```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
### Usage and Adaptation of Data Carpentry Materials:
Most material found in this document has been adapted from [Data Carpentry][https://datacarpentry.org/r-socialsci/] materials, under the [creative commons attribution license][https://creativecommons.org/licenses/by/4.0/]. Minor amendments have been made to allow for compatability in order.
### Exercise 0
Go ahead and load in tidyverse as usual.
```{r Tidyverse and Here Loading}
```
-------------
For this workshop, we will need to import a tidy data set. We will continue to use the data set from this morning.
```{r DataImport}
interviews <- read_csv("https://raw.githubusercontent.com/datacarpentry/r-socialsci/main/episodes/data/SAFI_clean.csv")
```
### Exercise 1
Using pipes, subset the `interviews` data to include interviews where respondents were members of an irrigation association (`memb_assoc`) and retain only the columns `affect_conflicts`, `liv_count`, and `no_meals`. Save this in an object called 'interviews_restricted'
```{r Subsetting Task}
#
```
-------------
### Exercise 2
Create a new dataframe from the `interviews` data that meets the following criteria: contains only the `village` column and a new column called `total_meals` containing a value that is equal to the total number of meals served in the household per day on average (`no_membrs` times `no_meals`). Only the rows where `total_meals` is greater than 20 should be shown in the final dataframe. This should be saved as 'interview_total_meals'
**Hint**: think about how the commands should be ordered to produce this data frame.
```{r New Variable Task}
#
```
-------------
### Exercise 3
How many households in the survey have an average of two meals per day? Three meals per day? Are there any other numbers of meals represented?
```{r Counting Task}
#
```
-------------
### Exercise 4
Use `group_by()` and `summarise()` to find the mean, min, and max number of household members for each village. Also add the number of observations (hint: see `?n`).
```{r Summary Task}
#
```
-------------
### Exercise 5
Create a new dataframe (named `interviews_months_lack_food`) that has one column for each month and records `TRUE` or `FALSE` for whether each interview respondent was lacking food in that month.
```{r Pivoting Task}
#
```