r_note.Rmd

# R_note

## 1.1 command

```{r}
# setwd("C:/Documents") change our directory to the documents folder on the C drive
```

```{r}
install.packages(c("sf", "tmap", "tmaptools", "RSQLite", "tidyverse"), 
                 repos = "https://www.stats.bris.ac.uk/R/")
```

## 1.2 numeric

```{r}
#编辑数据时注意在形式上区分浮点和整型
  a_integer <- 1
  a_double <- 0.1

  is.integer(a_integer)  #function(augument)
  #FALSE r默认所有输出都是double
  is.integer(a_double)
  #TRUE
  a_integer <- as.integer(a_integer)  # as.  Force an Object to Belong to a Class
  is.integer(a_integer)
  #TRUE
```

## 1.3 character

```{r}
a_character <- "Hello world"
  a_num_character <- "123"

  is.character(a_character)
  #TRUE
  is.numeric(a_num_character)
  #FALSE
  a_num_character <- as.numeric(a_num_character)
  is.numeric(a_num_character)
  #TRUE

  a_paste1 <- paste("Hello world","123",sep = "_") #Concatenate Strings
  #"Hello world_123"
  a_paste2 <- paste0(a_character,"_",a_num_character)

  b_substr1 <- substr(a_character,start=2,stop=5)
```

## 1.4 logical

```{r}
as.logical()
```

## 1.5 Data Type

```{r}
#vector, matrix, dataframe(table), array, list
```

## 2.1 vector

```{r}
  b_numeric_vector_1 <- c(1,2,3)
  b_numeric_vector_2 <- c(1,2:3)
  #vector 取值[]
  b_numeric_vector_1[c(2,3)]
  #vector 替换
  b_numeric_vector_3 <- b_numeric_vector_1[c(2,3)] <- c(6,6)
```

## 2.2 matrix

```{r}
  matrix_1 <- matrix(c(1:50),nrow = 5) #先列后行
  matrix_2 <- matrix(c(1:50),nrow = 5,byrow = TRUE) #先行后列 
  
  median(matrix_1)
  
  apply(matrix_1,1,sum)
  #1 indicates rows, 2 indicates columns, c(1, 2) indicates rows and columns
```

## 2.3 dataframe

```{r}
name1 <- c("bob","tom","sam")
score1 <- c(80,90,100)
class1 <- c(1,3,2)
student_score <- data.frame(name=name1,
                            score=score1,
                            class=class1)
#dataframe取值
rownames(student_score) <- student_score[,1]
student_score_name <- student_score$
```
### 2.3.1 tibble
```{r}
#data type for every column
#更适合在tidyverse中使用
library(tibble)

tibble(
    name = c('张三', '李四', '王五', '赵六'),
    age = 12:15,
    score = c(98.5, 72, 88, 96)
)
```

```{r}
library(tidyverse)
df %>% 
  head()
df %>% 
  tail()
```
### 2.3.2 elements of dataframe
```{r}
#data.frame[row,column]
df <- iris
df[1:10, 1]
df[5:15,]
df[c(2,3,6),2]
df[,1]
```


## packages
dplyr
```{r}
library(dplyr)
## a grammar of data manipulation, it has multiple verbs that allow you to change your data into a suitable format

#rename
df <- df %>% 
  dplyr::rename(column = Data1, column2=Data2)
#select
df %>% 
  dplyr::select(column1)
  #not all spatial data is compatible with dplyr yet, such as raster data
  df$column1
  df[["column1"]]

```


##syntax
```{r}
# ::
## use a function my_function from a package called my_package
my_package::my_function()

```


## 3.1 read data

```{r}

```


- Select points or polygons in a polygon = Selecting data by location = spatial sub-setting

- Determine where datasets overlap (or touch, or don’t overlap) and extract those parts = spatial clipping

- Join two spatial datasets together = spatial joining, which can use spatial subsetting functions as the default is st_intersects(). This function joins spatial data.

- Selecting data by attributes = filtering or selecting rows / columns with dplyr