download_this(
output_name = "gerals",
output_extension = ".xlsx", # Excel file type
button_label = "Download Excel",
button_type = "success", # change button type
)
dplyr package
select
It aims to extract variables or columns as a new vector or table.
Example
contains argument: selects variables that contain …
select(mpg, starts_with(match = "m"))# A tibble: 234 × 2
manufacturer model
<chr> <chr>
1 audi a4
2 audi a4
3 audi a4
4 audi a4
5 audi a4
6 audi a4
7 audi a4
8 audi a4 quattro
9 audi a4 quattro
10 audi a4 quattro
# ℹ 224 more rowsends_whith argument: selects variables that end with …
select(mpg, ends_with(match = "y"))# A tibble: 234 × 2
cty hwy
<int> <int>
1 18 29
2 21 29
3 20 31
4 21 30
5 16 26
6 18 26
7 18 27
8 18 26
9 16 25
10 20 28
# ℹ 224 more rowsnum_range argument: the use of this argument is valid when the data set contains variables with the same initials and numbered in sequence, as is the case with variables that begin with “wk” in the “billboard” data set.
# A tibble: 317 × 6
wk10 wk11 wk12 wk13 wk14 wk15
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 NA NA NA NA NA NA
2 NA NA NA NA NA NA
3 51 51 51 47 44 38
4 61 61 59 61 66 72
5 57 64 70 75 76 78
6 6 7 22 29 36 47
7 NA NA NA NA NA NA
8 36 37 37 38 49 61
9 10 9 8 6 1 2
10 59 66 68 61 67 59
# ℹ 307 more rowsone_off argument: selects specific variables.
# A tibble: 234 × 3
model year cyl
<chr> <int> <int>
1 a4 1999 4
2 a4 1999 4
3 a4 2008 4
4 a4 2008 4
5 a4 1999 6
6 a4 1999 6
7 a4 2008 6
8 a4 quattro 1999 4
9 a4 quattro 1999 4
10 a4 quattro 2008 4
# ℹ 224 more rowsmatches argument: selects columns using a regular expression.
# A tibble: 317 × 76
wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9 wk10 wk11 wk12 wk13
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 87 82 72 77 87 94 99 NA NA NA NA NA NA
2 91 87 92 NA NA NA NA NA NA NA NA NA NA
3 81 70 68 67 66 57 54 53 51 51 51 51 47
4 76 76 72 69 67 65 55 59 62 61 61 59 61
5 57 34 25 17 17 31 36 49 53 57 64 70 75
6 51 39 34 26 26 19 2 2 3 6 7 22 29
7 97 97 96 95 100 NA NA NA NA NA NA NA NA
8 84 62 51 41 38 35 35 38 38 36 37 37 38
9 59 53 38 28 21 18 16 14 12 10 9 8 6
10 76 76 74 69 68 67 61 58 57 59 66 68 61
# ℹ 307 more rows
# ℹ 63 more variables: wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, wk17 <dbl>,
# wk18 <dbl>, wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, wk22 <dbl>, wk23 <dbl>,
# wk24 <dbl>, wk25 <dbl>, wk26 <dbl>, wk27 <dbl>, wk28 <dbl>, wk29 <dbl>,
# wk30 <dbl>, wk31 <dbl>, wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>,
# wk36 <dbl>, wk37 <dbl>, wk38 <dbl>, wk39 <dbl>, wk40 <dbl>, wk41 <dbl>,
# wk42 <dbl>, wk43 <dbl>, wk44 <dbl>, wk45 <dbl>, wk46 <dbl>, wk47 <dbl>, …rename
Its aims to rename variables or columns.
Example
Renaming the manufacturer and model variables from the mpg dataset.
rename(mpg, mnfc = manufacturer, mod = model)# A tibble: 234 × 11
mnfc mod displ year cyl trans drv cty hwy fl class
<chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compact
2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compact
3 audi a4 2 2008 4 manual(m6) f 20 31 p compact
4 audi a4 2 2008 4 auto(av) f 21 30 p compact
5 audi a4 2.8 1999 6 auto(l5) f 16 26 p compact
6 audi a4 2.8 1999 6 manual(m5) f 18 26 p compact
7 audi a4 3.1 2008 6 auto(av) f 18 27 p compact
8 audi a4 quattro 1.8 1999 4 manual(m5) 4 18 26 p compact
9 audi a4 quattro 1.8 1999 4 auto(l5) 4 16 25 p compact
10 audi a4 quattro 2 2008 4 manual(m6) 4 20 28 p compact
# ℹ 224 more rowsSelecting and renaming variables.
select(mpg, mnf = manufacturer, mod = model)# A tibble: 234 × 2
mnf mod
<chr> <chr>
1 audi a4
2 audi a4
3 audi a4
4 audi a4
5 audi a4
6 audi a4
7 audi a4
8 audi a4 quattro
9 audi a4 quattro
10 audi a4 quattro
# ℹ 224 more rowsSelecting and renaming variable while keeping all variables in the dataset.
select(mpg, mnf = manufacturer, mod = model, everything())# A tibble: 234 × 11
mnf mod displ year cyl trans drv cty hwy fl class
<chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compact
2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compact
3 audi a4 2 2008 4 manual(m6) f 20 31 p compact
4 audi a4 2 2008 4 auto(av) f 21 30 p compact
5 audi a4 2.8 1999 6 auto(l5) f 16 26 p compact
6 audi a4 2.8 1999 6 manual(m5) f 18 26 p compact
7 audi a4 3.1 2008 6 auto(av) f 18 27 p compact
8 audi a4 quattro 1.8 1999 4 manual(m5) 4 18 26 p compact
9 audi a4 quattro 1.8 1999 4 auto(l5) 4 16 25 p compact
10 audi a4 quattro 2 2008 4 manual(m6) 4 20 28 p compact
# ℹ 224 more rowsmutate
Its aims to create new variables or columns in a data table.
transmute
Its aims to create new variables by discarding existing variables.
Example
mutate(mpg, media = (cty+hwy)/2)# A tibble: 234 × 12
manufacturer model displ year cyl trans drv cty hwy fl class
<chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
1 audi a4 1.8 1999 4 auto… f 18 29 p comp…
2 audi a4 1.8 1999 4 manu… f 21 29 p comp…
3 audi a4 2 2008 4 manu… f 20 31 p comp…
4 audi a4 2 2008 4 auto… f 21 30 p comp…
5 audi a4 2.8 1999 6 auto… f 16 26 p comp…
6 audi a4 2.8 1999 6 manu… f 18 26 p comp…
7 audi a4 3.1 2008 6 auto… f 18 27 p comp…
8 audi a4 quattro 1.8 1999 4 manu… 4 18 26 p comp…
9 audi a4 quattro 1.8 1999 4 auto… 4 16 25 p comp…
10 audi a4 quattro 2 2008 4 manu… 4 20 28 p comp…
# ℹ 224 more rows
# ℹ 1 more variable: media <dbl>gt::gt(mutate(mpg, carro = paste(manufacturer, model, sep = " "),
"cyl / trans" = paste(cyl, "cylinders", " / ", trans, " transmission", sep = " "))[c(1:10),])%>%
tab_header(title = "Mutate") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)gt::gt(transmute(mpg, carro = paste(manufacturer, model, sep = " "),
"cyl / trans" = paste(cyl, "cylinders", " / ", trans, " transmission", sep = " "))[c(1:10),])%>%
tab_header(title = "Transmute") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)filter
Aims to extract or obtain subsets of observations (rows) from the data table according to a logical condition.
slice
Extracts or selects lines by line position.
Example
gt::gt(filter(mpg, manufacturer == "audi")[c(1:10),])%>%
tab_header(title = "Filter") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)gt::gt(filter(mpg, manufacturer == "audi" & year == 1999)[c(1:10),])%>%
tab_header(title = "Filter") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)gt::gt(filter(mpg, manufacturer == "audi" | manufacturer == "dodge")[c(1:10),])%>%
tab_header(title = "Filter") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)gt::gt(filter(mpg, hwy >= 30)[c(1:10),])%>%
tab_header(title = "Filter") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)gt::gt(filter(mpg, year != 1999)[c(1:10),])%>%
tab_header(title = "Filter") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)gt::gt(slice(mpg, 1:10)[c(1:10),])%>%
tab_header(title = "Filter") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)gt::gt(slice(mpg, 20:30)[c(1:10),])%>%
tab_header(title = "Filter") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)gt::gt(slice(mpg, (nrow(mpg)-9):nrow(mpg))[c(1:10),])%>%
tab_header(title = "Filter") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)gt::gt(slice(mpg, (nrow(mpg)-9):nrow(mpg))[c(1:10),])%>%
tab_header(title = "Filter") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)arrange
Aims to select observations (rows) by values from one or more columns.
Example
gt::gt(arrange(mpg, year)[c(1:10),])%>%
tab_header(title = "Filter") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)gt::gt(arrange(mpg, desc(year))[c(1:10),])%>%
tab_header(title = "Filter") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)gt::gt(arrange(mpg, year, cyl, displ)[c(1:10),])%>%
tab_header(title = "Filter") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)distinct
Its aims to remove observations (lines) with duplicate values.
Example
data <- select(mpg, manufacturer, model, year, cyl)
gt::gt(distinct(data)[c(1:10),])%>%
tab_header(title = "Filter") %>%
opt_stylize(style = 2, color = "blue")%>%
tab_source_note(attach_excel)