1. Introduction to
dplyr dplyr is a data analysis package of R language, similar to pandas in python, which can do very convenient data processing and analysis operations on dataframe type data. At first I was also surprised by the weird name dplyr. I found one of the explanations-d stands for dataframe-plyr is a homophone for plier in English
dplyr, like most R packages, is functional programming, which is very different from Python object-oriented programming. The advantage is that beginners are more likely to accept this kind of functional thinking, which is similar to an assembly line. Each function is a workshop, and multiple workshops work together to complete a production (data analysis) task.
In dplyr, there is a pipe symbol %>%. The left side of the symbol represents the input of data, and the right side represents the downstream data processing link.
2. Install and import
the p_load function of the dplyr library pacman library includes
- install.packages(“dplyr”)
- library(dplyr)
is easier to use
pacman::p_load("dplyr")
**3. 读取数据**
#Set the working directory setwd("/Users/thunderhit/Desktop/dplyr_learn")
#Import csv data aapl <- read.csv('aapl.csv',
header=TRUE,
sep=',',
stringsAsFactors = FALSE) %>% as_tibble()
head( aapl )
A tibble: 6 × 6
查看数据类型class(aapl)
'tbl_df'
'tbl'
'data.frame'
查看数据的字段
colnames (aapl)
'Date'
'Open'
'High'
'Low'
'Close'
'Volume'
查看记录数、字段数dim (aapl)
251
6
**4. dplyr常用函数**
**4.1 Arrange**
对appl数据按照字段Volume进行降序排序arrange(aapl, -Volume)
A tibble: 6 × 6
我们也可以用管道符 %>% ，两种写法得到的运行结果是一致的，可能用久了会觉得管道符 %>% 可读性更强，后面我们都会用 %>% 来写代码。aapl %>% arrange(-Volume)
A tibble: 6 × 6
**4.2 Select**
选取 Date、Close和Volume三列aapl %>% select(Date, Close, Volume)
只选取Date、Close和Volume三列，其实另外一种表达方式是“排除Open、High、Low，选择剩下的字段的数据”。aapl %>% select(-c("Open", "High", "Low"))
**4.3 Filter**
按照筛选条件选择数据#Select from the data the transaction data with appl stock price greater than $150
aapl %>% filter(Close>=150)
从数据中选择appl - 股价大于150美元 且 收盘价大于开盘价 的交易数据aapl %>% filter((Close>=150) & (Close>Open))
**4.4 Mutate**
将现有的字段经过计算后生成新字段。#Define the result of the best price High minus the lowest price Low as maxDif, and take log aapl
%>% mutate(maxDif = High-Low,
log_maxDif=log(maxDif))
得到记录的位置(行数)aapl %>% mutate(n=row_number())
**4.5 Group_By**
对资料进行分组，这里导入新的 数据集 weather
#Import csv data weather <- read.csv('weather.csv',
header=TRUE,
sep=',',
stringsAsFactors = FALSE) %>% as_tibble()
weather
按照城市分组weather %>% group_by(city)
In order to let everyone see the effect of grouping, let’s calculate the average temperature by city.
weather %>% group_by(city) %>% summarise(mean_temperature = mean(temperature))
`summarise()` ungrouping output (override with `.groups` argument)
weather %>% summarise(mean_temperature = mean(temperature))
