From 9879bd971df74bdd90164d780d3a82f382460bbb Mon Sep 17 00:00:00 2001 From: lei-zhang Date: Mon, 1 Apr 2019 14:24:31 +0200 Subject: [PATCH] update --- 01.R_basics/.Rhistory | 254 +++++++++--------- .../.Rproj.user/E5E84BA0/console06/INDEX001 | 2 +- .../E5E84BA0/pcs/windowlayoutstate.pper | 4 +- .../E5E84BA0/pcs/workbench-pane.pper | 2 +- 4 files changed, 131 insertions(+), 131 deletions(-) diff --git a/01.R_basics/.Rhistory b/01.R_basics/.Rhistory index 0b59ea2..1c84c2e 100644 --- a/01.R_basics/.Rhistory +++ b/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) diff --git a/01.R_basics/.Rproj.user/E5E84BA0/console06/INDEX001 b/01.R_basics/.Rproj.user/E5E84BA0/console06/INDEX001 index d5e563f..2265700 100644 --- a/01.R_basics/.Rproj.user/E5E84BA0/console06/INDEX001 +++ b/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}] \ No newline at end of file +[{"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}] \ No newline at end of file diff --git a/01.R_basics/.Rproj.user/E5E84BA0/pcs/windowlayoutstate.pper b/01.R_basics/.Rproj.user/E5E84BA0/pcs/windowlayoutstate.pper index 066f9ce..c3635a1 100644 --- a/01.R_basics/.Rproj.user/E5E84BA0/pcs/windowlayoutstate.pper +++ b/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 } diff --git a/01.R_basics/.Rproj.user/E5E84BA0/pcs/workbench-pane.pper b/01.R_basics/.Rproj.user/E5E84BA0/pcs/workbench-pane.pper index 0e24b84..92c5223 100644 --- a/01.R_basics/.Rproj.user/E5E84BA0/pcs/workbench-pane.pper +++ b/01.R_basics/.Rproj.user/E5E84BA0/pcs/workbench-pane.pper @@ -1,6 +1,6 @@ { "TabSet1" : 0, - "TabSet2" : 0, + "TabSet2" : 3, "TabZoom" : { } } \ No newline at end of file