The goal of ensumr is to analyse enquate data easily.
You can install the development version of ensumr from GitHub with:
# install.packages("remotes")
remotes::install_github("matutosi/ensumr")You can use dummy data.
library(ensumr)
dummy_data
#> # A tibble: 50 × 4
#> q1 q2_ord q3_multi q4_comment
#> <chr> <fct> <chr> <chr>
#> 1 u never "A;B;C;D;E" The odor of spring makes young hearts jump.
#> 2 j often "E" The goose was brought straight from the old mark…
#> 3 m rarely "B;C;D" A young child should not suffer fright.
#> 4 n often "C;D" Mark the spot with a sign painted red.
#> 5 x sometimes "A;D" Plead with the lawyer to drop the lost cause.
#> 6 d never "A;B;C;D;E" The fight will end in just six minutes.
#> 7 c rarely "A;B;C;D;E" The three story house was built of stone.
#> 8 y rarely "" Time brings us many changes.
#> 9 b rarely "A;B;C;D;E" Whitings are small fish caught in nets.
#> 10 b rarely "A;B;D;E" The baby puts his right foot in his mouth.
#> # ℹ 40 more rowsYou can show the summary of enquate by enq_summary().
enq_summary(dummy_data)
#> $q1
#> # A tibble: 21 × 2
#> q1 n
#> <chr> <int>
#> 1 b 6
#> 2 c 4
#> 3 m 4
#> 4 n 4
#> 5 u 4
#> 6 j 3
#> 7 r 3
#> 8 s 3
#> 9 a 2
#> 10 g 2
#> # ℹ 11 more rows
#>
#> $q2_ord
#> # A tibble: 5 × 2
#> q2_ord n
#> <fct> <int>
#> 1 always 4
#> 2 often 10
#> 3 sometimes 12
#> 4 rarely 14
#> 5 never 10
#>
#> $q3_multi
#> # A tibble: 6 × 2
#> q3_multi n
#> <chr> <int>
#> 1 "A" 32
#> 2 "B" 24
#> 3 "D" 24
#> 4 "C" 23
#> 5 "E" 21
#> 6 "" 8enq_summary(dummy_data, simplify = TRUE)
#> # A tibble: 32 × 3
#> item option n
#> <chr> <chr> <int>
#> 1 q1 b 6
#> 2 q1 c 4
#> 3 q1 m 4
#> 4 q1 n 4
#> 5 q1 u 4
#> 6 q1 j 3
#> 7 q1 r 3
#> 8 q1 s 3
#> 9 q1 a 2
#> 10 q1 g 2
#> # ℹ 22 more rowsYou can analyse a item by enq_simple(), which can be used for both
singular or multiple options. When the column name has “_ord”, the
result will be arranged by options. Otherwise, the results will be
arranged by counted numbers. When multiple options will be separated by
deliminator (default is “;”).
enq_simple(dummy_data, "q1")
#> # A tibble: 21 × 2
#> q1 n
#> <chr> <int>
#> 1 b 6
#> 2 c 4
#> 3 m 4
#> 4 n 4
#> 5 u 4
#> 6 j 3
#> 7 r 3
#> 8 s 3
#> 9 a 2
#> 10 g 2
#> # ℹ 11 more rowsenq_simple(dummy_data, "q2_ord")
#> # A tibble: 5 × 2
#> q2_ord n
#> <fct> <int>
#> 1 always 4
#> 2 often 10
#> 3 sometimes 12
#> 4 rarely 14
#> 5 never 10enq_simple(dummy_data, "q3_multi")
#> # A tibble: 6 × 2
#> q3_multi n
#> <chr> <int>
#> 1 "A" 32
#> 2 "B" 24
#> 3 "D" 24
#> 4 "C" 23
#> 5 "E" 21
#> 6 "" 8You can analyse two item (cross tabulation) by enq_cross().
enq_cross(dummy_data, "q1", "q2_ord")
#> # A tibble: 21 × 7
#> q1 q2_ord_always q2_ord_often q2_ord_sometimes q2_ord_rarely q2_ord_never
#> <chr> <int> <int> <int> <int> <int>
#> 1 b 0 0 1 5 0
#> 2 c 1 1 0 1 1
#> 3 m 0 0 1 2 1
#> 4 n 0 3 1 0 0
#> 5 u 0 0 1 1 2
#> 6 j 0 1 1 1 0
#> 7 r 1 0 0 1 1
#> 8 s 0 1 1 1 0
#> 9 a 0 0 1 0 1
#> 10 g 0 1 0 0 1
#> # ℹ 11 more rows
#> # ℹ 1 more variable: total <int>enq_cross(dummy_data, "q1", "q3_multi")
#> # A tibble: 21 × 8
#> q1 q3_multi_ q3_multi_A q3_multi_B q3_multi_C q3_multi_D q3_multi_E total
#> <chr> <int> <int> <int> <int> <int> <int> <int>
#> 1 b 0 6 4 3 4 4 21
#> 2 u 0 4 2 3 4 2 15
#> 3 c 1 3 3 2 2 3 14
#> 4 m 0 3 3 2 2 2 12
#> 5 g 0 2 2 1 1 2 8
#> 6 n 1 2 1 2 1 1 8
#> 7 l 0 1 1 1 2 1 6
#> 8 s 1 1 1 1 1 1 6
#> 9 t 0 1 1 1 2 1 6
#> 10 a 1 1 1 1 1 0 5
#> # ℹ 11 more rowsenq_cross(dummy_data, "q2_ord", "q3_multi", arrange_total = FALSE)
#> # A tibble: 5 × 8
#> q2_ord q3_multi_ q3_multi_A q3_multi_B q3_multi_C q3_multi_D q3_multi_E total
#> <fct> <int> <int> <int> <int> <int> <int> <int>
#> 1 always 1 3 1 2 1 2 10
#> 2 often 3 3 3 5 4 4 22
#> 3 someti… 2 9 3 6 5 4 29
#> 4 rarely 2 10 10 6 7 6 41
#> 5 never 0 7 7 4 7 5 30enq_cross_split(dummy_data, "q1", "q2_ord", "q3_multi")
#> [[1]]
#> # A tibble: 8 × 6
#> q1 q2_ord_always q2_ord_often q2_ord_sometimes q2_ord_rarely total
#> <chr> <int> <int> <int> <int> <int>
#> 1 a 0 0 1 0 1
#> 2 c 1 0 0 0 1
#> 3 j 0 0 0 1 1
#> 4 n 0 1 0 0 1
#> 5 p 0 1 0 0 1
#> 6 q 0 1 0 0 1
#> 7 s 0 0 1 0 1
#> 8 y 0 0 0 1 1
#>
#> $A
#> # A tibble: 18 × 7
#> q1 q2_ord_always q2_ord_often q2_ord_sometimes q2_ord_rarely q2_ord_never
#> <chr> <int> <int> <int> <int> <int>
#> 1 b 0 0 1 5 0
#> 2 u 0 0 1 1 2
#> 3 c 0 1 0 1 1
#> 4 m 0 0 1 1 1
#> 5 g 0 1 0 0 1
#> 6 n 0 1 1 0 0
#> 7 a 0 0 0 0 1
#> 8 d 0 0 0 0 1
#> 9 h 1 0 0 0 0
#> 10 k 0 0 1 0 0
#> 11 l 0 0 1 0 0
#> 12 r 1 0 0 0 0
#> 13 s 0 0 0 1 0
#> 14 t 1 0 0 0 0
#> 15 v 0 0 0 1 0
#> 16 x 0 0 1 0 0
#> 17 y 0 0 1 0 0
#> 18 z 0 0 1 0 0
#> # ℹ 1 more variable: total <int>
#>
#> $B
#> # A tibble: 15 × 7
#> q1 q2_ord_always q2_ord_often q2_ord_sometimes q2_ord_rarely q2_ord_never
#> <chr> <int> <int> <int> <int> <int>
#> 1 b 0 0 0 4 0
#> 2 c 0 1 0 1 1
#> 3 m 0 0 1 1 1
#> 4 g 0 1 0 0 1
#> 5 u 0 0 0 1 1
#> 6 a 0 0 0 0 1
#> 7 d 0 0 0 0 1
#> 8 l 0 0 1 0 0
#> 9 n 0 1 0 0 0
#> 10 p 0 0 0 0 1
#> 11 r 0 0 0 1 0
#> 12 s 0 0 0 1 0
#> 13 t 1 0 0 0 0
#> 14 v 0 0 0 1 0
#> 15 z 0 0 1 0 0
#> # ℹ 1 more variable: total <int>
#>
#> $C
#> # A tibble: 16 × 7
#> q1 q2_ord_always q2_ord_often q2_ord_sometimes q2_ord_rarely q2_ord_never
#> <chr> <int> <int> <int> <int> <int>
#> 1 b 0 0 0 3 0
#> 2 u 0 0 1 1 1
#> 3 c 0 1 0 1 0
#> 4 m 0 0 1 1 0
#> 5 n 0 2 0 0 0
#> 6 a 0 0 0 0 1
#> 7 d 0 0 0 0 1
#> 8 g 0 1 0 0 0
#> 9 h 1 0 0 0 0
#> 10 j 0 0 1 0 0
#> 11 k 0 0 1 0 0
#> 12 l 0 0 1 0 0
#> 13 r 0 0 0 0 1
#> 14 s 0 1 0 0 0
#> 15 t 1 0 0 0 0
#> 16 z 0 0 1 0 0
#> # ℹ 1 more variable: total <int>
#>
#> $D
#> # A tibble: 14 × 7
#> q1 q2_ord_always q2_ord_often q2_ord_sometimes q2_ord_rarely q2_ord_never
#> <chr> <int> <int> <int> <int> <int>
#> 1 b 0 0 0 4 0
#> 2 u 0 0 1 1 2
#> 3 c 0 0 0 1 1
#> 4 l 0 0 1 0 1
#> 5 m 0 0 1 1 0
#> 6 t 1 1 0 0 0
#> 7 a 0 0 0 0 1
#> 8 d 0 0 0 0 1
#> 9 g 0 1 0 0 0
#> 10 n 0 1 0 0 0
#> 11 p 0 0 0 0 1
#> 12 s 0 1 0 0 0
#> 13 x 0 0 1 0 0
#> 14 z 0 0 1 0 0
#> # ℹ 1 more variable: total <int>
#>
#> $E
#> # A tibble: 13 × 7
#> q1 q2_ord_always q2_ord_often q2_ord_sometimes q2_ord_rarely q2_ord_never
#> <chr> <int> <int> <int> <int> <int>
#> 1 b 0 0 0 4 0
#> 2 c 0 1 0 1 1
#> 3 g 0 1 0 0 1
#> 4 m 0 0 1 0 1
#> 5 u 0 0 0 1 1
#> 6 d 0 0 0 0 1
#> 7 h 1 0 0 0 0
#> 8 j 0 1 0 0 0
#> 9 l 0 0 1 0 0
#> 10 n 0 0 1 0 0
#> 11 s 0 1 0 0 0
#> 12 t 1 0 0 0 0
#> 13 y 0 0 1 0 0
#> # ℹ 1 more variable: total <int>df <- dummy_data
bigram <- function(df, col = "", delim = " "){
id <- "id"
"word_1"
df |>
tibble::rownames_to_column(var = id) |>
dplyr::select({{ id }}, contains(col)) |>
tidyr::separate_longer_delim(contains(col), delim = delim)
}Toshikazu Matsumura (2024) ensumr. Analyse Enquate Data Easily. https://github.com/matutosi/ensumr/.
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