03-data-manipulation-solutions.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}
library(tidyverse)
library(ggplot2)
```

# Import data: chds6162_data

Import the chds6162_data to a dataframe called `data`. 

```{r}
data <- read_csv("data/chds6162_data.csv")
```



# 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}
data %>% 
    select(marital)
```

`select` for multiple variables:

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

```{r}
data %>% 
    select(marital,ed)
```

Use  `select`  for a range of columns. 

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

```{r}
# option one:

data %>% 
    select(wt:number)

# option 2:

data %>% 
    select(wt:last_col())
```

Drop the `race` variable.

```{r}
data %>% 
    select(-race)
```

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

```{r}

data %>% 
    select(-(drace:dwt))

```

# 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}
data %>% 
    mutate(country = "US") 
```


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}
data %>% 
    mutate(gestation_weeks = round(gestation / 7,2)) %>% 
    select(gestation, gestation_weeks)
```

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}
data %>% 
    mutate(dwt = dwt / 2.205) %>% 
    select(dwt)
```

# 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}

range(data$age,na.rm = TRUE)

data %>% 
  mutate(age_group = case_when(
    age %in% 10:19 ~ "10s",
    age %in% 20:29 ~ "20s",
    age %in% 30:39 ~ "30s",
    age %in% 40:49 ~ "40s",
    age %in% 50:59 ~ "50s"
  ))

```


# 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}
data %>% 
    filter(marital == 3) %>% 
    select(marital)
```

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

```{r}
data %>% 
    filter(marital != 3) %>% 
    select(marital)
```

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}
data %>% 
    filter(race %in% c(0:5))
```

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

```{r}
data %>% 
    filter(ed == 5) %>% 
    filter(marital != 5) %>% 
    select(ed, marital)
```


# summarize or summarise

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

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

```{r}
data %>% 
    summarize(mean_dad_weight = mean(dwt,na.rm = TRUE),
              median_dad_weight = median(dwt, na.rm = TRUE),
              min_dad_weight = min(dwt, na.rm = TRUE),
              max_dad_weight = max(dwt, na.rm = TRUE))
```

# 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}
data %>% 
    group_by(ded) %>% 
    drop_na(ded) %>%
    summarize(dad_weight = mean(dwt,
                                na.rm = TRUE))
```
# 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}
data %>% 
  drop_na(drace) %>%
  group_by(drace) %>%
  summarize(across(dage:dwt,mean,na.rm = TRUE))
```


# 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 of the variables to a different position using "relocate"

```{r}
mothers_below30 <- data %>% 
    filter(age < 30) %>% 
    mutate(gestation_w = gestation/7) %>%
    group_by(ed) %>% 
    summarize(mean_gestation_w = mean(gestation_w,na.rm = TRUE))
write_csv(mothers_below30,"exports/moms_under30.csv")
```


With Bonus:

```{r}
mothers_below30 <- data %>% 
    filter(age < 30) %>% 
    mutate(gestation_w = gestation/7) %>%
    group_by(ed) %>% 
    summarize(mean_gestation_w = mean(gestation_w,na.rm = TRUE)) %>%
    relocate (mean_gestation_w, .after=ed)
write_csv(mothers_below30,"exports/moms_under30.csv")
```