Steele H. Valenzuela shv38339
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| # ANOVA testing by group | |
| data %>% | |
| group_by(your_group) %>% | |
| do(tidy(aov(y ~ factor(x), data = .))) %>% | |
| select(your_group, p.value) | |
| # CHISQUARE testing by group | |
| data %>% | |
| group_by(your_group) %>% | |
| summarise(pvalue = chisq.test(x, y)$p.value) %>% |
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| #libraries | |
| library(shiny) | |
| library(ggplot2) | |
| library(dplyr) | |
| library(DT) | |
| library(tableone) | |
| # data | |
| # mtcars[, c("cyl", "vs", "am", "gear", "carb")] <- lapply(mtcars[, c("cyl", "vs", "am", "gear", "carb")], factor) | |
| # data1 |
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| ggplot(${1:data = }, aes(${2:x = )}) + | |
| geom_histogram(aes(y = ..density..), ${3:binwidth = }, colour = "black", fill = "white") + | |
| geom_density(alpha = .2, fill = "#FF6666") + | |
| geom_vline(aes(xintercept = mean(${4:}), color = "mean"), linetype = "dashed", size = 2) + | |
| geom_vline(aes(xintercept = median(${5:}), color = "median"), linetype = "dashed", size = 2) + | |
| scale_color_manual(name = "Dispersion", values = c(median = "blue", mean = "red")) |
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| # try to do table function in base R | |
| tbl_steele <- function(data, var, ...){ | |
| require(descr) | |
| require(htmlTable) | |
| levels_logic <- lapply(data[, var], function(x) levels(x)) | |
| levels_logic1 <- lapply(levels_logic, function(x) is.null(x)) # combine these statements in the future | |
| if(sum(unlist(levels_logic1)) < length(var)){ | |
| cat("Are all of your variables properly labelled?\n") |
shv38339
/ model_output.r
Created
April 21, 2017 21:55
This function accept glm and geeglm/gee objects. It outputs the variable names, variables levels, followed by the odds ratios, confidence intervals, and pvalues. Additionally, the reference levels are included in the output which is not included in the original R glm objects.
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| model_output <- function(mod_obj){ | |
| # libraries | |
| require(geepack); require(tibble); require(dplyr) | |
| xvar <- names(lm_object$model)[-1] | |
| yvar <- names(lm_object$model)[1] # idky i need this but just in case | |
| data <- lm_object$data | |
| # xvar levels | |
| xvar_levels <- sapply(data[, xvar], levels) | |
| xvar_levels_unlist <- unlist(lapply(seq_along(xvar_levels), |
shv38339
/ html_table.r
Last active
April 18, 2017 20:33
Code snippet displays a customized HTML table function and an example. My next step is to copy and paste this HTML table into an excel file, which I then copy and paste into a word document.
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| # Quite a few requirements so read closely | |
| # - variable must filter out NAs | |
| # - variable must be a factor | |
| # - variable must have factor labels (i think) | |
| # - must be slightly comfortable with dplyr | |
| # FUNCTION | |
| tbl_steele <- function(data, names){ | |
| require(htmlTable) | |
| a <- lapply(data, function(x) freq(x, plot = F)) |
shv38339
/ list_levels.r
Created
April 18, 2017 16:13
Combine list names with list levels in order to imitate the coefficient output from summary(lm(...)) objects in R (aka smushed variable names with variable levels)
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| # place list levels in object | |
| list_levels <- | |
| mtcars %>% select(vs, gear) %>% | |
| transmute(vs_fctr = factor(vs, labels = c("0. Zero", "1. One")), | |
| gear_fctr = factor(gear, labels = c("3. Three", "4. Four", "5. Five"))) %>% | |
| sapply(levels) # sapply extracts unique levels from all variables | |
| # i still don't understand this code but it works... | |
| lapply(seq_along(list_levels), | |
| function(i) paste(names(list_levels)[[i]], list_levels[[i]], sep = "")) %>% |