R code Chapter 3

Mae Jemstone character from Discovering Statistics using R and RStudio

This document contains abridged sections from Discovering Statistics Using R and RStudio by Andy Field so there are some copyright considerations. You can use this material for teaching and non-profit activities but please do not meddle with it or claim it as your own work. See the full license terms at the bottom of the page.

Confidence intervals using `Hmisc` and `ggplot2`

fb_tib <- tibble::tibble(
    friends = c(57, 40, 103, 234, 93, 53, 116, 98, 108, 121, 22)
  )

ggplot2::mean_cl_normal(fb_tib$friends)

##    y     ymin     ymax
## 1 95 56.85094 133.1491

Something other than a 95% interval:

ggplot2::mean_cl_normal(fb_tib$friends, conf.int = 0.90)

##    y     ymin    ymax
## 1 95 63.96796 126.032

Access individual values by using $ to access the variable name (y for the estimate of the mean, ymin for the lower boundary of the CI and ymax for the upper boundary). Executing this gives us the lower boundary of the 95% confidence interval:

ggplot2::mean_cl_normal(fb_tib$friends)$ymin

## [1] 56.85094

Use extracted value to create tables of summary statistics:

# Tidyverse sumptuousness:

fb_tib %>%
  dplyr::summarize(
    Mean =  ggplot2::mean_cl_normal(friends)$y,
    `95% CI Lower` = ggplot2::mean_cl_normal(friends)$ymin,
    `95% CI Upper` = ggplot2::mean_cl_normal(friends)$ymax,
    ) %>% 
  round(., 2)

## # A tibble: 1 x 3
##    Mean `95% CI Lower` `95% CI Upper`
##   <dbl>          <dbl>          <dbl>
## 1    95           56.8           133.

You can also combine these with other functions to get summary statistics:

fb_tib %>%
  dplyr::summarize(
    Mean =  ggplot2::mean_cl_normal(friends)$y,
    `95% CI Lower` = ggplot2::mean_cl_normal(friends)$ymin,
    `95% CI Upper` = ggplot2::mean_cl_normal(friends)$ymax,
    IQR = IQR(friends),
    `Std. dev.` = sd(friends)
    ) %>% 
  round(., 2)

## # A tibble: 1 x 5
##    Mean `95% CI Lower` `95% CI Upper`   IQR `Std. dev.`
##   <dbl>          <dbl>          <dbl> <dbl>       <dbl>
## 1    95           56.8           133.    57        56.8

Confidence intervals using `gmodels`

gmodels::ci(fb_tib$friends)

##   Estimate   CI lower   CI upper Std. Error 
##   95.00000   56.85094  133.14906   17.12149

Something other than a 95% interval:

gmodels::ci(fb_tib$friends, confidence = 0.90)

##   Estimate   CI lower   CI upper Std. Error 
##   95.00000   63.96796  126.03204   17.12149

Access individual values by appending their label in square brackets to the function. Executing this gives us the lower boundary of the 95% confidence interval:

gmodels::ci(fb_tib$friends)["CI lower"]

## CI lower 
## 56.85094

Use extracted value to create tables of summary statistics:

# Tidyverse sumptuousness:

fb_tib %>%
  dplyr::summarize(
    Mean =  gmodels::ci(friends)["Estimate"],
    `95% CI Lower` = gmodels::ci(friends)["CI lower"],
    `95% CI Upper` = gmodels::ci(friends)["CI upper"],
    ) %>% 
  round(., 2)

## # A tibble: 1 x 3
##    Mean `95% CI Lower` `95% CI Upper`
##   <dbl>          <dbl>          <dbl>
## 1    95           56.8           133.

You can also combine these with other functions to get summary statistics:

fb_tib %>%
  dplyr::summarize(
    Mean =  gmodels::ci(friends)["Estimate"],
    `95% CI Lower` = gmodels::ci(friends)["CI lower"],
    `95% CI Upper` = gmodels::ci(friends)["CI upper"],
    IQR = IQR(friends),
    `Std. dev.` = sd(friends)
    ) %>% 
  round(., 2)

## # A tibble: 1 x 5
##    Mean `95% CI Lower` `95% CI Upper`   IQR `Std. dev.`
##   <dbl>          <dbl>          <dbl> <dbl>       <dbl>
## 1    95           56.8           133.    57        56.8

Last updated on Jul 8, 2020

R code Chapter 3

Confidence intervals using Hmisc and ggplot2

Confidence intervals using gmodels

Confidence intervals using `Hmisc` and `ggplot2`

Confidence intervals using `gmodels`