秒答网

 找回密码
 注册秒答

QQ登录

只需一步,快速开始

搜索
热搜: 价格查询
查看: 61|回复: 4
收起左侧

[2k以上] 2000,R语言代做

[复制链接]

9万

智力

4068

体力

10万

品德

管理员

博士

Rank: 9Rank: 9Rank: 9

QQ
这个不是很难 数据待会儿发给你
1. The housework consists of 6 tasks. A maximum of 90 points can be achieved. Solve all problems in the programming language R.
2. A binding registration for the event (i.e., a pseudo-acquisition) takes place with handing over the housework.
3. Group taxes are not allowed. Your contribution must have been processed by you alone. If Unterschleif is suspected, a report is sent to the Examination Office for Statistics. The affected
Levies can be assessed as failed.
4. Submission of the paperwork takes place via the Moodle website of the course. It must be both
a .Rmd file as well as a PDF file created from the submitted .Rmd file. In the absence of any of the files, the term paper is considered failed and will not be corrected. It follows that your submitted .Rmd file must be compilable.
5. Use the template provided by us on the Moodle website for your .Rmd file.
6. First enter your full name and matriculation number in the
.Rmd template provided in the header of the file.
7. Name your submission files according to the following scheme: statsoft_firstname_surname.rmd or statsoft_firstname_surname.pdf
8. The processing period is 26.07.2019 - 23.08.2019.
9. Last possible deadline: 23.08.2019 - 23:00 CEST (Central European Summer Time).
10. Only use commands from the basic package, unless an additional package was explicitly requested in the task. If you do not require additional packages, you can not get any points
be forgiven.
11. In the PDF output of your .Rmd file, both code and output must be visible for all tasks
his.
12. Write only the code required in the claim. Running additional
Commands such as not requiring viewing of objects in the R console results in confusing
PDF output and makes the correction more difficult. Failure to observe a deduction is possible.
13. Be sure to write legible and concise code.
14. Comment out your code. Pay attention to clear and succinct phrases and put yours
Comments behind a diamond sign (#) to the corresponding places in the code.
1

In this paper, a partial dataset of a German bank on the risk of lending is to be examined. The variables of the data record have the following meaning:
variable
checking_status
duration
credit_amount installment_commitment personal_status residence_since
age num_dependents investments
class
description
Credit balance of existing current account in EUR loan period in months
Loan amount in EUR or USD
Installment as a percentage of disposable income Personal status and gender
Current resident for X years
Age in years
Number of related persons
Long-term investments such as stocks or bonds in EUR Good or bad credit
Task 1: Import data (4 points)
Read the credit_data.csv file from your working directory into R, i. the data should be in the same directory as your .Rmd file. The column names should already be named correctly after importing. Check the data type of the object. It should correspond to a DataFrame. Next, populate the structure of the data in the R console. So do not look at the record itself.
Task 2: Data preparation
Exercise 2.1: Identifying columns with missing values (2 points)
Some values of the data are missing. Using a command of your choice, create a 10-length vector that has a value of TRUE for each column of missing values and a value of FALSE for each column that has no missing values. Look at the vector in the R console.
Exercise 2.2: Deleting observations with missing values (3 points)
Only delete those observations that contain missing values. Then check the dimension of the data (number of rows and columns). The dataset should now consist of exactly 900 rows and 10 columns.
Exercise 2.3: Strings - Creating Gender Variables (4 points)
The personal_status variable contains a combined gender and marital status string. Use this information to add to the DataFrame a gender variable that contains the gender of a person. Then output a table that shows the number of observations per factor
(female and male) of the new variable gender.
Exercise 2.4: Strings - Currency Conversion (5 points)
The variable credit_amount was added in different currencies (EUR and USD) and coded as a factor variable. Transform credit_amount into a numeric variable where all observations are made
来自QQ,兮_。
回复 来自安卓客户端来自安卓客户端

使用道具 举报

9万

智力

4068

体力

10万

品德

管理员

博士

Rank: 9Rank: 9Rank: 9

QQ
 楼主| 合作共赢 发表于 7 天前 来自手机 | 显示全部楼层
回复

使用道具 举报

9万

智力

4068

体力

10万

品德

管理员

博士

Rank: 9Rank: 9Rank: 9

QQ
 楼主| 合作共赢 发表于 7 天前 | 显示全部楼层
法国
回复 来自安卓客户端来自安卓客户端

使用道具 举报

9万

智力

4068

体力

10万

品德

管理员

博士

Rank: 9Rank: 9Rank: 9

QQ
 楼主| 合作共赢 发表于 7 天前 | 显示全部楼层
宏运matlab python stata r博士在读15
回复 来自安卓客户端来自安卓客户端

使用道具 举报

9万

智力

4068

体力

10万

品德

管理员

博士

Rank: 9Rank: 9Rank: 9

QQ
 楼主| 合作共赢 发表于 7 天前 | 显示全部楼层
Derrick
回复 来自安卓客户端来自安卓客户端

使用道具 举报

您需要登录后才可以回帖 登录 | 注册秒答

本版积分规则

QQ|价格查询|地图|秒答网 ( 粤ICP备15056337号-1 )

GMT+8, 2019-8-19 22:37

Powered by Discuz! X3.4

© 2001-2017 Comsenz Inc.

快速回复 返回顶部 返回列表