# Environmental Data Analysis & Visualization

Into the tidyverse

## Warm-up activity

• Create a file system for this week and add a new Quarto document for today’s lecture.

• Have a look at sparts and income16 variables: what might these different values represent?

• Go to the links on Canvas and take a look at the metadata for each variable. Can you think of a way to modify the data to get a meaningful plot out of it?

## Sensor Dataset of the day

General Social Survey

https://gss.norc.org/

## Data

Data are individual pieces of information.

## Messy data

The process of data entry is often designed and conducted with limited consideration given to the process of data analysis.

https://openscapes.org/blog/2020-10-12-tidy-data/

## Tidy data principles

Each variable forms a column

Each observation forms a row

Each cell contains a single value

After Wickham, H. 2014. Tidy data. The Journal of Statistical Software 59.

## Activity: Making a mess of data

With the sticky notes you’ve been given, complete the data table that’s been started on the door to the data lab. Use Google Maps or similar to estimate transportation times.

friendsofthefells.org

## Exploring the data

Compare university year to drive time.

## Exploring the data

Compare university year to drive time.

## Exploring the data

Compare university year to drive time.

## Exploring the survey data

Compare university year to public transit time.

## Exploring the survey data

Compare university year to public transit time.

## Exploring the survey data

Compare mode of transportation to time spent in transit.

## Exploring the survey data

Compare mode of transportation to time spent in transit.

## Data wrangling: the big three

Column headers are values, not variable names.

## Data wrangling: the big three

Multiple variables are stored in a single column.

## Data wrangling: the big three

Variables stored in both rows and columns.

## Into the tidyverse

library(tidyverse)

## Data wrangling in the tidyverse

epirhandbook.com/en/pivoting-data.html

## Data wrangling in the tidyverse

fellsData<-read_csv("data/fakeFellsData.csv")
fellsData
# A tibble: 64 × 4
universityYear walkTime driveTime ptTime
<chr>             <dbl>     <dbl>  <dbl>
1 first                65        12     37
2 first                60        12     51
3 first                66        10     33
4 first                58        14     60
5 first                70        12     44
6 first                62        11     40
7 first                62        11     52
8 first                57         9     60
9 first                66        11     50
10 first                66        11     55
# ℹ 54 more rows

## Data wrangling in the tidyverse

fellsData2<-pivot_longer(
data=fellsData,
cols=walkTime:ptTime,
names_to="method",
values_to="time"
)
fellsData2
# A tibble: 192 × 3
universityYear method     time
<chr>          <chr>     <dbl>
1 first          walkTime     65
2 first          driveTime    12
3 first          ptTime       37
4 first          walkTime     60
5 first          driveTime    12
6 first          ptTime       51
7 first          walkTime     66
8 first          driveTime    10
9 first          ptTime       33
10 first          walkTime     58
# ℹ 182 more rows

## Data wrangling in the tidyverse

fellsData2<-pivot_longer(
data=fellsData,
cols=walkTime:ptTime,
names_to="method",
values_to="time",
names_pattern = "(.*)Time"
)
fellsData2
# A tibble: 192 × 3
universityYear method  time
<chr>          <chr>  <dbl>
1 first          walk      65
2 first          drive     12
3 first          pt        37
4 first          walk      60
5 first          drive     12
6 first          pt        51
7 first          walk      66
8 first          drive     10
9 first          pt        33
10 first          walk      58
# ℹ 182 more rows

## Data wrangling in the tidyverse

ggplot(data=fellsData2,aes(x=method,y=time))+
geom_boxplot()

## Summing up

• Data messiness can arise in a number of ways from a variety of sources, but will inevitably slow the process

• Data that follows tidy principles enables vectorization, querying, consistency, and ease of re-use

• As data scientists, we need to develop practices for quickly manipulating and transforming datasets to be more amenable to analysis and visualization