An exercise of tidying the data by changing the format

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

library(tidyr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

# 1. I create a new variable by pasting Pos_to variable position.  2. choose the variable "ID" "position1" "Response_Time 3. change the long format to wide format 
dta %>% mutate(position1= paste0('Pos_',dta$Position)) %>% select(ID,position1,Response_Time)%>%
  tidyr::spread(key="position1", value="Response_Time")

##     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