From e19bb7a6bb359b4e72d731673d358766a0b2cad1 Mon Sep 17 00:00:00 2001
From: Nics-Github <nicholasschwab@gmail.com>
Date: Tue, 22 Oct 2024 19:31:37 -0400
Subject: [PATCH] day 21

---
 docs/index.html                               |  14 +-
 folder/lab_4_solutions.rmarkdown              | 239 ++++++++++++++++++
 .../execute-results/html.json                 |  17 ++
 .../figure-html/unnamed-chunk-14-1.png        | Bin 0 -> 19566 bytes
 index.qmd                                     |  14 +-
 5 files changed, 280 insertions(+), 4 deletions(-)
 create mode 100644 folder/lab_4_solutions.rmarkdown
 create mode 100644 folder/lab_4_solutions_files/execute-results/html.json
 create mode 100644 folder/lab_4_solutions_files/figure-html/unnamed-chunk-14-1.png

diff --git a/docs/index.html b/docs/index.html
index 46ac785..577b5cd 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -7,7 +7,7 @@
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 <meta name="author" content="Nicholas Schwab">
-<meta name="dcterms.date" content="2024-10-20">
+<meta name="dcterms.date" content="2024-10-22">
 
 <title>SDS 192 - SDS 192: Intro to Data Science</title>
 <style>
@@ -185,6 +185,7 @@ <h2 id="toc-title">On this page</h2>
    
   <ul>
   <li><a href="#schedule" id="toc-schedule" class="nav-link active" data-scroll-target="#schedule">Schedule</a></li>
+  <li><a href="#day-21" id="toc-day-21" class="nav-link" data-scroll-target="#day-21">Day 21</a></li>
   <li><a href="#day-20-pivots" id="toc-day-20-pivots" class="nav-link" data-scroll-target="#day-20-pivots">Day 20 Pivots</a></li>
   <li><a href="#day-19-lab-5" id="toc-day-19-lab-5" class="nav-link" data-scroll-target="#day-19-lab-5">Day 19 lab 5</a></li>
   <li><a href="#day-18-joining-tables" id="toc-day-18-joining-tables" class="nav-link" data-scroll-target="#day-18-joining-tables">Day 18 Joining tables</a></li>
@@ -234,7 +235,7 @@ <h1 class="title">SDS 192: Intro to Data Science</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">October 20, 2024</p>
+      <p class="date">October 22, 2024</p>
     </div>
   </div>
   
@@ -256,7 +257,7 @@ <h1 class="title">SDS 192: Intro to Data Science</h1>
 <h1>Schedule</h1>
 <!-- This is the wesbite for the schedule below:  -->
 <!-- https://docs.google.com/spreadsheets/d/1j1Mub7lb1CJKpG-PySzdbzzgvryVQ3XrlvNgPRUcN0I/edit?usp=sharing -->
-<iframe src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTG_b6upDZcPxXB92LHYIhV1uAXgeVGaMsZ3sDRPuSQN74JdhkKhZQVjw6T68I3zsP8KGYzDSVTZW64/pubhtml?gid=0&amp;single=true&amp;widget=true&amp;headers=false&amp;gid=0&amp;range=A1:E24
+<iframe src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTG_b6upDZcPxXB92LHYIhV1uAXgeVGaMsZ3sDRPuSQN74JdhkKhZQVjw6T68I3zsP8KGYzDSVTZW64/pubhtml?gid=0&amp;single=true&amp;widget=true&amp;headers=false&amp;gid=0&amp;range=A1:E32
 " width="100%" height="300">
 </iframe>
 <!-- # Day 38  -->
@@ -400,6 +401,13 @@ <h1>Schedule</h1>
 <!-- [dpylr cheatsheet for wrangling](https://nyu-cdsc.github.io/learningr/assets/data-transformation.pdf) -->
 <!-- [Here is the link](https://classroom.github.com/a/eSF9MUxn) for Lab 4. -->
 </section>
+<section id="day-21" class="level1">
+<h1>Day 21</h1>
+<p>Lab 5 reflection - personal information</p>
+<p>Many to one join warning.</p>
+<p><a href="https://mdsr-book.github.io/mdsr3e/06-dataII.html#dataII-exercises">Pivot Practice - Problem 5 chapter 6</a></p>
+<p><a href="https://classroom.github.com/a/kprELIeW">Project 2</a></p>
+</section>
 <section id="day-20-pivots" class="level1">
 <h1>Day 20 Pivots</h1>
 <p><a href="./course-materials/lectures/20_SQF.html">Reflection on Lab 4</a></p>
diff --git a/folder/lab_4_solutions.rmarkdown b/folder/lab_4_solutions.rmarkdown
new file mode 100644
index 0000000..13cd069
--- /dev/null
+++ b/folder/lab_4_solutions.rmarkdown
@@ -0,0 +1,239 @@
+---
+title: 'Lab 4: Exploratory Data Analysis'
+subtitle: "solutions"
+format:
+  html:
+    self-contained: true
+editor: source
+knitr:
+  opts_chunk:
+    message: false
+---
+
+```{r}
+#| warning: false 
+#| messages: false
+#| document: show
+library(tidyverse)
+sqf_url <- "https://www1.nyc.gov/assets/nypd/downloads/zip/analysis_and_planning/stop-question-frisk/sqf-2011-csv.zip"
+temp <- tempfile()
+download.file(sqf_url, temp)
+sqf_zip <- unzip(temp, "2011.csv")
+sqf_2011 <- read.csv(sqf_zip, stringsAsFactors = FALSE) 
+sqf_2011_race_cat <- read.csv("https://raw.githubusercontent.com/lindsaypoirier/STS-101/master/Data/SQF/sqf_race_categories.csv", stringsAsFactors = FALSE) 
+rm(sqf_url)
+rm(temp)
+#rm(sqf_zip)
+```
+
+```{r}
+#| warning: false
+#| messages: false
+#| document: show
+sqf_2011 <- 
+  sqf_2011 |> 
+  select(pct, race, age, frisked, pistol, riflshot, asltweap, knifcuti, machgun, othrweap, sumissue, arstmade) |>
+  left_join(sqf_2011_race_cat, by = "race") |>
+  mutate(across(frisked:arstmade, 
+         ~ case_when(. == "Y" ~ 1, . == "N" ~ 0)))
+rm(sqf_2011_race_cat)
+```
+
+###### Question
+::: question
+Add two new columns. The first should indicate whether a weapon was found, and the second should indicate whether an arrest/summons was made.
+:::
+
+```{r}
+#| eval: true
+sqf_2011 <- 
+  sqf_2011 |>
+  #Add a variable for weapon found
+  mutate(wpnfound = case_when(pistol == 1 |
+                               riflshot == 1 | 
+                               asltweap == 1 |
+                               knifcuti == 1 | 
+                               machgun == 1 | 
+                               othrweap == 1 ~ 1,
+                             TRUE ~ 0))
+sqf_2011 <- 
+  sqf_2011 |>
+  #Add a variable for arrest made or summons issued
+  mutate(arrestsumm = case_when(sumissue == 1 | 
+                                arstmade == 1 ~ 1,
+                               TRUE ~ 0))
+```
+
+###### Question
+::: question
+Subset the dataset to the six variables listed in the data dictionary above.
+:::
+
+```{r}
+#| eval: true
+sqf_2011 <-
+  sqf_2011 |>
+  select(pct, arrestsumm, age, wpnfound, race_cat, frisked)
+```
+
+###### Question
+::: question
+Calculate the number of stops in 2011. If you are not sure which function to use below, you may want to refer to the list of Summary functions in the the Data Wrangling cheatsheet. Remember that each row in the data frame is a stop.
+:::
+
+```{r}
+#| eval: true
+total_stops <-
+  sqf_2011 |>
+  summarize(Count = n()) |>
+  pull()
+total_stops
+```
+
+###### Question
+::: question
+How many stops did not result in an arrest or summons in 2011? What percentage of stops did not result in an arrest or summons?
+:::
+
+```{r}
+#| eval: true
+sqf_2011 |>
+  #Subset to rows where suspect innocent
+  filter(arrestsumm == 0) |> 
+  #Calculate number of observations
+  summarise(total_innocent = n(), 
+            percent_innocent = n() / total_stops * 100)
+```
+
+###### Question
+::: question
+In how many stops were the individuals aged 14-24? In what percentage of stops were the individuals aged 14-24?
+:::
+
+```{r}
+#| eval: true
+sqf_2011 |>
+  #Subset to rows where suspect age 14-24
+  filter(age >= 14 & age <= 24) |> 
+  #Calculate number of observations and percentage of observations
+  summarise(total_14_24 = n(), 
+            percent_14_24 = n() / total_stops * 100)
+```
+
+###### Question
+::: question
+Fix the code below to calculate the correct number of stops for individuals 14-24. 
+:::
+
+```{r}
+#| eval: true
+total_stops_age_recorded <-
+  sqf_2011 |>
+  #Subset to rows where age is not 999
+  filter(age != 999) |> 
+  summarize(Count = n()) |>
+  pull()
+sqf_2011 |>
+  filter(age >= 14 & age <= 24) |>
+  summarize(total_14_24 = n(), 
+            percent_14_24 = n() / total_stops_age_recorded * 100)
+```
+
+```{r}
+#| eval: true
+total_stops_age_recorded <-
+  sqf_2011 |>
+  #Subset to rows where age is not 999
+  filter(age != 999) |> 
+  summarize(Count = n()) |>
+  pull()
+sqf_2011 |>
+  filter(age >= 14 & age < 24) |>
+  summarize(total_14_24 = n(), 
+            percent_14_24 = n() / total_stops_age_recorded * 100)
+```
+
+This still doesn't match the values we see on the website, but it does match the values we see in the NYCLU's 2011 report on Stop, Question, and Frisk data. This is typically when I would reach out to a representative at the NYCLU to inquire about the discrepancy.
+###### Question
+::: question
+How many stops were there per race in 2011? What percentage of stops per race in 2011? Arrange by number of stops in descending order.
+:::
+
+```{r}
+#| eval: true
+total_stops_race_recorded <-
+  sqf_2011 |>
+  #Subset to rows where race_cat is not NA or "OTHER"
+  filter(!is.na(race_cat) & race_cat != "OTHER") |> 
+  summarize(Count = n()) |>
+  pull()
+sqf_2011 |>
+  #Subset to rows where race_cat is not NA or "OTHER"
+  filter(!is.na(race_cat) & race_cat != "OTHER") |> 
+  #Group by race
+  group_by(race_cat) |> 
+  #Calculate number of observations
+  summarise(stops = n(), 
+            percent_stops = n() / total_stops_race_recorded * 100) |>
+  #Sort by stops in descending order
+  arrange(desc(stops)) 
+```
+
+###### Question
+::: question
+In how many stops were the individuals identified as Latinx (i.e. "WHITE-HISPANIC" or "BLACK-HISPANIC")? In what percentage of stops were the individuals identified as Latinx?
+:::
+
+```{r}
+#| eval: true
+sqf_2011 |>
+  #Subset to rows where race_cat is "WHITE-HISPANIC" or "BLACK-HISPANIC"
+  filter(race_cat %in% c("WHITE-HISPANIC", "BLACK-HISPANIC")) |> 
+  #Calculate number of observations
+  summarise(stops_Latinx = n(), 
+            percent_Latinx = n() / total_stops_race_recorded * 100)
+```
+
+###### Question
+::: question
+What percentage of stops in 2011 resulted in a frisk per race? What percentage of stops in 2011 resulted in a weapon found per race? What percentage of stops in 2011 resulted in an arrest or summons per race? In your resulting data table, each row should be a race, and there should be columns for `stops`, `percent_stops`, `percent_frisked`, `percent_wpnfound` , and `percent_arrestsumm`.
+:::
+
+```{r}
+#| eval: true
+# Write code here. 
+sqf_2011_percents <- sqf_2011 |>
+  filter(race_cat %in% c(
+    "BLACK","WHITE-HISPANIC",   "WHITE",  "BLACK-HISPANIC", "ASIAN/PACIFIC ISLANDER", "AMERICAN INDIAN/ALASKAN NATIVE")
+    ) |> 
+  group_by(race_cat) |>
+  summarise(stops = n(), 
+            percent_stops = n()/total_stops *100,
+            percent_frisked =  sum(frisked)/n() *100,
+            percent_wpnfound = sum(wpnfound)/n() *100,
+            percent_arrestsumm = sum(arrestsumm)/n() *100
+  )|>
+  kableExtra::kable()
+sqf_2011_percents
+```
+
+###### Question
+::: question
+Below, in 2-3 sentences, summarize what you learn from reviewing the summary statistics from the code above. What does this tell us about the constitutionality of 2011 stop and frisk activity in NYC?
+:::
+
+```{r}
+#| document: show
+#Make a data visualization
+```
+
+# Any lab visualization is acceptable. 
+
+```{r}
+sqf_2011_percents |>
+  filter(!is.na(race_cat) & race_cat != "OTHER") |> 
+  ggplot() +
+  geom_col(aes(x=race_cat , y= percent_frisked)) +
+  coord_flip()
+```
+
diff --git a/folder/lab_4_solutions_files/execute-results/html.json b/folder/lab_4_solutions_files/execute-results/html.json
new file mode 100644
index 0000000..97171c7
--- /dev/null
+++ b/folder/lab_4_solutions_files/execute-results/html.json
@@ -0,0 +1,17 @@
+{
+  "hash": "00be1c39f37dba9fcf5e1bf5c09f406a",
+  "result": {
+    "engine": "knitr",
+    "markdown": "---\ntitle: 'Lab 4: Exploratory Data Analysis'\nsubtitle: \"solutions\"\nformat:\n  html:\n    self-contained: true\neditor: source\nknitr:\n  opts_chunk:\n    message: false\n---\n\n::: {.cell messages='false' document='show'}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nsqf_url <- \"https://www1.nyc.gov/assets/nypd/downloads/zip/analysis_and_planning/stop-question-frisk/sqf-2011-csv.zip\"\ntemp <- tempfile()\ndownload.file(sqf_url, temp)\nsqf_zip <- unzip(temp, \"2011.csv\")\nsqf_2011 <- read.csv(sqf_zip, stringsAsFactors = FALSE) \nsqf_2011_race_cat <- read.csv(\"https://raw.githubusercontent.com/lindsaypoirier/STS-101/master/Data/SQF/sqf_race_categories.csv\", stringsAsFactors = FALSE) \nrm(sqf_url)\nrm(temp)\nrm(sqf_zip)\n```\n:::\n\n::: {.cell messages='false' document='show'}\n\n```{.r .cell-code}\nsqf_2011 <- \n  sqf_2011 |> \n  select(pct, race, age, frisked, pistol, riflshot, asltweap, knifcuti, machgun, othrweap, sumissue, arstmade) |>\n  left_join(sqf_2011_race_cat, by = \"race\") |>\n  mutate(across(frisked:arstmade, \n         ~ case_when(. == \"Y\" ~ 1, . == \"N\" ~ 0)))\nrm(sqf_2011_race_cat)\n```\n:::\n\n###### Question\n::: question\nAdd two new columns. The first should indicate whether a weapon was found, and the second should indicate whether an arrest/summons was made.\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsqf_2011 <- \n  sqf_2011 |>\n  #Add a variable for weapon found\n  mutate(wpnfound = case_when(pistol == 1 |\n                               riflshot == 1 | \n                               asltweap == 1 |\n                               knifcuti == 1 | \n                               machgun == 1 | \n                               othrweap == 1 ~ 1,\n                             TRUE ~ 0))\nsqf_2011 <- \n  sqf_2011 |>\n  #Add a variable for arrest made or summons issued\n  mutate(arrestsumm = case_when(sumissue == 1 | \n                                arstmade == 1 ~ 1,\n                               TRUE ~ 0))\n```\n:::\n\n###### Question\n::: question\nSubset the dataset to the six variables listed in the data dictionary above.\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsqf_2011 <-\n  sqf_2011 |>\n  select(pct, arrestsumm, age, wpnfound, race_cat, frisked)\n```\n:::\n\n###### Question\n::: question\nCalculate the number of stops in 2011. If you are not sure which function to use below, you may want to refer to the list of Summary functions in the the Data Wrangling cheatsheet. Remember that each row in the data frame is a stop.\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ntotal_stops <-\n  sqf_2011 |>\n  summarize(Count = n()) |>\n  pull()\ntotal_stops\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 685724\n```\n\n\n:::\n:::\n\n###### Question\n::: question\nHow many stops did not result in an arrest or summons in 2011? What percentage of stops did not result in an arrest or summons?\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsqf_2011 |>\n  #Subset to rows where suspect innocent\n  filter(arrestsumm == 0) |> \n  #Calculate number of observations\n  summarise(total_innocent = n(), \n            percent_innocent = n() / total_stops * 100)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n  total_innocent percent_innocent\n1         605328         88.27575\n```\n\n\n:::\n:::\n\n###### Question\n::: question\nIn how many stops were the individuals aged 14-24? In what percentage of stops were the individuals aged 14-24?\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsqf_2011 |>\n  #Subset to rows where suspect age 14-24\n  filter(age >= 14 & age <= 24) |> \n  #Calculate number of observations and percentage of observations\n  summarise(total_14_24 = n(), \n            percent_14_24 = n() / total_stops * 100)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n  total_14_24 percent_14_24\n1      346226      50.49058\n```\n\n\n:::\n:::\n\n###### Question\n::: question\nFix the code below to calculate the correct number of stops for individuals 14-24. \n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ntotal_stops_age_recorded <-\n  sqf_2011 |>\n  #Subset to rows where age is not 999\n  filter(age != 999) |> \n  summarize(Count = n()) |>\n  pull()\nsqf_2011 |>\n  filter(age >= 14 & age <= 24) |>\n  summarize(total_14_24 = n(), \n            percent_14_24 = n() / total_stops_age_recorded * 100)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n  total_14_24 percent_14_24\n1      346226       50.5542\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ntotal_stops_age_recorded <-\n  sqf_2011 |>\n  #Subset to rows where age is not 999\n  filter(age != 999) |> \n  summarize(Count = n()) |>\n  pull()\nsqf_2011 |>\n  filter(age >= 14 & age < 24) |>\n  summarize(total_14_24 = n(), \n            percent_14_24 = n() / total_stops_age_recorded * 100)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n  total_14_24 percent_14_24\n1      321381      46.92646\n```\n\n\n:::\n:::\n\nThis still doesn't match the values we see on the website, but it does match the values we see in the NYCLU's 2011 report on Stop, Question, and Frisk data. This is typically when I would reach out to a representative at the NYCLU to inquire about the discrepancy.\n###### Question\n::: question\nHow many stops were there per race in 2011? What percentage of stops per race in 2011? Arrange by number of stops in descending order.\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ntotal_stops_race_recorded <-\n  sqf_2011 |>\n  #Subset to rows where race_cat is not NA or \"OTHER\"\n  filter(!is.na(race_cat) & race_cat != \"OTHER\") |> \n  summarize(Count = n()) |>\n  pull()\nsqf_2011 |>\n  #Subset to rows where race_cat is not NA or \"OTHER\"\n  filter(!is.na(race_cat) & race_cat != \"OTHER\") |> \n  #Group by race\n  group_by(race_cat) |> \n  #Calculate number of observations\n  summarise(stops = n(), \n            percent_stops = n() / total_stops_race_recorded * 100) |>\n  #Sort by stops in descending order\n  arrange(desc(stops)) \n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 3\n  race_cat                        stops percent_stops\n  <chr>                           <int>         <dbl>\n1 BLACK                          350743        52.9  \n2 WHITE-HISPANIC                 175302        26.4  \n3 WHITE                           61805         9.32 \n4 BLACK-HISPANIC                  48438         7.30 \n5 ASIAN/PACIFIC ISLANDER          23932         3.61 \n6 AMERICAN INDIAN/ALASKAN NATIVE   2897         0.437\n```\n\n\n:::\n:::\n\n###### Question\n::: question\nIn how many stops were the individuals identified as Latinx (i.e. \"WHITE-HISPANIC\" or \"BLACK-HISPANIC\")? In what percentage of stops were the individuals identified as Latinx?\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsqf_2011 |>\n  #Subset to rows where race_cat is \"WHITE-HISPANIC\" or \"BLACK-HISPANIC\"\n  filter(race_cat %in% c(\"WHITE-HISPANIC\", \"BLACK-HISPANIC\")) |> \n  #Calculate number of observations\n  summarise(stops_Latinx = n(), \n            percent_Latinx = n() / total_stops_race_recorded * 100)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n  stops_Latinx percent_Latinx\n1       223740       33.74065\n```\n\n\n:::\n:::\n\n###### Question\n::: question\nWhat percentage of stops in 2011 resulted in a frisk per race? What percentage of stops in 2011 resulted in a weapon found per race? What percentage of stops in 2011 resulted in an arrest or summons per race? In your resulting data table, each row should be a race, and there should be columns for `stops`, `percent_stops`, `percent_frisked`, `percent_wpnfound` , and `percent_arrestsumm`.\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# Write code here. \nsqf_2011_percents <- sqf_2011 |>\n  filter(race_cat %in% c(\n    \"BLACK\",\"WHITE-HISPANIC\",   \"WHITE\",  \"BLACK-HISPANIC\", \"ASIAN/PACIFIC ISLANDER\", \"AMERICAN INDIAN/ALASKAN NATIVE\")\n    ) |> \n  group_by(race_cat) |>\n  summarise(stops = n(), \n            percent_stops = n()/total_stops *100,\n            percent_frisked =  sum(frisked)/n() *100,\n            percent_wpnfound = sum(wpnfound)/n() *100,\n            percent_arrestsumm = sum(arrestsumm)/n() *100\n  )\nsqf_2011_percents\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 6\n  race_cat                  stops percent_stops percent_frisked percent_wpnfound\n  <chr>                     <int>         <dbl>           <dbl>            <dbl>\n1 AMERICAN INDIAN/ALASKAN…   2897         0.422            49.4            1.10 \n2 ASIAN/PACIFIC ISLANDER    23932         3.49             47.1            0.827\n3 BLACK                    350743        51.1              57.9            1.06 \n4 BLACK-HISPANIC            48438         7.06             58.5            1.09 \n5 WHITE                     61805         9.01             44.2            1.89 \n6 WHITE-HISPANIC           175302        25.6              56.7            1.16 \n# ℹ 1 more variable: percent_arrestsumm <dbl>\n```\n\n\n:::\n:::\n\n###### Question\n::: question\nBelow, in 2-3 sentences, summarize what you learn from reviewing the summary statistics from the code above. What does this tell us about the constitutionality of 2011 stop and frisk activity in NYC?\n:::\n\n::: {.cell document='show'}\n\n```{.r .cell-code}\n#Make a data visualization\n```\n:::\n\n# Any lab visualization is acceptable. \n\n::: {.cell}\n\n```{.r .cell-code}\nsqf_2011_percents |>\n  filter(!is.na(race_cat) & race_cat != \"OTHER\") |> \n  ggplot() +\n  geom_col(aes(x=race_cat , y= percent_frisked)) +\n  coord_flip()\n```\n\n::: {.cell-output-display}\n![](lab_4_solutions_files/figure-html/unnamed-chunk-14-1.png){width=672}\n:::\n:::\n",
+    "supporting": [
+      "lab_4_solutions_files"
+    ],
+    "filters": [
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+    "engineDependencies": {},
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+    "postProcess": true
+  }
+}
\ No newline at end of file
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diff --git a/index.qmd b/index.qmd
index fb150f4..d3b6e95 100644
--- a/index.qmd
+++ b/index.qmd
@@ -61,7 +61,7 @@ h1{font-weight: 400;}
 <!-- https://docs.google.com/spreadsheets/d/1j1Mub7lb1CJKpG-PySzdbzzgvryVQ3XrlvNgPRUcN0I/edit?usp=sharing -->
 
 ```{=html}
-<iframe src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTG_b6upDZcPxXB92LHYIhV1uAXgeVGaMsZ3sDRPuSQN74JdhkKhZQVjw6T68I3zsP8KGYzDSVTZW64/pubhtml?gid=0&amp;single=true&amp;widget=true&amp;headers=false&amp;gid=0&amp;range=A1:E24
+<iframe src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTG_b6upDZcPxXB92LHYIhV1uAXgeVGaMsZ3sDRPuSQN74JdhkKhZQVjw6T68I3zsP8KGYzDSVTZW64/pubhtml?gid=0&amp;single=true&amp;widget=true&amp;headers=false&amp;gid=0&amp;range=A1:E32
 " width="100%" height="300">
 ```
 </iframe>
@@ -348,8 +348,20 @@ h1{font-weight: 400;}
 
 <!-- [Here is the link](https://classroom.github.com/a/eSF9MUxn) for Lab 4. -->
 
+# Day 21 
+
+Lab 5 reflection - personal information
+
+Many to one join warning.
+
+[Pivot Practice - Problem 5 chapter 6 ](https://mdsr-book.github.io/mdsr3e/06-dataII.html#dataII-exercises)
+
+[Project 2](https://classroom.github.com/a/kprELIeW)
+
 # Day 20 Pivots
 
+
+
 [Reflection on Lab 4](course-materials/lectures/20_SQF.qmd)
 
 [Pivots](course-materials/lectures/18_pivots.qmd)