| Title: | Support Functions for Wrangling and Visualization |
|---|---|
| Description: | Suite of helper functions for data wrangling and visualization. The only theme for these functions is that they tend towards simple, short, and narrowly-scoped. These functions are built for tasks that often recur but are not large enough in scope to warrant an ecosystem of interdependent functions. |
| Authors: | Nicholas J Lyon [aut, cre, cph]
|
| Maintainer: | Nicholas J Lyon <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.4.0.900 |
| Built: | 2024-11-20 17:21:21 UTC |
| Source: | https://github.com/njlyon0/supportr |
Melts an array of dimensions x, y, and z into a dataframe containing columns x, y, z, and value where value is whatever was stored in the array at those coordinates.
array_melt(array = NULL)array_melt(array = NULL)
array |
(array) array object to melt into a dataframe |
(dataframe) object containing the "flattened" array in dataframe format
# First we need to create an array to melt ## Make data to fill the array vec1 <- c(5, 9, 3) vec2 <- c(10:15) ## Create dimension names (x = col, y = row, z = which matrix) x_vals <- c("Col_1","Col_2","Col_3") y_vals <- c("Row_1","Row_2","Row_3") z_vals <- c("Mat_1","Mat_2") ## Make an array from these components g <- array(data = c(vec1, vec2), dim = c(3, 3, 2), dimnames = list(x_vals, y_vals, z_vals)) ## "Melt" the array into a dataframe array_melt(array = g)# First we need to create an array to melt ## Make data to fill the array vec1 <- c(5, 9, 3) vec2 <- c(10:15) ## Create dimension names (x = col, y = row, z = which matrix) x_vals <- c("Col_1","Col_2","Col_3") y_vals <- c("Row_1","Row_2","Row_3") z_vals <- c("Mat_1","Mat_2") ## Make an array from these components g <- array(data = c(vec1, vec2), dim = c(3, 3, 2), dimnames = list(x_vals, y_vals, z_vals)) ## "Melt" the array into a dataframe array_melt(array = g)
Counts the number of occurrences of each element in the provided vector. Counting of NAs in addition to non-NA values is supported.
count(vec = NULL)count(vec = NULL)
vec |
(vector) vector containing elements to count |
(dataframe) two-column dataframe with as many rows as there are unique elements in the provided vector. First column is named "value" and includes the unique elements of the vector, second column is named "count" and includes the number of occurrences of each vector element.
# Count instances of vector elements count(vec = c(1, 1, NA, "a", 1, "a", NA, "x"))# Count instances of vector elements count(vec = c(1, 1, NA, "a", 1, "a", NA, "x"))
Accepts a symmetric data object and replaces the chosen triangle with NAs. Also allows user to choose whether to keep or drop the diagonal of the data object
crop_tri(data = NULL, drop_tri = "upper", drop_diag = FALSE)crop_tri(data = NULL, drop_tri = "upper", drop_diag = FALSE)
data |
(dataframe, dataframe-like, or matrix) symmetric data object to remove one of the triangles from |
drop_tri |
(character) which triangle to replace with NAs, either "upper" or "lower" |
drop_diag |
(logical) whether to drop the diagonal of the data object (defaults to FALSE) |
(dataframe or dataframe-like) data object with desired triangle removed and either with or without the diagonal
# Define a simple matrix wtih symmetric dimensions mat <- matrix(data = c(1:2, 2:1), nrow = 2, ncol = 2) # Crop off it's lower triangle supportR::crop_tri(data = mat, drop_tri = "lower", drop_diag = FALSE)# Define a simple matrix wtih symmetric dimensions mat <- matrix(data = c(1:2, 2:1), nrow = 2, ncol = 2) # Crop off it's lower triangle supportR::crop_tri(data = mat, drop_tri = "lower", drop_diag = FALSE)
Identifies any elements in the column(s) that would be changed to NA if as.Date is used on the column(s). This is useful for quickly identifying only the "problem" entries of ostensibly date column(s) that is/are read in as a character.
date_check(data = NULL, col = NULL)date_check(data = NULL, col = NULL)
data |
(dataframe) object containing at least one column of supposed dates |
col |
(character or numeric) name(s) or column number(s) of the column(s) containing putative dates in the data object |
(list) malformed dates from each supplied column in separate list elements
# Make a dataframe to test the function loc <- c("LTR", "GIL", "PYN", "RIN") time <- c("2021-01-01", "2021-01-0w", "1990", "2020-10-xx") time2 <- c("1880-08-08", "2021-01-02", "1992", "2049-11-01") time3 <- c("2022-10-31", "tomorrow", "1993", NA) # Assemble our vectors into a dataframe sites <- data.frame("site" = loc, "first_visit" = time, "second" = time2, "third" = time3) # Use `date_check()` to return only the entries that would be lost date_check(data = sites, col = c("first_visit", "second", "third"))# Make a dataframe to test the function loc <- c("LTR", "GIL", "PYN", "RIN") time <- c("2021-01-01", "2021-01-0w", "1990", "2020-10-xx") time2 <- c("1880-08-08", "2021-01-02", "1992", "2049-11-01") time3 <- c("2022-10-31", "tomorrow", "1993", NA) # Assemble our vectors into a dataframe sites <- data.frame("site" = loc, "first_visit" = time, "second" = time2, "third" = time3) # Use `date_check()` to return only the entries that would be lost date_check(data = sites, col = c("first_visit", "second", "third"))
In a column containing multiple date formats (e.g., MM/DD/YYYY, "YYYY/MM/DD, etc.) identifies probable format of each date. Provision of a grouping column improves inference. Any formats that cannot be determined are flagged as "FORMAT UNCERTAIN" for human double-checking. This is useful for quickly sorting the bulk of ambiguous dates into clear categories for later conditional wrangling.
date_format_guess( data = NULL, date_col = NULL, groups = TRUE, group_col = NULL, return = "dataframe", quiet = FALSE )date_format_guess( data = NULL, date_col = NULL, groups = TRUE, group_col = NULL, return = "dataframe", quiet = FALSE )
data |
(dataframe) object containing at least one column of ambiguous dates |
date_col |
(character) name of column containing ambiguous dates |
groups |
(logical) whether groups exist in the dataframe / should be used (defaults to TRUE) |
group_col |
(character) name of column containing grouping variable |
return |
(character) either "dataframe" or "vector" depending on whether the user wants the date format "guesses" returned as a new column on the dataframe or a vector |
quiet |
(logical) whether certain optional messages should be displayed (defaults to FALSE) |
(dataframe or character) object containing date format guesses
# Create dataframe of example ambiguous dates & grouping variable my_df <- data.frame('data_enterer' = c('person A', 'person B', 'person B', 'person B', 'person C', 'person D', 'person E', 'person F', 'person G'), 'bad_dates' = c('2022.13.08', '2021/2/02', '2021/2/03', '2021/2/04', '1899/1/15', '10-31-1901', '26/11/1901', '08.11.2004', '6/10/02')) # Now we can invoke the function! date_format_guess(data = my_df, date_col = "bad_dates", group_col = "data_enterer", return = "dataframe") # If preferred, do it without groups and return a vector date_format_guess(data = my_df, date_col = "bad_dates", groups = FALSE, return = "vector")# Create dataframe of example ambiguous dates & grouping variable my_df <- data.frame('data_enterer' = c('person A', 'person B', 'person B', 'person B', 'person C', 'person D', 'person E', 'person F', 'person G'), 'bad_dates' = c('2022.13.08', '2021/2/02', '2021/2/03', '2021/2/04', '1899/1/15', '10-31-1901', '26/11/1901', '08.11.2004', '6/10/02')) # Now we can invoke the function! date_format_guess(data = my_df, date_col = "bad_dates", group_col = "data_enterer", return = "dataframe") # If preferred, do it without groups and return a vector date_format_guess(data = my_df, date_col = "bad_dates", groups = FALSE, return = "vector")
Reflexively compares two vectors and identifies (1) elements that are found in the first but not the second (i.e., "lost" components) and (2) elements that are found in the second but not the first (i.e., "gained" components). This is particularly helpful when manipulating a dataframe and comparing what columns are lost or gained between wrangling steps. Alternately it can compare the contents of two columns to see how two dataframes differ.
diff_check(old = NULL, new = NULL, sort = TRUE, return = FALSE)diff_check(old = NULL, new = NULL, sort = TRUE, return = FALSE)
old |
(vector) starting / original object |
new |
(vector) ending / modified object |
sort |
(logical) whether to sort the difference between the two vectors |
return |
(logical) whether to return the two vectors as a 2-element list |
No return value (unless return = TRUE), called for side effects. If return = TRUE, returns a two-element list
# Make two vectors vec1 <- c("x", "a", "b") vec2 <- c("y", "z", "a") # Compare them! diff_check(old = vec1, new = vec2, return = FALSE) # Return the difference for later use diff_out <- diff_check(old = vec1, new = vec2, return = TRUE) diff_out# Make two vectors vec1 <- c("x", "a", "b") vec2 <- c("y", "z", "a") # Compare them! diff_check(old = vec1, new = vec2, return = FALSE) # Return the difference for later use diff_out <- diff_check(old = vec1, new = vec2, return = TRUE) diff_out
Coerces a vector into a numeric vector and automatically silences NAs introduced by coercion warning. Useful for cases where non-numbers are known to exist in vector and their coercion to NA is expected / unremarkable. Essentially just a way of forcing this coercion more succinctly than wrapping as.numeric in suppressWarnings.
force_num(x = NULL)force_num(x = NULL)
x |
(non-numeric) vector containing elements to be coerced into class numeric |
(numeric) vector of numeric values
# Coerce a character vector to numeric without throwing a warning force_num(x = c(2, "A", 4))# Coerce a character vector to numeric without throwing a warning force_num(x = c(2, "A", 4))
Accepts a GitHub repository URL and identifies all files in the specified folder. If no folder is specified, lists top-level repository contents. Recursive listing of sub-folders is supported by an additional argument. This function only works on repositories (public or private) to which you have access.
github_ls(repo = NULL, folder = NULL, recursive = TRUE, quiet = FALSE)github_ls(repo = NULL, folder = NULL, recursive = TRUE, quiet = FALSE)
repo |
(character) full URL for a GitHub repository (including "github.com") |
folder |
(NULL/character) either |
recursive |
(logical) whether to recursively list contents (i.e., list contents of sub-folders identified within previously identified sub-folders) |
quiet |
(logical) whether to print an informative message as the contents of each folder is being listed |
(dataframe) three-column dataframe including (1) the names of the contents, (2) the type of each content item (e.g., file/directory/etc.), and (3) the full path from the starting folder to each item
## Not run: # List complete contents of the `supportR` package repository github_ls(repo = "https://github.com/njlyon0/supportR", recursive = TRUE, quiet = FALSE) ## End(Not run)## Not run: # List complete contents of the `supportR` package repository github_ls(repo = "https://github.com/njlyon0/supportR", recursive = TRUE, quiet = FALSE) ## End(Not run)
Accepts a GitHub repository URL and identifies all files in the specified folder. If no folder is specified, lists top-level repository contents. This function only works on repositories (public or private) to which you have access.
github_ls_single(repo = NULL, folder = NULL)github_ls_single(repo = NULL, folder = NULL)
repo |
(character) full URL for a GitHub repository (including "github.com") |
folder |
(NULL/character) either |
(dataframe) two-column dataframe including (1) the names of the contents and (2) the type of each content item (e.g., file/directory/etc.)
## Not run: # List contents of the top-level of the `supportR` package repository github_ls_single(repo = "https://github.com/njlyon0/supportR") ## End(Not run)## Not run: # List contents of the top-level of the `supportR` package repository github_ls_single(repo = "https://github.com/njlyon0/supportR") ## End(Not run)
Recursively identifies all files in a GitHub repository and creates a file tree using the data.tree package to create a simple, human-readable visualization of the folder hierarchy. Folders can be specified for exclusion in which case the number of elements within them is listed but not the names of those objects. This function only works on repositories (public or private) to which you have access.
github_tree(repo = NULL, exclude = NULL, quiet = FALSE)github_tree(repo = NULL, exclude = NULL, quiet = FALSE)
repo |
(character) full URL for a github repository (including "github.com") |
exclude |
(character) vector of folder names to exclude from the file tree. If |
quiet |
(logical) whether to print an informative message as the contents of each folder is being listed and as the tree is prepared from that information |
(node / R6) data.tree package object class
## Not run: # Create a file tree for the `supportR` package GitHub repository github_tree(repo = "github.com/njlyon0/supportR", exclude = c("man", "docs", ".github")) ## End(Not run)## Not run: # Create a file tree for the `supportR` package GitHub repository github_tree(repo = "github.com/njlyon0/supportR", exclude = c("man", "docs", ".github")) ## End(Not run)
Create a named vector in a single line without either manually defining names at the outset (e.g., c("name_1" = 1, "name_2" = 2, ...) or spending a second line to assign names to an existing vector (e.g., names(vec) <- c("name_1", "name_2", ...)). Useful in cases where you need a named vector within a pipe and don't want to break into two pipes just to define a named vector (see tidyr::separate_wider_position)
name_vec(content = NULL, name = NULL)name_vec(content = NULL, name = NULL)
content |
(vector) content of vector |
name |
(vector) names to assign to vector (must be in same order) |
(named vector) vector with contents from the content argument and names from the name argument
# Create a named vector name_vec(content = 1:10, name = paste0("text_", 1:10))# Create a named vector name_vec(content = 1:10, name = paste0("text_", 1:10))
![[Superseded]](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHhtbG5zOnhsaW5rPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5L3hsaW5rIiB3aWR0aD0iMTI4IiBoZWlnaHQ9IjIwIj48bGluZWFyR3JhZGllbnQgaWQ9ImIiIHgyPSIwIiB5Mj0iMTAwJSI+PHN0b3Agb2Zmc2V0PSIwIiBzdG9wLWNvbG9yPSIjYmJiIiBzdG9wLW9wYWNpdHk9Ii4xIi8+PHN0b3Agb2Zmc2V0PSIxIiBzdG9wLW9wYWNpdHk9Ii4xIi8+PC9saW5lYXJHcmFkaWVudD48Y2xpcFBhdGggaWQ9ImEiPjxyZWN0IHdpZHRoPSIxMjgiIGhlaWdodD0iMjAiIHJ4PSIzIiBmaWxsPSIjZmZmIi8+PC9jbGlwUGF0aD48ZyBjbGlwLXBhdGg9InVybCgjYSkiPjxwYXRoIGZpbGw9IiM1NTUiIGQ9Ik0wIDBoNTV2MjBIMHoiLz48cGF0aCBmaWxsPSIjMDA3ZWM2IiBkPSJNNTUgMGg3M3YyMEg1NXoiLz48cGF0aCBmaWxsPSJ1cmwoI2IpIiBkPSJNMCAwaDEyOHYyMEgweiIvPjwvZz48ZyBmaWxsPSIjZmZmIiB0ZXh0LWFuY2hvcj0ibWlkZGxlIiBmb250LWZhbWlseT0iRGVqYVZ1IFNhbnMsVmVyZGFuYSxHZW5ldmEsc2Fucy1zZXJpZiIgZm9udC1zaXplPSIxMTAiPiA8dGV4dCB4PSIyODUiIHk9IjE1MCIgZmlsbD0iIzAxMDEwMSIgZmlsbC1vcGFjaXR5PSIuMyIgdHJhbnNmb3JtPSJzY2FsZSguMSkiIHRleHRMZW5ndGg9IjQ1MCI+bGlmZWN5Y2xlPC90ZXh0Pjx0ZXh0IHg9IjI4NSIgeT0iMTQwIiB0cmFuc2Zvcm09InNjYWxlKC4xKSIgdGV4dExlbmd0aD0iNDUwIj5saWZlY3ljbGU8L3RleHQ+PHRleHQgeD0iOTA1IiB5PSIxNTAiIGZpbGw9IiMwMTAxMDEiIGZpbGwtb3BhY2l0eT0iLjMiIHRyYW5zZm9ybT0ic2NhbGUoLjEpIiB0ZXh0TGVuZ3RoPSI2MzAiPnN1cGVyc2VkZWQ8L3RleHQ+PHRleHQgeD0iOTA1IiB5PSIxNDAiIHRyYW5zZm9ybT0ic2NhbGUoLjEpIiB0ZXh0TGVuZ3RoPSI2MzAiPnN1cGVyc2VkZWQ8L3RleHQ+PC9nPiA8L3N2Zz4=)
This function has been superseded by ordination because this is just a special case of that function. Additionally, ordination provides users much more control over the internal graphics functions used to create the fundamental elements of the graph
Produces Non-Metric Multi-dimensional Scaling (NMS) ordinations for up to 10 groups. Assigns a unique color for each group and draws an ellipse around the standard deviation of the points. Automatically adds stress (see vegan::metaMDS for explanation of "stress") as legend title. Because there are only five hollow shapes (see ?graphics::pch()) all shapes are re-used a maximum of 2 times when more than 5 groups are supplied.
nms_ord( mod = NULL, groupcol = NULL, title = NA, colors = c("#41b6c4", "#c51b7d", "#7fbc41", "#d73027", "#4575b4", "#e08214", "#8073ac", "#f1b6da", "#b8e186", "#8c96c6"), shapes = rep(x = 21:25, times = 2), lines = rep(x = 1, times = 10), pt_size = 1.5, pt_alpha = 1, lab_text_size = 1.25, axis_text_size = 1, leg_pos = "bottomleft", leg_cont = unique(groupcol) )nms_ord( mod = NULL, groupcol = NULL, title = NA, colors = c("#41b6c4", "#c51b7d", "#7fbc41", "#d73027", "#4575b4", "#e08214", "#8073ac", "#f1b6da", "#b8e186", "#8c96c6"), shapes = rep(x = 21:25, times = 2), lines = rep(x = 1, times = 10), pt_size = 1.5, pt_alpha = 1, lab_text_size = 1.25, axis_text_size = 1, leg_pos = "bottomleft", leg_cont = unique(groupcol) )
mod |
(metaMDS/monoMDS) object returned by |
groupcol |
(dataframe) column specification in the data that includes the groups (accepts either bracket or $ notation) |
title |
(character) string to use as title for plot |
colors |
(character) vector of colors (as hexadecimal codes) of length >= group levels (default not colorblind safe because of need for 10 built-in unique colors) |
shapes |
(numeric) vector of shapes (as values accepted by |
lines |
(numeric) vector of line types (as integers) of length >= group levels |
pt_size |
(numeric) value for point size (controlled by character expansion i.e., |
pt_alpha |
(numeric) value for transparency of points (ranges from 0 to 1) |
lab_text_size |
(numeric) value for axis label text size |
axis_text_size |
(numeric) value for axis tick text size |
leg_pos |
(character or numeric) legend position, either numeric vector of x/y coordinates or shorthand accepted by |
leg_cont |
(character) vector of desired legend entries. Defaults to |
(plot) base R ordination with an ellipse for each group
# Use data from the vegan package utils::data("varespec", package = 'vegan') resp <- varespec # Make some columns of known number of groups factor_4lvl <- c(rep.int("Trt1", (nrow(resp)/4)), rep.int("Trt2", (nrow(resp)/4)), rep.int("Trt3", (nrow(resp)/4)), rep.int("Trt4", (nrow(resp)/4))) # And combine them into a single data object data <- cbind(factor_4lvl, resp) # Actually perform multidimensional scaling mds <- vegan::metaMDS(data[-1], autotransform = FALSE, expand = FALSE, k = 2, try = 50) # With the scaled object and original dataframe we can use this function nms_ord(mod = mds, groupcol = data$factor_4lvl, title = '4-Level NMS', leg_pos = 'topright', leg_cont = as.character(1:4))# Use data from the vegan package utils::data("varespec", package = 'vegan') resp <- varespec # Make some columns of known number of groups factor_4lvl <- c(rep.int("Trt1", (nrow(resp)/4)), rep.int("Trt2", (nrow(resp)/4)), rep.int("Trt3", (nrow(resp)/4)), rep.int("Trt4", (nrow(resp)/4))) # And combine them into a single data object data <- cbind(factor_4lvl, resp) # Actually perform multidimensional scaling mds <- vegan::metaMDS(data[-1], autotransform = FALSE, expand = FALSE, k = 2, try = 50) # With the scaled object and original dataframe we can use this function nms_ord(mod = mds, groupcol = data$factor_4lvl, title = '4-Level NMS', leg_pos = 'topright', leg_cont = as.character(1:4))
Identifies any elements in the column(s) that would be changed to NA if as.numeric is used on the column(s). This is useful for quickly identifying only the "problem" entries of ostensibly numeric column(s) that is/are read in as a character.
num_check(data = NULL, col = NULL)num_check(data = NULL, col = NULL)
data |
(dataframe) object containing at least one column of supposed numbers |
col |
(character or numeric) name(s) or column number(s) of the column(s) containing putative numbers in the data object |
(list) malformed numbers from each supplied column in separate list elements
# Create dataframe with a numeric column where some entries would be coerced into NA spp <- c("salmon", "bass", "halibut", "eel") ct <- c(1, "14x", "_23", 12) ct2 <- c("a", "2", "4", "0") ct3 <- c(NA, "Y", "typo", "2") fish <- data.frame("species" = spp, "count" = ct, "num_col2" = ct2, "third_count" = ct3) # Use `num_check()` to return only the entries that would be lost num_check(data = fish, col = c("count", "num_col2", "third_count"))# Create dataframe with a numeric column where some entries would be coerced into NA spp <- c("salmon", "bass", "halibut", "eel") ct <- c(1, "14x", "_23", 12) ct2 <- c("a", "2", "4", "0") ct3 <- c(NA, "Y", "typo", "2") fish <- data.frame("species" = spp, "count" = ct, "num_col2" = ct2, "third_count" = ct3) # Use `num_check()` to return only the entries that would be lost num_check(data = fish, col = c("count", "num_col2", "third_count"))
Produces a Nonmetric Multidimensional Scaling (NMS) or Principal Coordinate Analysis (PCoA) for up to 10 groups. Draws an ellipse around the standard deviation of the points in each group. By default, assigns a unique color (colorblind-safe) and point shape for each group. If the user supplies colors/shapes then the function can support more than 10 groups. For NMS ordinations, includes the stress as the legend title (see ?vegan::metaMDS for explanation of "stress"). For PCoA ordinations includes the percent variation explained parenthetically in the axis labels.
ordination(mod = NULL, grps = NULL, ...)ordination(mod = NULL, grps = NULL, ...)
mod |
(pcoa | monoMDS/metaMDS) object returned by |
grps |
(vector) vector of categorical groups for data. Must be same length as number of rows in original data object |
... |
additional arguments passed to |
(plot) base R ordination with an ellipse for each group
# Use data from the vegan package utils::data("varespec", package = 'vegan') # Make some columns of known number of groups treatment <- c(rep.int("Trt1", (nrow(varespec)/4)), rep.int("Trt2", (nrow(varespec)/4)), rep.int("Trt3", (nrow(varespec)/4)), rep.int("Trt4", (nrow(varespec)/4))) # And combine them into a single data object data <- cbind(treatment, varespec) # Get a distance matrix from the data dist <- vegan::vegdist(varespec, method = 'kulczynski') # Perform PCoA / NMS pcoa_mod <- ape::pcoa(dist) nms_mod <- vegan::metaMDS(data[-1], autotransform = FALSE, expand = FALSE, k = 2, try = 50) # Create PCoA ordination (with optional agruments) ordination(mod = pcoa_mod, grps = data$treatment, bg = c("red", "blue", "purple", "orange"), lty = 2, col = "black") # Create NMS ordination ordination(mod = nms_mod, grps = data$treatment, alpha = 0.3, x = "topright", legend = LETTERS[1:4])# Use data from the vegan package utils::data("varespec", package = 'vegan') # Make some columns of known number of groups treatment <- c(rep.int("Trt1", (nrow(varespec)/4)), rep.int("Trt2", (nrow(varespec)/4)), rep.int("Trt3", (nrow(varespec)/4)), rep.int("Trt4", (nrow(varespec)/4))) # And combine them into a single data object data <- cbind(treatment, varespec) # Get a distance matrix from the data dist <- vegan::vegdist(varespec, method = 'kulczynski') # Perform PCoA / NMS pcoa_mod <- ape::pcoa(dist) nms_mod <- vegan::metaMDS(data[-1], autotransform = FALSE, expand = FALSE, k = 2, try = 50) # Create PCoA ordination (with optional agruments) ordination(mod = pcoa_mod, grps = data$treatment, bg = c("red", "blue", "purple", "orange"), lty = 2, col = "black") # Create NMS ordination ordination(mod = nms_mod, grps = data$treatment, alpha = 0.3, x = "topright", legend = LETTERS[1:4])
This function has been superseded by ordination because this is just a special case of that function. Additionally, ordination provides users much more control over the internal graphics functions used to create the fundamental elements of the graph
Produces Principal Coordinates Analysis (PCoA) ordinations for up to 10 groups. Assigns a unique color for each group and draws an ellipse around the standard deviation of the points. Automatically adds percent of variation explained by first two principal component axes parenthetically to axis labels. Because there are only five hollow shapes (see ?graphics::pch) all shapes are re-used a maximum of 2 times when more than 5 groups are supplied.
pcoa_ord( mod = NULL, groupcol = NULL, title = NA, colors = c("#41b6c4", "#c51b7d", "#7fbc41", "#d73027", "#4575b4", "#e08214", "#8073ac", "#f1b6da", "#b8e186", "#8c96c6"), shapes = rep(x = 21:25, times = 2), lines = rep(x = 1, times = 10), pt_size = 1.5, pt_alpha = 1, lab_text_size = 1.25, axis_text_size = 1, leg_pos = "bottomleft", leg_cont = unique(groupcol) )pcoa_ord( mod = NULL, groupcol = NULL, title = NA, colors = c("#41b6c4", "#c51b7d", "#7fbc41", "#d73027", "#4575b4", "#e08214", "#8073ac", "#f1b6da", "#b8e186", "#8c96c6"), shapes = rep(x = 21:25, times = 2), lines = rep(x = 1, times = 10), pt_size = 1.5, pt_alpha = 1, lab_text_size = 1.25, axis_text_size = 1, leg_pos = "bottomleft", leg_cont = unique(groupcol) )
mod |
(pcoa) object returned by |
groupcol |
(dataframe) column specification in the data that includes the groups (accepts either bracket or $ notation) |
title |
(character) string to use as title for plot |
colors |
(character) vector of colors (as hexadecimal codes) of length >= group levels (default not colorblind safe because of need for 10 built-in unique colors) |
shapes |
(numeric) vector of shapes (as values accepted by |
lines |
(numeric) vector of line types (as integers) of length >= group levels |
pt_size |
(numeric) value for point size (controlled by character expansion i.e., |
pt_alpha |
(numeric) value for transparency of points (ranges from 0 to 1) |
lab_text_size |
(numeric) value for axis label text size |
axis_text_size |
(numeric) value for axis tick text size |
leg_pos |
(character or numeric) legend position, either numeric vector of x/y coordinates or shorthand accepted by |
leg_cont |
(character) vector of desired legend entries. Defaults to |
(plot) base R ordination with an ellipse for each group
# Use data from the vegan package data("varespec", package = 'vegan') resp <- varespec # Make some columns of known number of groups factor_4lvl <- c(rep.int("Trt1", (nrow(resp)/4)), rep.int("Trt2", (nrow(resp)/4)), rep.int("Trt3", (nrow(resp)/4)), rep.int("Trt4", (nrow(resp)/4))) # And combine them into a single data object data <- cbind(factor_4lvl, resp) # Get a distance matrix from the data dist <- vegan::vegdist(resp, method = 'kulczynski') # Perform a PCoA on the distance matrix to get points for an ordination pnts <- ape::pcoa(dist) # Test the function for 4 groups pcoa_ord(mod = pnts, groupcol = data$factor_4lvl)# Use data from the vegan package data("varespec", package = 'vegan') resp <- varespec # Make some columns of known number of groups factor_4lvl <- c(rep.int("Trt1", (nrow(resp)/4)), rep.int("Trt2", (nrow(resp)/4)), rep.int("Trt3", (nrow(resp)/4)), rep.int("Trt4", (nrow(resp)/4))) # And combine them into a single data object data <- cbind(factor_4lvl, resp) # Get a distance matrix from the data dist <- vegan::vegdist(resp, method = 'kulczynski') # Perform a PCoA on the distance matrix to get points for an ordination pnts <- ape::pcoa(dist) # Test the function for 4 groups pcoa_ord(mod = pnts, groupcol = data$factor_4lvl)
Finds all non-ASCII (American Standard Code for Information Interchange) characters in a character vector and replaces them with ASCII characters that are as visually similar as possible. For example, various special dash types (e.g., em dash, en dash, etc.) are replaced with a hyphen. The function will return a warning if it finds any non-ASCII characters for which it does not have a hard-coded replacement. Please open a GitHub Issue if you encounter this warning and have a suggestion for what the replacement character should be for that particular character.
replace_non_ascii(x = NULL, include_letters = FALSE)replace_non_ascii(x = NULL, include_letters = FALSE)
x |
(character) vector in which to replace non-ASCII characters |
include_letters |
(logical) whether to include letters with accents (e.g., u with an umlaut, etc.). Defaults to |
(character) vector where all non-ASCII characters have been replaced by ASCII equivalents
# Make a vector of the hexadecimal codes for several non-ASCII characters ## This function accepts the characters themselves but CRAN checks do not non_ascii <- c("\u201C", "\u00AC", "\u00D7") # Invoke function (ascii <- replace_non_ascii(x = non_ascii))# Make a vector of the hexadecimal codes for several non-ASCII characters ## This function accepts the characters themselves but CRAN checks do not non_ascii <- c("\u201C", "\u00AC", "\u00D7") # Invoke function (ascii <- replace_non_ascii(x = non_ascii))
This function allows you to knit a specified R Markdown file locally and export it to the Google Drive folder for which you provided a link. NOTE that if you have not used googledrive::drive_auth this will prompt you to authorize a Google account in a new browser tab. If you do not check the box in that screen before continuing you will not be able to use this function until you clear your browser cache and re-authenticate. I recommend invoking drive_auth beforehand to reduce the chances of this error
rmd_export( rmd = NULL, out_path = getwd(), out_name = NULL, out_type = "html", drive_link )rmd_export( rmd = NULL, out_path = getwd(), out_name = NULL, out_type = "html", drive_link )
rmd |
(character) name and path to R markdown file to knit |
out_path |
(character) path to the knit file's destination (defaults to path returned by |
out_name |
(character) desired name for knit file (with or without file suffix) |
out_type |
(character) either "html" or "pdf" depending on what YAML entry you have in the |
drive_link |
(character) full URL of drive folder to upload the knit document |
No return value, called to knit R Markdown file
## Not run: # Authorize R to interact with GoogleDrive googledrive::drive_auth() ## NOTE: See warning about possible misstep at this stage # Use `rmd_export()` to knit and export an .Rmd file rmd_export(rmd = "my_markdown.Rmd", in_path = getwd(), out_path = getwd(), out_name = "my_markdown", out_type = "html", drive_link = "<Google Drive folder URL>") ## End(Not run)## Not run: # Authorize R to interact with GoogleDrive googledrive::drive_auth() ## NOTE: See warning about possible misstep at this stage # Use `rmd_export()` to knit and export an .Rmd file rmd_export(rmd = "my_markdown.Rmd", in_path = getwd(), out_path = getwd(), out_name = "my_markdown", out_type = "html", drive_link = "<Google Drive folder URL>") ## End(Not run)
Replaces specified column names with user-defined vector of new column name(s). This operation is done "safely" because it specifically matches each 'bad' name with its corresponding 'good' name and thus minimizes the risk of accidentally replacing the wrong column name.
safe_rename(data = NULL, bad_names = NULL, good_names = NULL)safe_rename(data = NULL, bad_names = NULL, good_names = NULL)
data |
(dataframe or dataframe-like) object with column names that match the values passed to the |
bad_names |
(character) vector of column names to replace in original data object. Order does not need to match data column order but must match the |
good_names |
(character) vector of column names to use as replacements for data object. Order does not need to match data column order but must match the |
(dataframe or dataframe-like) with renamed columns
# Make a dataframe to demonstrate df <- data.frame("first" = 1:3, "middle" = 4:6, "second" = 7:9) # Invoke the function safe_rename(data = df, bad_names = c("second", "middle"), good_names = c("third", "second"))# Make a dataframe to demonstrate df <- data.frame("first" = 1:3, "middle" = 4:6, "second" = 7:9) # Invoke the function safe_rename(data = df, bad_names = c("second", "middle"), good_names = c("third", "second"))
Calculates mean, standard deviation, sample size, and standard error of a given response variable within user-defined grouping variables. This is meant as a convenience instead of doing dplyr::group_by followed by dplyr::summarize iteratively themselves.
summary_table( data = NULL, groups = NULL, response = NULL, drop_na = FALSE, round_digits = 2 )summary_table( data = NULL, groups = NULL, response = NULL, drop_na = FALSE, round_digits = 2 )
data |
(dataframe or dataframe-like) object with column names that match the values passed to the |
groups |
(character) vector of column names to group by |
response |
(character) name of the column name to calculate summary statistics for (the column must be numeric) |
drop_na |
(logical) whether to drop NAs in grouping variables. Defaults to FALSE |
round_digits |
(numeric) number of digits to which mean, standard deviation, and standard error should be rounded |
(dataframe) summary table containing the mean, standard deviation, sample size, and standard error of the supplied response variable
Accepts one markdown file (i.e., "md" file extension) and returns its content as a table. Nested heading structure in markdown file–as defined by hashtags / pounds signs (#)–is identified and preserved as columns in the resulting tabular format. Each line of non-heading content in the file is preserved in the right-most column of one row of the table.
tabularize_md(file = NULL)tabularize_md(file = NULL)
file |
(character/url connection) name and file path of markdown file to transform into a table or a connection object to a URL of a markdown file (see |
(dataframe) table with one additional column than there are heading levels in the document (e.g., if first and second level headings are in the document, the resulting table will have three columns) and one row per line of non-heading content in the markdown file.
## Not run: # Identify URL to the NEWS.md file in `supportR` GitHub repo md_cxn <- url("https://raw.githubusercontent.com/njlyon0/supportR/main/NEWS.md") # Transform it into a table md_df <- tabularize_md(file = md_cxn) # Close connection (just good housekeeping to do so) close(md_cxn) # Check out the table format str(md_df) ## End(Not run)## Not run: # Identify URL to the NEWS.md file in `supportR` GitHub repo md_cxn <- url("https://raw.githubusercontent.com/njlyon0/supportR/main/NEWS.md") # Transform it into a table md_df <- tabularize_md(file = md_cxn) # Close connection (just good housekeeping to do so) close(md_cxn) # Check out the table format str(md_df) ## End(Not run)
ggplot2 Theme for Non-Data AestheticsCustom alternative to the ggtheme options built into ggplot2. Removes gray boxes and grid lines from plot background. Increases font size of tick marks and axis labels. Removes gray box from legend background and legend key. Removes legend title.
theme_lyon(title_size = 16, text_size = 13)theme_lyon(title_size = 16, text_size = 13)
title_size |
(numeric) size of font in axis titles |
text_size |
(numeric) size of font in tick labels |
(ggplot theme) list of ggplot2 theme elements