Kemal Koçaklı R Notebook

#2. Learn your Working Directory and check files, under your working directory

getwd()

[1] "C:/Users/Asus/Desktop"

list.files()

 [1] "~$toğraf Analizleri.docx"     "~$yet-mektubu-ornegi-tr.docx" "~$ynakçaa.docx"              
 [4] "~$YZAK KARAKTER TİPLERİ.docx" "ALL CBS"                      "ARKEOLOJİK TEORİ"            
 [7] "AYBE 2019 Bahar"              "AYBE 2019 Güz"                "Bildiriler"                  
[10] "delice_GIS"                   "desktop.ini"                  "Evraklar"                    
[13] "final_project.nb.html"        "final_project.Rmd"            "Foto for CV"                 
[16] "Google Chrome.lnk"            "İLHAN KAYAN"                  "Java Web Start.lnk"          
[19] "PhD_Tezis"                    "TAY_Project"                  "WhatsApp.lnk"                
[22] "YÖK BURSU"                    "YÜKSEK LİSANS"               

#3. Solve this math. Is your solution bigger than 4 ? TRUE or FALSE.

math<- (5 ^ 3) - (8 * (3/2)) + 4 * (64 ^ 1/2) + 7 ^ (-2 ^ (4.68 - 1/3) )
print (math)

[1] 241

math > 4

[1] TRUE

#4. Create a vector with six numeric and two integer elements, and than assign it to a new variable (vec1)

vector <- c(1,2,3,4,5,6, 1L,2L)
vec1 <- vector
vec1

[1] 1 2 3 4 5 6 1 2

#5. Check the class vec1. Coerce vec1 to be a character vector, assign it as vec2, and check the class vec2

class(vec1)

[1] "numeric"

vec2 <- as.character(vec1)
vec2

[1] "1" "2" "3" "4" "5" "6" "1" "2"

class(vec2)

[1] "character"

#6. Create a vector with eight numeric elements using sequence function, and assign it to a new variable (vec3)

vec <- c(1,2,3,4,5,6,7,8)
vec3 <- vec
vec3 <- seq(1:8)
vec3

[1] 1 2 3 4 5 6 7 8

Check the length and calculate the mean of vec3.

length(vec3)

[1] 8

mean(vec3)

[1] 4.5

#8. Print the second element of vec3 and change it as "TRUE

print(vec3[2])

[1] 2

vec3[2] <- "TRUE"
vec3[2]

[1] "TRUE"

#9. Create a new vector with 16 elements using the function that generate random uniform numbers, and assign it as vec4.

set.seed(1)
vec4 <- runif(16,20,45)
vec4

 [1] 26.63772 29.30310 34.32133 42.70519 25.04205 42.45974 43.61688 36.51994 35.72785 21.54466
[11] 25.14936 24.41392 37.17557 29.60259 39.24604 32.44248

vec4

 [1] 26.63772 29.30310 34.32133 42.70519 25.04205 42.45974 43.61688 36.51994 35.72785 21.54466
[11] 25.14936 24.41392 37.17557 29.60259 39.24604 32.44248

#10. Create a new matrix, with 3 rows and 8 columns using vec3 and vec4 by row. Assign it as mat1.

mat1 <- matrix(c(vec3,vec4), nrow = 3, ncol = 8, byrow = TRUE)
mat1

     [,1]               [,2]               [,3]               [,4]              
[1,] "1"                "TRUE"             "3"                "4"               
[2,] "26.6377165785525" "29.3030974909198" "34.3213340837974" "42.7051947498694"
[3,] "35.727851097472"  "21.5446567616891" "25.1493643724825" "24.4139188132249"
     [,5]               [,6]               [,7]               [,8]              
[1,] "5"                "6"                "7"                "8"               
[2,] "25.0420482759364" "42.4597421241924" "43.6168817151338" "36.5199448121712"
[3,] "37.1755711664446" "29.6025929553434" "39.2460354999639" "32.4424810521305"

#11. Create a new array with 6 rows, 4 columns and 2 layers using mat1. Assign it as arr1

arr1 <- array(mat1, dim = c(6,4,2))    
arr1

#12. Check structure and dimensions of mat1 and arr1

dim(mat1)
dim(arr1)

str(mat1)

 chr [1:3, 1:8] "1" "26.6377165785525" "35.727851097472" "TRUE" "29.3030974909198" ...

str(arr1)

 chr [1:6, 1:4, 1:2] "1" "26.6377165785525" "35.727851097472" "TRUE" "29.3030974909198" ...

#13. Select 3rd row, 2nd column and change it with NA for each layers

arr1[3,2,] <- NA
arr1

, , 1

     [,1]               [,2]               [,3]               [,4]              
[1,] "1"                "3"                "5"                "7"               
[2,] "26.6377165785525" "34.3213340837974" "25.0420482759364" "43.6168817151338"
[3,] "35.727851097472"  NA                 "37.1755711664446" "39.2460354999639"
[4,] "TRUE"             "4"                "6"                "8"               
[5,] "29.3030974909198" "42.7051947498694" "42.4597421241924" "36.5199448121712"
[6,] "21.5446567616891" "24.4139188132249" "29.6025929553434" "32.4424810521305"

, , 2

     [,1]               [,2]               [,3]               [,4]              
[1,] "1"                "3"                "5"                "7"               
[2,] "26.6377165785525" "34.3213340837974" "25.0420482759364" "43.6168817151338"
[3,] "35.727851097472"  NA                 "37.1755711664446" "39.2460354999639"
[4,] "TRUE"             "4"                "6"                "8"               
[5,] "29.3030974909198" "42.7051947498694" "42.4597421241924" "36.5199448121712"
[6,] "21.5446567616891" "24.4139188132249" "29.6025929553434" "32.4424810521305"

14. Create a data frame with vec1, vec2 and vec3. Assign it as df1. Print 2nd column of df1.

df1 <- data.frame (vec1, vec2, vec3)
df1

print(df1[,2])

[1] 1 2 3 4 5 6 1 2
Levels: 1 2 3 4 5 6

#15. Create a list with vec1, mat1, arr1 and df1. Assign it as list1. Print 4th element of list1.

list1 <- list(vec1, mat1, arr1, df1)
list1

[[1]]
[1] 1 2 3 4 5 6 1 2

[[2]]
     [,1]               [,2]               [,3]               [,4]              
[1,] "1"                "TRUE"             "3"                "4"               
[2,] "26.6377165785525" "29.3030974909198" "34.3213340837974" "42.7051947498694"
[3,] "35.727851097472"  "21.5446567616891" "25.1493643724825" "24.4139188132249"
     [,5]               [,6]               [,7]               [,8]              
[1,] "5"                "6"                "7"                "8"               
[2,] "25.0420482759364" "42.4597421241924" "43.6168817151338" "36.5199448121712"
[3,] "37.1755711664446" "29.6025929553434" "39.2460354999639" "32.4424810521305"

[[3]]
, , 1

     [,1]               [,2]               [,3]               [,4]              
[1,] "1"                "3"                "5"                "7"               
[2,] "26.6377165785525" "34.3213340837974" "25.0420482759364" "43.6168817151338"
[3,] "35.727851097472"  NA                 "37.1755711664446" "39.2460354999639"
[4,] "TRUE"             "4"                "6"                "8"               
[5,] "29.3030974909198" "42.7051947498694" "42.4597421241924" "36.5199448121712"
[6,] "21.5446567616891" "24.4139188132249" "29.6025929553434" "32.4424810521305"

, , 2

     [,1]               [,2]               [,3]               [,4]              
[1,] "1"                "3"                "5"                "7"               
[2,] "26.6377165785525" "34.3213340837974" "25.0420482759364" "43.6168817151338"
[3,] "35.727851097472"  NA                 "37.1755711664446" "39.2460354999639"
[4,] "TRUE"             "4"                "6"                "8"               
[5,] "29.3030974909198" "42.7051947498694" "42.4597421241924" "36.5199448121712"
[6,] "21.5446567616891" "24.4139188132249" "29.6025929553434" "32.4424810521305"


[[4]]

print(list1[4])

[[1]]
  vec1 vec2 vec3
1    1    1    1
2    2    2 TRUE
3    3    3    3
4    4    4    4
5    5    5    5
6    6    6    6
7    1    1    7
8    2    2    8

#16. Go to main web page and download Istanbul_Cekmekoy_Omerli_26072017-29072017_15min.txt in your working directory and Read the station data in R-Studio (be careful about file path, header and seperator). Assign it as ****

getwd()

[1] "C:/Users/Asus/Desktop"

omerli <- read.csv("Istanbul_Cekmekoy_Omerli_26072017-29072017_15min -txt", sep=";")
sta_data1 <- omerli<- read.csv("Istanbul_Cekmekoy_Omerli_26072017-29072017_15min -txt", sep=";")
sta_data1

#17. Check the structure and attributes of sta_data. Print and plot the precipiptation and temperature of sta_data1

str(sta_data1)

'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(sta_data1)

$names
 [1] "sta_no"            "year"              "month"             "day"              
 [5] "hour"              "minutes"           "temp"              "precipitation"    
 [9] "pressure"          "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  19  20  21  22  23
 [24]  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46
 [47]  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69
 [70]  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92
 [93]  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115
[116] 116 117 118 119 120 121

print(sta_data1)

plot(sta_data1$temp)

plot(sta_data1$precipitation)

#18. Change temperature with NA if the value is lower than 20. Assign it as sta_data2

Temp <- c(sta_data1$temp)
Temp

  [1] 23.9 23.9 23.8 23.8 23.6 23.2 23.2 23.1 23.0 22.8 22.5 22.4 22.2 22.3 22.2 21.7 21.9 21.7
 [19] 21.6 22.2 22.2 22.1 22.3 22.5 22.3 22.2 22.5 22.6 22.6 22.6 22.6 22.7 22.6 22.5 22.6 22.5
 [37] 22.5 22.4 22.5 22.4 22.5 22.6 23.0 23.2 24.2 25.1 25.5 26.1 27.1 26.9 27.6 28.0 28.4 28.5
 [55] 29.3 30.2 30.1 30.1 30.4 30.4 30.8 30.9 31.0 31.5 31.2 30.9 30.9 30.4 30.4 30.0 29.2 29.5
 [73] 29.4 29.3 29.6 28.8 29.0 29.0 29.2 28.4 27.8 27.4 26.6 26.2 25.8 25.6 25.4 24.2 19.2 19.5
 [91] 20.1 20.8 21.2 21.4 21.4 21.4 21.2 21.0 20.8 20.9 20.8 20.7 20.8 20.8 20.9 20.6 20.6 20.5
[109] 20.7 20.8 20.4 20.4 20.6 20.5 20.4 20.5 20.5 20.6 20.5 20.5 20.4

plot(Temp, main = "sta_data1" , type = "l", xlab = "")

#19. Write sta_data2 as a new txt file in your working directory.

write.table(sta_data2, file = "Istanbul_Cekmekoy_Omerli_26072017-29072017_15min -txt")
list.files()

 [1] "~$toğraf Analizleri.docx"                             
 [2] "~$yet-mektubu-ornegi-tr.docx"                         
 [3] "~$ynakçaa.docx"                                       
 [4] "~$YZAK KARAKTER TİPLERİ.docx"                         
 [5] "ALL CBS"                                              
 [6] "ARKEOLOJİK TEORİ"                                     
 [7] "AYBE 2019 Bahar"                                      
 [8] "AYBE 2019 Güz"                                        
 [9] "Bildiriler"                                           
[10] "delice_GIS"                                           
[11] "desktop.ini"                                          
[12] "Evraklar"                                             
[13] "final_project.nb.html"                                
[14] "final_project.Rmd"                                    
[15] "Foto for CV"                                          
[16] "Google Chrome.lnk"                                    
[17] "İLHAN KAYAN"                                          
[18] "Istanbul_Cekmekoy_Omerli_26072017-29072017_15min -txt"
[19] "Java Web Start.lnk"                                   
[20] "PhD_Tezis"                                            
[21] "station.txt"                                          
[22] "TAY_Project"                                          
[23] "WhatsApp.lnk"                                         
[24] "YÖK BURSU"                                            
[25] "YÜKSEK LİSANS"                                        

#20. Install “ncdf4” package and call it into R-Studio from library.

install.packages("ncdf4")

WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/
Installing package into 㤼㸱C:/Users/Asus/Documents/R/win-library/3.6㤼㸲
(as 㤼㸱lib㤼㸲 is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.6/ncdf4_1.17.zip'
Content type 'application/zip' length 3270524 bytes (3.1 MB)
downloaded 3.1 MB

package ‘ncdf4’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\Asus\AppData\Local\Temp\Rtmp46m2y1\downloaded_packages

library(ncdf4)