# phe_mean: Calculate Means using phe_mean In PHEindicatormethods: Common Public Health Statistics and their Confidence Intervals

## Description

Calculates means with confidence limits using Student's t-distribution method.

## Usage

 `1` ```phe_mean(data, x, type = "full", confidence = 0.95) ```

## Arguments

 `data` a data.frame containing the data to calculate means for, pre-grouped if multiple means required; unquoted string; no default `x` field name from data containing the values to calculate the means for; unquoted string; no default `type` defines the data and metadata columns to be included in output; can be "value", "lower", "upper", "standard" (for all data) or "full" (for all data and metadata); quoted string; default = "full" `confidence` the required level of confidence expressed as a number between 0.9 and 1 or a number between 90 and 100 or can be a vector of 0.95 and 0.998, for example, to output both 95% and 99.8% CIs; numeric; default 0.95

## Value

When type = "full", returns a data.frame of value_sum, value_count, stdev, value, lowercl, uppercl, confidence, statistic and method for each grouping set

Other PHEindicatormethods package functions: `phe_dsr()`, `phe_isr()`, `phe_life_expectancy()`, `phe_proportion()`, `phe_quantile()`, `phe_rate()`, `phe_sii()`, `phe_smr()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```library(dplyr) df <- data.frame(values = c(30,40,50,60)) ## default execution phe_mean(df, values) ## calculate 95% and 99.8% CIs in single execution phe_mean(df, values, confidence = c(0.95, 0.998)) ## calculate multiple means in a single execution df2 <- data.frame(area = rep(c("Area1", "Area2"),each=3), values = c(20,30,40,200,300,400)) %>% group_by(area) phe_mean(df2,values) phe_mean(df2,values,type="standard", confidence=0.998) ```