This package does variety of things I find useful when working with R Tidyverse:
- Import commonly used packages
- Changes the default ggplot theme
- Overrides dplyr::summarise to remove leftover grouping
- Contains various functions for use with ggplot
- Contains various functions for use in tidy code
devtools::install_github("ymer/jsmp")
-
gg_hist_percent: Draws a histogram with y-axis as a percentage -
gg_legend_remove: Removes the legend -
gg_legend_notitle: Removes the legend title -
gg_legend_bottom: Moves the legend to the bottom of the plot -
gg_loess_line: Plots a locally weighed regression line -
gg_regression_line: Plots a regression line -
gg_x_remove: Removes the x-axis -
gg_x_rotate: Rotates x-axis labels 45° -
gg_x_wrap: Wraps text on x-axis labels -
gg_y_big: Changes the y-axis to avoid scientific notation for big numbers -
gg_y_percent: Changes the y-axis to percentage -
gg_y_percent_zero: Changes the y-axis to percentage, and adjusts the y-axis to start at 0. -
gg_y_remove: Removes the y-axis -
gg_y_zero: Adjusts the y-axis to start at exactly 0
summarise: Overrides the standard summarise function, so that leftover grouping is dropped after summarising
mtcars |>
group_by(am, gear) |>
dplyr::summarise(mean_mpg = mean(mpg)) |>
group_vars()
#> [1] "am"mtcars |>
group_by(am, gear) |>
summarise(mean_mpg = mean(mpg)) |>
group_vars()
#> character(0)%notin%: As%in%but exclusionary
d: Formats table (usinggtas base)
ci_means: Finds the means and confidence intervals
ToothGrowth |>
group_by(supp) |>
ci_means(len) |>
ggplot(aes(y = supp, x = mean)) +
geom_crossbar(aes(xmin = ci.lower, xmax = ci.upper), width = 0.3, size = 0.7, color = c1)
ci_proportions: Finds the proportions and confidence intervals
mtcars |>
mutate(cyl = as.character(cyl)) |>
count(cyl) |>
mutate(total = sum(n)) |>
ci_proportions() |>
ggplot(aes(x = cyl, y = proportion)) +
geom_col(position = position_dodge()) +
gg_y_percent_zero() +
gg_legend_notitle() +
labs(y = "", x = "") +
geom_errorbar(aes(ymin = ci.lower, ymax = ci.upper),
width=.2, position=position_dodge(.9))
do_if: Use a condition in a pipe flow
only_high_values <- T
mtcars |> do_if(only_high_values, ~ . |> filter(disp > 180)) |>
summarise(mean(disp))
#> mean(disp)
#> 1 339.15only_high_values <- F
mtcars |> do_if(only_high_values, ~ . |> filter(disp > 180)) |>
summarise(mean(disp))
#> mean(disp)
#> 1 230.7219high_or_low <- "low"
mtcars |> do_if(high_or_low == "high",
~ .x |> filter(disp >= 180),
~ .x |> filter(disp < 180)) |>
summarise(mean(disp))
#> mean(disp)
#> 1 122.2938filter_duplicates: selects duplicated rows
mtcars |> filter_duplicates(wt)
#> # A tibble: 5 x 12
#> # Groups: wt [2]
#> mpg cyl disp hp drat wt qsec vs am gear carb n
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 3
#> 2 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 3
#> 3 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 4 3
#> 4 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 2
#> 5 15 8 301 335 3.54 3.57 14.6 0 1 5 8 2fix_names: Changes the column names to tidy style
iris |> fix_names() |>
head()
#> sepal_length sepal_width petal_length petal_width species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosaleft_join0: Performs a left_join, while setting values in missing rows to 0 instead of NA.
df1 <- tribble(
~id, ~v1,
1, 2,
2, 2,
3, 10)
df2 <- tribble(
~id, ~v2,
1, 2,
3, 4)
left_join(df1, df2)
#> # A tibble: 3 x 3
#> id v1 v2
#> <dbl> <dbl> <dbl>
#> 1 1 2 2
#> 2 2 2 NA
#> 3 3 10 4left_join0(df1, df2)
#> # A tibble: 3 x 3
#> id v1 v2
#> <dbl> <dbl> <dbl>
#> 1 1 2 2
#> 2 2 2 0
#> 3 3 10 4percent: Returns proportion formatted as percentage
percent(0.173234235)
#> [1] "17.3%"rows: Facilitates looping
df <- mtcars |> head()
for (row in rows(df)){
print(row$mpg)
}
#> [1] 21
#> [1] 21
#> [1] 22.8
#> [1] 21.4
#> [1] 18.7
#> [1] 18.1tab: Orderedcountincluding percentage
ToothGrowth |>
filter(len > 20) |>
tab(supp)
#> supp n percent
#> 1 OJ 18 64.3%
#> 2 VC 10 35.7%transpose: Flips rows and columns
mtcars |>
rownames_to_column() |>
transpose()
#> # A tibble: 11 x 33
#> attribute `Mazda RX4` `Mazda RX4 Wag` `Datsun 710` `Hornet 4 Drive`
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 mpg 21 21 22.8 21.4
#> 2 cyl 6 6 4 6
#> 3 disp 160 160 108 258
#> 4 hp 110 110 93 110
#> 5 drat 3.9 3.9 3.85 3.08
#> 6 wt 2.62 2.88 2.32 3.22
#> 7 qsec 16.5 17.0 18.6 19.4
#> 8 vs 0 0 1 1
#> 9 am 1 1 1 0
#> 10 gear 4 4 4 3
#> 11 carb 4 4 1 1
#> # ... with 28 more variables: Hornet Sportabout <dbl>, Valiant <dbl>,
#> # Duster 360 <dbl>, Merc 240D <dbl>, Merc 230 <dbl>, Merc 280 <dbl>,
#> # Merc 280C <dbl>, Merc 450SE <dbl>, Merc 450SL <dbl>, Merc 450SLC <dbl>,
#> # Cadillac Fleetwood <dbl>, Lincoln Continental <dbl>,
#> # Chrysler Imperial <dbl>, Fiat 128 <dbl>, Honda Civic <dbl>,
#> # Toyota Corolla <dbl>, Toyota Corona <dbl>, Dodge Challenger <dbl>,
#> # AMC Javelin <dbl>, Camaro Z28 <dbl>, Pontiac Firebird <dbl>, ...