day 28 inclusion

Author

Jen Richmond

Published

April 28, 2025

Stats NZ recently released an update to their Wellbeing 2023 dataset that included variables related to digital inclusion. I downloaded the raw dataset from the site and used this script to do a bit of cleaning before reading the data into RStudio and plotting how internet use and satisfaction differs across the lifespan below.

I found it particularly interesting that more than half of kiwis 75+ use the internet daily, but older people are less likely to say they are very satisfied with the internet than are younger people.

get data

Code 
library(tidyverse)
library(here)
library(ggeasy)


age_use <- read_csv(here("charts/2025-04-28_inclusion/age_group.csv")) %>%
  filter(category == "Internet use") %>%
   mutate(response = factor(response, levels = c("Many times a day",  "Once or twice a day",  "A few times a week or less", "Never use it"))) %>%
   mutate(age = factor(age, levels = c("15-24", "25-34","35-44", "45-54","55-64","65-74","75+"))) %>%
  mutate(estimate = as.numeric(estimate)) %>%
  select(category, age, response, estimate)

      
age_sat <- read_csv(here("charts/2025-04-28_inclusion/age_group.csv")) %>%
  filter(category == "Internet satisfaction") %>%
   mutate(response = factor(response, levels = c("Very satisfied",  "Satisfied", 
                                           "No feeling either way", "Dissatisfied/very dissatisfied
"))) %>%
   mutate(age = factor(age, levels = c("15-24", "25-34","35-44", "45-54","55-64","65-74","75+"))) %>%
  mutate(estimate = as.numeric(estimate)) %>%
  select(category, age, response, estimate)

plot

age_use %>%
  ggplot(aes(x = age, y = estimate, fill = response)) +
  geom_col()

Basic plot check! Things I would like to change…

  • colour scheme
  • theme
  • axis and legend labels
  • title/caption
  • make same style plot for internet satisfaction
  • combine plot using panel tabset

colours/themes etc

palette1 <- c ("#A092B7", "#7d9fc2", "#C582B2", "#51806a") # Kereru from manu package

palette2 <- c("#85BEDC",  "#647588" , "#CCBBCD") # Korora from manu package


age_use %>%
  ggplot(aes(x = age, y = estimate, fill = response)) +
  geom_col() +
  scale_fill_manual(values = palette1) +
  easy_add_legend_title("Frequency") +
  labs(y = "Percent of respondents", x = "Age group", , 
       title = "Digital inclusion: Wellbeing NZ 2023", 
       subtitle = "Internet use", caption = "Data from StatsNZ") +
  theme_minimal() +
  easy_remove_gridlines(axis = "y")

same plot for satisfaction

age_sat %>%
  ggplot(aes(x = age, y = estimate, fill = response)) +
  geom_col() +
  scale_fill_manual(values = palette2) +
  easy_add_legend_title("Satisfaction") +
  labs(y = "Percent of respondents", x = "Age group", 
        title = "Digital inclusion: Wellbeing NZ 2023", 
       subtitle = "Internet satisfaction", caption = "Data from StatsNZ") +
  theme_minimal() +
  easy_remove_gridlines(axis = "y")

combine plots

Digital inclusion: Wellbeing NZ 2023