포스팅 목차
[변수와 관측치 선택] 3. 변수와 관측치를 동시에 선택
1. Proc SQL
- 성별이 남성인 관측치 중에서 선택된 변수로 데이터 구성.
| SAS Programming |
proc sql;
create table mydata3 as
select gender,q1,q2,q3,q4
from BACK.mydata
where gender='f';
select * from mydata3;
quit;
proc sql;
create table mydata3 as
select gender,q1,q2,q3,q4
from BACK.mydata(keep=gender q:)
where gender='f';
select * from mydata3;
quit;
proc sql;
create table mydata3(keep=gender q:) as
select gender,q1,q2,q3,q4
from BACK.mydata
where gender='f';
select * from mydata3;
quit;
proc sql;
create table mydata3(where=(gender='f')) as
select gender,q1,q2,q3,q4
from BACK.mydata;
select * from mydata3;
quit;
proc sql;
create table mydata3 as
select gender,q1,q2,q3,q4
from BACK.mydata(drop=id workshop)
where gender='f';
select * from mydata3;
quit;
| Results |
gender q1 q2 q3 q4
------------------------------------------------
f 1 1 5 1
f 2 1 4 1
f 2 2 4 3
f 3 1 . 3
2. SAS Programming
- 성별이 남성인 관측치 중에서 선택된 변수로 데이터 구성.
| SAS Programming |
data mydata3;
set BACK.mydata;
where gender='f';
KEEP gender q1--q4;
run;
proc print;run;
data mydata3(where=(gender='f'));
set BACK.mydata;
KEEP gender q1--q4;
run;
proc print;run;
data mydata3;
set BACK.mydata;
if gender='f' then output;
KEEP gender q1--q4;
run;
proc print;run;
data mydata3;
set BACK.mydata;
where gender='f';
KEEP gender q:;
run;
proc print;run;
data mydata3;
set BACK.mydata(keep=gender q:);
where gender='f';
run;
proc print;run;
data mydata3;
set BACK.mydata(drop=id workshop);
where gender='f';
run;
proc print;run;
| Results |
OBS gender q1 q2 q3 q4
1 f 1 1 5 1
2 f 2 1 4 1
3 f 2 2 4 3
4 f 3 1 . 3
3. SPSS
| SPSS Programming |
GET FILE=("c:\data\mydata.sav")
COMPUTE X1=0.
IF (q4 EQ 5 ) X1=1.
COMPUTE X2=0.
IF (q4 GE 4) X2=1.
COMPUTE X3=0.
IF (gender EQ 'f' AND Q4 GE 5) X3=1.
COMPUTE scoreA=3*q1+q2.
IF (gender='f') scoreA=2*q1+q2.
COMPUTE scoreB=3*q1+q2.
IF (workshop EQ 1 AND q4 GE 5) scoreB=2*q1+q2.
EXECUTE.
4. R Programming (R-PROJECT)
| R Programming |
from rpy2.robjects import r
%load_ext rpy2.ipython
| Results |
The rpy2.ipython extension is already loaded. To reload it, use:
%reload_ext rpy2.ipython
| R Programming |
%%R
library(tidyverse)
load(file="c://work/data/mydata.Rdata")
withmooc = mydata
attach(withmooc) # mydata를 기본 데이터 세트로 지정.
withmooc
| Results |
R[write to console]: -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
From cffi callback :
Traceback (most recent call last):
File "C:\Users\BACK\anaconda3\lib\site-packages\rpy2\rinterface_lib\callbacks.py", line 131, in _consolewrite_ex
s = conversion._cchar_to_str_with_maxlen(buf, maxlen=n)
File "C:\Users\BACK\anaconda3\lib\site-packages\rpy2\rinterface_lib\conversion.py", line 130, in _cchar_to_str_with_maxlen
s = ffi.string(c, maxlen).decode('utf-8')
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa1 in position 0: invalid start byte
R[write to console]: -- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
| Results |
workshop gender q1 q2 q3 q4
1 1 f 1 1 5 1
2 2 f 2 1 4 1
3 1 f 2 2 4 3
4 2 f 3 1 NA 3
5 1 m 4 5 2 4
6 2 m 5 4 5 5
7 1 m 5 3 4 4
8 2 m 4 5 5 5
| R Programming |
%%R
withmooc[1:4,2:6]
| Results |
gender q1 q2 q3 q4
1 f 1 1 5 1
2 f 2 1 4 1
3 f 2 2 4 3
4 f 3 1 NA 3
- 성별이 남성인 관측치 중에서 선택된 변수로 데이터 구성.
| R Programming |
%%R
print( mydata[gender=="f", c("gender","q1","q2","q3","q4")] )
| Results |
gender q1 q2 q3 q4
1 f 1 1 5 1
2 f 2 1 4 1
3 f 2 2 4 3
4 f 3 1 NA 3
| R Programming |
%%R
subset(mydata,subset=gender=="f",select=gender:q4)
| Results |
gender q1 q2 q3 q4
1 f 1 1 5 1
2 f 2 1 4 1
3 f 2 2 4 3
4 f 3 1 NA 3
5. R - Tidyverse
| R Programming |
%%R
library(tidyverse)
load(file="c://work/data/mydata.Rdata")
withmooc = mydata
attach(withmooc) # mydata를 기본 데이터 세트로 지정.
withmooc
| Results |
R[write to console]: The following objects are masked from withmooc (pos = 3):
gender, q1, q2, q3, q4, workshop
| Results |
workshop gender q1 q2 q3 q4
1 1 f 1 1 5 1
2 2 f 2 1 4 1
3 1 f 2 2 4 3
4 2 f 3 1 NA 3
5 1 m 4 5 2 4
6 2 m 5 4 5 5
7 1 m 5 3 4 4
8 2 m 4 5 5 5
| R Programming |
%%R
withmooc %>%
dplyr::slice(1:4) %>%
dplyr::select(2:6)
| Results |
gender q1 q2 q3 q4
1 f 1 1 5 1
2 f 2 1 4 1
3 f 2 2 4 3
4 f 3 1 NA 3
| R Programming |
%%R
myMales <- withmooc %>%
dplyr::filter(gender == "f") %>%
dplyr::select(2:6)
print(myMales)
| Results |
gender q1 q2 q3 q4
1 f 1 1 5 1
2 f 2 1 4 1
3 f 2 2 4 3
4 f 3 1 NA 3
| R Programming |
%%R
withmooc %>%
dplyr::filter(gender=="f") %>%
dplyr::select("gender","q1","q2","q3","q4")
| Results |
gender q1 q2 q3 q4
1 f 1 1 5 1
2 f 2 1 4 1
3 f 2 2 4 3
4 f 3 1 NA 3
| R Programming |
%%R
withmooc[gender=="f", c("gender","q1","q2","q3","q4")]
| Results |
gender q1 q2 q3 q4
1 f 1 1 5 1
2 f 2 1 4 1
3 f 2 2 4 3
4 f 3 1 NA 3
6. Python - Pandas
| Python Programming |
import pandas as pd
import numpy as np
import sweetviz as sv
mydata = pd.read_csv("C:/work/data/mydata.csv",sep=",",
dtype={'id':object,'workshop':object,
'q1':int, 'q2':int, 'q3':float, 'q4':int},
na_values=['NaN'],skipinitialspace =True)
mydata =mydata.drop('id',axis=1)
withmooc= mydata.copy()
withmooc
| Results |
workshop gender q1 q2 q3 q4
0 1 f 1 1 5.0 1
1 2 f 2 1 4.0 1
2 1 f 2 2 4.0 3
3 2 f 3 1 NaN 3
4 1 m 4 5 2.0 4
5 2 m 5 4 5.0 5
6 1 m 5 3 4.0 4
7 2 m 4 5 5.0 5
- 성별이 남성인 관측치 중에서 선택된 변수로 데이터 구성.
| Python Programming |
withmooc.iloc[0:4,1:6]
# 동일한 결과를 산출하는 프로그램.
withmooc.loc[mydata.gender=="f", ["gender","q1","q2","q3","q4"]]
| Results |
gender q1 q2 q3 q4
0 f 1 1 5.0 1
1 f 2 1 4.0 1
2 f 2 2 4.0 3
3 f 3 1 NaN 3
7. Python - dfply
| Python Programming |
import pandas as pd
from dfply import *
mydata = pd.read_csv("C:/work/data/mydata.csv",sep=",",
dtype={'id':object,'workshop':object,
'q1':int, 'q2':int, 'q3':float, 'q4':int},
na_values=['NaN'],skipinitialspace =True)
mydata =mydata.drop('id',axis=1)
withmooc= mydata.copy()
withmooc
| Results |
workshop gender q1 q2 q3 q4
0 1 f 1 1 5.0 1
1 2 f 2 1 4.0 1
2 1 f 2 2 4.0 3
3 2 f 3 1 NaN 3
4 1 m 4 5 2.0 4
5 2 m 5 4 5.0 5
6 1 m 5 3 4.0 4
7 2 m 4 5 5.0 5
| Python Programming |
withmooc >> row_slice([0,1,2,3]) >> select(X.gender, X.q1, X.q2, X.q3,X.q4)
| Results |
gender q1 q2 q3 q4
0 f 1 1 5.0 1
1 f 2 1 4.0 1
2 f 2 2 4.0 3
3 f 3 1 NaN 3
| Python Programming |
withmooc >> row_slice((X.gender=="f")) >> select(1,2,3,4,5)
| Results |
gender q1 q2 q3 q4
0 f 1 1 5.0 1
1 f 2 1 4.0 1
2 f 2 2 4.0 3
3 f 3 1 NaN 3
| 통계프로그램 비교 목록(Proc sql, SAS, SPSS, R 프로그래밍, R Tidyverse, Python Pandas, Python Dfply) |
| [Oracle, Pandas, R Prog, Dplyr, Sqldf, Pandasql, Data.Table] 오라클 함수와 R & Python 비교 사전 목록 링크 |
| [SQL, Pandas, R Prog, Dplyr, SQLDF, PANDASQL, DATA.TABLE] SQL EMP 예제로 만나는 테이블 데이터 처리 방법 리스트 링크 |
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