This script contains pre-processing of the Joint Action Task related to participants who took part in our TMS study on race, requesting to cooperate with either a Black or White avatar.
First, we load packages we need for analyses
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## smithslibrary(here)## here() starts at C:/Users/Giovy/Downloads/Progetti Attivi/TMS-race/EXP_3/Analisi Joint Tasklibrary(readr)
library(utils)PRE-PROCESSING First, we set the directory
here::i_am("data_cleaning_TMS_BW.Rmd")## here() starts at C:/Users/Giovy/Downloads/Progetti Attivi/TMS-race/EXP_3/Analisi Joint Tasksetwd("C:/Users/Giovy/Downloads/Progetti Attivi/TMS-race/EXP_3/Analisi Joint Task")
bala = read.table("balan_bw.csv",sep = ";",header=T) #upload balancing fileThen, we specify file names and the data directory
file_names <- dir("C:/Users/Giovy/Downloads/Progetti Attivi/TMS-race/EXP_3/Script analisi in R_Valerio/dati")
setwd("C:/Users/Giovy/Downloads/Progetti Attivi/TMS-race/EXP_3/Analisi Joint Task/dati") We create a dataframe
BW =read_delim(file_names, delim = ";",
escape_double = FALSE, trim_ws = TRUE) #adding a column with the filenames, header=F if columns have no title
BW_raw = merge(bala, BW, all=T) #merging with the balancing file
names <- c("Subject","Session", "bw","sito")
BW_raw[,names] <- lapply(BW_raw[,names] , factor) #declaring factorsWe now clean the dataframe removing Catch Trials (CatchTrial_CORR =1) and Inaccurate trials (StartSoggetto1 = 1; StopSoggetto1 = 1)
BW_Clean = subset(BW_raw, BW_raw$CatchTrial_CORR != 1 &
BW_raw$StartSoggetto1 != 1 & BW_raw$StopSoggetto1 != 1)We create a new dataframe in which Inaccurate trials are = NA (AccSogg1 = 0)
#BW_Clean$Hmax_ok = ifelse(BW_Clean$AccSogg1 == 1, BW_Clean$HmaxPolso1y,NA)
#BW_Clean$MaxAp_ok = ifelse(BW_Clean$AccSogg1 == 1, BW_Clean$MaxAp1,NA)
BW_Clean$Asynchrony_ok = ifelse(BW_Clean$AccSogg1 == 1, BW_Clean$RTNetto,NA)
BW_Clean$Start_ok = ifelse(BW_Clean$AccSogg1 == 1, BW_Clean$StartSoggetto1,NA)
BW_Clean$Stop_ok = ifelse(BW_Clean$AccSogg1 == 1, BW_Clean$StopSoggetto1,NA)
BW_Clean$TM_ok = ifelse(BW_Clean$AccSogg1 == 1, BW_Clean$TMSoggetto1,NA)Then, we add the independent variables and rename the factors level
BW_Clean$Grasp = ifelse(BW_Clean$CorrectResponse == 3, "Precision","Power")
BW_Clean$Movement = ifelse(BW_Clean$OP_UG == 0, "Complementary","Imitative")
BW_Clean$Grasp = as.factor(BW_Clean$Grasp)
BW_Clean$Movement = as.factor(BW_Clean$Movement) OUTLIER REMOVAL We proceed to remove each trial that is 2.5 STDV above or below the Mean in each Condition in each Subject
First, we create a dataframe of means and stdv for each subj and cond
ordinaper <- BW_Clean %>% group_by(Subject,bw, sito, Movement, Grasp)
head(ordinaper)## # A tibble: 0 × 10
## # Groups: Subject, bw, sito, Movement, Grasp [0]
## # … with 10 variables: Subject <fct>, Session <fct>, bw <fct>, sito <fct>,
## # Asynchrony_ok <lgl>, Start_ok <lgl>, Stop_ok <lgl>, TM_ok <lgl>,
## # Grasp <fct>, Movement <fct>tabella1 = ordinaper %>% summarise(
av_Asy = mean(Asynchrony_ok,na.rm = TRUE),
sd_Asy = sd(Asynchrony_ok,na.rm = TRUE),
av_TM = mean(TM_ok,na.rm = TRUE),
sd_TM = sd(TM_ok,na.rm = TRUE))## `summarise()` has grouped output by 'Subject', 'bw', 'sito', 'Movement'. You
## can override using the `.groups` argument.Ee now merge all our tables
df2 = merge(BW_Clean, tabella1, all=T)We create a new dataset without the grasp level in wich we merge the “start” RT
ordinaper2 <- BW_Clean %>% group_by(Subject,bw, sito, Movement)
head(ordinaper2)## # A tibble: 0 × 10
## # Groups: Subject, bw, sito, Movement [0]
## # … with 10 variables: Subject <fct>, Session <fct>, bw <fct>, sito <fct>,
## # Asynchrony_ok <lgl>, Start_ok <lgl>, Stop_ok <lgl>, TM_ok <lgl>,
## # Grasp <fct>, Movement <fct>tabella2 = ordinaper2 %>% summarise(
av_Start = mean(Start_ok,na.rm = TRUE),
sd_Start = sd(Start_ok,na.rm = TRUE))## `summarise()` has grouped output by 'Subject', 'bw', 'sito'. You can override
## using the `.groups` argument.We merge the last two dataset we created
df3 = merge(df2, tabella2, all=T)
str(df3)## 'data.frame': 0 obs. of 16 variables:
## $ Subject : Factor w/ 0 levels:
## $ bw : Factor w/ 0 levels:
## $ sito : Factor w/ 0 levels:
## $ Movement : Factor w/ 0 levels:
## $ Grasp : Factor w/ 0 levels:
## $ Session : Factor w/ 0 levels:
## $ Asynchrony_ok: logi
## $ Start_ok : logi
## $ Stop_ok : logi
## $ TM_ok : logi
## $ av_Asy : num
## $ sd_Asy : num
## $ av_TM : num
## $ sd_TM : num
## $ av_Start : num
## $ sd_Start : numFinally, we remove the outliers
df3$Asynchrony = ifelse(df3$Asynchrony_ok > df3$av_Asy + 2.5 * df3$sd_Asy |
df3$Asynchrony_ok < df3$av_Asy - 2.5 * df3$sd_Asy, NA,df3$Asynchrony_ok )
df3$MovTime = ifelse(df3$TM_ok > df3$av_TM + 2.5 * df3$sd_TM |
df3$TM_ok < df3$av_TM - 2.5 * df3$sd_TM, NA, df3$TM_ok )
df3$Start = ifelse(df3$Start_ok > df3$av_Start + 2.5 * df3$sd_Start |
df3$Start_ok < df3$av_Start - 2.5 * df3$sd_Start, NA, df3$Start_ok)Then, the final Dataset is create in Excel, with only the columns of interest
str(df3) ## 'data.frame': 0 obs. of 19 variables:
## $ Subject : Factor w/ 0 levels:
## $ bw : Factor w/ 0 levels:
## $ sito : Factor w/ 0 levels:
## $ Movement : Factor w/ 0 levels:
## $ Grasp : Factor w/ 0 levels:
## $ Session : Factor w/ 0 levels:
## $ Asynchrony_ok: logi
## $ Start_ok : logi
## $ Stop_ok : logi
## $ TM_ok : logi
## $ av_Asy : num
## $ sd_Asy : num
## $ av_TM : num
## $ sd_TM : num
## $ av_Start : num
## $ sd_Start : num
## $ Asynchrony : logi
## $ MovTime : logi
## $ Start : logikeeps <- c("Subject","Session", "bw","sito","Movement","Grasp","Session","Asynchrony",
"MovTime","Start")
df = df3[keeps]
str(df)## 'data.frame': 0 obs. of 10 variables:
## $ Subject : Factor w/ 0 levels:
## $ Session : Factor w/ 0 levels:
## $ bw : Factor w/ 0 levels:
## $ sito : Factor w/ 0 levels:
## $ Movement : Factor w/ 0 levels:
## $ Grasp : Factor w/ 0 levels:
## $ Session.1 : Factor w/ 0 levels:
## $ Asynchrony: logi
## $ MovTime : logi
## $ Start : logiwrite.table(df, file = "DatiTMSBW.csv", sep = ",", row.names = F) #export in xlsx