search for: q75

I am trying to replicate the SAS proc univariate in R. I got most of the stats I needed for a by grouping in a data frame using: all1 <- ddply(all,"ACT_NAME", summarise, mean=mean(COUNTS), sd=sd(COUNTS), q25=quantile(COUNTS,.25),median=quantile(COUNTS,.50), q75=quantile(COUNTS,.75), q90=quantile(COUNTS,.90), q95=quantile(COUNTS,.95), q99=quantile(COUNTS,.99) ) So I got the mean, median std dev, quantiles etc. IS there any way I can add the mode to the mixt. Thanks ahead for any suggestions. -- View this message in context: http://r.789...

...t_depth, t_alt_count, t_alt_ratio), names_to = "measure") %>% dplyr::group_by(dat, measure) %>% dplyr::summarize(minimum = min(value,na.rm=T), q25 = quantile(value, probs = 0.25,na.rm=T), med = median(value,na.rm=T), q75 = quantile(value, probs = 0.75,na.rm=T), maximum = max(value,na.rm=T), average = mean(value,na.rm=T), #standard_deviation = sd(value), .groups = "drop" ) Or, simpler, no need to group_by anymore. It can b...

...ot;TDS", "TIC", "TKN", "TOC", "TSS", "TVSS", "Zinc"), row.names = 54:80, class = "data.frame") library(prettyR) library(moments) Q25 <- function(x, na.rm=TRUE){quantile(as.numeric(x), 0.25, na.rm = na.rm, names = FALSE)} Q75 <- function(x, na.rm=TRUE){quantile(as.numeric(x), 0.75, na.rm = na.rm, names = FALSE)} describe(x.f, num.desc=c("mean", "median", "sd", "min", "max", "skewness", "Q25", "Q75","valid.n")) [[alternative HT...

I am using the describe function in prettyR. I would like to add the 25% 75% quartiles to the summary table how do I do this I have tried describe(x.f, num.desc=c("mean", "median", "sd", "min", "max", "skewness", "quantile(x.f, na.rm=T, probs=seq(0.25, 0.75))", "valid.n")) help -- Let's not spend our time

...th, t_alt_count, t_alt_ratio), names_to = "measure") %>% dplyr::group_by(dat, measure) %>% dplyr::summarize(minimum = min(value,na.rm=T), q25 = quantile(value, probs = 0.25,na.rm=T), med = median(value,na.rm=T), q75 = quantile(value, probs = 0.75,na.rm=T), maximum = max(value,na.rm=T), average = mean(value,na.rm=T), #standard_deviation = sd(value), .groups = "drop" ) Or, simpler, no need to group_by anymore....

...<- anno1148ft %>% pivot_longer(c(t_depth, t_alt_count, t_alt_ratio), names_to = "measure") %>% group_by(dat, measure) %>% summarize(minimum = min(value,na.rm=T), q25 = quantile(value, probs = 0.25,na.rm=T), med = median(value,na.rm=T), q75 = quantile(value, probs = 0.75,na.rm=T), maximum = max(value,na.rm=T), average = mean(value,na.rm=T), #standard_deviation = sd(value), .groups = "drop" ) summary_anno1148ft <-t(summary_anno1148ft) ------------------------------------...

...<- anno1148ft %>% pivot_longer(c(t_depth, t_alt_count, t_alt_ratio), names_to = "measure") %>% group_by(dat, measure) %>% summarize(minimum = min(value,na.rm=T), q25 = quantile(value, probs = 0.25,na.rm=T), med = median(value,na.rm=T), q75 = quantile(value, probs = 0.75,na.rm=T), maximum = max(value,na.rm=T), average = mean(value,na.rm=T), #standard_deviation = sd(value), .groups = "drop" ) summary_anno1148ft <-t(summary_anno1148ft) ------------------------------------...

...plot2) library(lattice) library(latticeExtra) # User functions Factor <- function(...) factor(..., levels = ...) PAN <- function(x1, y1, x2, y2, txt, ...) { panel.rect(x1, y1, x2, y2) panel.text(mean(c(x1, x2)), mean(c(y1, y2)), txt, ...) } q25 <- function(.x) quantile(.x, 0.25) q75 <- function(.x) quantile(.x, 0.75) # Create a dummy dataset ecg <- expand.grid( trt = Factor(c('Placebo', '100mg', '200mg', '400mg')), subject = 1:50, time = Factor(c('Screening', 'Pre-dose', 'Day 1', 'Day 2',...

...t_longer(c(t_depth, t_alt_count, t_alt_ratio), names_to = "measure") > %>% > group_by(dat, measure) %>% > summarize(minimum = min(value,na.rm=T), > q25 = quantile(value, probs = 0.25,na.rm=T), > med = median(value,na.rm=T), > q75 = quantile(value, probs = 0.75,na.rm=T), > maximum = max(value,na.rm=T), > average = mean(value,na.rm=T), > #standard_deviation = sd(value), > .groups = "drop" > ) > summary_anno1148ft <-t(summary_anno1148ft) > >...

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