## ----include = FALSE-------------------------------- # Load libraries source("libraries.R") # Set up chunk for all slides source("chunk_options_and_themes.R") ## ----read TB incidence data and check, results='hide'---- tb <- read_csv("data/TB_notifications_2025-07-22.csv") tb %>% # first we get the tb data filter(year == 2023) %>% # then we focus on the most recent year group_by(country) %>% # then we group by country summarize( cases = sum(c_newinc, na.rm=TRUE) # to create a summary of all new cases ) %>% arrange(desc(cases)) # then we sort countries to show highest number of new cases first ## ----read TB incidence data and check base pipe, results='hide'---- tb <- read_csv("data/TB_notifications_2025-07-22.csv") tb |> # first we get the tb data filter(year == 2023) |> # then we focus on the most recent year group_by(country) |> # then we group by country summarize( cases = sum(c_newinc, na.rm=TRUE) # to create a summary of all new cases ) |> arrange(desc(cases)) # then we sort countries to show highest number new cases first ## ----show-results, echo=FALSE----------------------- tb <- read_csv("data/TB_notifications_2025-07-22.csv") tb |> # first we get the tb data filter(year == 2023) |> # then we focus on the most recent year group_by(country) |> # then we group by country summarize( cases = sum(c_newinc, na.rm=TRUE) # to create a summary of all new cases ) |> arrange(desc(cases)) # then we sort countries to show highest number of new cases first ## ----example 1 What are the variables, echo=FALSE---- grad <- read_csv("data/graduate-programs.csv") head(grad[c(2,3,4,6)]) ## ----example 2 whats in the column names, echo=FALSE---- genes <- read_csv("data/genes.csv") head(genes) ## ----example 3 what are the variables and records, echo=FALSE---- melbtemp <- read.fwf("data/ASN00086282.dly", c(11, 4, 2, 4, rep(c(5, 1, 1, 1), 31)), fill=T) head(melbtemp[,c(1,2,3,4,seq(5,100,4))]) ## ----example 4 what are the variables and experimental units, echo=FALSE---- tb <- read_csv("data/tb.csv") tail(tb) #colnames(tb) ## ----example 4 what are the variables and observations, echo=FALSE---- pew <- read.delim( file = "http://stat405.had.co.nz/data/pew.txt", header = TRUE, stringsAsFactors = FALSE, check.names = F ) pew[1:5, 1:5] ## ----example 6 what are the factors measurements and experimental units, echo = FALSE---- load("data/french_fries.rda") head(french_fries, 4) ## ----setup a simple example, echo = FALSE----------- dframe <- data.frame(id = 1:2, trtA=c(2.5,4.6), trtB = c(45, 35)) ## ----gather the example data into long form--------- # wide format dframe # long format dframe |> pivot_longer(trtA:trtB, names_to="treatment", values_to="outcome") ## ----read in and process the TB data---------------- read_csv("data/TB_notifications_2025-07-22.csv") |> dplyr::select(country, iso3, year, starts_with("new_sp_")) |> na.omit() |> head() ## ----turn TB data into long form-------------------- tb1 <- read_csv("data/TB_notifications_2025-07-22.csv") |> dplyr::select(country, iso3, year, starts_with("new_sp_")) |> pivot_longer(starts_with("new_sp_")) tb1 |> na.omit() |> head() ## ----extract variable names from original column names---- tb2 <- tb1 |> separate_wider_delim( name, delim = "_", names=c("toss_new", "toss_sp", "sexage")) tb2 |> na.omit() |> head() ## ----continue extracting variable names------------- tb3 <- tb2 %>% dplyr::select(-starts_with("toss")) |> # remove the `toss` variables separate_wider_position( sexage, widths = c(sex = 1, age = 4), too_few = "align_start" ) tb3 |> na.omit() |> head() ## --------------------------------------------------- genes <- read_csv("data/genes.csv") names(genes) ## --------------------------------------------------- #| label: code solution to genes wrangling #| code-fold: true gtidy <- genes |> pivot_longer(-id, names_to="variable", values_to="expr") |> separate_wider_delim(variable, names=c("trt", "leftover"), delim = "-") |> separate_wider_delim(leftover, names=c("time", "rep"), delim = ".") |> mutate(trt = sub("W", "", trt)) |> mutate(rep = sub("R", "", rep)) ## --------------------------------------------------- head(gtidy) ## ----compute group means, fig.show='hide'----------- gmean <- gtidy |> group_by(id, trt, time) |> summarise(expr = mean(expr)) gtidy |> ggplot(aes(x = trt, y = expr, colour=time)) + geom_point() + geom_line(data = gmean, aes(group = time)) + facet_wrap(~id) + scale_colour_brewer("", palette="Set1") ## ----plot the genes data overlaid with group means, echo=FALSE, fig.width=5, fig.height = 5---- gtidy |> ggplot(aes(x = trt, y = expr, colour=time)) + geom_point() + geom_line(data = gmean, aes(group = time)) + facet_wrap(~id) + scale_colour_brewer("", palette="Set1") ## ----echo = FALSE----------------------------------- xlsx_df <- read_excel("data/lecture3-example.xlsx", col_types = c("text", "date", "text", "numeric")) |> mutate(id = as.factor(id), date = as.character(date), date = as.Date(date, format = "%Y-%d-%m")) ## ----echo = TRUE------------------------------------ vis_dat(xlsx_df) ## ----echo = TRUE------------------------------------ df <- read_excel("data/lecture3-example.xlsx") df ## ----echo = TRUE------------------------------------ df <- read_excel("data/lecture3-example.xlsx", col_types = c("text", "date", "text", "numeric")) df |> mutate(id = as.factor(id), date = ydm(date)) |> mutate( day = day(date), month = month(date), year = year(date)) ## --------------------------------------------------- options(width=80) raw_dat <- read_csv("data/world-development-indicators.csv", na = "..", n_max = 11935) glimpse(raw_dat) ## --------------------------------------------------- country_code_df <- raw_dat |> distinct(`Country Name`, `Country Code`) |> rename_all(janitor::make_clean_names) |> left_join( countrycode::codelist |> select(iso3c, region, continent), by = c("country_code" = "iso3c") ) |> arrange(continent, region) ## --------------------------------------------------- #| echo: false options(width=80) glimpse(country_code_df) country_code_df |> count(continent) country_code_df |> count(region) ## --------------------------------------------------- country_code_df |> filter(is.na(continent)) ## --------------------------------------------------- wdi_vars <- raw_dat |> select(`Series Name`, `Series Code`) |> distinct() |> rename_all(janitor::make_clean_names) ## --------------------------------------------------- #| echo: false wdi_vars |> gt::gt() ## --------------------------------------------------- wdi <- raw_dat |> select(`Country Code`, `Series Code`, `1969 [YR1969]`:`2018 [YR2018]`) |> rename_all(janitor::make_clean_names) |> pivot_longer(x1969_yr1969:x2018_yr2018, names_to = "year", values_to = "value") |> mutate(year = as.numeric(str_sub(year, 2, 5)) ) |> pivot_wider(names_from = series_code, values_from = value) wdi2017 <- wdi |> filter(year == 2017) ## --------------------------------------------------- #| fig-width: 8 #| fig-height: 7 #| code-fold: true vis_miss(wdi, sort_miss = TRUE) ## --------------------------------------------------- #| fig-width: 8 #| fig-height: 6 #| code-fold: true gg_miss_var(wdi, show_pct=TRUE) ## --------------------------------------------------- #| code-fold: true #| fig-width: 2 #| fig-height: 8 #| out-width: 100% wdi_cnt_miss <- wdi |> filter(!is.na(country_code)) |> bind_shadow() |> select(country_code, year, SP.ADO.TFRT_NA:SP.URB.GROW_NA) |> pivot_longer(SP.ADO.TFRT_NA:SP.URB.GROW_NA, names_to="var", values_to="value") |> group_by(country_code) |> count(value) |> mutate(value = fct_recode(value, miss="NA", not="!NA")) |> pivot_wider(names_from = value, values_from = n) |> mutate(p_miss = miss/(miss+not)) |> select(country_code, p_miss) wdi_cnt_p <- wdi_cnt_miss |> ggplot(aes(x=1, y=p_miss, label=country_code)) + geom_quasirandom() + ylim(c(0,1)) + ylab("Prop miss") ggplotly(wdi_cnt_p) ## --------------------------------------------------- #| fig-width: 8 #| fig-height: 6 #| code-fold: true wdi_cri <- wdi |> filter(country_code == "CRI") vis_miss(wdi_cri, sort_miss=TRUE) ## --------------------------------------------------- #| fig-width: 6 #| fig-height: 3 #| code-fold: true wdi_cri_p <- wdi_cri |> ggplot(aes(x=year, y=SE.PRM.CMPT.ZS)) + geom_miss_point() + theme(aspect.ratio=0.5, legend.position = "none") wdi_cri_p ## --------------------------------------------------- #| fig-width: 6 #| fig-height: 3 #| code-fold: true wdi_cri_v1 <- wdi_cri |> mutate(SE.PRM.CMPT.ZS = na_ma(SE.PRM.CMPT.ZS)) wdi_cri_v1 |> ggplot(aes(x=year, y=SE.PRM.CMPT.ZS)) + geom_point() + geom_smooth(se=F, colour="#D55E00") + theme(aspect.ratio=0.5) ## --------------------------------------------------- #| echo: false 1 + 1 ## --------------------------------------------------- #| echo: false library(ggplot2) data(cars) table_data <- head(cars, 5) knitr::kable(table_data, caption = "Speed and stopping distances of cars") |> kableExtra::kable_paper( full_width = TRUE ) ## --------------------------------------------------- #| label: cars-plot #| fig-cap: "Distance taken for a car to stop, against it's speed during the test." #| fig-height: 3 library(ggplot2) ggplot(cars, aes(x = speed, y = dist) ) + geom_point() ## ----out.width="110%", echo=FALSE------------------- knitr::include_app("https://ebsmonash.shinyapps.io/VICfire/", height = "550px")