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ehumphehumph
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update dataset
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‎data/Wastewater_covid_level_data.csv

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Date,Viral Activity Level
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1/1/2022,16.86
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1/8/2022,23.77
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1/15/2022,23.11
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1/22/2022,19.33
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1/29/2022,14.04
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2/5/2022,9.19
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2/12/2022,5.83
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2/19/2022,3.72
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2/26/2022,2.47
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3/5/2022,1.62
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3/12/2022,1.35
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3/19/2022,1.13
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3/26/2022,1.23
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4/2/2022,1.17
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4/9/2022,1.53
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4/16/2022,2.02
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4/23/2022,3.05
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4/30/2022,3.87
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5/7/2022,4.97
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5/14/2022,5.56
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5/21/2022,6.61
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5/28/2022,7.15
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6/4/2022,7.01
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6/11/2022,6.73
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6/18/2022,6.63
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6/25/2022,7.11
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7/2/2022,7.81
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7/9/2022,9.15
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7/16/2022,8.72
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7/23/2022,9.32
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7/30/2022,8.8
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8/6/2022,7.89
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8/13/2022,7.27
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8/20/2022,6.79
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8/27/2022,6.37
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9/3/2022,5.69
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9/10/2022,5.45
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9/17/2022,5.01
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9/24/2022,4.52
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10/1/2022,3.71
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10/8/2022,3.33
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10/15/2022,3.32
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10/22/2022,3.32
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10/29/2022,3.91
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11/5/2022,4
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11/12/2022,4
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11/19/2022,4.52
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11/26/2022,5.28
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12/3/2022,8.34
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12/10/2022,8.4
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12/17/2022,8.91
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12/24/2022,9.38
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12/31/2022,10.52
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1/7/2023,8.21
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1/14/2023,5.93
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1/21/2023,5.59
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1/28/2023,5.77
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2/4/2023,6.13
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2/11/2023,6.07
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2/18/2023,6.24
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2/25/2023,5.79
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3/4/2023,5.13
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3/11/2023,4.37
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3/18/2023,4.54
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3/25/2023,4.53
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4/1/2023,3.42
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4/8/2023,2.9
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4/15/2023,2.71
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4/22/2023,2.12
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4/29/2023,1.86
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5/6/2023,1.98
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5/13/2023,1.93
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5/20/2023,1.66
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5/27/2023,1.53
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6/3/2023,1.27
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6/10/2023,1.12
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6/17/2023,1.14
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6/24/2023,1.13
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7/1/2023,1.3
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7/8/2023,1.52
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7/15/2023,1.67
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7/22/2023,1.95
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7/29/2023,2.41
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8/5/2023,2.82
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8/12/2023,3.67
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8/19/2023,3.95
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8/26/2023,4.42
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9/2/2023,5.08
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9/9/2023,5.61
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9/16/2023,5.1
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9/23/2023,4.69
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9/30/2023,4.46
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10/7/2023,3.78
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10/14/2023,3.31
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10/21/2023,3.38
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10/28/2023,3.53
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11/4/2023,3.67
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11/11/2023,4.06
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11/18/2023,4.73
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11/25/2023,5.31
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12/2/2023,6.95
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12/9/2023,7.67
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12/16/2023,8.95
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12/23/2023,10.61
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12/30/2023,12.66
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1/6/2024,11.67
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1/13/2024,9.06
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1/20/2024,7.48
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1/27/2024,7.17
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2/3/2024,6.05
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2/10/2024,6
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2/17/2024,5.71
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2/24/2024,5.39
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3/2/2024,4.52
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3/9/2024,3.55
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3/16/2024,2.65
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3/23/2024,2.21
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3/30/2024,2.04
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4/6/2024,1.86
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4/13/2024,1.57
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4/20/2024,1.38
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4/27/2024,1.23
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5/4/2024,1.16

‎modules/Data_Classes/lab/Data_Classes_Lab.Rmd

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library(tidyverse)
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library(dplyr)
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library(lubridate)
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library(jhur)
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library(dasehr)
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```
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Create some data to work with by running the following code chunk.
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### 1.6
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Read in the Charm City Circulator data using `read_circulator()` function from `jhur` package using the code supplied in the chunk. Or alternatively using the url link.
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Read in the Wastewater Covid Viral Load data from `dasehr` package using the code supplied in the chunk. Alternatively using the url link.
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```{r}
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circ <- read_circulator()
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circ <- read_csv(file = "https://daseh.org/data/Charm_City_Circulator_Ridership.csv")
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#wwviral <-
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wwviral <- read_csv(file = "https://daseh.org/data/Wastewater_covid_level_data.csv")
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```
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Use the `str()` function to take a look at the data and learn about the column types.
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### 1.7
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Use the `mutate()` function to create a new column named `date_formatted` that is of `Date` class. The new variable is created from `date` column. Hint: use `mdy()` function. Reassign to `circ`.
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Use the `mutate()` function to create a new column named `date_formatted` that is of `Date` class. The new variable is created from `date` column. Hint: use `mdy()` function. Reassign to `wwviral`.
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```
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# General format

‎modules/Data_Classes/lab/Data_Classes_Lab_Key.Rmd

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library(tidyverse)
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library(dplyr)
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library(lubridate)
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library(jhur)
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library(dasehr)
2020
```
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Create some data to work with by running the following code chunk.
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### 1.6
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Read in the Charm City Circulator data using `read_circulator()` function from `jhur` package using the code supplied in the chunk. Or alternatively using the url link.
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Read in the Wastewater Covid Viral Load data from `dasehr` package using the code supplied in the chunk. Alternatively using the url link.
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```{r}
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circ <- read_circulator()
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circ <- read_csv(file = "https://daseh.org/data/Charm_City_Circulator_Ridership.csv")
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#wwviral <-
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wwviral <- read_csv(file = "https://daseh.org/data/Wastewater_covid_level_data.csv")
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```
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Use the `str()` function to take a look at the data and learn about the column types.
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```{r 1.6response}
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str(circ)
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str(wwviral)
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```
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### 1.7
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Use the `mutate()` function to create a new column named `date_formatted` that is of `Date` class. The new variable is created from `date` column. Hint: use `mdy()` function. Reassign to `circ`.
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Use the `mutate()` function to create a new column named `date_formatted` that is of `Date` class. The new variable is created from `date` column. Hint: use `mdy()` function. Reassign to `wwviral`.
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```
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# General format
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NEWDATA <- OLD_DATA %>% mutate(NEW_COLUMN = OLD_COLUMN)
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```
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```{r 7response}
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circ <- mutate(circ, date_formatted = mdy(date))
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wwviral <- mutate(wwviral, date_formatted = mdy(date))
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```
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```
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```{r P.1response}
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circ <- circ %>% relocate(date_formatted, .before = date)
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wwviral <- wwviral %>% relocate(date_formatted, .before = Date)
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# alternative
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# circ <- circ %>% select(day, date_formatted, everything()) %>% head()
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# wwviral <- wwviral %>% select(day, date_formatted, everything()) %>% head()
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glimpse(circ)
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glimpse(wwviral)
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```
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Use `range()` function on `date_formatted` variable to display the range of dates in the data set. How does this compare to that of `date`? Why? (Hint: use the pull function first to pull the values.)
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```{r P.2response}
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pull(circ, date_formatted) %>% range()
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pull(circ, date) %>% range()
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pull(wwviral, date_formatted) %>% range()
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pull(wwviral, date) %>% range()
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# The max of `pull(circ, date) %>% range()` is numerical not based on date.
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# The max of `pull(wwviral, date) %>% range()` is numerical not based on date.
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```

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