03-data-manipulation-exercises.Rmd

---
title: "Data Manipulation Exercises"
author: "Joscelin Rocha Hidalgo"
output: 
    html_document:
        css: slides/style.css
        toc: true
        toc_depth: 1
        toc_float: true
        df_print: paged
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Load Packages

Load the `tidyverse` package and the `ggplot2` package.

```{r}

```

# Import data: chds6162_data

Import the chds6162_data to a dataframe called `data`. 

```{r}

```



# select

![](slides/images/select.png)
![](https://swcarpentry.github.io/r-novice-gapminder/fig/13-dplyr-fig1.png)

Use `select` to show just the `marital` variable.

```{r}


```



`select` for multiple variables:

Use `select` to show `marital` and `ed` variables.

```{r}


```

Use  `select`  for a range of columns. 

`select` all the variables from `wt` to the end.


```{r}


```

Drop the `race` variable.


```{r}


```

Drop the variables that belonged to the father from `drace` to `dwt`

```{r}


```

# mutate

![](slides/images/mutate.png)
![art by @allison_horst](slides/images/dplyr_mutate.png)
art by @allison_horst


Create a **new variable with a specific value**

Create a new variable called `country` and fill that variable with "US".

```{r}


```


Create a **new variable based on other variables**

Create a new variable called `gestation_weeks` and calculate gestation length in weeks rather than days (try rounding this number to only 2 decimals). Remember that the `gestation` variable is a measure of the **gestation length** in days. Then `select` both variables. 

```{r}


```

Change an **existing variable**

Convert the `dwt` variable to kilos by dividing by 2.205 (it's in pounds now). Then, use `select` to show only the `dwt` variable.

```{r}


```

# case_when

![art by @allison_horst](slides/images/dplyr_case_when.png)
art by @allison_horst

Using mutate and case_when, let's create a variable called `age_group`. In this variable we will use the mom's age variable and recode it so all moms whose age are now "20s", "30s", and so on. Then select the `id` and the new variable

*tip= Wanna know the range of your sample age? Use `range(dataframe$variable,na.rm = TRUE)`

** Use `%in%` (you just learned it 😉)

```{r}



```


# filter
![](slides/images/filter.png)
![art by @allison_horst](https://raw.githubusercontent.com/allisonhorst/stats-illustrations/master/rstats-artwork/dplyr_filter.jpg)
art by @allison_horst

We use `filter` to choose a subset of cases.

Use `filter` to keep only respondents who are divorced (`marital` = 3). 

```{r}


```

Use `filter` to keep only respondents who are **not** divorced. 

```{r}



```

Use `%in%` within the `filter` function to keep only those whose mothers' race was reported as white. In this dataset, values from 0 to 5 represent "white" under the `race`variable.

```{r}



```

Create a chain that keeps only those are college grads (value of 5). Then, `filter` to keep only those who are not married. Finally, use `select` to show only the `ed` and `marital` variables.

```{r}



```


# summarize or summarise

![](slides/images/summarize.png)


Get the mean, median, min, and max weight for the fathers `dwt`.

```{r}



```


# group_by

![](slides/images/group-by.png)
![art by @allison_horst](slides/images/group_by_ungroup.png)
art by @allison_horst

Calculate the mean weight for fathers based on their education levels (`ded`variable has some NAs, don't forget to remove them!)
 with `na.rm=`)
 
```{r}



```
# across
![art by @allison_horst](slides/images/dplyr_across.png)
art by @allison_horst

We messed up and we found out that out collaborators need the mean of all the variables for dad by their race category (dads'). 

Use `across` to get them all!
* Don't forget to remove NAs otherwise it will yell at you!

```{r}



```


# Big exercise

A new data frame called `mothers_below30` that:

1. Uses `filter` to only include those age younger than 30
2. Uses `mutate` to transform the `gestation` variable into weeks rather than days
3. Uses `group_by` to create mom education groups
4. Uses `summarise` to calculate a new variable called `mean_gestation_w`
5. Make sure to export it as a csv!

+ Once you have that data frame just move the variables to a different position using "relocate"

```{r}





```