R - ilya-khadykin/notes-outdated GitHub Wiki
TO DO:
Features:
- operators have contextual behaviour;
- most of operations on vectors (arrays) doesn't require writing loops;
Style guide: Google's R Style Guide
Syntax
Operators
| operator | comment |
|---|---|
<- |
assignment |
1:10 |
returns a list of numbers between 1 and 10 |
x <- 1:5 # assignment operator
y <- c(5,6,7,8,9,10)
Comments
# single line comment
Functions
print("Hello World!")
| function | result |
|---|---|
help("library") |
shows help for a particular function |
x <- c(1,2,3,4) |
concatenate elements |
ls() |
list objects |
str(test.csv) |
show structure of an object |
install.packages("devtools") |
install packages |
library(AnomalyDetection) |
library and require load and attach add-on packages. |
Read CSV file
test.csv <- read.csv("C:\\Users\\ikhadykin\\Desktop\\test.csv", header = TRUE, sep = ",") # Windows
d <- read.csv("C:\\Users\\Ilya\\Documents\\R\\subset_rockstar_playercount_xboxonemp_metrics.csv", header = T, sep = ";")
Data type conversions
Convert data from R's factor to string in a data frame
# convert data from R's factor to string in a data frame
f <- sapply(d, is.factor)
d[f] <- lapply(d[f], as.character)
Converting character (string) to timestamp data format (R friendly)
# converting string to timestamp data format (R friendly)
for (i in 1:length(d$metric_datetime[i])) d$metric_datetime <- strptime(d$metric_datetime[i], "%Y-%m-%d %H.%M.%OS")
Packages
| Package | Comment |
|---|---|
| twitter/AnomalyDetection | AnomalyDetection R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend |
| hadley/devtools | devtools makes package development easier by providing R functions that simplify common tasks |
devtools and proxy settings
require(httr)
set_config(
use_proxy(url="18.91.12.23", port=8080, username="user",password="password")
)
One can also add this to .Rprofile for consistency
Examples
# converting string to timestamp data format (R friendly)
for (i in 1:length(d$metric_datetime[i])) d$metric_datetime <- strptime(d$metric_datetime[i], "%Y-%m-%d %H.%M.%OS")
# reordering columns in a data frame
d <- d[c("metric_datetime", "metric_value")]