---
# System prepended metadata

title: List of remote data
tags: [bym4102]

---

---
tags: bym4102
---

# List of remote data

This page is accessible at [HackMD page](https://hackmd.io/Vn4OaR4NTNmTEusPIOPREA?both)

## me

* [Traffic data, Istanbul, January 2020](https://data.ibb.gov.tr/dataset/3ee6d744-5da2-40c8-9cd6-0e3e41f1928f/resource/db9c7fb3-e7f9-435a-92f4-1b917e357821/download/traffic_density_202001.csv)
* [Meteorolgy data, Istanbul, January 2020](https://data.ibb.gov.tr/dataset/94197af2-67f4-428a-a031-432e1848cc59/resource/8ca0081a-333c-4e86-8ea2-ab8741bbfc95/download/meteorology_observation_202001.csv)


## by Elif Nur Yıldız

* [CDC15 yeast gene expression data set of Spellman et al](http://www.exploredata.net/ftp/Spellman.csv)
* [Microbiome dataset of Turnbaugh et al](http://www.exploredata.net/ftp/MicrobiomeWithMetadata.csv)
  * (Diet, Source, Donor, Collection Met, Sex, OTU 0-...)  (675 satır, 6701 sütun)
* [Set of social, economic, health, and political indicators using data from WHO](http://www.exploredata.net/ftp/WHO.csv)
  * (Country,Country ID,Continent,Adolescent fertility rate (%),Adult literacy rate (%),Gross national income per capita (PPP international $),Net primary school enrolment ratio female (%),Net primary school enrolment ratio male (%),Population (in thousands) total,Population annual growth rate (%),Population in urban areas (%),Population living below the poverty line (%))
* [DNAm age and age acceleration](https://static-content.springer.com/esm/art%3A10.1186%2Fgb-2013-14-10-r115/MediaObjects/13059_2013_3156_MOESM19_ESM.csv)
  * (Cell Line, Age, DNAm Age, Cell Line1, Tissue, Short Cell Line, CCLE name, Cell line primary name, Cell line aliases, Site Primary, Histology, Age Acceleration)

## by Furkan Zahid Tunç
* [CDC15 yeast gene expression data set](http://www.exploredata.net/ftp/Spellman.csv) 
  * gen ismi, 40-250 arası zaman
* [RNA tissue expression](https://v15.proteinatlas.org/download/rna_tissue.csv.zip)
  * ("Gene","Gene name","Sample","Value","Unit","Abundance")  
* [subcellular locations of proteins](https://v15.proteinatlas.org/download/subcellular_location.csv.zip)
  * (Gen ismi, ekspresyon lokasyoları ve ekspresyon tipi )
* [RNA transcription in cellline](https://v15.proteinatlas.org/download/transcript_rna_celline.tsv.zip)
  * (cellline ismi ve transkripsiyon düzeyleri)
* [normal tissue protein expression](https://v15.proteinatlas.org/download/normal_tissue.csv.zip)
  * (gen ismi, dokular ve güvenilirlik)
 
## by Maide Canyakar

* [Bitcoin fiyat tahmini](https://storage.googleapis.com/kagglesdsdata/datasets/1927/3320/bitcoin_price_1week_Test%20-%20Test.csv?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210401%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210401T065526Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
* [US covid19](https://www.kaggle.com/fireballbyedimyrnmom/us-counties-covid-19-dataset)
* [IMDB](https://www.kaggle.com/columbine/imdb-dataset-sentiment-analysis-in-csv-format)
* [Restoran müşteri yorumu](https://www.kaggle.com/vigneshwarsofficial/reviews)
* [Foursquare checkins](https://www.kaggle.com/chetanism/foursquare-nyc-and-tokyo-checkin-dataset)


## by Furkan Akif Aladağ
* [GDP](https://pkgstore.datahub.io/core/gdp/gdp_csv/data/0048bc8f6228d0393d41cac4b663b90f/gdp_csv.csv)
* [Restaurant Business Rankings 2020](https://www.kaggle.com/team-ai/bitcoin-price-prediction)
* [Dummy Marketing and Sales Data](https://storage.googleapis.com/kagglesdsdata/datasets/1206038/2015190/Dummy%20Data%20HSS.csv?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210401%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210401T065955Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
* [Red Wine Quality](https://storage.googleapis.com/kagglesdsdata/datasets/4458/8204/winequality-red.csv?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210401%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210401T065919Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
* [Anime Recommendation Database 2020](https://storage.googleapis.com/kaggle-data-sets/1225408/2048259/compressed/anime.csv.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210401%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210401T065826Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
* [Students Performance in Exams](https://storage.googleapis.com/kagglesdsdata/datasets/74977/169835/StudentsPerformance.csv?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210401%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210401T071417Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
  * ("gender","race/ethnicity","parental level of education","lunch","test preparation course","math score","reading score", "writing score") 
  
## by Furkan Kıvanç
* [IMDb Movies](https://www.kaggle.com/stefanoleone992/imdb-extensive-dataset?select=IMDb+movies.csv)
    * ("imdb_title_id", "title", "original_title", "year", "date_published","genre", "duration", "country", "language", "director" )
* [IMDb Ratings](https://www.kaggle.com/stefanoleone992/imdb-extensive-dataset?select=IMDb+ratings.csv)
* [Stroke Prediction](https://www.kaggle.com/fedesoriano/stroke-prediction-dataset?select=healthcare-dataset-stroke-data.csv)
* [Turkey Covid-19 cases](https://www.kaggle.com/gkhan496/covid19-in-turkey?select=covid_19_data_tr.csv)
* [Yotube Trending](https://www.kaggle.com/datasnaek/youtube-new?select=CAvideos.csv)

## by Elif Nur Akın




* [Cigarette Consumption Data](https://vincentarelbundock.github.io/Rdatasets/csv/AER/CigarettesSW.csv)
* [birth and death rates](https://vincentarelbundock.github.io/Rdatasets/csv/HSAUR/birthdeathrates.csv)
* [Brain and body weights of animals](https://vincentarelbundock.github.io/Rdatasets/csv/MASS/Animals.csv)


## Muhammet Gylyjov

* [Student Alcohol Consumption](https://storage.googleapis.com/kagglesdsdata/datasets/251/561/student-mat.csv?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210401%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210401T072516Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
* [students performance in exams](https://storage.googleapis.com/kagglesdsdata/datasets/74977/169835/StudentsPerformance.csv?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210401%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210401T072212Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
* [world happiness report](https://storage.googleapis.com/kagglesdsdata/datasets/894/813759/2015.csv?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210401%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210401T072404Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
* [red wine quality](https://storage.googleapis.com/kagglesdsdata/datasets/4458/8204/winequality-red.csv?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210401%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210401T072438Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
* [Amazon Top 50 Bestselling Books](https://storage.googleapis.com/kagglesdsdata/datasets/918976/1556647/bestsellers%20with%20categories.csv?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210401%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210401T072625Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=24886c433b7af0be9b52471066fce5d9de93e5be9cf98569a5084db751b84d02106a566da151becbfe9120747e64ebc5f30668da12fd1266015bc7287d00d2e4526ffa59217e4295fa06520f35e2b8e5f04d061be86168f9fdffefcc2b0da71f8d79d7d8b09571220ffdab7af4bbb3f9da9f475670a1a2c1326b51095416512c7425bd4fc28ac2803fdab1f2073cf0460a70b537a5764d0c5519b758fd13caf8a44266ccb501a26b16369dbba40b7cfbc6bfbaff2c97f844d85b8910a61480e4fcdc53d0a665dfde9fe593ef142fd39147197bf2c6a29e6c82cb7b40ea0a56062ebdd36dbd9425a5e8250e64a98bcbb64f812d30e8c11be83963084be4421d08)

## by Zekiye Akdağ 
* [Skin Cancer](https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000?select=hmnist_8_8_RGB.csv)
* [Brain Tumor](https://datahub.io/machine-learning/primary-tumor/r/primary-tumor.csv)
* [cRNA](http://crispr-pacman.stanford.edu/minipools/coronaviridae/minipool.tsv)

# Importing data

## by me

Please find the country with highest median age of population

```r
library(tidyverse)

read_csv("http://www.exploredata.net/ftp/WHO.csv") %>% 
  select(Country, `Population median age (years)`) %>% 
  arrange(desc(`Population median age (years)`))
```
* [X] ENY  
* [x] ENA
* [X] FAA 
* [X] FK
* [X] FZT
* [X] MC
* [x] MG


## by ENY

Question: Find the cell lines for each tissue with maximum age acceleration

Solution:
```r
library(tidyverse)
read_csv("https://static-content.springer.com/esm/art%3A10.1186%2Fgb-2013-14-10-r115/MediaObjects/13059_2013_3156_MOESM19_ESM.csv") %>%
select(CellLine, Tissue, AgeAcceleration) %>% 
   group_by(Tissue) %>%
   top_n(1, AgeAcceleration)
```

* [x] ENA
* [x] FAA 
* [X] FK
* [x] FZT
* [x] MC
* [ ] MG
* [x] AY :+1: 

## by ENA

Question: Find the max income value for cigarrete consumption data.

- Solution1: Base R

```r
a <- read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/AER/CigarettesSW.csv")

which.max(a$income)
```

- Solution2: Tidyverse

```r
library(tidyverse)
read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/AER/CigarettesSW.csv") %>%
   top_n(1, income)
```

* [ ] ENY
* [x] FAA 
* [X] FK
* [X] FZT
* [ ] MC: Solution 1 works but 2 gives an error: Missing column names filled in: 'X1' [1] 
* [ ] MG
* [x] AY

## by FAA

Question:
Find the total "profitInMillion" and total "invested" money for the advertisements
Solution:

```r
library(tidyverse)
dummy<-read_csv("https://storage.googleapis.com/kagglesdsdata/datasets/1206038/2015190/Dummy%20Data%20HSS.csv?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210401%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210401T065955Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=8181f3c54e9c4ff33fe3ce3766a3ded92f3c57f4b196154836e46a4de9b73c9a28be484efed5baa0e115aa0d868eb7bde67d355214e90062ffdea82eb42b0f42db7f607546fcdbea6be2b3f676e7d35bbe3eccd7ade0ab84796ee3c9e711f6fe48fb4d94414e8ca41e4619a3297deedcbc978e960c80de086e1770942d7c633dfae7ee6c5a4111757ac8462dbe85d01d7fe355856ee300f0cb73a5996335c69f1d0d041d8a693112d9ece272046aab70fa3b740304e21e970f3ff386b6b645fa3a7546fd170159a5b15c757292412db3b8ac127c1d2470853aaf48fca0f9b2d7026cce3d2500ad04975ee696a3030c5aef8b695ac3f19657693ee42bb4da022c")%>% 
  mutate(profitInMillion=Sales-(`Social Media`+Radio+TV)) %>% 
  mutate(invested=(`Social Media`+Radio+TV)) %>% 
           arrange(desc(profitInMillion))
dummy
```
* [x] ENY
* [ ] ENA
* [X] FK: Error in open.connection(con, "rb") : HTTP error 400. Icouldn't find the solution on RStudio Cloud
* [x] FZT
* [x] MC
* [ ] MG
* [x] AY

## by FK
Question: Find the expected population for each countries which has grater  Adolescent fertility rate (%) than 50 %  

Solution:

```r
read_csv("http://www.exploredata.net/ftp/WHO.csv") %>% 
  #janitor::clean_names() %>% 
  filter (`Adolescent fertility rate (%)` >= 50 )  %>% 
  mutate(expected_pop = `Population (in thousands) total` + (`Population (in thousands) total`) * (`Population annual growth rate (%)` /100 ) ) %>% 
  select(Country, expected_pop)

  ```

* [ ] ENY
* [x] ENA
* [x] FAA
* [x] FZT
* [x] MC
* [X] MG
* [x] AY

## by FZT

Question: Detection of biggest invasiveness age, its location and BRCA1 and A2 test suitability

Solution: 

```r
library(tidyverse)
p <-read_csv("https://pkgstore.datahub.io/machine-learning/breast-cancer/breast-cancer_csv/data/cfade1d9730bf286d3a83abf7d48829d/breast-cancer_csv.csv") %>% 
  select(age, menopause, `deg-malig`, breast) %>% 
  arrange(-`deg-malig`)
malignant <- p$`deg-malig`
p$BRCA1_A2_suitable <- malignant > 1
```
Arkadaşlar tekrardan bakabilirsiniz.

Suggestion by AY, probably you meant:
```r
read_csv("https://pkgstore.datahub.io/machine-learning/breast-cancer/breast-cancer_csv/data/cfade1d9730bf286d3a83abf7d48829d/breast-cancer_csv.csv") %>%
  select(age, menopause, `deg-malig`, breast) %>% 
  filter(`deg-malig` > 1) %>% 
  arrange(-`deg-malig`)
```



* [x] ENY: used AY's suggestion
* [x] ENA
* [x] FAA 
* [X] FK: used the AYs suggestion
* [ ] MC: arranging is not in the right order
* [ ] MG
* [x] AY

## by MC

Question: Sort dates from maximum market cap to minimum

Solution:

```r
library(tidyverse)
read_csv("https://storage.googleapis.com/kagglesdsdata/datasets/1927/3320/bitcoin_price_1week_Test%20-%20Test.csv?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210401%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210401T065526Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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") %>%
    select(Date, `Market Cap`) %>%
    arrange(desc(`Market Cap`))

```
* [x] ENY
* [x] ENA: Error in open.connection(con, “rb”) : HTTP error 400. Icouldn’t find the solution on RStudio Cloud
* [x] FAA 
* [X] FK: Error in open.connection(con, "rb") : HTTP error 400. Icouldn't find the solution on RStudio Cloud
* [X] FZT
* [X] MG
* [x] AY

## by MG

Question: average number of reviews in fiction genre

Solution:

```r
readr::read_csv("https://storage.googleapis.com/kagglesdsdata/datasets/918976/1556647/bestsellers%20with%20categories.csv?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210401%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210401T072625Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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") %>%
  dplyr::group_by(Genre) %>% 
  dplyr::summarise(mean_rev = mean(Reviews))
```
* [x] ENY
* [x] ENA
* [x] FAA 
* [X] FK:Error in open.connection(con, "rb") : HTTP error 400. Icouldn't find the solution on RStudio Cloud
* [X] FZT
* [x] MC
* [x] AY

# Conclusions / reflections

* spaces or other special characters are not pleasent to write in pipe. we need to use special quation mark, back tick (<kbd>AltGr+, </kbd> then *maybe* <kbd>Space</kbd>) *Solution:* use `janitor::clean_names()` instead
* `read_csv` accepts compressed file which is not equivalent to "archive file"
* kaggle links are very loooooong! we might use URL shorteners such as bit.ly
* we learned how to prepare [MWE](https://en.wikipedia.org/wiki/Minimal_working_example)