 18397;2017;7;26;18;15;23.9;0;1003.1;95
## 3 18397;2017;7;26;18;30;23.8;0;1003.2;96
## 4 18397;2017;7;26;18;45;23.8;0;1003.2;96
## 5 18397;2017;7;26;19;0;23.6;0;1003.2;96
## 6 18397;2017;7;26;19;15;23.2;0;1003.1;97
## 7 18397;2017;7;26;19;30;23.2;0;1003.1;97
## 8 18397;2017;7;26;19;45;23.1;0;1003.1;98
## 9 18397;2017;7;26;20;0;23;0;1003.1;98
## 10 18397;2017;7;26;20;15;22.8;0;1003;98
## 11 18397;2017;7;26;20;30;22.5;0;1003;98
## 12 18397;2017;7;26;20;45;22.4;0;1003;99
## 13 18397;2017;7;26;21;0;22.2;0;1003;99
## 14 18397;2017;7;26;21;15;22.3;0;1003;99
## 15 18397;2017;7;26;21;30;22.2;0;1003.1;99
## 16 18397;2017;7;26;21;45;21.7;0;1003.1;99
## 17 18397;2017;7;26;22;0;21.9;0;1003.2;99
## 18 18397;2017;7;26;22;15;21.7;0;1003.3;99
## 19 18397;2017;7;26;22;30;21.6;0;1003.3;99
## 20 18397;2017;7;26;22;45;22.2;0;1003.4;100
## 21 18397;2017;7;26;23;0;22.2;0;1003.4;100
## 22 18397;2017;7;26;23;15;22.1;0;1003.5;100
## 23 18397;2017;7;26;23;30;22.3;0;1003.4;100
## 24 18397;2017;7;26;23;45;22.5;0;1003.4;100
## 25 18397;2017;7;27;0;0;22.3;0;1003.4;100
## 26 18397;2017;7;27;0;15;22.2;0;1003.2;100
## 27 18397;2017;7;27;0;30;22.5;0;1003.2;100
## 28 18397;2017;7;27;0;45;22.6;0;1003.2;100
## 29 18397;2017;7;27;1;0;22.6;0;1003.3;100
## 30 18397;2017;7;27;1;15;22.6;0;1003.4;100
## 31 18397;2017;7;27;1;30;22.6;0;1003.2;100
## 32 18397;2017;7;27;1;45;22.7;0;1003.2;100
## 33 18397;2017;7;27;2;0;22.6;0;1003.3;100
## 34 18397;2017;7;27;2;15;22.5;0;1003.2;100
## 35 18397;2017;7;27;2;30;22.6;0;1003.2;100
## 36 18397;2017;7;27;2;45;22.5;0;1003.1;100
## 37 18397;2017;7;27;3;0;22.5;0;1003.1;100
## 38 18397;2017;7;27;3;15;22.4;0;1003;100
## 39 18397;2017;7;27;3;30;22.5;0;1003.1;100
## 40 18397;2017;7;27;3;45;22.4;0;1003.3;100
## 41 18397;2017;7;27;4;0;22.5;0;1003.4;100
## 42 18397;2017;7;27;4;15;22.6;0;1003.5;100
## 43 18397;2017;7;27;4;30;23;0;1003.5;100
## 44 18397;2017;7;27;4;45;23.2;0;1003.5;100
## 45 18397;2017;7;27;5;0;24.2;0;1003.6;100
## 46 18397;2017;7;27;5;15;25.1;0;1003.5;97
## 47 18397;2017;7;27;5;30;25.5;0;1003.4;84
## 48 18397;2017;7;27;5;45;26.1;0;1003.3;82
## 49 18397;2017;7;27;6;0;27.1;0;1003.3;79
## 50 18397;2017;7;27;6;15;26.9;0;1003.3;78
## 51 18397;2017;7;27;6;30;27.6;0;1003.3;78
## 52 18397;2017;7;27;6;45;28;0;1003.2;76
## 53 18397;2017;7;27;7;0;28.4;0;1003.1;76
## 54 18397;2017;7;27;7;15;28.5;0;1003.1;75
## 55 18397;2017;7;27;7;30;29.3;0;1003;73
## 56 18397;2017;7;27;7;45;30.2;0;1002.9;65
## 57 18397;2017;7;27;8;0;30.1;0;1002.8;57
## 58 18397;2017;7;27;8;15;30.1;0;1002.8;60
## 59 18397;2017;7;27;8;30;30.4;0;1002.8;53
## 60 18397;2017;7;27;8;45;30.4;0;1002.8;52
## 61 18397;2017;7;27;9;0;30.8;0;1002.9;51
## 62 18397;2017;7;27;9;15;30.9;0;1002.8;51
## 63 18397;2017;7;27;9;30;31;0;1002.6;50
## 64 18397;2017;7;27;9;45;31.5;0;1002.6;53
## 65 18397;2017;7;27;10;0;31.2;0;1002.6;52
## 66 18397;2017;7;27;10;15;30.9;0;1002.4;57
## 67 18397;2017;7;27;10;30;30.9;0;1002.4;58
## 68 18397;2017;7;27;10;45;30.4;0;1002.3;59
## 69 18397;2017;7;27;11;0;30.4;0;1002.1;60
## 70 18397;2017;7;27;11;15;30;0;1001.9;61
## 71 18397;2017;7;27;11;30;29.2;0;1001.9;65
## 72 18397;2017;7;27;11;45;29.5;0;1001.7;66
## 73 18397;2017;7;27;12;0;29.4;0;1001.6;67
## 74 18397;2017;7;27;12;15;29.3;0;1001.3;66
## 75 18397;2017;7;27;12;30;29.6;0;1001.2;68
## 76 18397;2017;7;27;12;45;28.8;0;1001.3;70
## 77 18397;2017;7;27;13;0;29;0;1001.1;68
## 78 18397;2017;7;27;13;15;29;0;1001.2;69
## 79 18397;2017;7;27;13;30;29.2;0;1001.3;69
## 80 18397;2017;7;27;13;45;28.4;0;1001.5;71
## 81 18397;2017;7;27;14;0;27.8;0;1001.6;72
## 82 18397;2017;7;27;14;15;27.4;0;1001.6;72
## 83 18397;2017;7;27;14;30;26.6;0;1001.5;77
## 84 18397;2017;7;27;14;45;26.2;0;1001.2;79
## 85 18397;2017;7;27;15;0;25.8;0;1001.1;80
## 86 18397;2017;7;27;15;15;25.6;0;1001;82
## 87 18397;2017;7;27;15;30;25.4;0;1000.9;84
## 88 18397;2017;7;27;15;45;24.2;0;1001.8;79
## 89 18397;2017;7;27;16;0;19.2;7.01;1003.7;99
## 90 18397;2017;7;27;16;15;19.5;8.8;1003.2;100
## 91 18397;2017;7;27;16;30;20.1;0.25;1003.1;100
## 92 18397;2017;7;27;16;45;20.8;0;1003.7;100
## 93 18397;2017;7;27;17;0;21.2;1.13;-9999;100
## 94 18397;2017;7;27;17;15;21.4;0.02;1005.6;100
## 95 18397;2017;7;27;17;30;21.4;1.25;1005.4;100
## 96 18397;2017;7;27;17;45;21.4;2.75;1005.1;100
## 97 18397;2017;7;27;18;0;21.2;0;1005.1;100
## 98 18397;2017;7;27;18;15;21;0;-9999;100
## 99 18397;2017;7;27;18;30;20.8;0;1006.3;100
## 100 18397;2017;7;27;18;45;20.9;0;-9999;100
## 101 18397;2017;7;27;19;0;20.8;0.19;1005.7;100
## 102 18397;2017;7;27;19;15;20.7;0;1006.2;100
## 103 18397;2017;7;27;19;30;20.8;0.2;1003.6;100
## 104 18397;2017;7;27;19;45;20.8;0.22;1003.7;100
## 105 18397;2017;7;27;20;0;20.9;0;-9999;100
## 106 18397;2017;7;27;20;15;20.6;0;-9999;100
## 107 18397;2017;7;27;20;30;20.6;0;1005.1;100
## 108 18397;2017;7;27;20;45;20.5;0;1005.6;100
## 109 18397;2017;7;27;21;0;20.7;0;1005.5;100
## 110 18397;2017;7;27;21;15;20.8;0;1005.7;100
## 111 18397;2017;7;27;21;30;20.4;0;1005.6;100
## 112 18397;2017;7;27;21;45;20.4;0;1005.8;100
## 113 18397;2017;7;27;22;0;20.6;0;1005.8;100
## 114 18397;2017;7;27;22;15;20.5;0;1005.9;100
## 115 18397;2017;7;27;22;30;20.4;0;1006;100
## 116 18397;2017;7;27;22;45;20.5;0;1005.9;100
## 117 18397;2017;7;27;23;0;20.5;0;1005.9;100
## 118 18397;2017;7;27;23;15;20.6;0;1005.9;100
## 119 18397;2017;7;27;23;30;20.5;0;1006;100
## 120 18397;2017;7;27;23;45;20.5;0;1006;100
## 121 18397;2017;7;28;0;0;20.4;0;1006;100read.csv("https://web.itu.edu.tr/~tokerem/18397_Cekmekoy_Omerli_15dk.txt", sep = ";")## sta_no year month day hour minutes temp precipitation pressure
## 1 18397 2017 7 26 18 0 23.9 0.00 1003.0
## 2 18397 2017 7 26 18 15 23.9 0.00 1003.1
## 3 18397 2017 7 26 18 30 23.8 0.00 1003.2
## 4 18397 2017 7 26 18 45 23.8 0.00 1003.2
## 5 18397 2017 7 26 19 0 23.6 0.00 1003.2
## 6 18397 2017 7 26 19 15 23.2 0.00 1003.1
## 7 18397 2017 7 26 19 30 23.2 0.00 1003.1
## 8 18397 2017 7 26 19 45 23.1 0.00 1003.1
## 9 18397 2017 7 26 20 0 23.0 0.00 1003.1
## 10 18397 2017 7 26 20 15 22.8 0.00 1003.0
## 11 18397 2017 7 26 20 30 22.5 0.00 1003.0
## 12 18397 2017 7 26 20 45 22.4 0.00 1003.0
## 13 18397 2017 7 26 21 0 22.2 0.00 1003.0
## 14 18397 2017 7 26 21 15 22.3 0.00 1003.0
## 15 18397 2017 7 26 21 30 22.2 0.00 1003.1
## 16 18397 2017 7 26 21 45 21.7 0.00 1003.1
## 17 18397 2017 7 26 22 0 21.9 0.00 1003.2
## 18 18397 2017 7 26 22 15 21.7 0.00 1003.3
## 19 18397 2017 7 26 22 30 21.6 0.00 1003.3
## 20 18397 2017 7 26 22 45 22.2 0.00 1003.4
## 21 18397 2017 7 26 23 0 22.2 0.00 1003.4
## 22 18397 2017 7 26 23 15 22.1 0.00 1003.5
## 23 18397 2017 7 26 23 30 22.3 0.00 1003.4
## 24 18397 2017 7 26 23 45 22.5 0.00 1003.4
## 25 18397 2017 7 27 0 0 22.3 0.00 1003.4
## 26 18397 2017 7 27 0 15 22.2 0.00 1003.2
## 27 18397 2017 7 27 0 30 22.5 0.00 1003.2
## 28 18397 2017 7 27 0 45 22.6 0.00 1003.2
## 29 18397 2017 7 27 1 0 22.6 0.00 1003.3
## 30 18397 2017 7 27 1 15 22.6 0.00 1003.4
## 31 18397 2017 7 27 1 30 22.6 0.00 1003.2
## 32 18397 2017 7 27 1 45 22.7 0.00 1003.2
## 33 18397 2017 7 27 2 0 22.6 0.00 1003.3
## 34 18397 2017 7 27 2 15 22.5 0.00 1003.2
## 35 18397 2017 7 27 2 30 22.6 0.00 1003.2
## 36 18397 2017 7 27 2 45 22.5 0.00 1003.1
## 37 18397 2017 7 27 3 0 22.5 0.00 1003.1
## 38 18397 2017 7 27 3 15 22.4 0.00 1003.0
## 39 18397 2017 7 27 3 30 22.5 0.00 1003.1
## 40 18397 2017 7 27 3 45 22.4 0.00 1003.3
## 41 18397 2017 7 27 4 0 22.5 0.00 1003.4
## 42 18397 2017 7 27 4 15 22.6 0.00 1003.5
## 43 18397 2017 7 27 4 30 23.0 0.00 1003.5
## 44 18397 2017 7 27 4 45 23.2 0.00 1003.5
## 45 18397 2017 7 27 5 0 24.2 0.00 1003.6
## 46 18397 2017 7 27 5 15 25.1 0.00 1003.5
## 47 18397 2017 7 27 5 30 25.5 0.00 1003.4
## 48 18397 2017 7 27 5 45 26.1 0.00 1003.3
## 49 18397 2017 7 27 6 0 27.1 0.00 1003.3
## 50 18397 2017 7 27 6 15 26.9 0.00 1003.3
## 51 18397 2017 7 27 6 30 27.6 0.00 1003.3
## 52 18397 2017 7 27 6 45 28.0 0.00 1003.2
## 53 18397 2017 7 27 7 0 28.4 0.00 1003.1
## 54 18397 2017 7 27 7 15 28.5 0.00 1003.1
## 55 18397 2017 7 27 7 30 29.3 0.00 1003.0
## 56 18397 2017 7 27 7 45 30.2 0.00 1002.9
## 57 18397 2017 7 27 8 0 30.1 0.00 1002.8
## 58 18397 2017 7 27 8 15 30.1 0.00 1002.8
## 59 18397 2017 7 27 8 30 30.4 0.00 1002.8
## 60 18397 2017 7 27 8 45 30.4 0.00 1002.8
## 61 18397 2017 7 27 9 0 30.8 0.00 1002.9
## 62 18397 2017 7 27 9 15 30.9 0.00 1002.8
## 63 18397 2017 7 27 9 30 31.0 0.00 1002.6
## 64 18397 2017 7 27 9 45 31.5 0.00 1002.6
## 65 18397 2017 7 27 10 0 31.2 0.00 1002.6
## 66 18397 2017 7 27 10 15 30.9 0.00 1002.4
## 67 18397 2017 7 27 10 30 30.9 0.00 1002.4
## 68 18397 2017 7 27 10 45 30.4 0.00 1002.3
## 69 18397 2017 7 27 11 0 30.4 0.00 1002.1
## 70 18397 2017 7 27 11 15 30.0 0.00 1001.9
## 71 18397 2017 7 27 11 30 29.2 0.00 1001.9
## 72 18397 2017 7 27 11 45 29.5 0.00 1001.7
## 73 18397 2017 7 27 12 0 29.4 0.00 1001.6
## 74 18397 2017 7 27 12 15 29.3 0.00 1001.3
## 75 18397 2017 7 27 12 30 29.6 0.00 1001.2
## 76 18397 2017 7 27 12 45 28.8 0.00 1001.3
## 77 18397 2017 7 27 13 0 29.0 0.00 1001.1
## 78 18397 2017 7 27 13 15 29.0 0.00 1001.2
## 79 18397 2017 7 27 13 30 29.2 0.00 1001.3
## 80 18397 2017 7 27 13 45 28.4 0.00 1001.5
## 81 18397 2017 7 27 14 0 27.8 0.00 1001.6
## 82 18397 2017 7 27 14 15 27.4 0.00 1001.6
## 83 18397 2017 7 27 14 30 26.6 0.00 1001.5
## 84 18397 2017 7 27 14 45 26.2 0.00 1001.2
## 85 18397 2017 7 27 15 0 25.8 0.00 1001.1
## 86 18397 2017 7 27 15 15 25.6 0.00 1001.0
## 87 18397 2017 7 27 15 30 25.4 0.00 1000.9
## 88 18397 2017 7 27 15 45 24.2 0.00 1001.8
## 89 18397 2017 7 27 16 0 19.2 7.01 1003.7
## 90 18397 2017 7 27 16 15 19.5 8.80 1003.2
## 91 18397 2017 7 27 16 30 20.1 0.25 1003.1
## 92 18397 2017 7 27 16 45 20.8 0.00 1003.7
## 93 18397 2017 7 27 17 0 21.2 1.13 -9999.0
## 94 18397 2017 7 27 17 15 21.4 0.02 1005.6
## 95 18397 2017 7 27 17 30 21.4 1.25 1005.4
## 96 18397 2017 7 27 17 45 21.4 2.75 1005.1
## 97 18397 2017 7 27 18 0 21.2 0.00 1005.1
## 98 18397 2017 7 27 18 15 21.0 0.00 -9999.0
## 99 18397 2017 7 27 18 30 20.8 0.00 1006.3
## 100 18397 2017 7 27 18 45 20.9 0.00 -9999.0
## 101 18397 2017 7 27 19 0 20.8 0.19 1005.7
## 102 18397 2017 7 27 19 15 20.7 0.00 1006.2
## 103 18397 2017 7 27 19 30 20.8 0.20 1003.6
## 104 18397 2017 7 27 19 45 20.8 0.22 1003.7
## 105 18397 2017 7 27 20 0 20.9 0.00 -9999.0
## 106 18397 2017 7 27 20 15 20.6 0.00 -9999.0
## 107 18397 2017 7 27 20 30 20.6 0.00 1005.1
## 108 18397 2017 7 27 20 45 20.5 0.00 1005.6
## 109 18397 2017 7 27 21 0 20.7 0.00 1005.5
## 110 18397 2017 7 27 21 15 20.8 0.00 1005.7
## 111 18397 2017 7 27 21 30 20.4 0.00 1005.6
## 112 18397 2017 7 27 21 45 20.4 0.00 1005.8
## 113 18397 2017 7 27 22 0 20.6 0.00 1005.8
## 114 18397 2017 7 27 22 15 20.5 0.00 1005.9
## 115 18397 2017 7 27 22 30 20.4 0.00 1006.0
## 116 18397 2017 7 27 22 45 20.5 0.00 1005.9
## 117 18397 2017 7 27 23 0 20.5 0.00 1005.9
## 118 18397 2017 7 27 23 15 20.6 0.00 1005.9
## 119 18397 2017 7 27 23 30 20.5 0.00 1006.0
## 120 18397 2017 7 27 23 45 20.5 0.00 1006.0
## 121 18397 2017 7 28 0 0 20.4 0.00 1006.0
## relative_humidity
## 1 94
## 2 95
## 3 96
## 4 96
## 5 96
## 6 97
## 7 97
## 8 98
## 9 98
## 10 98
## 11 98
## 12 99
## 13 99
## 14 99
## 15 99
## 16 99
## 17 99
## 18 99
## 19 99
## 20 100
## 21 100
## 22 100
## 23 100
## 24 100
## 25 100
## 26 100
## 27 100
## 28 100
## 29 100
## 30 100
## 31 100
## 32 100
## 33 100
## 34 100
## 35 100
## 36 100
## 37 100
## 38 100
## 39 100
## 40 100
## 41 100
## 42 100
## 43 100
## 44 100
## 45 100
## 46 97
## 47 84
## 48 82
## 49 79
## 50 78
## 51 78
## 52 76
## 53 76
## 54 75
## 55 73
## 56 65
## 57 57
## 58 60
## 59 53
## 60 52
## 61 51
## 62 51
## 63 50
## 64 53
## 65 52
## 66 57
## 67 58
## 68 59
## 69 60
## 70 61
## 71 65
## 72 66
## 73 67
## 74 66
## 75 68
## 76 70
## 77 68
## 78 69
## 79 69
## 80 71
## 81 72
## 82 72
## 83 77
## 84 79
## 85 80
## 86 82
## 87 84
## 88 79
## 89 99
## 90 100
## 91 100
## 92 100
## 93 100
## 94 100
## 95 100
## 96 100
## 97 100
## 98 100
## 99 100
## 100 100
## 101 100
## 102 100
## 103 100
## 104 100
## 105 100
## 106 100
## 107 100
## 108 100
## 109 100
## 110 100
## 111 100
## 112 100
## 113 100
## 114 100
## 115 100
## 116 100
## 117 100
## 118 100
## 119 100
## 120 100
## 121 100sta1 <- read.csv("https://web.itu.edu.tr/~tokerem/18397_Cekmekoy_Omerli_15dk.txt", sep = ";")str(sta1)## 'data.frame': 121 obs. of 10 variables:
## $ sta_no : int 18397 18397 18397 18397 18397 18397 18397 18397 18397 18397 ...
## $ year : int 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 ...
## $ month : int 7 7 7 7 7 7 7 7 7 7 ...
## $ day : int 26 26 26 26 26 26 26 26 26 26 ...
## $ hour : int 18 18 18 18 19 19 19 19 20 20 ...
## $ minutes : int 0 15 30 45 0 15 30 45 0 15 ...
## $ temp : num 23.9 23.9 23.8 23.8 23.6 23.2 23.2 23.1 23 22.8 ...
## $ precipitation : num 0 0 0 0 0 0 0 0 0 0 ...
## $ pressure : num 1003 1003 1003 1003 1003 ...
## $ relative_humidity: int 94 95 96 96 96 97 97 98 98 98 ...attributes(sta1)## $names
## [1] "sta_no" "year" "month"
## [4] "day" "hour" "minutes"
## [7] "temp" "precipitation" "pressure"
## [10] "relative_humidity"
##
## $class
## [1] "data.frame"
##
## $row.names
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## [18] 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
## [35] 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
## [52] 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68
## [69] 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85
## [86] 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102
## [103] 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119
## [120] 120 121sta1[25:121,2:10]## year month day hour minutes temp precipitation pressure
## 25 2017 7 27 0 0 22.3 0.00 1003.4
## 26 2017 7 27 0 15 22.2 0.00 1003.2
## 27 2017 7 27 0 30 22.5 0.00 1003.2
## 28 2017 7 27 0 45 22.6 0.00 1003.2
## 29 2017 7 27 1 0 22.6 0.00 1003.3
## 30 2017 7 27 1 15 22.6 0.00 1003.4
## 31 2017 7 27 1 30 22.6 0.00 1003.2
## 32 2017 7 27 1 45 22.7 0.00 1003.2
## 33 2017 7 27 2 0 22.6 0.00 1003.3
## 34 2017 7 27 2 15 22.5 0.00 1003.2
## 35 2017 7 27 2 30 22.6 0.00 1003.2
## 36 2017 7 27 2 45 22.5 0.00 1003.1
## 37 2017 7 27 3 0 22.5 0.00 1003.1
## 38 2017 7 27 3 15 22.4 0.00 1003.0
## 39 2017 7 27 3 30 22.5 0.00 1003.1
## 40 2017 7 27 3 45 22.4 0.00 1003.3
## 41 2017 7 27 4 0 22.5 0.00 1003.4
## 42 2017 7 27 4 15 22.6 0.00 1003.5
## 43 2017 7 27 4 30 23.0 0.00 1003.5
## 44 2017 7 27 4 45 23.2 0.00 1003.5
## 45 2017 7 27 5 0 24.2 0.00 1003.6
## 46 2017 7 27 5 15 25.1 0.00 1003.5
## 47 2017 7 27 5 30 25.5 0.00 1003.4
## 48 2017 7 27 5 45 26.1 0.00 1003.3
## 49 2017 7 27 6 0 27.1 0.00 1003.3
## 50 2017 7 27 6 15 26.9 0.00 1003.3
## 51 2017 7 27 6 30 27.6 0.00 1003.3
## 52 2017 7 27 6 45 28.0 0.00 1003.2
## 53 2017 7 27 7 0 28.4 0.00 1003.1
## 54 2017 7 27 7 15 28.5 0.00 1003.1
## 55 2017 7 27 7 30 29.3 0.00 1003.0
## 56 2017 7 27 7 45 30.2 0.00 1002.9
## 57 2017 7 27 8 0 30.1 0.00 1002.8
## 58 2017 7 27 8 15 30.1 0.00 1002.8
## 59 2017 7 27 8 30 30.4 0.00 1002.8
## 60 2017 7 27 8 45 30.4 0.00 1002.8
## 61 2017 7 27 9 0 30.8 0.00 1002.9
## 62 2017 7 27 9 15 30.9 0.00 1002.8
## 63 2017 7 27 9 30 31.0 0.00 1002.6
## 64 2017 7 27 9 45 31.5 0.00 1002.6
## 65 2017 7 27 10 0 31.2 0.00 1002.6
## 66 2017 7 27 10 15 30.9 0.00 1002.4
## 67 2017 7 27 10 30 30.9 0.00 1002.4
## 68 2017 7 27 10 45 30.4 0.00 1002.3
## 69 2017 7 27 11 0 30.4 0.00 1002.1
## 70 2017 7 27 11 15 30.0 0.00 1001.9
## 71 2017 7 27 11 30 29.2 0.00 1001.9
## 72 2017 7 27 11 45 29.5 0.00 1001.7
## 73 2017 7 27 12 0 29.4 0.00 1001.6
## 74 2017 7 27 12 15 29.3 0.00 1001.3
## 75 2017 7 27 12 30 29.6 0.00 1001.2
## 76 2017 7 27 12 45 28.8 0.00 1001.3
## 77 2017 7 27 13 0 29.0 0.00 1001.1
## 78 2017 7 27 13 15 29.0 0.00 1001.2
## 79 2017 7 27 13 30 29.2 0.00 1001.3
## 80 2017 7 27 13 45 28.4 0.00 1001.5
## 81 2017 7 27 14 0 27.8 0.00 1001.6
## 82 2017 7 27 14 15 27.4 0.00 1001.6
## 83 2017 7 27 14 30 26.6 0.00 1001.5
## 84 2017 7 27 14 45 26.2 0.00 1001.2
## 85 2017 7 27 15 0 25.8 0.00 1001.1
## 86 2017 7 27 15 15 25.6 0.00 1001.0
## 87 2017 7 27 15 30 25.4 0.00 1000.9
## 88 2017 7 27 15 45 24.2 0.00 1001.8
## 89 2017 7 27 16 0 19.2 7.01 1003.7
## 90 2017 7 27 16 15 19.5 8.80 1003.2
## 91 2017 7 27 16 30 20.1 0.25 1003.1
## 92 2017 7 27 16 45 20.8 0.00 1003.7
## 93 2017 7 27 17 0 21.2 1.13 -9999.0
## 94 2017 7 27 17 15 21.4 0.02 1005.6
## 95 2017 7 27 17 30 21.4 1.25 1005.4
## 96 2017 7 27 17 45 21.4 2.75 1005.1
## 97 2017 7 27 18 0 21.2 0.00 1005.1
## 98 2017 7 27 18 15 21.0 0.00 -9999.0
## 99 2017 7 27 18 30 20.8 0.00 1006.3
## 100 2017 7 27 18 45 20.9 0.00 -9999.0
## 101 2017 7 27 19 0 20.8 0.19 1005.7
## 102 2017 7 27 19 15 20.7 0.00 1006.2
## 103 2017 7 27 19 30 20.8 0.20 1003.6
## 104 2017 7 27 19 45 20.8 0.22 1003.7
## 105 2017 7 27 20 0 20.9 0.00 -9999.0
## 106 2017 7 27 20 15 20.6 0.00 -9999.0
## 107 2017 7 27 20 30 20.6 0.00 1005.1
## 108 2017 7 27 20 45 20.5 0.00 1005.6
## 109 2017 7 27 21 0 20.7 0.00 1005.5
## 110 2017 7 27 21 15 20.8 0.00 1005.7
## 111 2017 7 27 21 30 20.4 0.00 1005.6
## 112 2017 7 27 21 45 20.4 0.00 1005.8
## 113 2017 7 27 22 0 20.6 0.00 1005.8
## 114 2017 7 27 22 15 20.5 0.00 1005.9
## 115 2017 7 27 22 30 20.4 0.00 1006.0
## 116 2017 7 27 22 45 20.5 0.00 1005.9
## 117 2017 7 27 23 0 20.5 0.00 1005.9
## 118 2017 7 27 23 15 20.6 0.00 1005.9
## 119 2017 7 27 23 30 20.5 0.00 1006.0
## 120 2017 7 27 23 45 20.5 0.00 1006.0
## 121 2017 7 28 0 0 20.4 0.00 1006.0
## relative_humidity
## 25 100
## 26 100
## 27 100
## 28 100
## 29 100
## 30 100
## 31 100
## 32 100
## 33 100
## 34 100
## 35 100
## 36 100
## 37 100
## 38 100
## 39 100
## 40 100
## 41 100
## 42 100
## 43 100
## 44 100
## 45 100
## 46 97
## 47 84
## 48 82
## 49 79
## 50 78
## 51 78
## 52 76
## 53 76
## 54 75
## 55 73
## 56 65
## 57 57
## 58 60
## 59 53
## 60 52
## 61 51
## 62 51
## 63 50
## 64 53
## 65 52
## 66 57
## 67 58
## 68 59
## 69 60
## 70 61
## 71 65
## 72 66
## 73 67
## 74 66
## 75 68
## 76 70
## 77 68
## 78 69
## 79 69
## 80 71
## 81 72
## 82 72
## 83 77
## 84 79
## 85 80
## 86 82
## 87 84
## 88 79
## 89 99
## 90 100
## 91 100
## 92 100
## 93 100
## 94 100
## 95 100
## 96 100
## 97 100
## 98 100
## 99 100
## 100 100
## 101 100
## 102 100
## 103 100
## 104 100
## 105 100
## 106 100
## 107 100
## 108 100
## 109 100
## 110 100
## 111 100
## 112 100
## 113 100
## 114 100
## 115 100
## 116 100
## 117 100
## 118 100
## 119 100
## 120 100
## 121 100sta2 <- sta1[25:121,2:10]plot(sta2$temp)
plot(sta2$precipitation)
sta2$temp<20## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
## [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [78] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [89] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEwhich(sta2$temp<20)## [1] 65 66temp_min_indis <- which(sta2$temp<20)temp_min_indis## [1] 65 66sta2$precipitation[temp_min_indis]## [1] 7.01 8.80sta2$precipitation[sta2$temp<20]## [1] 7.01 8.80max(sta2$precipitation)## [1] 8.8plot(sta2$temp,sta2$precipitation)
cov(sta2$temp,sta2$precipitation)## [1] -1.078493cor(sta2$temp,sta2$precipitation)## [1] -0.2345707List
v1 <- c("ahmet","mehmet","hüseyin")
v2 <- c( TRUE, FALSE, FALSE)
m1 <- matrix(1:6 , 3, 2)
df1 <- data.frame(v1,v2,m1)
l1 <- list(v1,v2,m1,df1)
l1## [[1]]
## [1] "ahmet" "mehmet" "hüseyin"
##
## [[2]]
## [1] TRUE FALSE FALSE
##
## [[3]]
## [,1] [,2]
## [1,] 1 4
## [2,] 2 5
## [3,] 3 6
##
## [[4]]
## v1 v2 X1 X2
## 1 ahmet TRUE 1 4
## 2 mehmet FALSE 2 5
## 3 hüseyin FALSE 3 6l1[1]## [[1]]
## [1] "ahmet" "mehmet" "hüseyin"l1[1][1]## [[1]]
## [1] "ahmet" "mehmet" "hüseyin"l1[[1]]## [1] "ahmet" "mehmet" "hüseyin"l1[[1]][1]## [1] "ahmet"l1[1:2]## [[1]]
## [1] "ahmet" "mehmet" "hüseyin"
##
## [[2]]
## [1] TRUE FALSE FALSEl1[1:2,2:3]str(l1)## List of 4
## $ : chr [1:3] "ahmet" "mehmet" "hüseyin"
## $ : logi [1:3] TRUE FALSE FALSE
## $ : int [1:3, 1:2] 1 2 3 4 5 6
## $ :'data.frame': 3 obs. of 4 variables:
## ..$ v1: Factor w/ 3 levels "ahmet","hüseyin",..: 1 3 2
## ..$ v2: logi [1:3] TRUE FALSE FALSE
## ..$ X1: int [1:3] 1 2 3
## ..$ X2: int [1:3] 4 5 6attributes(l1)## NULLl1[4]## [[1]]
## v1 v2 X1 X2
## 1 ahmet TRUE 1 4
## 2 mehmet FALSE 2 5
## 3 hüseyin FALSE 3 6df2 <- l1[4]
df2
df2 <- l1[[4]]
df2Factor
char1 <- c("a","b","c","a","d","d","a")
fac1 <- factor(char1)
fac1## [1] a b c a d d a
## Levels: a b c dfac1[4]## [1] a
## Levels: a b c dnum1 <- c(1,4,2,5,7,3,2,1,1,1,2)
fac2 <- factor(num1)
fac2## [1] 1 4 2 5 7 3 2 1 1 1 2
## Levels: 1 2 3 4 5 7Table
char1 <- c("a","b","c","a","d","d","a")
tab1 <- table(char1)
tab1## char1
## a b c d
## 3 1 1 2tab1[2]## b
## 1num1 <- c(1,4,2,5,7,3,2,1,1,1,2)
tab2 <- table(num1)
tab2## num1
## 1 2 3 4 5 7
## 4 3 1 1 1 1Which
which(letters == "g")## [1] 7num1 <- c(1,4,2,5,7,3,2,1,1,1,2)
which(num1 != 1)## [1] 2 3 4 5 6 7 11which(num1 != c(1,2))## Warning in num1 != c(1, 2): longer object length is not a multiple of
## shorter object length## [1] 2 3 4 5 6 7 8 10 11which(num1 != 1 & num1 != 2)## [1] 2 4 5 6head(sta1)## sta_no year month day hour minutes temp precipitation pressure
## 1 18397 2017 7 26 18 0 23.9 0 1003.0
## 2 18397 2017 7 26 18 15 23.9 0 1003.1
## 3 18397 2017 7 26 18 30 23.8 0 1003.2
## 4 18397 2017 7 26 18 45 23.8 0 1003.2
## 5 18397 2017 7 26 19 0 23.6 0 1003.2
## 6 18397 2017 7 26 19 15 23.2 0 1003.1
## relative_humidity
## 1 94
## 2 95
## 3 96
## 4 96
## 5 96
## 6 97which((sta1$hour == 18) & (sta1$minutes == 15))## [1] 2 98m2 <- matrix(1:16,4,4)
m2## [,1] [,2] [,3] [,4]
## [1,] 1 5 9 13
## [2,] 2 6 10 14
## [3,] 3 7 11 15
## [4,] 4 8 12 16which.min(m2)## [1] 1which(m2-7 > 0)## [1] 8 9 10 11 12 13 14 15 16which(m2-7 > 0, arr.ind=TRUE)## row col
## [1,] 4 2
## [2,] 1 3
## [3,] 2 3
## [4,] 3 3
## [5,] 4 3
## [6,] 1 4
## [7,] 2 4
## [8,] 3 4
## [9,] 4 4which(m2-7 > 0, arr.ind=TRUE, useNames = FALSE)## [,1] [,2]
## [1,] 4 2
## [2,] 1 3
## [3,] 2 3
## [4,] 3 3
## [5,] 4 3
## [6,] 1 4
## [7,] 2 4
## [8,] 3 4
## [9,] 4 4which(ll <- c(TRUE, FALSE, TRUE, NA, FALSE, FALSE, TRUE))## [1] 1 3 7ll## [1] TRUE FALSE TRUE NA FALSE FALSE TRUEseq(ll)## [1] 1 2 3 4 5 6 7letters[seq(ll)]## [1] "a" "b" "c" "d" "e" "f" "g"apply Group
apply
apply(X, MARGIN, FUN)
takes data frame or matrix as an input
- x: an array or matrix
- MARGIN: take a value or range between 1 and 2 to define where to apply the function: - MARGIN=1
: the manipulation is performed on rows - **MARGIN**=2: the manipulation is performed on columns - MARGIN=c(1,2)` the manipulation is performed on rows and columns - FUN: tells which function to apply. Built functions like mean, median, sum, min, max..
gives output in vector, list or array
m1 <- matrix( 1:10 , nrow=5, ncol=6)
m1## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1 6 1 6 1 6
## [2,] 2 7 2 7 2 7
## [3,] 3 8 3 8 3 8
## [4,] 4 9 4 9 4 9
## [5,] 5 10 5 10 5 10a_m1 <- apply(m1, 2, sum)
a_m1## [1] 15 40 15 40 15 40a_m1## [1] 15 40 15 40 15 40
lapply
lapply(X, FUN)
takes list, vector or data frame as input
- X: A vector or an object
- FUN: Function applied to each element of x
lapply() function does not need MARGIN
gives output in list
v1## [1] "ahmet" "mehmet" "hüseyin"lapply(v1,max)## [[1]]
## [1] "ahmet"
##
## [[2]]
## [1] "mehmet"
##
## [[3]]
## [1] "hüseyin"l1## [[1]]
## [1] "ahmet" "mehmet" "hüseyin"
##
## [[2]]
## [1] TRUE FALSE FALSE
##
## [[3]]
## [,1] [,2]
## [1,] 1 4
## [2,] 2 5
## [3,] 3 6
##
## [[4]]
## v1 v2 X1 X2
## 1 ahmet TRUE 1 4
## 2 mehmet FALSE 2 5
## 3 hüseyin FALSE 3 6#l1
lapply(l1, FUN = dim)## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
## [1] 3 2
##
## [[4]]
## [1] 3 4#l1
lapply(l1,"[",1)## [[1]]
## [1] "ahmet"
##
## [[2]]
## [1] TRUE
##
## [[3]]
## [1] 1
##
## [[4]]
## v1
## 1 ahmet
## 2 mehmet
## 3 hüseyin
l2 <- list(x = 1:5, y = 6:10, z = 11:15)
lapply(l2, FUN = median)## $x
## [1] 3
##
## $y
## [1] 8
##
## $z
## [1] 13head(cars)## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10df3 <- cars
lapply(df3, min)## $speed
## [1] 4
##
## $dist
## [1] 2sapply
sapply(X, FUN)
takes list, vector or data frame as input
- X: A vector or an object
- FUN: Function applied to each element of x
gives output in vector or matrix
#head(cars)
#df3 <- cars
lapply(df3, min)## $speed
## [1] 4
##
## $dist
## [1] 2sapply(df3, min)## speed dist
## 4 2tapply
tapply(X, INDEX, FUN = NULL)
for each factor variable in a vector
- X: An object, usually a vector
- INDEX: A list containing factor
- FUN: Function applied to each element of x
iris## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicatapply(iris$Sepal.Width, iris$Species, median)## setosa versicolor virginica
## 3.4 2.8 3.0mapply
mapply - For when you have several data structures (e.g. vectors, lists) and you want to apply a function to the 1st elements of each, and then the 2nd elements of each
sum(1:5, 1:5, 1:5)## [1] 45rep(1:4, 4:1) # replicates## [1] 1 1 1 1 2 2 2 3 3 4mapply(sum, 1:5, 1:5, 1:5) ## [1] 3 6 9 12 15mapply(rep, 1:4, 4:1) # replicates## [[1]]
## [1] 1 1 1 1
##
## [[2]]
## [1] 2 2 2
##
## [[3]]
## [1] 3 3
##
## [[4]]
## [1] 4sweep
m3 <- matrix(4:15,4,3)
m3## [,1] [,2] [,3]
## [1,] 4 8 12
## [2,] 5 9 13
## [3,] 6 10 14
## [4,] 7 11 15m3_mean <- apply(m3, 2, mean)
m3_mean## [1] 5.5 9.5 13.5m3_sd <- apply(m3, 2, sd)
m3_sd## [1] 1.290994 1.290994 1.290994m3_a <- sweep(m3, 2, m3_mean,"-")
m3_a## [,1] [,2] [,3]
## [1,] -1.5 -1.5 -1.5
## [2,] -0.5 -0.5 -0.5
## [3,] 0.5 0.5 0.5
## [4,] 1.5 1.5 1.5m3_b <- sweep(m3_a, 2, m3_sd, "/")
m3_b## [,1] [,2] [,3]
## [1,] -1.1618950 -1.1618950 -1.1618950
## [2,] -0.3872983 -0.3872983 -0.3872983
## [3,] 0.3872983 0.3872983 0.3872983
## [4,] 1.1618950 1.1618950 1.1618950aggregate
sta2## year month day hour minutes temp precipitation pressure
## 25 2017 7 27 0 0 22.3 0.00 1003.4
## 26 2017 7 27 0 15 22.2 0.00 1003.2
## 27 2017 7 27 0 30 22.5 0.00 1003.2
## 28 2017 7 27 0 45 22.6 0.00 1003.2
## 29 2017 7 27 1 0 22.6 0.00 1003.3
## 30 2017 7 27 1 15 22.6 0.00 1003.4
## 31 2017 7 27 1 30 22.6 0.00 1003.2
## 32 2017 7 27 1 45 22.7 0.00 1003.2
## 33 2017 7 27 2 0 22.6 0.00 1003.3
## 34 2017 7 27 2 15 22.5 0.00 1003.2
## 35 2017 7 27 2 30 22.6 0.00 1003.2
## 36 2017 7 27 2 45 22.5 0.00 1003.1
## 37 2017 7 27 3 0 22.5 0.00 1003.1
## 38 2017 7 27 3 15 22.4 0.00 1003.0
## 39 2017 7 27 3 30 22.5 0.00 1003.1
## 40 2017 7 27 3 45 22.4 0.00 1003.3
## 41 2017 7 27 4 0 22.5 0.00 1003.4
## 42 2017 7 27 4 15 22.6 0.00 1003.5
## 43 2017 7 27 4 30 23.0 0.00 1003.5
## 44 2017 7 27 4 45 23.2 0.00 1003.5
## 45 2017 7 27 5 0 24.2 0.00 1003.6
## 46 2017 7 27 5 15 25.1 0.00 1003.5
## 47 2017 7 27 5 30 25.5 0.00 1003.4
## 48 2017 7 27 5 45 26.1 0.00 1003.3
## 49 2017 7 27 6 0 27.1 0.00 1003.3
## 50 2017 7 27 6 15 26.9 0.00 1003.3
## 51 2017 7 27 6 30 27.6 0.00 1003.3
## 52 2017 7 27 6 45 28.0 0.00 1003.2
## 53 2017 7 27 7 0 28.4 0.00 1003.1
## 54 2017 7 27 7 15 28.5 0.00 1003.1
## 55 2017 7 27 7 30 29.3 0.00 1003.0
## 56 2017 7 27 7 45 30.2 0.00 1002.9
## 57 2017 7 27 8 0 30.1 0.00 1002.8
## 58 2017 7 27 8 15 30.1 0.00 1002.8
## 59 2017 7 27 8 30 30.4 0.00 1002.8
## 60 2017 7 27 8 45 30.4 0.00 1002.8
## 61 2017 7 27 9 0 30.8 0.00 1002.9
## 62 2017 7 27 9 15 30.9 0.00 1002.8
## 63 2017 7 27 9 30 31.0 0.00 1002.6
## 64 2017 7 27 9 45 31.5 0.00 1002.6
## 65 2017 7 27 10 0 31.2 0.00 1002.6
## 66 2017 7 27 10 15 30.9 0.00 1002.4
## 67 2017 7 27 10 30 30.9 0.00 1002.4
## 68 2017 7 27 10 45 30.4 0.00 1002.3
## 69 2017 7 27 11 0 30.4 0.00 1002.1
## 70 2017 7 27 11 15 30.0 0.00 1001.9
## 71 2017 7 27 11 30 29.2 0.00 1001.9
## 72 2017 7 27 11 45 29.5 0.00 1001.7
## 73 2017 7 27 12 0 29.4 0.00 1001.6
## 74 2017 7 27 12 15 29.3 0.00 1001.3
## 75 2017 7 27 12 30 29.6 0.00 1001.2
## 76 2017 7 27 12 45 28.8 0.00 1001.3
## 77 2017 7 27 13 0 29.0 0.00 1001.1
## 78 2017 7 27 13 15 29.0 0.00 1001.2
## 79 2017 7 27 13 30 29.2 0.00 1001.3
## 80 2017 7 27 13 45 28.4 0.00 1001.5
## 81 2017 7 27 14 0 27.8 0.00 1001.6
## 82 2017 7 27 14 15 27.4 0.00 1001.6
## 83 2017 7 27 14 30 26.6 0.00 1001.5
## 84 2017 7 27 14 45 26.2 0.00 1001.2
## 85 2017 7 27 15 0 25.8 0.00 1001.1
## 86 2017 7 27 15 15 25.6 0.00 1001.0
## 87 2017 7 27 15 30 25.4 0.00 1000.9
## 88 2017 7 27 15 45 24.2 0.00 1001.8
## 89 2017 7 27 16 0 19.2 7.01 1003.7
## 90 2017 7 27 16 15 19.5 8.80 1003.2
## 91 2017 7 27 16 30 20.1 0.25 1003.1
## 92 2017 7 27 16 45 20.8 0.00 1003.7
## 93 2017 7 27 17 0 21.2 1.13 -9999.0
## 94 2017 7 27 17 15 21.4 0.02 1005.6
## 95 2017 7 27 17 30 21.4 1.25 1005.4
## 96 2017 7 27 17 45 21.4 2.75 1005.1
## 97 2017 7 27 18 0 21.2 0.00 1005.1
## 98 2017 7 27 18 15 21.0 0.00 -9999.0
## 99 2017 7 27 18 30 20.8 0.00 1006.3
## 100 2017 7 27 18 45 20.9 0.00 -9999.0
## 101 2017 7 27 19 0 20.8 0.19 1005.7
## 102 2017 7 27 19 15 20.7 0.00 1006.2
## 103 2017 7 27 19 30 20.8 0.20 1003.6
## 104 2017 7 27 19 45 20.8 0.22 1003.7
## 105 2017 7 27 20 0 20.9 0.00 -9999.0
## 106 2017 7 27 20 15 20.6 0.00 -9999.0
## 107 2017 7 27 20 30 20.6 0.00 1005.1
## 108 2017 7 27 20 45 20.5 0.00 1005.6
## 109 2017 7 27 21 0 20.7 0.00 1005.5
## 110 2017 7 27 21 15 20.8 0.00 1005.7
## 111 2017 7 27 21 30 20.4 0.00 1005.6
## 112 2017 7 27 21 45 20.4 0.00 1005.8
## 113 2017 7 27 22 0 20.6 0.00 1005.8
## 114 2017 7 27 22 15 20.5 0.00 1005.9
## 115 2017 7 27 22 30 20.4 0.00 1006.0
## 116 2017 7 27 22 45 20.5 0.00 1005.9
## 117 2017 7 27 23 0 20.5 0.00 1005.9
## 118 2017 7 27 23 15 20.6 0.00 1005.9
## 119 2017 7 27 23 30 20.5 0.00 1006.0
## 120 2017 7 27 23 45 20.5 0.00 1006.0
## 121 2017 7 28 0 0 20.4 0.00 1006.0
## relative_humidity
## 25 100
## 26 100
## 27 100
## 28 100
## 29 100
## 30 100
## 31 100
## 32 100
## 33 100
## 34 100
## 35 100
## 36 100
## 37 100
## 38 100
## 39 100
## 40 100
## 41 100
## 42 100
## 43 100
## 44 100
## 45 100
## 46 97
## 47 84
## 48 82
## 49 79
## 50 78
## 51 78
## 52 76
## 53 76
## 54 75
## 55 73
## 56 65
## 57 57
## 58 60
## 59 53
## 60 52
## 61 51
## 62 51
## 63 50
## 64 53
## 65 52
## 66 57
## 67 58
## 68 59
## 69 60
## 70 61
## 71 65
## 72 66
## 73 67
## 74 66
## 75 68
## 76 70
## 77 68
## 78 69
## 79 69
## 80 71
## 81 72
## 82 72
## 83 77
## 84 79
## 85 80
## 86 82
## 87 84
## 88 79
## 89 99
## 90 100
## 91 100
## 92 100
## 93 100
## 94 100
## 95 100
## 96 100
## 97 100
## 98 100
## 99 100
## 100 100
## 101 100
## 102 100
## 103 100
## 104 100
## 105 100
## 106 100
## 107 100
## 108 100
## 109 100
## 110 100
## 111 100
## 112 100
## 113 100
## 114 100
## 115 100
## 116 100
## 117 100
## 118 100
## 119 100
## 120 100
## 121 100aggregate(sta2$temp, by=sta2["hour"], FUN=sum)## hour x
## 1 0 110.0
## 2 1 90.5
## 3 2 90.2
## 4 3 89.8
## 5 4 91.3
## 6 5 100.9
## 7 6 109.6
## 8 7 116.4
## 9 8 121.0
## 10 9 124.2
## 11 10 123.4
## 12 11 119.1
## 13 12 117.1
## 14 13 115.6
## 15 14 108.0
## 16 15 101.0
## 17 16 79.6
## 18 17 85.4
## 19 18 83.9
## 20 19 83.1
## 21 20 82.6
## 22 21 82.3
## 23 22 82.0
## 24 23 82.1Functions
celsius_to_kelvin <- function(temp_C) {
temp_K <- temp_C + 273.15
return(temp_K)
}
celsius_to_kelvin(19.5)## [1] 292.65sum.of.squares <- function(x,y) {
x^2 + y^2
}
sum.of.squares(3,5)## [1] 34red.plot <- function(x, y) {
plot(x, y, col="red")
}
red.plot(runif(20), runif(20))
red.plot <- function(x, y, ...) {
plot(x, y, col="red", ...)
}
red.plot(runif(20), runif(20), xlab="My x axis", ylab="My y axis", main="redddd")
red.plot <- function(x, y, ...) {
plot(x, y, col="red", ...)
}
red.plot(sta1$temp, sta1$relative_humidity, xlab="My x axis", ylab="My y axis", main="redddd")
lets_guess <- function(x)
{
n=28
if(missing(x)) print("enter a value");
if(x<0 | x>40) print("value must be 0<=x<=40)")
else if(x<n) print("enter a higher value")
else if(x>n) print("enter a lower value")
else if(x==n) print(c("you got it ",x))
else print("what are you doing? guess a number")
}String Manipulation
- grep()
- nchar()
- paste()
- sprintf()
- substr()
- strsplit()
- regex()
- gregexpr()
grep("b+", c("abc", "bda", "cca a", "abd"))## [1] 1 2 4str <- "Big Data at DataFlair"
nchar(str)## [1] 21paste("Hadoop", "Spark", "and", "Flink")## [1] "Hadoop Spark and Flink"paste("Hadoop", "Spark", "and", "Flink",sep = "")## [1] "HadoopSparkandFlink"num <- "12345678"
substr(num, 4, 5)## [1] "45"str = "Splitting sentence into words"
strsplit(str, " ")## [[1]]
## [1] "Splitting" "sentence" "into" "words"strsplit(str, "i")## [[1]]
## [1] "Spl" "tt" "ng sentence " "nto words"x <- "Learning To MANIPULATE strinGS in R"
tolower(x)## [1] "learning to manipulate strings in r"toupper(x)## [1] "LEARNING TO MANIPULATE STRINGS IN R"x <- "This is A string."
chartr(old = "A", new = "a", x)## [1] "This is a string."y <- "Tomorrow I plzn do lezrn zbout dexduzl znzlysis."
chartr(old = "dz", new = "ta", y)## [1] "Tomorrow I plan to learn about textual analysis."streets <- c("Main", "Elm", "Riverbend", "Mario", "Frederick")
abbreviate(streets)## Main Elm Riverbend Mario Frederick
## "Main" "Elm" "Rvrb" "Mari" "Frdr"abbreviate(streets, minlength = 2)## Main Elm Riverbend Mario Frederick
## "Mn" "El" "Rv" "Mr" "Fr"