Context

To make this a seamless process, I cleaned the data and delete many variables that I thought were not important to our dataset. I then uploaded all of those files to Kaggle for each of you to download. The rideshare_data has both lyft and uber but it is still a cleaned version from the dataset we downloaded from Kaggle.

Use of Data Files

You can easily subset the data into the car types that you will be modeling by first loading the csv into R, here is the code for how you do this:

This loads the file into R

df<-read.csv('uber.csv')

The next codes is to subset the data into specific car types. The example below only has Uber 'Black' car types.

df_black<-subset(uber_df, uber_df$name == 'Black')

This next portion of code will be to load it into R. First, we must write this dataframe into a csv file on our computer in order to load it into R.

write.csv(df_black, "nameofthefileyouwanttosaveas.csv")

The file will appear in you working directory. If you are not familiar with your working directory. Run this code:

getwd()

The output will be the file path to your working directory. You will find the file you just created in that folder.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This scoping report is prepared by Ever Green Tech Environmental Services and Training Co., Ltd for Myanmar Pongpipat Co., Ltd., for EIA of Heinda Mine in Heinda Village, Myitta Township, Tanintharyi Region. Myanmar Pongpipat Co., Ltd. Will operate surface mining for tin in Heinda Mine. The project area is over 809.37 hectares and the total investment is USD 230 million. The main purpose of this scoping is to focus the environmental assessment on a manageable number of important questions.

Mercury emissions from artisanal and small-scale gold mining throughout the Global South exceed coal combustion as the largest global source of mercury. We examined mercury deposition and storage in an area of the Peruvian Amazon heavily impacted by artisanal gold mining. Intact forests in the Peruvian Amazon near gold mining receive extremely high inputs of mercury and experience elevated total mercury and methylmercury in the atmosphere, canopy foliage, and soils. Here we show for the first time that an intact forest canopy near artisanal gold mining intercepts large amounts of particulate and gaseous mercury, at a rate proportional with total leaf area. We document substantial mercury accumulation in soils, biomass, and resident songbirds in some of the Amazon’s most protected and biodiverse areas, raising important questions about how mercury pollution may constrain modern and future conservation efforts in these tropical ecosystems.

this graph was created in OurDataWorld:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F14b9b7c99a81a4feb818b5b05a69ea91%2Fgraph1.png?generation=1726527845157283&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F5b84706cee4dbd1d10497b65779e6f9b%2Fgraph2.png?generation=1726527851090215&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F1d4c7ef62aad10b95e91e273832eb07b%2Fgraph3.png?generation=1726527857902757&alt=media" alt="">

Metals and minerals have played a crucial role in building the modern world. These materials are essential in construction and manufacturing, from buildings and bridges to cars and electronics.

Critical minerals like lithium, copper, and cobalt will play an increasingly important role in the energy transition as countries move away from fossil fuels towards clean energy.

This raises important questions about whether the world has enough of these minerals to power the energy transition, the environmental impacts of mining, and socioeconomic issues such as working conditions in supply chains.

On this page, you find our data, charts, and writing related to metals and minerals. It gives an overview of global statistics on crucial minerals: which countries have these resources, where they are mined and refined, and how they’re traded across the world.

If we want to build a low-carbon economy, we'll need to mine a lot of different minerals. To build solar panels, we’ll need silicon, nickel, silver, and manganese. We’ll need iron and steel for wind turbines, uranium for nuclear power, and lithium and graphite for batteries.1

This raises the concern that a move to clean energy might drive a huge increase in global mining.

It looks this way if you only look at the mining requirements of a low-carbon energy system in isolation. We’ll indeed need to dig out tens to hundreds of millions of tonnes of minerals every year for decades.

But zero mining is not the right baseline to compare it to. The relevant comparison is what we already mine for our current fossil fuel system. The alternative to low-carbon energy is not a zero-energy economy: it’s maintaining the status quo of a system powered mostly by fossil fuels.

When we run the numbers, we find that moving to renewables or nuclear power actually reduces the material requirements for electricity.

Let’s take a look at the data.