Addressing manipulating data buggies

modules/Manipulating_Data_in_R/Manipulating_Data_in_R.Rmd

@@ -35,7 +35,7 @@ library(tidyverse)
 
 📃[Cheatsheet](https://daseh.org/modules/cheatsheets/Day-5.pdf)
 
-## Manipulating Data 
+## Manipulating Data
 
 In this module, we will show you how to:
 
@@ -89,7 +89,7 @@ ex_wide <- tibble(State = c("Alabama", "Alaska"),
 ex_long <- pivot_longer(ex_wide, cols = !State)
 ```
 
-Wide: multiple columns per individual, values spread across multiple columns 
+Wide: multiple columns per individual, values spread across multiple columns
 
 ```{r, echo = FALSE}
 ex_wide
@@ -136,7 +136,7 @@ You might see old functions `gather` and `spread` when googling. These are older
 
 # `pivot_longer`...
 
-## Reshaping data from **wide to long** {.codesmall} 
+## Reshaping data from **wide to long** {.codesmall}
 
 `pivot_longer()` - puts column data into rows (`tidyr` package)
 
@@ -161,7 +161,7 @@ long_vacc <- wide_vacc %>% pivot_longer(cols = everything())
 long_vacc
 ```
 
-## Reshaping wide to long: Better column names {.codesmall} 
+## Reshaping wide to long: Better column names {.codesmall}
 
 `pivot_longer()` - puts column data into rows (`tidyr` package)
 
@@ -189,7 +189,7 @@ long_vacc
 
 Newly created column names are enclosed in quotation marks.
 
-## Data used: Nitrate exposure 
+## Data used: Nitrate exposure
 
 Nitrate exposure by quarter for populations on public water systems in the state of Washington for 1999-2020.
 
@@ -239,7 +239,7 @@ Un-pivoted columns (`year`, `quarter`, `pop_on_sampled_PWS`) are still columns.
 long
 ```
 
-## Cleaning up long data{.codesmall} 
+## Cleaning up long data{.codesmall}
 
 Let's make the `conc_count` into a proportion.
 
@@ -254,8 +254,8 @@ long
 Now our data is more tidy, and we can take the averages easily!
 
 ```{r}
-long %>% 
-  group_by(conc_cat) %>% 
+long %>%
+  group_by(conc_cat) %>%
   summarize("avg_prop" = mean(conc_prop))
 ```
 
@@ -275,7 +275,7 @@ There are many ways to **select** the columns we want. Check out https://dplyr.t
 
 <div class = "codeexample">
 ```{r, eval=FALSE}
-{wide_data} <- {long_data} %>% 
+{wide_data} <- {long_data} %>%
   pivot_wider(names_from = {Old column name: contains new column names},
               values_from = {Old column name: contains new cell values})
 ```
@@ -285,12 +285,12 @@ There are many ways to **select** the columns we want. Check out https://dplyr.t
 
 ```{r}
 long_vacc
-wide_vacc <- long_vacc %>% pivot_wider(names_from = "Month", 
-                                       values_from = "Rate") 
+wide_vacc <- long_vacc %>% pivot_wider(names_from = "Month",
+                                       values_from = "Rate")
 wide_vacc
 ```
 
-## Reshaping nitrate exposure data{.codesmall} 
+## Reshaping nitrate exposure data{.codesmall}
 
 What if we wanted different columns for each quarter?
 
@@ -335,7 +335,7 @@ knitr::include_graphics("images/joins.png")
 * Merging/joining data sets together - usually on key variables, usually "id"
 * `?join` - see different types of joining for `dplyr`
 * `inner_join(x, y)` - only rows that match for `x` and `y` are kept
-* `full_join(x, y)` - all rows of `x` and `y` are kept 
+* `full_join(x, y)` - all rows of `x` and `y` are kept
 * `left_join(x, y)` - all rows of `x` are kept even if not merged with `y`
 * `right_join(x, y)` - all rows of `y` are kept even if not merged with `x`
 * `anti_join(x, y)` - all rows from `x` not in `y` keeping just columns from `x`.
@@ -545,11 +545,11 @@ anti_join(data_cold, data_As, by = "State") # order switched
 * Merging/joining data sets together - assumes all column names that overlap
      - use the `by = c("a" = "b")` if they differ
 * `inner_join(x, y)` - only rows that match for `x` and `y` are kept
-* `full_join(x, y)` - all rows of `x` and `y` are kept 
+* `full_join(x, y)` - all rows of `x` and `y` are kept
 * `left_join(x, y)` - all rows of `x` are kept even if not merged with `y`
 * `right_join(x, y)` - all rows of `y` are kept even if not merged with `x`
 * Use the `tidylog` package for a detailed summary
-* `antijoin(x, y)` shows what is only in `x` (missing from `y`)
+* `anti_join(x, y)` shows what is only in `x` (missing from `y`)
 
 ## Lab Part 2
 
@@ -596,7 +596,7 @@ dplyr::setdiff(cold_states, A_states)
 
 ## Getting the set difference with `setdiff`
 
-Why did we use `dplyr::setdiff`? 
+Why did we use `dplyr::setdiff`?
 
 There is a base R function, also called `setdiff` that requires vectors.
 

modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab.Rmd

@@ -1,7 +1,7 @@
 ---
 title: "Manipulating Data in R Lab"
 output: html_document
-editor_options: 
+editor_options:
   chunk_output_type: console
 ---
 
@@ -41,7 +41,10 @@ Look at the column names using `colnames` - do you notice any patterns?
 
 ### 1.3
 
-Let's rename the column "2011" in "co2" to "CO2_2011" using `rename`. Repeat this for the years 2012, 2013, and 2014. Make sure to reassign to `co2` here and in subsequent steps.
+Let's rename the columns "co2" from this type of format: "2011" to this: "CO2_2011" using `rename`.
+Be sure to do this for all years 2012, 2013, and 2014. Make sure that you end up with the renamed columns in a data frame named `co2` here and in subsequent steps.
+
+Hint: If you run code to rename the columns and store back into a data frame of the same name like `co2` you will not be able to re-run the renaming code without error (the columns are already renamed so it won't be able to find the oldname of the column anymore)
 
 ```
 # General format
@@ -119,7 +122,7 @@ Take the code from Questions 1.1 and 1.3-1.7. Chain all of this code together us
 
 Modify the code from Question P.1:
 
--  Choose 4 different years to examine 
+-  Choose 4 different years to examine
 -  Select different countries to compare
 -  Call your data `co2_compare2`
 
@@ -176,7 +179,7 @@ What countries are present in "co2" that are not present in "cc"? Use `anti_join
 
 ```
 # General format
-anti_join(data1, data2, by = "") %>% select(index)
+anti_join(data1, data2, by = "") %>% select(columnname)
 ```
 
 ```{r 2.4response}

modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab_Key.Rmd

@@ -1,7 +1,7 @@
 ---
 title: "Manipulating Data in R Lab - Key"
 output: html_document
-editor_options: 
+editor_options:
   chunk_output_type: console
 ---
 
@@ -44,7 +44,10 @@ colnames(co2)
 
 ### 1.3
 
-Let's rename the column "2011" in "co2" to "CO2_2011" using `rename`. Repeat this for the years 2012, 2013, and 2014. Make sure to reassign to `co2` here and in subsequent steps.
+Let's rename the columns "co2" from this type of format: "2011" to this: "CO2_2011" using `rename`.
+Be sure to do this for all years 2012, 2013, and 2014. Make sure that you end up with the renamed columns in a data frame named `co2` here and in subsequent steps.
+
+Hint: If you run code to rename the columns and store back into a data frame of the same name like `co2` you will not be able to re-run the renaming code without error (the columns are already renamed so it won't be able to find the oldname of the column anymore)
 
 ```
 # General format
@@ -146,7 +149,7 @@ co2_compare
 
 Modify the code from Question P.1:
 
--  Choose 4 different years to examine 
+-  Choose 4 different years to examine
 -  Select different countries to compare
 -  Call your data `co2_compare2`
 
@@ -177,7 +180,7 @@ Open the `Yearly_CC_Disasters` dataset using the url below. Save the dataset as
 
 
 ```{r 2.1response}
-cc <- read_csv("https://daseh.org/data/Yearly_CC_Disasters.csv") %>% 
+cc <- read_csv("https://daseh.org/data/Yearly_CC_Disasters.csv") %>%
   rename(country = Country)
 ```
 
@@ -218,7 +221,7 @@ What countries are present in "co2" that are not present in "cc"? Use `anti_join
 
 ```
 # General format
-anti_join(data1, data2, by = "") %>% select(index)
+anti_join(data1, data2, by = "") %>% select(columnname)
 ```
 
 ```{r 2.4response}
@@ -234,7 +237,7 @@ anti_join(cc, co2, by = "country") %>% select(country) %>% distinct()
 Take the code from 2.2 and save the output as an object "co2_cc". Filter the dataset. Filter so that you only keep Indonesia and Canada.
 
 ```{r P.3response}
-co2_cc <- full_join(co2, cc, by = "country") %>% 
+co2_cc <- full_join(co2, cc, by = "country") %>%
   filter(country %in% c("Indonesia", "Canada"))
 ```
 
@@ -279,7 +282,7 @@ Pivot the dataset so that there are columns for country, emissions, and a column
 
 ```{r P.6response}
 co2_cc %>% pivot_wider(
-  names_from = Indicator, 
+  names_from = Indicator,
   values_from = disasters
 )
 ```