library(readxl)
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
library(ggplot2)
library(tibble)
library(magrittr)
setwd("D:/UIUC/Becklab/Master project/John's data")
data = read_excel("cortex_experiment_3_data.xlsx",sheet=1)
accuracy_check =
data %>%
group_by(subject) %>%
summarise(accuracy = mean(accuracy, na.rm = TRUE))
accuracy_cutoff = .6
good_subjs = accuracy_check$subject[which(accuracy_check$accuracy > accuracy_cutoff)]
diffs = as_tibble(matrix(ncol = 4, nrow = (length(good_subjs) * 2)))
colnames(diffs) = c("participant", "accuracy", "rt", "condition")
diffs$participant = rep(good_subjs, 2)
diffs$condition =
c(rep("inner_invalid horizontal-vertical", length(good_subjs)),
rep("outer_invalid horizontal-vertical", length(good_subjs)))
for (i in 1:length(good_subjs)) {
current = filter(data, subject == good_subjs[i])
diffs$accuracy[i] = current$accuracy[1] - current$accuracy[2]
diffs$rt[i] = current$rt[2] - current$rt[1]
diffs$accuracy[i + length(good_subjs)] = current$accuracy[4] - current$accuracy[5]
diffs$rt[i + length(good_subjs)] = current$rt[4] - current$rt[5]
}
options(max.print = 20000)
print.data.frame(diffs)
## participant accuracy rt condition
## 1 8 0.04166667 -63.2231660 inner_invalid horizontal-vertical
## 2 9 -0.18750000 43.8838475 inner_invalid horizontal-vertical
## 3 10 -0.08333333 15.6352941 inner_invalid horizontal-vertical
## 4 11 0.25000000 105.7272727 inner_invalid horizontal-vertical
## 5 12 -0.18750000 46.7624633 inner_invalid horizontal-vertical
## 6 13 -0.12500000 5.2891263 inner_invalid horizontal-vertical
## 7 14 0.02083333 190.4642857 inner_invalid horizontal-vertical
## 8 15 -0.08333333 -12.6522727 inner_invalid horizontal-vertical
## 9 16 0.08333333 150.5142857 inner_invalid horizontal-vertical
## 10 17 0.02083333 114.0064516 inner_invalid horizontal-vertical
## 11 18 -0.08333333 98.5820272 inner_invalid horizontal-vertical
## 12 20 0.08333333 -0.6473214 inner_invalid horizontal-vertical
## 13 21 0.12500000 -182.0745192 inner_invalid horizontal-vertical
## 14 22 -0.12500000 -50.7505828 inner_invalid horizontal-vertical
## 15 8 0.16666667 20.1212121 outer_invalid horizontal-vertical
## 16 9 0.08333333 174.1667765 outer_invalid horizontal-vertical
## 17 10 -0.08333333 -11.7919799 outer_invalid horizontal-vertical
## 18 11 0.02083333 -29.0567376 outer_invalid horizontal-vertical
## 19 12 0.16666667 -73.3616030 outer_invalid horizontal-vertical
## 20 13 0.02083333 -46.1843854 outer_invalid horizontal-vertical
## 21 14 0.04166667 125.3272727 outer_invalid horizontal-vertical
## 22 15 0.00000000 -0.4347826 outer_invalid horizontal-vertical
## 23 16 -0.06250000 -67.7993243 outer_invalid horizontal-vertical
## 24 17 0.00000000 32.3000000 outer_invalid horizontal-vertical
## 25 18 -0.04166667 19.3520951 outer_invalid horizontal-vertical
## 26 20 0.22916667 -120.8821138 outer_invalid horizontal-vertical
## 27 21 0.10416667 -103.1117886 outer_invalid horizontal-vertical
## 28 22 0.08333333 -3.3972154 outer_invalid horizontal-vertical
t.test(diffs$accuracy[diffs$condition == 'inner_invalid horizontal-vertical'],
diffs$accuracy[diffs$condition == 'outer_invalid horizontal-vertical'], paired=T)
##
## Paired t-test
##
## data: diffs$accuracy[diffs$condition == "inner_invalid horizontal-vertical"] and diffs$accuracy[diffs$condition == "outer_invalid horizontal-vertical"]
## t = -1.6795, df = 13, p-value = 0.1169
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1599072 0.0200263
## sample estimates:
## mean of the differences
## -0.06994048