A few days ago I was given code by a client for a function that, given a path to a patient’s file, generates a useful ID for the patient. I won’t post the actual function, but it was something along the lines of this: library(stringr)library(dplyr)patient_name<-function(path){...
# select_if() if you want to apply the function to columns of a certain characteristic (e.g. data type) # select_with() if you want to apply the function to columns and include another function within it # 创建新列 # mutate函数 marine5 <- marine4 %>% mutate(genus_species = paste...
I realize that this is a slightly more complicated application, but in reality, this is a very common way to use case_when in R. We commonly use case_when to create new variables in a dataframe, in conjunction with the mutate function. EXAMPLE 4: Create new variable by multiple conditions...
About 16% of the flights in this dataset arrived more than 30 minutes late. R flight_data %>% count(arr_delay) %>% mutate(prop = n/sum(n)) Thedestfeature has 104 flight destinations. R unique(flight_data$dest) There are 16 distinct carriers. ...
The query function has two modes of operation which correspond to the two protocols the Postgres server provides for sending queries to the database server: Simple protocol: you only pass in a single argument, the query string Extended protocol: you pass in a query with parameter placeholders (...
For example, thegather()function intidyrcan be used to convert wide data into long data. Here's an example: R # convert the stock data into longer datalibrary(tidyr) stocksL <- gather(data = stocks, key = stock, value = price, X, Y, Z) stocksL ...
const MUTATION = ` mutation CreateTodo($todoTitle string) { todo(title: $todoTitle) { id title } } ` function App() { const [todoTitle, setTodoTitle] = useState('') const request = useFetch('http://example.com') const createtodo = () => request.mutate(MUTATION, { todoTitle })...
# librarylibrary(ggplot2)library(ggiraph)library(tidyverse)library(gapminder)library(dplyr)library(hrbrthemes)library(viridis)# The dataset is provided in the gapminder librarydata<-gapminder%>%filter(year=="2007")%>%dplyr::select(-year)%>%arrange(desc(pop))%>%mutate(country =factor(country, ...
mutates and twists around its swelling chorus. Shut tight. Loud as bodies. Imagine if we answered all these blushed curious inquiries to rewrite backwards retrogrades spoken softly enough to understand its sacred feedback. ___ title is William Stafford’s reference to “that feeling you have wh...
We briefly passed over this in the "mutate" values part of the intro above - any static value block will editable as a code window on the Rabbit canvas. Integer, string, float, map, etc - it's an easy way to test things out / experiment....