目录
插入不同的代码块
插入R代码块
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car_data<- head(cars)
head(car_data)
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## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
插入Python代码块
## Hello Python!
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import numpy as np
a = np.array([[1,2,4.0],[3,6,9]])
a
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## array([[1., 2., 4.],
## [3., 6., 9.]])
## 2
插入Stata代码块
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sysuse auto
summarize
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##
##
## . sysuse a(1978 Automobile Data)
##
## . summarize
##
## Variable | Obs Mean Std. Dev. Min Max
## -------------+---------------------------------------------------------
## make | 0
## price | 74 6165.257 2949.496 3291 15906
## mpg | 74 21.2973 5.785503 12 41
## rep78 | 69 3.405797 .9899323 1 5
## headroom | 74 2.993243 .8459948 1.5 5
## -------------+---------------------------------------------------------
## trunk | 74 13.75676 4.277404 5 23
## weight | 74 3019.459 777.1936 1760 4840
## length | 74 187.9324 22.26634 142 233
## turn | 74 39.64865 4.399354 31 51
## displacement | 74 197.2973 91.83722 79 425
## -------------+---------------------------------------------------------
## gear_ratio | 74 3.014865 .4562871 2.19 3.89
## foreign | 74 .2972973 .4601885 0 1
R与Python的数据交换
由Python到R的数据传递
Create a variable x in the Python session:
Access the Python variable x in an R code chunk:
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data_py_to_r1<-py$x;data_py_to_r1
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## [1] 1 2 3
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data_py_to_r2<-py$a;data_py_to_r2
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## [,1] [,2] [,3]
## [1,] 1 2 4
## [2,] 3 6 9
由R到Python的数据传递
## speed dist
## 0 4.0 2.0
## 1 4.0 10.0
## 2 7.0 4.0
## 3 7.0 22.0
## 4 8.0 16.0
## 5 9.0 10.0
在 R中运行 Stata
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library(RStata) ##加载包
options("RStata.StataPath" = "\"C:\\Program Files\\Stata16\\StataMP-64\"")
options("RStata.StataVersion" = 16) ##设置版本
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stata_src <- "
sysuse auto, clear
reg mpg weight "
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## .
## . sysuse auto, clear
## (1978 Automobile Data)
## . reg mpg weight
##
## Source | SS df MS Number of obs = 74
## -------------+---------------------------------- F(1, 72) = 134.62
## Model | 1591.9902 1 1591.9902 Prob > F = 0.0000
## Residual | 851.469256 72 11.8259619 R-squared = 0.6515
## -------------+---------------------------------- Adj R-squared = 0.6467
## Total | 2443.45946 73 33.4720474 Root MSE = 3.4389
##
## ------------------------------------------------------------------------------
## mpg | Coef. Std. Err. t P>|t| [95% Conf. Interval]
## -------------+----------------------------------------------------------------
## weight | -.0060087 .0005179 -11.60 0.000 -.0070411 -.0049763
## _cons | 39.44028 1.614003 24.44 0.000 36.22283 42.65774
## ------------------------------------------------------------------------------
在 Stata中运行R
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clear
webuse grunfeld,clear //利用webuse从网络读取数据
list in 1/10 // 显示该数据集的前10行
xtset company year, yearly //设置面板数据格式
xtreg invest mvalue kstock, fe //fe表示固定效应
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##
##
## . cl. webuse grunfeld,clear //利用webuse从网络读取数据
##
## . list in 1/10 // 显示该数据集的前10行
##
## +--------------------------------------------------+
## | company year invest mvalue kstock time |
## |--------------------------------------------------|
## 1. | 1 1935 317.6 3078.5 2.8 1 |
## 2. | 1 1936 391.8 4661.7 52.6 2 |
## 3. | 1 1937 410.6 5387.1 156.9 3 |
## 4. | 1 1938 257.7 2792.2 209.2 4 |
## 5. | 1 1939 330.8 4313.2 203.4 5 |
## |--------------------------------------------------|
## 6. | 1 1940 461.2 4643.9 207.2 6 |
## 7. | 1 1941 512 4551.2 255.2 7 |
## 8. | 1 1942 448 3244.1 303.7 8 |
## 9. | 1 1943 499.6 4053.7 264.1 9 |
## 10. | 1 1944 547.5 4379.3 201.6 10 |
## +--------------------------------------------------+
##
## . xtset company year, yearly //设置面板数据格式
## panel variable: company (strongly balanced)
## time variable: year, 1935 to 1954
## delta: 1 year
##
## . xtreg invest mvalue kstock, fe //fe表示固定效应
##
## Fixed-effects (within) regression Number of obs = 200
## Group variable: company Number of groups = 10
##
## R-sq: Obs per group:
## within = 0.7668 min = 20
## between = 0.8194 avg = 20.0
## overall = 0.8060 max = 20
##
## F(2,188) = 309.01
## corr(u_i, Xb) = -0.1517 Prob > F = 0.0000
##
## ------------------------------------------------------------------------------
## invest | Coef. Std. Err. t P>|t| [95% Conf. Interval]
## -------------+----------------------------------------------------------------
## mvalue | .1101238 .0118567 9.29 0.000 .0867345 .1335131
## kstock | .3100653 .0173545 17.87 0.000 .2758308 .3442999
## _cons | -58.74393 12.45369 -4.72 0.000 -83.31086 -34.177
## -------------+----------------------------------------------------------------
## sigma_u | 85.732501
## sigma_e | 52.767964
## rho | .72525012 (fraction of variance due to u_i)
## ------------------------------------------------------------------------------
## F test that all u_i=0: F(9, 188) = 49.18 Prob > F = 0.0000