03-30-In-class exercises-6

#load data& see it

dta <- read.table("C:/Users/Tjlee/Desktop/weather/probeL.txt", header = T)
dim(dta)

## [1] 55  3

head(dta)

##    ID Response_Time Position
## 1 S01            51        1
## 2 S01            36        2
## 3 S01            50        3
## 4 S01            35        4
## 5 S01            42        5
## 6 S02            27        1

str(dta)

## 'data.frame':    55 obs. of  3 variables:
##  $ ID           : Factor w/ 11 levels "S01","S02","S03",..: 1 1 1 1 1 2 2 2 2 2 ...
##  $ Response_Time: int  51 36 50 35 42 27 20 26 17 27 ...
##  $ Position     : int  1 2 3 4 5 1 2 3 4 5 ...

#Use tidyr spred the data (turn rows into columns)
library(tidyr)  
dtanew <- dta %>% tidyr::spread(key= Position, value=Response_Time)
head(dtanew)

##    ID  1  2  3  4  5
## 1 S01 51 36 50 35 42
## 2 S02 27 20 26 17 27
## 3 S03 37 22 41 37 30
## 4 S04 42 36 32 34 27
## 5 S05 27 18 33 14 29
## 6 S06 43 32 43 35 40

#recolname

colnames(dtanew)[-1]<- c("Pos_1", "Pos_2", "Pos_3", "Pos_4", "Pos_5")

dtanew

##     ID Pos_1 Pos_2 Pos_3 Pos_4 Pos_5
## 1  S01    51    36    50    35    42
## 2  S02    27    20    26    17    27
## 3  S03    37    22    41    37    30
## 4  S04    42    36    32    34    27
## 5  S05    27    18    33    14    29
## 6  S06    43    32    43    35    40
## 7  S07    41    22    36    25    38
## 8  S08    38    21    31    20    16
## 9  S09    36    23    27    25    28
## 10 S10    26    31    31    32    36
## 11 S11    29    20    25    26    25