update

01.R_basics/.Rhistory

@@ -1,130 +1,3 @@
-sprintf('sub%02i/raw_data_sub%04.2f.txt',10,9)
-sprintf('sub%02i/raw_data_sub%05.2f.txt',10,9)
-sprintf('sub%02i/raw_data_sub%06.2f.txt',10,9)
-sprintf('sub%02i/raw_data_sub%06.2f.txt',10,9)
-sprintf('sub%02d/raw_data_sub%06.2f.txt',10,9)
-sprintf('sub%03d/raw_data_sub%06.2f.txt',10,9)
-sprintf('sub%03i/raw_data_sub%06.2f.txt',10,9)
-sprintf('sub%3i/raw_data_sub%06.2f.txt',10,9)
-sprintf('sub%03i/raw_data_sub%06.2f.txt',10,9)
-sprintf('sub%03i/raw_data_sub%06.2f.txt',10,9)
-sprintf('sub%03d/raw_data_sub%06.2f.txt',10,9)
-sprintf('sub%03d/raw_data_sub%07.2f.txt',10,9)
-sprintf('sub%03d/raw_data_sub%03.2f.txt',10,9)
-sprintf('sub%03d/raw_data_sub%03.3f.txt',10,9)
-ns = 10
-data_dir = '_data/RL_raw_data'
-rawdata = c();
-for (s in 1:ns) {
-sub_file = file.path(data_dir, sprintf('sub%02i/raw_data_sub%02i.txt',s,s))
-sub_data = read.table(sub_file, header = T, sep = ",")
-rawdata = rbind(rawdata, sub_data)
-}
-dim(rawdata)
-rawdata = rawdata[complete.cases(rawdata),]
-dim(rawdata)
-rawdata$choice == rawdata$correct
-(rawdata$choice == rawdata$correct) * 1.0
-rawdata$accuracy = (rawdata$choice == rawdata$correct) * 1.0
-head(rawdara)
-head(rawdata)
-aggregate(rawdata$accuracy, by = list(rawdata$subjID), mean)
-acc_mean = aggregate(rawdata$accuracy, by = list(rawdata$subjID), mean)[,2]
-aggregate(rawdata$accuracy, by = list(rawdata$trialID), mean)
-#------------------------------------------------------------------------------
-# read descriptive data
-load('_data/RL_descriptive.RData')
-descriptive
-#------------------------------------------------------------------------------
-# read descriptive data
-load('_data/RL_descriptive.RData')
-descriptive$acc = acc_mean
-df = descriptive
-df
-#------------------------------------------------------------------------------
-# read descriptive data
-load('_data/RL_descriptive.RData')
-descriptive
-descriptive$acc = acc_mean
-descriptive$acc = acc_mean
-df = descriptive
-#------------------------------------------------------------------------------
-# one sample t-test , to test if 'acc' is above chance level
-t.test(df$acc, mu = 0.5)
-# simple correlation, to test if IQ is correlated with acc
-cor.test(df$IQ, df$acc)
-df
-g1 <- ggplot(df, aes(IQ,acc))
-library
-#------------------------------------------------------------------------------
-## plot the scatter and the regression line
-library(ggplot2)
-myconfig <- theme_bw(base_size = 20) +
-theme(panel.grid.major = element_blank(),
-panel.grid.minor = element_blank(),
-panel.background = element_blank() )
-# scatter plot
-g1 <- ggplot(df, aes(IQ,acc))
-g1
-library(ggplot2)
-myconfig <- theme_bw(base_size = 20) +
-theme(panel.grid.major = element_blank(),
-panel.grid.minor = element_blank(),
-panel.background = element_blank() )
-g1 <- ggplot(df, aes(IQ,acc))
-g1
-g1 <- g1 + geom_jitter(width=0.0, height=0.0, size=5, colour='skyblue', alpha=0.95)
-g1
-g1 + geom_jitter(width=0.0, height=0.0, size=5, colour='skyblue', alpha=0.1)
-g1 + geom_jitter(width=0.0, height=0.0, size=5, colour='skyblue', alpha=0.1)
-g1 + geom_jitter(width=0.0, height=0.0, size=5, colour='skyblue', alpha=0)
-g1 + geom_jitter(width=0.0, height=0.0, size=5, colour='skyblue', alpha=0.9)
-g1 + geom_jitter(width=0.0, height=0.0, size=5, colour='red', alpha=0.9)
-g1 + geom_jitter(width=0.0, height=0.0, size=1, colour='red', alpha=0.9)
-g1 + geom_jitter(width=0.0, height=0.0, size=1, colour='red', fill = 'green', alpha=0.9)
-g1 + geom_jitter(width=1, height=1, size=1, colour='red', fill = 'green', alpha=0.9)
-g1 + geom_jitter(width=1, height=1, size=1, colour='red', fill = 'green', alpha=0.9)
-g1 + geom_jitter(width=1, height=1, size=1, colour='red', fill = 'green', alpha=0.9)
-g1 + geom_jitter(width=1, height=1, size=1, colour='red', fill = 'green', alpha=0.9)
-g1 + geom_jitter(width=1, height=1, size=1, colour='red', fill = 'green', alpha=0.9)
-g1 + geom_jitter(width=1, height=1, size=1, colour='red', fill = 'green', alpha=0.9)
-g1 <- g1 + myconfig + labs(x = 'IQ', y = 'Choice accuracy (%)')
-g1
-g1 <- g1 + geom_smooth(method = "lm", se = T, colour='skyblue3')
-g1
-g1 <- ggplot(df, aes(Age,acc))
-g1 <- g1 + geom_jitter(width=0.0, height=0.0, size=5, colour='skyblue', alpha=0.95)
-g1 <- g1 + myconfig + labs(x = 'Age', y = 'Choice accuracy (%)')
-# add the regression line
-g1 <- g1 + geom_smooth(method = "lm", se = T, colour='skyblue3')
-g1
-cor.test(df$Age, df$acc)
-cor.test(df$IQ, df$acc)
-0.8631401 ^2
-# scatter plot
-g1 <- ggplot(df, aes(IQ,acc))
-?gemo_jitter
-?geom_jitter
-g1 + geom_jitter(width=0.0, height=0.0, size=2, colour='skyblue', alpha=0.95)
-g1 + geom_jitter(width=0.0, height=0.0, size=3, colour='skyblue', alpha=0.95)
-g1 + geom_jitter(width=0.0, height=0.0, size=3, colour='red', alpha=0.95)
-g1 + geom_jitter(width=0.0, height=0.0, size=3, colour='red', alpha=0.5)
-g1 + geom_jitter(width=0.0, height=0.0, size=3, colour='red', alpha=0.1)
-g1 + geom_jitter(width=0.0, height=0.0, size=3, colour='red', fill='green', alpha=0.1)
-g1 + geom_jitter(width=0.0, height=0.0, size=3, colour='red', alpha=0.1)
-g1 + geom_jitter(width=0.0, height=0.0, size=3, colour='red', alpha=0.9)
-g1 + geom_jitter(width=0.0, height=0.0, size=3, colour='skybule3', alpha=0.9)
-g1 + geom_jitter(width=0.0, height=0.0, size=3, colour='skyblue3', alpha=0.9)
-g1 + geom_jitter(width=0.0, height=0.0, size=3, colour='skyblue3', alpha=0.3)
-g1 + geom_jitter(width=0.0, height=0.0, size=10, colour='skyblue3', alpha=0.3)
-g1 + geom_jitter(width=0.0, height=0.0, size=10, colour='red', alpha=0.3)
-g1 + geom_jitter(width=0.0, height=0.0, size=10, colour='red', alpha=0.1)
-g1 + geom_jitter(width=0.0, height=0.0, size=10, colour='red', alpha=0.95)
-## simple regression
-fit1 = lm(acc ~ IQ, data = df)
-fit2 = lm(acc ~ Age, data = df)
-fit3 = lm(acc ~ IQ + Age, data = df)
-fit4 = lm(acc ~ IQ * Age, data = df)
 summary(fit1)
 summary(fit2)
 summary(fit3)
@@ -510,3 +383,130 @@ acc_mean = aggregate(rawdata$accuracy, by = list(rawdata$subjID), mean)[,2]
 install.packages('brms')
 install.packages('tidyverse')
 library(brms)
+data_dir = ('_data/RL_raw_data/sub01/raw_data_sub01.txt')
+data = read.table(data_dir, header = T, sep = ",")
+head(data)
+# rm NAs
+sum(complete.cases(data))
+data = data[complete.cases(data),]
+dim(data[complete.cases(data),])
+# indexing
+data[1,1]
+data[1,]
+data[,1]
+data[1:10,]
+data[,1:2]
+data[1:10, 1:2]
+data[c(1,3,5,6), c(2,4)]
+data$choice
+# read in all the data!
+ns = 10
+data_dir = '_data/RL_raw_data'
+rawdata = c();
+for (s in 1:ns) {
+sub_file = file.path(data_dir, sprintf('sub%02i/raw_data_sub%02i.txt',s,s))
+sub_data = read.table(sub_file, header = T, sep = ",")
+rawdata = rbind(rawdata, sub_data)
+}
+rawdata = rawdata[complete.cases(rawdata),]
+rawdata$accuracy = (rawdata$choice == rawdata$correct) * 1.0
+acc_mean = aggregate(rawdata$accuracy, by = list(rawdata$subjID), mean)[,2]
+acc_mean
+as.matrix(acc_mean, 10, 1)
+0.8631^2
+?cor.test
+load('_data/RL_descriptive.RData')
+descriptive$acc = acc_mean
+df = descriptive
+df
+ns = 10
+data_dir = '_data/RL_raw_data'
+rawdata = c();
+for (s in 1:ns) {
+sub_file = file.path(, sprintf('sub%02i/raw_data_sub%02i.txt',s,s))
+sub_data = read.table(sub_file, header = T, sep = ",")
+rawdata = rbind(rawdata, sub_data)
+}
+rawdata = rawdata[complete.cases(rawdata),]
+rawdata$accuracy = (rawdata$choice == rawdata$correct) * 1.0
+acc_mean = aggregate(rawdata$accuracy, by = list(rawdata$subjID), mean)[,2]
+ns = 10
+data_dir = '_data/RL_raw_data'
+rawdata = c();
+for (s in 1:ns) {
+sub_file = file.path(data_dir, sprintf('sub%02i/raw_data_sub%02i.txt',s,s))
+sub_data = read.table(sub_file, header = T, sep = ",")
+rawdata = rbind(rawdata, sub_data)
+}
+rawdata = rawdata[complete.cases(rawdata),]
+rawdata$accuracy = (rawdata$choice == rawdata$correct) * 1.0
+acc_mean = aggregate(rawdata$accuracy, by = list(rawdata$subjID), mean)[,2]
+library(ggplot2)
+myconfig <- theme_bw(base_size = 20) +
+theme(panel.grid.major = element_blank(),
+panel.grid.minor = element_blank(),
+panel.background = element_blank() )
+# scatter plot
+g1 <- ggplot(df, aes(IQ,acc))
+g1 <- g1 + geom_jitter(width=0.0, height=0.0, size=5, colour='skyblue', alpha=0.95)
+g1 <- g1 + myconfig + labs(x = 'IQ', y = 'Choice accuracy (%)')
+# add the regression line
+g1 <- g1 + geom_smooth(method = "lm", se = T, colour='skyblue3')
+g1
+df
+head(df)
+head(df, 8)
+tail(df)
+tail(df, 8)
+colnames(df)
+row(df)
+rownames(df)
+#------------------------------------------------------------------------------
+# one sample t-test , to test if 'acc' is above chance level
+t.test(df$acc, mu = 0.5)
+cor.test(df$IQ, df$acc)
+#install.packages("ggplot2")
+library(ggplot)
+library(ggplot2)
+myconfig <- theme_bw(base_size = 20) +
+theme(panel.grid.major = element_blank(),
+panel.grid.minor = element_blank(),
+panel.background = element_blank() )
+graphics.off()
+g1 <- ggplot(df, aes(IQ,acc))
+g1
+g1 <- g1 + geom_jitter(width=0.0, height=0.0, size=5, colour='skyblue', alpha=0.95)
+g1
+g1 <- g1 + myconfig + labs(x = 'IQ', y = 'Choice accuracy (%)')
+1
+g1
+g1 <- g1 + geom_smooth(method = "lm", se = T, colour='skyblue3')
+g1
+clc
+diag_ma
+library(rstan)
+lookup(diag)
+m = rnorm(3,1)
+m
+m = rnorm(3,2)
+m
+m = as.matrix(rnorm(12) ,3,4)
+m
+m = as.matrix(rnorm(12) ,c(3,4))
+m
+?as.matrix
+m = as.matrix(rnorm(12),3,4))
+m = as.matrix(rnorm(12),3,4)
+m
+rnorm(12)
+m = matrix(rnorm(12),3,4)
+m
+diag(m)
+m
+m = round(m)
+m
+m = round(m.1)
+m = round(matrix(rnorm(12),3,4),1)
+m
+m
+diag(m)

01.R_basics/.Rproj.user/E5E84BA0/console06/INDEX001

@@ -1 +1 @@
-[{"allow_restart":true,"alt_buffer":false,"autoclose":1,"buffered_output":"\n\n\n\n\n\n\n","caption":"Terminal 1","channel_id":"6076","channel_mode":1,"child_procs":false,"cols":103,"cwd":"","exit_code":15,"handle":"A9888C92","interaction_mode":2,"max_output_lines":1000,"restarted":true,"rows":11,"shell_type":1,"show_on_output":false,"terminal_sequence":1,"title":":/e/teaching/RL_R_Tromso_2018/R_intro_workshop/01.R_basics","track_env":false,"zombie":false}]
+[{"allow_restart":true,"alt_buffer":false,"autoclose":1,"buffered_output":"\n\n\n\n\n\n\n\n","caption":"Terminal 1","channel_id":"6076","channel_mode":1,"child_procs":false,"cols":103,"cwd":"","exit_code":15,"handle":"A9888C92","interaction_mode":2,"max_output_lines":1000,"restarted":true,"rows":11,"shell_type":1,"show_on_output":false,"terminal_sequence":1,"title":":/e/teaching/RL_R_Tromso_2018/R_intro_workshop/01.R_basics","track_env":false,"zombie":false}]

01.R_basics/.Rproj.user/E5E84BA0/pcs/windowlayoutstate.pper

@@ -1,13 +1,13 @@
 {
     "left" : {
         "panelheight" : 638,
-        "splitterpos" : 271,
+        "splitterpos" : 333,
         "topwindowstate" : "HIDE",
         "windowheight" : 676
     },
     "right" : {
         "panelheight" : 1118,
-        "splitterpos" : 701,
+        "splitterpos" : 1008,
         "topwindowstate" : "NORMAL",
         "windowheight" : 1156
     }

01.R_basics/.Rproj.user/E5E84BA0/pcs/workbench-pane.pper

@@ -1,6 +1,6 @@
 {
     "TabSet1" : 0,
-    "TabSet2" : 0,
+    "TabSet2" : 3,
     "TabZoom" : {
     }
 }