Questions

Question 1

Given the data Below, create 3 columns ie Sum_15, Sum_16, Sum_17 whereby sum_* is the sum of the values for the corresponding year and agegroup. The result should look like shown below:

AgeGroup Sum_15 Sum_16 Sum_17
A 321 342 372
B 391 339 345
C 353 361 363
D 356 388 359
E 351 390 386 
set.seed(100) # make results reproducible by setting seed
vars <- c("AgeGroup", paste0(month.abb, "_", rep(15:17, each = 12)))


(df <- cbind(LETTERS[1:5], matrix(rpois(n = (length(vars) - 1) * 5, 30), nrow = 5)) %>% 
    data.frame() %>%
    setNames(vars) %>% 
    tibble() %>% 
    mutate(across(-1, as.integer))
)
# A tibble: 5 × 37
  AgeGroup Jan_15 Feb_15 Mar_15 Apr_15 May_15 Jun_15 Jul_15 Aug_15 Sep_15 Oct_15
  <chr>     <int>  <int>  <int>  <int>  <int>  <int>  <int>  <int>  <int>  <int>
1 A            27     26     33     36     34     25     27     37     37     32
2 B            21     32     24     31     25     39     32     20     30     32
3 C            34     28     30     23     25     29     35     26     19     30
4 D            30     32     29     34     31     29     35     37     28     34
5 E            31     33     27     31     23     26     29     28     28     26
# ℹ 26 more variables: Nov_15 <int>, Dec_15 <int>, Jan_16 <int>, Feb_16 <int>,
#   Mar_16 <int>, Apr_16 <int>, May_16 <int>, Jun_16 <int>, Jul_16 <int>,
#   Aug_16 <int>, Sep_16 <int>, Oct_16 <int>, Nov_16 <int>, Dec_16 <int>,
#   Jan_17 <int>, Feb_17 <int>, Mar_17 <int>, Apr_17 <int>, May_17 <int>,
#   Jun_17 <int>, Jul_17 <int>, Aug_17 <int>, Sep_17 <int>, Oct_17 <int>,
#   Nov_17 <int>, Dec_17 <int>

Question 2

df1 <- structure(list(
  num_pp = c(1, 2, 3, 4, 5, 6), 
  nombre_dp1 = c(24,14, 2, 6, 6, 21), 
  nombre_dp05 = c(20, 28, 2, 9, 8, 21), 
  nombre_dp0 = c(24,20, 4, 11, 8, 20), 
  jugement_causal_dp1 = c("Oui", "Oui", "Oui","Oui", "Oui", "Oui"), 
  jugement_causal_dp05 = c("Non", "Oui","Non", "Non", "Oui", "Non"),
  jugement_causal_dp0 = c("Non", "Non","Oui", "Non", "Non", "Non"),
  confiance_dp1 = c(90, 80, 63, 80,90, 80),
  confiance_dp05 = c(60, 50, 86, 65, 50, 90),
  confiance_dp0 = c(65,60, 55, 43, 50, 80),
  age = c(33, 22, 20, 20, 18, 18),
  genre = c("Masculin","Feminin", "Feminin", "Feminin", "Feminin", "Feminin"),
  etude = c("L1","L1", "L1", "L1", "L1", "L1"),
  ordre = c("dp_05|dp_1|dp_0", "dp_0|dp_1|dp_05","dp_0|dp_1|dp_05", "dp_0|dp_05|dp_1", "dp_1|dp_05|dp_0", "dp_1|dp_05|dp_0"),
  wdif_dp1dp05 = c(-4, 14, 0, 3, 2, 0)),
  row.names = c(NA, -6L), class = c("tbl_df", "tbl", "data.frame"))

The dataset above represents a repeated measure design. ie It has multiple measures with each measure under 3 conditions.

For example, the measure nombre is repeated under conditions dp1, dp05 and dp0. In this dataset we have 3 measures ie:

matrix(str_subset(names(df1), "_dp\\d+$"), 3, byrow = TRUE)
     [,1]                  [,2]                   [,3]                 
[1,] "nombre_dp1"          "nombre_dp05"          "nombre_dp0"         
[2,] "jugement_causal_dp1" "jugement_causal_dp05" "jugement_causal_dp0"
[3,] "confiance_dp1"       "confiance_dp05"       "confiance_dp0"

Transform the data to have only the measures as the columns and include the conditions in their own column ie:

participant id condition measure1 measure2 measure3
1 1
1 5
1 0

Question 3

write regular expression patterns which will match all of the values in x and none of the values in y.

Ranges

x y
abac beam
accede buoy
adead canjac
babe chymia
bead corah
bebed cupula
bedad griece
bedded hafter
bedead idic
bedeaf lucy
caba martyr
caffa matron
dace messrs
dade mucose
daff relose
dead sonly
deed tegua
deface threap
faded towned
faff widish
feed yite

Back References

x y
allochirally anticker
anticovenanting corundum
barbary crabcatcher
calelectrical damnably
entablement foxtailed
ethanethiol galvanotactic
froufrou gummage
furfuryl gurniad
galagala hypergoddess
heavyheaded kashga
linguatuline nonimitative
mathematic parsonage
monoammonium pouchlike
perpera presumptuously
photophonic pylar
purpuraceous rachioparalysis
salpingonasal scherzando
testes swayed
trisectrix unbridledness
undergrounder unupbraidingly
untaunted wellside

Primes

x y
xx xxxx
xxx xxxxxx
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Question 4

Given the dataset below, transform it to have two columns, ie the Name column and the Year column:

Name
Percy Vere (2020)
Ginger Plant (2017)
Perry (2019)
Pat Thettick (2020)
Samuel (2022)
Fay Daway (2008)
Greg (2022)
Simon Sais (2011)
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