Get Started
27-May-2023
Load Libraries
A code chunk:
Things to know about R
Everything has a name
Everything is an object
c(1, 2, 3, 1, 3, 5, 25)## [1] 1 2 3 1 3 5 25my_numbers <- c(1, 2, 3, 1, 3, 5, 25)
your_numbers <- c(5, 31, 71, 1, 3, 21, 6)my_numbers## [1] 1 2 3 1 3 5 25You do things using functions
my_numbers## [1] 1 2 3 1 3 5 25mean()mean(x = my_numbers)## [1] 5.714286mean(x = your_numbers)## [1] 19.71429mean(my_numbers)## [1] 5.714286my_summary <- summary(my_numbers)my_summary## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.500 3.000 5.714 4.000 25.000Functions are bundled into thematic packages, which you load with
the library() command
Datasets can also be bundled into packages. Here we load the
drat library to allow us to install two data packages: covdata and nycdogs.
library(drat)
addRepo("kjhealy")
install.packages(c("covdata", "nycdogs"))## Installing packages into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)## Warning in install.packages(c("covdata", "nycdogs")): installation of package
## 'covdata' had non-zero exit statusYou only have to install a package once, or whenever it is
updated. But if you want to make use of it, you must load it
(with library()) at the start of your R session. You can do
this in the first chunk of your RMarkdown file, for example.
table(my_numbers)## my_numbers
## 1 2 3 5 25
## 2 1 2 1 1sd(my_numbers)## [1] 8.616153my_numbers * 5## [1] 5 10 15 5 15 25 125my_numbers + 1## [1] 2 3 4 2 4 6 26my_numbers + my_numbers## [1] 2 4 6 2 6 10 50If you’re not sure what an object is, ask for its class
class(my_numbers)## [1] "numeric"class(my_summary)## [1] "summaryDefault" "table"class(summary)## [1] "function"my_new_vector <- c(my_numbers, "Apple")
my_new_vector## [1] "1" "2" "3" "1" "3" "5" "25" "Apple"class(my_new_vector)## [1] "character"titanic## fate sex n percent
## 1 perished male 1364 62.0
## 2 perished female 126 5.7
## 3 survived male 367 16.7
## 4 survived female 344 15.6class(titanic)## [1] "data.frame"titanic$percent## [1] 62.0 5.7 16.7 15.6titanic_tb <- as_tibble(titanic)
titanic_tb## # A tibble: 4 × 4
## fate sex n percent
## <fct> <fct> <dbl> <dbl>
## 1 perished male 1364 62
## 2 perished female 126 5.7
## 3 survived male 367 16.7
## 4 survived female 344 15.6To see inside an object, ask for its structure, or use RStudio’s object inspector
str(my_numbers)## num [1:7] 1 2 3 1 3 5 25str(my_summary)## 'summaryDefault' Named num [1:6] 1 1.5 3 5.71 4 ...
## - attr(*, "names")= chr [1:6] "Min." "1st Qu." "Median" "Mean" ...Be patient with R, and with yourself
Here are three very specific things to watch out for:
- Make sure parentheses are balanced and that every opening
“
(” has a corresponding closing “)”. - Make sure you complete your expressions. If you think you have
completed typing your code, but instead of seeing the
>command prompt at the console you see the+character instead, that may mean R thinks you haven’t written a complete expression yet. You can hitEscorCtrl-Cto force your way back to the console and try typing your code again. - In ggplot specifically, as you will see, we will build up plots a
piece at a time by adding expressions to one another. When doing this,
make sure your
+character goes at the end of the line, and not the beginning. That is, write this:
ggplot(data = mpg, aes(x = displ, y = hwy)) +
geom_point()and not this:
ggplot(data = mpg, aes(x = displ, y = hwy))
+ geom_point()Get data into R
Remotely:
url <- "https://cdn.rawgit.com/kjhealy/viz-organdata/master/organdonation.csv"
organs <- read_csv(file = url)Or locally:
organs <- read_csv(file = "data/organdonation.csv")## Rows: 238 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): country, world, opt, consent.law, consent.practice, consistent, ccode
## dbl (14): year, donors, pop, pop.dens, gdp, gdp.lag, health, health.lag, pub...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.organs## # A tibble: 238 × 21
## country year donors pop pop.dens gdp gdp.lag health health.lag pubhealth
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Austra… NA NA 17065 0.220 16774 16591 1300 1224 4.8
## 2 Austra… 1991 12.1 17284 0.223 17171 16774 1379 1300 5.4
## 3 Austra… 1992 12.4 17495 0.226 17914 17171 1455 1379 5.4
## 4 Austra… 1993 12.5 17667 0.228 18883 17914 1540 1455 5.4
## 5 Austra… 1994 10.2 17855 0.231 19849 18883 1626 1540 5.4
## 6 Austra… 1995 10.2 18072 0.233 21079 19849 1737 1626 5.5
## 7 Austra… 1996 10.6 18311 0.237 21923 21079 1846 1737 5.6
## 8 Austra… 1997 10.3 18518 0.239 22961 21923 1948 1846 5.7
## 9 Austra… 1998 10.5 18711 0.242 24148 22961 2077 1948 5.9
## 10 Austra… 1999 8.67 18926 0.244 25445 24148 2231 2077 6.1
## # ℹ 228 more rows
## # ℹ 11 more variables: roads <dbl>, cerebvas <dbl>, assault <dbl>,
## # external <dbl>, txp.pop <dbl>, world <chr>, opt <chr>, consent.law <chr>,
## # consent.practice <chr>, consistent <chr>, ccode <chr>Make your first figure
gapminder## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # ℹ 1,694 more rowsp <- ggplot(data = gapminder,
mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point() +
geom_smooth(mapping = aes(color = continent, fill = continent)) +
scale_x_log10(labels = scales::dollar)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Life expectancy plotted against GDP per capita for a large number of country-years.