Linted
marcgauch
parent
afd7d1babf
commit
51cd711da8
90
report.html
90
report.html
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@ -1494,11 +1494,11 @@ border-radius: 0px;
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<div id="preparation" class="section level1" number="1">
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<h1><span class="header-section-number">1</span> Preparation</h1>
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<pre class="r"><code>if (!require(tidyverse)){
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<pre class="r"><code>if (!require(tidyverse)) {
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install.packages("tidyverse")
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library(tidyverse)
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}
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if (!require(moments)){
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if (!require(moments)) {
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install.packages("moments")
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library(moments)
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}</code></pre>
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@ -1508,28 +1508,26 @@ if (!require(moments)){
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<div id="frequency-table-ordered-from-wish.com" class="section level2" number="2.1">
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<h2><span class="header-section-number">2.1</span> Frequency Table
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ordered from wish.com</h2>
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<pre class="r"><code>freq <- function(data){
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na_count = length(data[is.na(data)])
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valid_count = length(data)-na_count
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<pre class="r"><code>freq <- function(data) {
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na_count <- length(data[is.na(data)])
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valid_count <- length(data) - na_count
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frequency <- table(data)
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p <- prop.table(frequency)
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percent <- round(p*100, digits = 2)
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percent <- round(p * 100, digits = 2)
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frequency_sum <- cumsum(frequency)
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hkum <- cumsum(p)
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percent_sum <- round(hkum*100, digits = 2)
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percent_sum <- round(hkum * 100, digits = 2)
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freq_table <- cbind(frequency, percent, frequency_sum, percent_sum)
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valid_percent <- round(valid_count / length(data)*100, digits = 2)
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na_percent <- round(na_count / length(data)*100, digits = 2)
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valid_percent <- round(valid_count / length(data) * 100, digits = 2)
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na_percent <- round(na_count / length(data) * 100, digits = 2)
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print(freq_table)
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count <- c(valid_count, na_count, valid_count+na_count)
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percent <- c(valid_percent, na_percent, valid_percent+na_percent)
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totall <- c(valid_count+na_count, valid_percent+na_percent)
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count <- c(valid_count, na_count, valid_count + na_count)
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percent <- c(valid_percent, na_percent, valid_percent + na_percent)
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df <- data.frame(count, percent, row.names = c("valid", "NA", "Total"))
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print(df)
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}</code></pre>
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<p><em>Source: <a href="https://tellmi.psy.lmu.de/tutorials/deskriptive-statistiken-und-grafiken.html#haeufigkeiten-diskret" class="uri">https://tellmi.psy.lmu.de/tutorials/deskriptive-statistiken-und-grafiken.html#haeufigkeiten-diskret</a>
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and adapted</em></p>
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@ -1539,7 +1537,7 @@ and adapted</em></p>
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<pre class="r"><code>getmode <- function(v) {
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uniqv <- unique(v)
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x <- tabulate(match(v, uniqv))
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uniqv[which(x==max(x))]
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uniqv[which(x == max(x))]
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}</code></pre>
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</div>
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</div>
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@ -1826,17 +1824,17 @@ numbers and <em>Keine Antwort</em> zu <em>NaN</em></h2>
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<p>The following code will <strong>NOT</strong> be run. The Idea is to
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show a way to automatically edit all columns. It works but some columns
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are NOT numeric.</p>
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<pre class="r"><code> # All colnames that exist
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litdataColnames <- colnames(litdata)
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# the ones we don't want to change
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litdataNonNumericCols <- c("submitdate", "startlanguage", "startdate", "datestamp", "lastpage", "seed")
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# the colnames that should be changed
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litdataColsToMakeNumeric <- litdataColnames[!(litdataColnames %in% litdataNonNumericCols)]
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print(litdataColsToMakeNumeric)
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litdataColsToMakeNumeric <- c("R1")
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for (col in litdataColsToMakeNumeric) {
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<pre class="r"><code># All colnames that exist
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litdataColnames <- colnames(litdata)
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# the ones we don't want to change
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litdataNonNumericCols <- c("submitdate", "startlanguage", "startdate", "datestamp", "lastpage", "seed")
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# the colnames that should be changed
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litdataColsToMakeNumeric <- litdataColnames[!(litdataColnames %in% litdataNonNumericCols)]
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print(litdataColsToMakeNumeric)
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litdataColsToMakeNumeric <- c("R1")
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for (col in litdataColsToMakeNumeric) {
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litdata[[col]] <- as.numeric(litdata[[col]])
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}</code></pre>
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}</code></pre>
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<p>First we rename all the columns</p>
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<pre class="r"><code>litdata <- litdata %>% rename(
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"A1" = "W001",
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@ -1848,7 +1846,6 @@ are NOT numeric.</p>
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"A7" = "W007",
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"A8" = "W008",
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"A9" = "W009",
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"B1" = "K001",
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"B2" = "K002",
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"B3" = "K003",
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@ -1858,37 +1855,30 @@ are NOT numeric.</p>
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"B7" = "K007",
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"B8" = "K008",
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"B9" = "K009",
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"C1_1" = "TK001_01",
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"C1_2" = "TK001_02",
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"C1_3" = "TK001_03",
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"C1_4" = "TK001_04",
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"C2_1" = "TK002_01",
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"C2_2" = "TK002_02",
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"C2_3" = "TK002_03",
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"C2_4" = "TK002_04",
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"C3_1" = "TK003_01",
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"C3_2" = "TK003_02",
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"C3_3" = "TK003_03",
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"C3_4" = "TK003_04",
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"C4_1" = "TK004_01",
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"C4_2" = "TK004_02",
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"C4_3" = "TK004_03",
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"C4_4" = "TK004_04",
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"C5_1" = "TK005_01",
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"C5_2" = "TK005_02",
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"C5_3" = "TK005_03",
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"C5_4" = "TK005_04",
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"C6_1" = "TK006_01",
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"C6_2" = "TK006_02",
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"C6_3" = "TK006_03",
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"C6_4" = "TK006_04",
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"D1_1" = "H001_001",
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"D1_2" = "H001_002",
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"D1_3" = "H001_003",
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@ -1896,23 +1886,15 @@ are NOT numeric.</p>
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"D1_5" = "H001_005",
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"D1_6" = "H001_006",
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"D1_7" = "H001_007",
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"D2" = "H002",
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"D3" = "H003",
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"D4" = "H004",
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"D4_comment" = "H004_other",
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"D5" = "H005",
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"D5_comment" = "H005_other",
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"D6" = "H006",
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"D7" = "H007",
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"D8" = "H008",
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"E1" = "R1"
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)</code></pre>
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<p>Then we change the datatype and fix the values</p>
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@ -2278,16 +2260,18 @@ Data</h2>
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tmp <- rename(tmp, value = all_of(column))
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tmp <- tmp %>%
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count(value) %>%
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mutate(percentage = prop.table(n)*100)
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mutate(percentage = prop.table(n) * 100)
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print(tmp, n = 100)
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ggplot(tmp,
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aes(x = value, y=n)) +
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ggplot(
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tmp,
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aes(x = value, y = n)
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) +
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geom_bar(stat = "identity") +
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theme(axis.text.x = element_text(angle = 45, hjust = 1))
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}</code></pre>
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<div id="a3-w003" class="section level3 unnumbered">
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<h3 class="unnumbered">A3 (W003)</h3>
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<pre class="r"><code> displayFunction1(litdata, "A3")</code></pre>
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<pre class="r"><code>displayFunction1(litdata, "A3")</code></pre>
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<pre><code>## # A tibble: 6 × 3
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## value n percentage
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## <dbl> <int> <dbl>
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@ -2302,7 +2286,7 @@ Data</h2>
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</div>
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<div id="b3-k003" class="section level3 unnumbered">
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<h3 class="unnumbered">B3 (K003)</h3>
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<pre class="r"><code> displayFunction1(litdata, "B3")</code></pre>
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<pre class="r"><code>displayFunction1(litdata, "B3")</code></pre>
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<pre><code>## # A tibble: 7 × 3
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## value n percentage
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## <dbl> <int> <dbl>
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@ -2318,7 +2302,7 @@ Data</h2>
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</div>
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<div id="d1_1-h001_001" class="section level3 unnumbered">
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<h3 class="unnumbered">D1_1 (H001_001)</h3>
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<pre class="r"><code> displayFunction1(litdata, "D1_1")</code></pre>
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<pre class="r"><code>displayFunction1(litdata, "D1_1")</code></pre>
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<pre><code>## # A tibble: 3 × 3
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## value n percentage
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## <lgl> <int> <dbl>
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@ -2329,7 +2313,7 @@ Data</h2>
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</div>
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<div id="d5-h005" class="section level3 unnumbered">
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<h3 class="unnumbered">D5 (H005)</h3>
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<pre class="r"><code> displayFunction1(litdata, "D5")</code></pre>
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<pre class="r"><code>displayFunction1(litdata, "D5")</code></pre>
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<pre><code>## # A tibble: 18 × 3
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## value n percentage
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## <fct> <int> <dbl>
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@ -2355,7 +2339,7 @@ Data</h2>
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</div>
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<div id="d7-h007" class="section level3 unnumbered">
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<h3 class="unnumbered">D7 (H007)</h3>
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<pre class="r"><code> displayFunction1(litdata, "D7")</code></pre>
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<pre class="r"><code>displayFunction1(litdata, "D7")</code></pre>
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<pre><code>## # A tibble: 24 × 3
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## value n percentage
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## <dbl> <int> <dbl>
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@ -2390,7 +2374,7 @@ gibt.</p>
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</div>
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<div id="d8-h008" class="section level3 unnumbered">
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<h3 class="unnumbered">D8 (H008)</h3>
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<pre class="r"><code> displayFunction1(litdata, "D8")</code></pre>
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<pre class="r"><code>displayFunction1(litdata, "D8")</code></pre>
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<pre><code>## # A tibble: 3 × 3
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## value n percentage
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## <fct> <int> <dbl>
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@ -2407,7 +2391,7 @@ gibt.</p>
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<pre class="r"><code>birthyears <- litdata$D7
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# remove NAs
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birthyears <- birthyears[!is.na(birthyears)]
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age <- 2021-birthyears</code></pre>
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age <- 2021 - birthyears</code></pre>
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<div id="frequency" class="section level2" number="6.1">
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<h2><span class="header-section-number">6.1</span> Frequency</h2>
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<pre class="r"><code>freq(age)</code></pre>
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@ -2440,7 +2424,7 @@ age <- 2021-birthyears</code></pre>
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## NA 0 0
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## Total 106 100</code></pre>
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<pre class="r"><code># with NA
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freq(2021-litdata$D7)</code></pre>
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freq(2021 - litdata$D7)</code></pre>
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<pre><code>## frequency percent frequency_sum percent_sum
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## 19 1 0.94 1 0.94
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## 20 2 1.89 3 2.83
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@ -2492,7 +2476,7 @@ Mittelwert</h3>
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</div>
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<div id="spannweite" class="section level3" number="6.2.4">
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<h3><span class="header-section-number">6.2.4</span> Spannweite</h3>
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<pre class="r"><code>max(age)-min(age)</code></pre>
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<pre class="r"><code>max(age) - min(age)</code></pre>
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<pre><code>## [1] 31</code></pre>
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</div>
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<div id="quartilsabstand" class="section level3" number="6.2.5">
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@ -2643,7 +2627,7 @@ sozialen Herkunft 5.6, er ist also genauso hoch wie in der
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Gesamtschweiz. Was ist zur Lage dieses Wertes bezogen auf die Verteilung
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in Graubünden zu sagen? Also: Wie viele Schüler in GR liegen mit ihrem
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Wert darunter?</p>
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<pre class="r"><code>pnorm(5.6, mean = 5.1, sd=2.0)</code></pre>
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<pre class="r"><code>pnorm(5.6, mean = 5.1, sd = 2.0)</code></pre>
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<pre><code>## [1] 0.5987063</code></pre>
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</div>
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</div>
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91
report.rmd
91
report.rmd
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@ -20,11 +20,11 @@ knitr::opts_chunk$set(echo = TRUE)
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# Preparation
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```{r, message=FALSE}
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if (!require(tidyverse)){
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if (!require(tidyverse)) {
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install.packages("tidyverse")
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library(tidyverse)
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}
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if (!require(moments)){
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if (!require(moments)) {
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install.packages("moments")
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library(moments)
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}
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## Frequency Table ordered from wish.com
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```{r}
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freq <- function(data){
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na_count = length(data[is.na(data)])
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valid_count = length(data)-na_count
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freq <- function(data) {
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na_count <- length(data[is.na(data)])
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valid_count <- length(data) - na_count
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frequency <- table(data)
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p <- prop.table(frequency)
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percent <- round(p*100, digits = 2)
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percent <- round(p * 100, digits = 2)
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frequency_sum <- cumsum(frequency)
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hkum <- cumsum(p)
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percent_sum <- round(hkum*100, digits = 2)
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percent_sum <- round(hkum * 100, digits = 2)
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freq_table <- cbind(frequency, percent, frequency_sum, percent_sum)
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valid_percent <- round(valid_count / length(data)*100, digits = 2)
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na_percent <- round(na_count / length(data)*100, digits = 2)
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valid_percent <- round(valid_count / length(data) * 100, digits = 2)
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na_percent <- round(na_count / length(data) * 100, digits = 2)
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print(freq_table)
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count <- c(valid_count, na_count, valid_count+na_count)
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percent <- c(valid_percent, na_percent, valid_percent+na_percent)
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totall <- c(valid_count+na_count, valid_percent+na_percent)
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count <- c(valid_count, na_count, valid_count + na_count)
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percent <- c(valid_percent, na_percent, valid_percent + na_percent)
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df <- data.frame(count, percent, row.names = c("valid", "NA", "Total"))
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print(df)
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}
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```
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*Source: https://tellmi.psy.lmu.de/tutorials/deskriptive-statistiken-und-grafiken.html#haeufigkeiten-diskret and adapted*
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@ -65,7 +63,7 @@ freq <- function(data){
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getmode <- function(v) {
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uniqv <- unique(v)
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x <- tabulate(match(v, uniqv))
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uniqv[which(x==max(x))]
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uniqv[which(x == max(x))]
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}
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```
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@ -106,17 +104,17 @@ litdata <- litdata %>%
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## Make it numeric
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The following code will **NOT** be run. The Idea is to show a way to automatically edit all columns. It works but some columns are NOT numeric.
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```{r, eval=FALSE}
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# All colnames that exist
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litdataColnames <- colnames(litdata)
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# the ones we don't want to change
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litdataNonNumericCols <- c("submitdate", "startlanguage", "startdate", "datestamp", "lastpage", "seed")
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# the colnames that should be changed
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litdataColsToMakeNumeric <- litdataColnames[!(litdataColnames %in% litdataNonNumericCols)]
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print(litdataColsToMakeNumeric)
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litdataColsToMakeNumeric <- c("R1")
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for (col in litdataColsToMakeNumeric) {
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# All colnames that exist
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litdataColnames <- colnames(litdata)
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# the ones we don't want to change
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litdataNonNumericCols <- c("submitdate", "startlanguage", "startdate", "datestamp", "lastpage", "seed")
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# the colnames that should be changed
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litdataColsToMakeNumeric <- litdataColnames[!(litdataColnames %in% litdataNonNumericCols)]
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print(litdataColsToMakeNumeric)
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litdataColsToMakeNumeric <- c("R1")
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for (col in litdataColsToMakeNumeric) {
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litdata[[col]] <- as.numeric(litdata[[col]])
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}
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}
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```
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First we rename all the columns
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@ -131,7 +129,6 @@ litdata <- litdata %>% rename(
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"A7" = "W007",
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"A8" = "W008",
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"A9" = "W009",
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"B1" = "K001",
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"B2" = "K002",
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"B3" = "K003",
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@ -141,37 +138,30 @@ litdata <- litdata %>% rename(
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"B7" = "K007",
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"B8" = "K008",
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"B9" = "K009",
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"C1_1" = "TK001_01",
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"C1_2" = "TK001_02",
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"C1_3" = "TK001_03",
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"C1_4" = "TK001_04",
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"C2_1" = "TK002_01",
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"C2_2" = "TK002_02",
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"C2_3" = "TK002_03",
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"C2_4" = "TK002_04",
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"C3_1" = "TK003_01",
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"C3_2" = "TK003_02",
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"C3_3" = "TK003_03",
|
||||
"C3_4" = "TK003_04",
|
||||
|
||||
"C4_1" = "TK004_01",
|
||||
"C4_2" = "TK004_02",
|
||||
"C4_3" = "TK004_03",
|
||||
"C4_4" = "TK004_04",
|
||||
|
||||
"C5_1" = "TK005_01",
|
||||
"C5_2" = "TK005_02",
|
||||
"C5_3" = "TK005_03",
|
||||
"C5_4" = "TK005_04",
|
||||
|
||||
"C6_1" = "TK006_01",
|
||||
"C6_2" = "TK006_02",
|
||||
"C6_3" = "TK006_03",
|
||||
"C6_4" = "TK006_04",
|
||||
|
||||
"D1_1" = "H001_001",
|
||||
"D1_2" = "H001_002",
|
||||
"D1_3" = "H001_003",
|
||||
|
|
@ -179,23 +169,15 @@ litdata <- litdata %>% rename(
|
|||
"D1_5" = "H001_005",
|
||||
"D1_6" = "H001_006",
|
||||
"D1_7" = "H001_007",
|
||||
|
||||
"D2" = "H002",
|
||||
|
||||
"D3" = "H003",
|
||||
|
||||
"D4" = "H004",
|
||||
"D4_comment" = "H004_other",
|
||||
|
||||
"D5" = "H005",
|
||||
"D5_comment" = "H005_other",
|
||||
|
||||
"D6" = "H006",
|
||||
|
||||
"D7" = "H007",
|
||||
|
||||
"D8" = "H008",
|
||||
|
||||
"E1" = "R1"
|
||||
)
|
||||
```
|
||||
|
|
@ -289,7 +271,6 @@ litdata$D7 <- as.numeric(litdata$D7)
|
|||
litdata$D8 <- as.factor(litdata$D8)
|
||||
|
||||
# skipping E1 because it's a free text
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
|
@ -321,44 +302,46 @@ displayFunction1 <- function(table, column) {
|
|||
tmp <- rename(tmp, value = all_of(column))
|
||||
tmp <- tmp %>%
|
||||
count(value) %>%
|
||||
mutate(percentage = prop.table(n)*100)
|
||||
mutate(percentage = prop.table(n) * 100)
|
||||
print(tmp, n = 100)
|
||||
ggplot(tmp,
|
||||
aes(x = value, y=n)) +
|
||||
ggplot(
|
||||
tmp,
|
||||
aes(x = value, y = n)
|
||||
) +
|
||||
geom_bar(stat = "identity") +
|
||||
theme(axis.text.x = element_text(angle = 45, hjust = 1))
|
||||
}
|
||||
```
|
||||
### A3 (W003) {-}
|
||||
```{r}
|
||||
displayFunction1(litdata, "A3")
|
||||
displayFunction1(litdata, "A3")
|
||||
```
|
||||
|
||||
### B3 (K003) {-}
|
||||
```{r}
|
||||
displayFunction1(litdata, "B3")
|
||||
displayFunction1(litdata, "B3")
|
||||
```
|
||||
|
||||
### D1_1 (H001_001) {-}
|
||||
```{r}
|
||||
displayFunction1(litdata, "D1_1")
|
||||
displayFunction1(litdata, "D1_1")
|
||||
```
|
||||
|
||||
### D5 (H005) {-}
|
||||
```{r}
|
||||
displayFunction1(litdata, "D5")
|
||||
displayFunction1(litdata, "D5")
|
||||
```
|
||||
|
||||
### D7 (H007) {-}
|
||||
```{r}
|
||||
displayFunction1(litdata, "D7")
|
||||
displayFunction1(litdata, "D7")
|
||||
```
|
||||
|
||||
Die Warnung resultiert daraus, dass es sehr viele *NA* gibt.
|
||||
|
||||
### D8 (H008) {-}
|
||||
```{r}
|
||||
displayFunction1(litdata, "D8")
|
||||
displayFunction1(litdata, "D8")
|
||||
```
|
||||
|
||||
# Selbststudium 2.1
|
||||
|
|
@ -368,14 +351,14 @@ We have the year 2021
|
|||
birthyears <- litdata$D7
|
||||
# remove NAs
|
||||
birthyears <- birthyears[!is.na(birthyears)]
|
||||
age <- 2021-birthyears
|
||||
age <- 2021 - birthyears
|
||||
```
|
||||
|
||||
## Frequency
|
||||
```{r}
|
||||
freq(age)
|
||||
# with NA
|
||||
freq(2021-litdata$D7)
|
||||
freq(2021 - litdata$D7)
|
||||
```
|
||||
|
||||
## Selbststudium 2
|
||||
|
|
@ -399,7 +382,7 @@ mean(age)
|
|||
|
||||
### Spannweite
|
||||
```{r}
|
||||
max(age)-min(age)
|
||||
max(age) - min(age)
|
||||
```
|
||||
|
||||
### Quartilsabstand
|
||||
|
|
@ -511,6 +494,6 @@ pnorm(5.1, mean = 5.6, sd = 1.8)
|
|||
In einem Bündner Ort beträgt der Mittelwert auf der Skala zur sozialen Herkunft 5.6, er ist also genauso hoch wie in der Gesamtschweiz. Was ist zur Lage dieses Wertes bezogen auf die Verteilung in Graubünden zu sagen? Also: Wie viele Schüler in GR liegen mit ihrem Wert darunter?
|
||||
|
||||
```{r}
|
||||
pnorm(5.6, mean = 5.1, sd=2.0)
|
||||
pnorm(5.6, mean = 5.1, sd = 2.0)
|
||||
```
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue