Task A
participants, containing
integer numbers from 1 to 20, using c() and
seq().conditions, of length
20, containing alternating values of “a” and “b” (“a”, “b”, “a”, “b”,
“a”, …), using rep().first_half, containing only the
first half of the participants vector’s values.participants and
conditions have length 20, and that first_half
has length 10, using length().conditions, insert a
missing value. Print the conditions after the change.participants_cond, by pasting
together (using paste()) participants and
conditions, separated by _. So, the first
element should be “1_a”.# task A1
participants = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20)
participants = seq(from=1, to=20)
# task A2
x = c("a", "b")
conditions = rep(x, times=10, each=1)
# task A3
first_half = participants[1:(length(participants)/2)]
print(first_half)## [1] 1 2 3 4 5 6 7 8 9 10# task A4
length(participants) == 20 & length(conditions) == 20## [1] TRUElength(first_half) == 10## [1] TRUE# task A5
conditions[5] = NA
print(conditions)## [1] "a" "b" "a" "b" NA "b" "a" "b" "a" "b" "a" "b" "a" "b" "a" "b" "a" "b" "a" "b"# task A6
participants_cond = paste(participants, conditions, sep="_")
print(participants_cond)## [1] "1_a" "2_b" "3_a" "4_b" "5_NA" "6_b" "7_a" "8_b" "9_a" "10_b" "11_a" "12_b" "13_a" "14_b" "15_a" "16_b"
## [17] "17_a" "18_b" "19_a" "20_b"# task A7
is.na(participants_cond[5])## [1] FALSEparticipants_cond[5] = NA
print(participants_cond)## [1] "1_a" "2_b" "3_a" "4_b" NA "6_b" "7_a" "8_b" "9_a" "10_b" "11_a" "12_b" "13_a" "14_b" "15_a" "16_b"
## [17] "17_a" "18_b" "19_a" "20_b"Task B
my_data, with as columns the
vectorsparticipants, conditions and
participants_cond that you created before. Print the data
frame to have a look if it worked.response_times made of 20 samples
from the normal distribution, with mean .8 and standard deviation 1.
Print the data frame to have a look if it worked.response_times column that are
negative and set them to 0. Print the data frame to have a look if it
worked.log_response_times, made of
the logarithm of response_times.correct_response made of 20 samples
from the binomial distribution, with size 1 and probability of success
.65. Print the data frame to have a look if it worked.data_correct and
data_incorrect made of, respectively, the subset of
my_data where correct_response is 1, and the
subset of my_data where correct_response is 0.
Print the data frame to have a look if it worked. Print the result to
check.# task B1
my_data = data.frame(participants, conditions, participants_cond)
print(my_data)## participants conditions participants_cond
## 1 1 a 1_a
## 2 2 b 2_b
## 3 3 a 3_a
## 4 4 b 4_b
## 5 5 <NA> <NA>
## 6 6 b 6_b
## 7 7 a 7_a
## 8 8 b 8_b
## 9 9 a 9_a
## 10 10 b 10_b
## 11 11 a 11_a
## 12 12 b 12_b
## 13 13 a 13_a
## 14 14 b 14_b
## 15 15 a 15_a
## 16 16 b 16_b
## 17 17 a 17_a
## 18 18 b 18_b
## 19 19 a 19_a
## 20 20 b 20_b# task B2
my_data$response_times = rnorm(n=20, mean=.8, sd=1)
print(my_data)## participants conditions participants_cond response_times
## 1 1 a 1_a 0.67497731
## 2 2 b 2_b 1.11035090
## 3 3 a 3_a 1.01776449
## 4 4 b 4_b -1.13302187
## 5 5 <NA> <NA> -0.49996216
## 6 6 b 6_b 0.70169411
## 7 7 a 7_a 1.05788834
## 8 8 b 8_b -0.02429025
## 9 9 a 9_a 2.53628578
## 10 10 b 10_b 0.16849823
## 11 11 a 11_a 1.19261085
## 12 12 b 12_b -0.84618119
## 13 13 a 13_a 1.37031098
## 14 14 b 14_b -0.68834881
## 15 15 a 15_a 1.71053945
## 16 16 b 16_b 3.06906284
## 17 17 a 17_a 0.32712329
## 18 18 b 18_b -0.24271330
## 19 19 a 19_a 1.47521986
## 20 20 b 20_b 0.53879215# task B3
my_data[my_data$response_times < 0, "response_times"] = 0
print(my_data)## participants conditions participants_cond response_times
## 1 1 a 1_a 0.6749773
## 2 2 b 2_b 1.1103509
## 3 3 a 3_a 1.0177645
## 4 4 b 4_b 0.0000000
## 5 5 <NA> <NA> 0.0000000
## 6 6 b 6_b 0.7016941
## 7 7 a 7_a 1.0578883
## 8 8 b 8_b 0.0000000
## 9 9 a 9_a 2.5362858
## 10 10 b 10_b 0.1684982
## 11 11 a 11_a 1.1926109
## 12 12 b 12_b 0.0000000
## 13 13 a 13_a 1.3703110
## 14 14 b 14_b 0.0000000
## 15 15 a 15_a 1.7105394
## 16 16 b 16_b 3.0690628
## 17 17 a 17_a 0.3271233
## 18 18 b 18_b 0.0000000
## 19 19 a 19_a 1.4752199
## 20 20 b 20_b 0.5387921# task B4
my_data$log_response_times = log(my_data$response_times)
print(my_data)## participants conditions participants_cond response_times log_response_times
## 1 1 a 1_a 0.6749773 -0.39307620
## 2 2 b 2_b 1.1103509 0.10467609
## 3 3 a 3_a 1.0177645 0.01760855
## 4 4 b 4_b 0.0000000 -Inf
## 5 5 <NA> <NA> 0.0000000 -Inf
## 6 6 b 6_b 0.7016941 -0.35425771
## 7 7 a 7_a 1.0578883 0.05627479
## 8 8 b 8_b 0.0000000 -Inf
## 9 9 a 9_a 2.5362858 0.93070072
## 10 10 b 10_b 0.1684982 -1.78083004
## 11 11 a 11_a 1.1926109 0.17614490
## 12 12 b 12_b 0.0000000 -Inf
## 13 13 a 13_a 1.3703110 0.31503771
## 14 14 b 14_b 0.0000000 -Inf
## 15 15 a 15_a 1.7105394 0.53680879
## 16 16 b 16_b 3.0690628 1.12137225
## 17 17 a 17_a 0.3271233 -1.11741814
## 18 18 b 18_b 0.0000000 -Inf
## 19 19 a 19_a 1.4752199 0.38880704
## 20 20 b 20_b 0.5387921 -0.61842541# task B5
my_data$correct_response = rbinom(n=20, size=1, prob=.65)
print(my_data)## participants conditions participants_cond response_times log_response_times correct_response
## 1 1 a 1_a 0.6749773 -0.39307620 1
## 2 2 b 2_b 1.1103509 0.10467609 1
## 3 3 a 3_a 1.0177645 0.01760855 1
## 4 4 b 4_b 0.0000000 -Inf 0
## 5 5 <NA> <NA> 0.0000000 -Inf 1
## 6 6 b 6_b 0.7016941 -0.35425771 1
## 7 7 a 7_a 1.0578883 0.05627479 1
## 8 8 b 8_b 0.0000000 -Inf 0
## 9 9 a 9_a 2.5362858 0.93070072 0
## 10 10 b 10_b 0.1684982 -1.78083004 0
## 11 11 a 11_a 1.1926109 0.17614490 1
## 12 12 b 12_b 0.0000000 -Inf 1
## 13 13 a 13_a 1.3703110 0.31503771 1
## 14 14 b 14_b 0.0000000 -Inf 1
## 15 15 a 15_a 1.7105394 0.53680879 1
## 16 16 b 16_b 3.0690628 1.12137225 1
## 17 17 a 17_a 0.3271233 -1.11741814 1
## 18 18 b 18_b 0.0000000 -Inf 0
## 19 19 a 19_a 1.4752199 0.38880704 1
## 20 20 b 20_b 0.5387921 -0.61842541 1# task B6
mean(my_data$response_times)## [1] 0.8475559mean(my_data$correct_response)## [1] 0.75# task B7
data_correct = my_data[my_data$correct_response == 1,]
data_incorrect = my_data[my_data$correct_response == 0,]
print(data_correct)## participants conditions participants_cond response_times log_response_times correct_response
## 1 1 a 1_a 0.6749773 -0.39307620 1
## 2 2 b 2_b 1.1103509 0.10467609 1
## 3 3 a 3_a 1.0177645 0.01760855 1
## 5 5 <NA> <NA> 0.0000000 -Inf 1
## 6 6 b 6_b 0.7016941 -0.35425771 1
## 7 7 a 7_a 1.0578883 0.05627479 1
## 11 11 a 11_a 1.1926109 0.17614490 1
## 12 12 b 12_b 0.0000000 -Inf 1
## 13 13 a 13_a 1.3703110 0.31503771 1
## 14 14 b 14_b 0.0000000 -Inf 1
## 15 15 a 15_a 1.7105394 0.53680879 1
## 16 16 b 16_b 3.0690628 1.12137225 1
## 17 17 a 17_a 0.3271233 -1.11741814 1
## 19 19 a 19_a 1.4752199 0.38880704 1
## 20 20 b 20_b 0.5387921 -0.61842541 1print(data_incorrect)## participants conditions participants_cond response_times log_response_times correct_response
## 4 4 b 4_b 0.0000000 -Inf 0
## 8 8 b 8_b 0.0000000 -Inf 0
## 9 9 a 9_a 2.5362858 0.9307007 0
## 10 10 b 10_b 0.1684982 -1.7808300 0
## 18 18 b 18_b 0.0000000 -Inf 0