Kemal’s R Notebook

getwd()

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

read.delim("Istanbul_Goztepe_Mean_Temperature_1839-2013_Monthly_data.txt")

read.table("Istanbul_Goztepe_Mean_Temperature_1839-2013_Monthly_data.txt")

read.csv2("Istanbul_Goztepe_Mean_Temperature_1839-2013_Monthly_data.txt")

temp_1 <- read.table("Istanbul_Goztepe_Mean_Temperature_1839-2013_Monthly_data.txt")
temp_1

NA

getwd()

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

path_my_file <- read.table("Istanbul_Goztepe_Mean_Temperature_1839-2013_Monthly_data.txt")
path_my_file

temp_2 <- path_my_file$temp
temp_2

NULL

#WAY 3 - IMPORT THE FILE #16. Use “Import Datase” #17. Assign your data as “temp_3”

temp_3 <- read.table("Istanbul_Goztepe_Mean_Temperature_1839-2013_Monthly_data.txt")
temp_3

NA

#WAY 4 - DOWNLOAD THE FILE #18. Copy the LINK of data #19. Use your best read() function #20. Read the file with this function and LINK #21. Assign your data as “temp_4” #22. Choose your favorite " temp_1 or _2 or _3 or _4" and assign as just “temp”

read.table("Istanbul_Goztepe_Mean_Temperature_1839-2013_Monthly_data.txt")

temp_4 <- read.table("Istanbul_Goztepe_Mean_Temperature_1839-2013_Monthly_data.txt")
temp_4

temp_4 <- read.table("Istanbul_Goztepe_Mean_Temperature_1839-2013_Monthly_data.txt")
temp <- temp_4
temp

#PART-2 Play with the Data #Meet with the Data #1. Look at structure #2. Learn attributes and dimensions #3. Rename attributes

str(temp)

'data.frame':   175 obs. of  13 variables:
 $ V1 : int  1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 ...
 $ V2 : num  -999.9 4.3 6.6 -999.9 -999.9 ...
 $ V3 : num  -999.9 3.8 4.2 -999.9 -999.9 ...
 $ V4 : num  -999.9 4.3 4.9 -999.9 -999.9 ...
 $ V5 : num  -999.9 7.7 10.7 -999.9 -999.9 ...
 $ V6 : num  -999.9 16.6 15.5 -999.9 -999.9 ...
 $ V7 : num  -999.9 19 21.3 -999.9 -999.9 ...
 $ V8 : num  -999.9 24.5 -999.9 -999.9 -999.9 ...
 $ V9 : num  -999.9 22.7 -999.9 -999.9 -999.9 ...
 $ V10: num  -999.9 20.3 -999.9 -999.9 -999.9 ...
 $ V11: num  -999.9 15.7 -999.9 -999.9 -999.9 ...
 $ V12: num  -999.9 12.6 -999.9 -999.9 -999.9 ...
 $ V13: num  6.9 3.7 -999.9 -999.9 -999.9 ...

dim(temp)

[1] 175  13

attributes(temp)

$names
 [1] "V1"  "V2"  "V3"  "V4"  "V5"  "V6"  "V7"  "V8"  "V9"  "V10" "V11" "V12"
[13] "V13"

$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]  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
 [37]  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
 [55]  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72
 [73]  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90
 [91]  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108
[109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
[127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
[145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
[163] 163 164 165 166 167 168 169 170 171 172 173 174 175

#Clear NA and Choose Colomn #4. Print “temp” #5. Delete rows which include NA ( na.omit() ) #6. Assign it as “temp_b” #7. Select summer season #8. Assign it as “temp_b_summer”

print(temp)

temp[temp==-999.9]<- NA
temp_b <- temp
temp_b <- na.omit(temp_b)

print(temp_b)

temp_new_summer <- temp_b 
temp_new_summer

help (names)
names(temp_new_summer)[1] <- "june"
names(temp_new_summer)[2] <- "july"
names(temp_new_summer)[3] <- "august"
temp_new_summer

mean(temp_new_summer$"june")

[1] 1934.014

mean(temp_new_summer$"july")

[1] 5.611972

mean(temp_new_summer$"august")

[1] 5.658451

june_mean_temperature <- mean(temp_new_summer$"june")
july_mean_temperature <-mean(temp_new_summer$"july")
august_mean_temperature <- mean(temp_new_summer$"august")

#Use Condition Statements - if #9. Compare June Mean Temperature and July Mean Temperature #10. IF June Mean Temperature is LOWER than July then print “June has LOWER Mean Temperature.”

june_mean_temperature <- mean(temp_new_summer$"june")
july_mean_temperature <-mean(temp_new_summer$"july")
if(june_mean_temperature < july_mean_temperature){
  print("june has LOVER Mean Temperature")
}else{
  print("June has H搼㹤GHER Mean Temperature")
}
temp_new_summer

colMeans(temp_new_summer, na.rm = FALSE, dims = 1)

#Plot #13. Plot temperature for June #14. Add title and unit #15. Edit x-axis, which years are they ? #16. What about July and August ? Plot them. #17. Is there any strangeness thing, what do you think ? Compare three plots.

plot(temp_new_summer$"june")

plot(temp_new_summer$"july")

plot(temp_new_summer$"august")