From 77a7de35936e4614f908742baa0036ac89eac474 Mon Sep 17 00:00:00 2001 From: Manny Gimond Date: Fri, 23 Feb 2024 11:49:58 -0500 Subject: [PATCH] sticking to summarise() syntax --- docs/group_by.html | 12 ++++++------ docs/search.json | 2 +- group_by.qmd | 6 +++--- ...unk-1_9fb98a5adc80a23c31eedbe88b93a06a.RData | Bin 164 -> 0 bytes ...chunk-1_9fb98a5adc80a23c31eedbe88b93a06a.rdb | Bin 1370 -> 0 bytes ...chunk-1_9fb98a5adc80a23c31eedbe88b93a06a.rdx | Bin 158 -> 0 bytes ...unk-2_cd2338c52672295a1db060b6e3202081.RData | Bin 785 -> 787 bytes ...chunk-2_cd2338c52672295a1db060b6e3202081.rdb | Bin 318 -> 320 bytes ...chunk-2_cd2338c52672295a1db060b6e3202081.rdx | Bin 205 -> 205 bytes ...unk-4_d81779338be38082a9993debd3a81a92.RData | Bin 384 -> 0 bytes ...unk-4_db15eeb225ba52d8d0d6c4ebaf271c32.RData | Bin 0 -> 383 bytes ...hunk-4_db15eeb225ba52d8d0d6c4ebaf271c32.rdb} | 0 ...hunk-4_db15eeb225ba52d8d0d6c4ebaf271c32.rdx} | Bin ...unk-6_6de698b8961425ae8b983a1493c2e376.RData | Bin 0 -> 429 bytes ...hunk-6_6de698b8961425ae8b983a1493c2e376.rdb} | 0 ...hunk-6_6de698b8961425ae8b983a1493c2e376.rdx} | Bin ...unk-6_f31d24f5b42ab4b84a1c2090a1293f36.RData | Bin 430 -> 0 bytes 17 files changed, 10 insertions(+), 10 deletions(-) delete mode 100644 group_by_cache/html/unnamed-chunk-1_9fb98a5adc80a23c31eedbe88b93a06a.RData delete mode 100644 group_by_cache/html/unnamed-chunk-1_9fb98a5adc80a23c31eedbe88b93a06a.rdb delete mode 100644 group_by_cache/html/unnamed-chunk-1_9fb98a5adc80a23c31eedbe88b93a06a.rdx delete mode 100644 group_by_cache/html/unnamed-chunk-4_d81779338be38082a9993debd3a81a92.RData create mode 100644 group_by_cache/html/unnamed-chunk-4_db15eeb225ba52d8d0d6c4ebaf271c32.RData rename group_by_cache/html/{unnamed-chunk-4_d81779338be38082a9993debd3a81a92.rdb => unnamed-chunk-4_db15eeb225ba52d8d0d6c4ebaf271c32.rdb} (100%) rename group_by_cache/html/{unnamed-chunk-4_d81779338be38082a9993debd3a81a92.rdx => unnamed-chunk-4_db15eeb225ba52d8d0d6c4ebaf271c32.rdx} (100%) create mode 100644 group_by_cache/html/unnamed-chunk-6_6de698b8961425ae8b983a1493c2e376.RData rename group_by_cache/html/{unnamed-chunk-6_f31d24f5b42ab4b84a1c2090a1293f36.rdb => unnamed-chunk-6_6de698b8961425ae8b983a1493c2e376.rdb} (100%) rename group_by_cache/html/{unnamed-chunk-6_f31d24f5b42ab4b84a1c2090a1293f36.rdx => unnamed-chunk-6_6de698b8961425ae8b983a1493c2e376.rdx} (100%) delete mode 100644 group_by_cache/html/unnamed-chunk-6_f31d24f5b42ab4b84a1c2090a1293f36.RData diff --git a/docs/group_by.html b/docs/group_by.html index 296dc586..25c89a95 100644 --- a/docs/group_by.html +++ b/docs/group_by.html @@ -336,7 +336,7 @@
  • - + 23  A working example: t-tests and re-expression
    • @@ -512,13 +512,13 @@

    The data table has three variables: Weekday, Quarter and Delay. Delay is the value we will summarize which leaves us with one variable to collapse: Quarter. In doing so, we will compute the Delay statistics for all quarters associated with a unique Weekday value.

    -

    This workflow requires two operations: a grouping operation using the group_by function and a summary operation using the summarise function. Here, we’ll compute two summary statistics: minimum delay time and maximum delay time.

    -
    +

    This workflow requires two operations: a grouping operation using the group_by function and a summary operation using the summarise/summarize function. Here, we’ll compute two summary statistics: minimum delay time and maximum delay time.

    +
    library(dplyr)
 
 df %>% 
   group_by(Weekday) %>% 
-  summarize(min_delay = min(Delay), max_delay = max(Delay))
    + summarise(min_delay = min(Delay), max_delay = max(Delay))
    # A tibble: 5 × 3
   Weekday min_delay max_delay
@@ -546,10 +546,10 @@ 
    
 
    This time, the data table has four variables. We are wanting to summarize by Quater and Week which leaves one variable, Direction, that needs to be collapsed.
    
-
    
+
    
 
    df2 %>% 
   group_by(Quarter, Week) %>% 
-  summarize(min_delay = min(Delay), max_delay = max(Delay))
    
+  summarise(min_delay = min(Delay), max_delay = max(Delay))
    # A tibble: 8 × 4
 # Groups:   Quarter [4]
diff --git a/docs/search.json b/docs/search.json
index fa1d6c52..5c97cd79 100755
--- a/docs/search.json
+++ b/docs/search.json
@@ -326,7 +326,7 @@
     "href": "group_by.html#summarizing-data-by-group",
     "title": "10  Grouping and summarizing",
     "section": "10.1 Summarizing data by group",
-    "text": "10.1 Summarizing data by group\nLet’s first create a dataframe listing the average delay time in minutes, by day of the week and by quarter, for Logan airport’s 2014 outbound flights.\n\ndf <- data.frame(\n  Weekday = factor(rep(c(\"Mon\", \"Tues\", \"Wed\", \"Thurs\", \"Fri\"), each = 4), \n                   levels = c(\"Mon\", \"Tues\", \"Wed\", \"Thurs\", \"Fri\")),\n  Quarter = paste0(\"Q\", rep(1:4, each = 5)), \n  Delay = c(9.9, 5.4, 8.8, 6.9, 4.9, 9.7, 7.9, 5, 8.8, 11.1, 10.2, 9.3, 12.2,\n            10.2, 9.2, 9.7, 12.2, 8.1, 7.9, 5.6))\n\nThe goal will be to summarize the table by Weekday as shown in the following graphic.\n\nThe data table has three variables: Weekday, Quarter and Delay. Delay is the value we will summarize which leaves us with one variable to collapse: Quarter. In doing so, we will compute the Delay statistics for all quarters associated with a unique Weekday value.\nThis workflow requires two operations: a grouping operation using the group_by function and a summary operation using the summarise function. Here, we’ll compute two summary statistics: minimum delay time and maximum delay time.\n\nlibrary(dplyr)\n\ndf %>% \n  group_by(Weekday) %>% \n  summarize(min_delay = min(Delay), max_delay = max(Delay))\n\n# A tibble: 5 × 3\n  Weekday min_delay max_delay\n  <fct>       <dbl>     <dbl>\n1 Mon           5.4       9.9\n2 Tues          4.9       9.7\n3 Wed           8.8      11.1\n4 Thurs         9.2      12.2\n5 Fri           5.6      12.2\n\n\nNote that the weekday follows the chronological order as defined in the Weekday factor.\nYou’ll also note that the output is a tibble. This data class is discussed at the end of this page.\n\n10.1.1 Grouping by multiple variables\nYou can group by more than one variable. For example, let’s build another dataframe listing the average delay time in minutes, by quarter, by weekend/weekday and by inbound/outbound status for Logan airport’s 2014 outbound flights.\n\ndf2 <- data.frame(\n  Quarter = paste0(\"Q\", rep(1:4, each = 4)), \n  Week = rep(c(\"Weekday\", \"Weekend\"), each=2, times=4),\n  Direction = rep(c(\"Inbound\", \"Outbound\"), times=8),\n  Delay = c(10.8, 9.7, 15.5, 10.4, 11.8, 8.9, 5.5, \n            3.3, 10.6, 8.8, 6.6, 5.2, 9.1, 7.3, 5.3, 4.4))\n\nThe goal will be to summarize the delay time by Quarter and by Week type as shown in the following graphic.\n\nThis time, the data table has four variables. We are wanting to summarize by Quater and Week which leaves one variable, Direction, that needs to be collapsed.\n\ndf2 %>% \n  group_by(Quarter, Week) %>% \n  summarize(min_delay = min(Delay), max_delay = max(Delay))\n\n# A tibble: 8 × 4\n# Groups:   Quarter [4]\n  Quarter Week    min_delay max_delay\n  <chr>   <chr>       <dbl>     <dbl>\n1 Q1      Weekday       9.7      10.8\n2 Q1      Weekend      10.4      15.5\n3 Q2      Weekday       8.9      11.8\n4 Q2      Weekend       3.3       5.5\n5 Q3      Weekday       8.8      10.6\n6 Q3      Weekend       5.2       6.6\n7 Q4      Weekday       7.3       9.1\n8 Q4      Weekend       4.4       5.3\n\n\nThe following section demonstrates other grouping/summarizing operations on a larger dataset."
+    "text": "10.1 Summarizing data by group\nLet’s first create a dataframe listing the average delay time in minutes, by day of the week and by quarter, for Logan airport’s 2014 outbound flights.\n\ndf <- data.frame(\n  Weekday = factor(rep(c(\"Mon\", \"Tues\", \"Wed\", \"Thurs\", \"Fri\"), each = 4), \n                   levels = c(\"Mon\", \"Tues\", \"Wed\", \"Thurs\", \"Fri\")),\n  Quarter = paste0(\"Q\", rep(1:4, each = 5)), \n  Delay = c(9.9, 5.4, 8.8, 6.9, 4.9, 9.7, 7.9, 5, 8.8, 11.1, 10.2, 9.3, 12.2,\n            10.2, 9.2, 9.7, 12.2, 8.1, 7.9, 5.6))\n\nThe goal will be to summarize the table by Weekday as shown in the following graphic.\n\nThe data table has three variables: Weekday, Quarter and Delay. Delay is the value we will summarize which leaves us with one variable to collapse: Quarter. In doing so, we will compute the Delay statistics for all quarters associated with a unique Weekday value.\nThis workflow requires two operations: a grouping operation using the group_by function and a summary operation using the summarise/summarize function. Here, we’ll compute two summary statistics: minimum delay time and maximum delay time.\n\nlibrary(dplyr)\n\ndf %>% \n  group_by(Weekday) %>% \n  summarise(min_delay = min(Delay), max_delay = max(Delay))\n\n# A tibble: 5 × 3\n  Weekday min_delay max_delay\n  <fct>       <dbl>     <dbl>\n1 Mon           5.4       9.9\n2 Tues          4.9       9.7\n3 Wed           8.8      11.1\n4 Thurs         9.2      12.2\n5 Fri           5.6      12.2\n\n\nNote that the weekday follows the chronological order as defined in the Weekday factor.\nYou’ll also note that the output is a tibble. This data class is discussed at the end of this page.\n\n10.1.1 Grouping by multiple variables\nYou can group by more than one variable. For example, let’s build another dataframe listing the average delay time in minutes, by quarter, by weekend/weekday and by inbound/outbound status for Logan airport’s 2014 outbound flights.\n\ndf2 <- data.frame(\n  Quarter = paste0(\"Q\", rep(1:4, each = 4)), \n  Week = rep(c(\"Weekday\", \"Weekend\"), each=2, times=4),\n  Direction = rep(c(\"Inbound\", \"Outbound\"), times=8),\n  Delay = c(10.8, 9.7, 15.5, 10.4, 11.8, 8.9, 5.5, \n            3.3, 10.6, 8.8, 6.6, 5.2, 9.1, 7.3, 5.3, 4.4))\n\nThe goal will be to summarize the delay time by Quarter and by Week type as shown in the following graphic.\n\nThis time, the data table has four variables. We are wanting to summarize by Quater and Week which leaves one variable, Direction, that needs to be collapsed.\n\ndf2 %>% \n  group_by(Quarter, Week) %>% \n  summarise(min_delay = min(Delay), max_delay = max(Delay))\n\n# A tibble: 8 × 4\n# Groups:   Quarter [4]\n  Quarter Week    min_delay max_delay\n  <chr>   <chr>       <dbl>     <dbl>\n1 Q1      Weekday       9.7      10.8\n2 Q1      Weekend      10.4      15.5\n3 Q2      Weekday       8.9      11.8\n4 Q2      Weekend       3.3       5.5\n5 Q3      Weekday       8.8      10.6\n6 Q3      Weekend       5.2       6.6\n7 Q4      Weekday       7.3       9.1\n8 Q4      Weekend       4.4       5.3\n\n\nThe following section demonstrates other grouping/summarizing operations on a larger dataset."
   },
   {
     "objectID": "group_by.html#a-working-example",
diff --git a/group_by.qmd b/group_by.qmd
index 1ad6a9cf..9926e941 100644
--- a/group_by.qmd
+++ b/group_by.qmd
@@ -30,14 +30,14 @@ The goal will be to summarize the table by `Weekday` as shown in the following g
 
 The data table has three variables: `Weekday`, `Quarter` and `Delay`. `Delay` is the value we will summarize which leaves us with one variable to *collapse*: `Quarter`. In doing so, we will compute the `Delay` statistics for all quarters associated with a unique `Weekday` value.
 
-This workflow requires two operations: a grouping operation using the `group_by` function and a summary operation using the `summarise` function. Here, we'll compute two summary statistics: minimum delay time and maximum delay time.
+This workflow requires two operations: a grouping operation using the `group_by` function and a summary operation using the `summarise`/`summarize` function. Here, we'll compute two summary statistics: minimum delay time and maximum delay time.
 
 ```{r}
 library(dplyr)
 
 df %>% 
   group_by(Weekday) %>% 
-  summarize(min_delay = min(Delay), max_delay = max(Delay))
+  summarise(min_delay = min(Delay), max_delay = max(Delay))
 ```
 
 Note that the weekday follows the chronological order as defined in the `Weekday` factor.
@@ -66,7 +66,7 @@ This time, the data table has four variables. We are wanting to summarize by `Qu
 ```{r}
 df2 %>% 
   group_by(Quarter, Week) %>% 
-  summarize(min_delay = min(Delay), max_delay = max(Delay))
+  summarise(min_delay = min(Delay), max_delay = max(Delay))
 ```
 
 The following section demonstrates other  grouping/summarizing operations on a larger dataset.
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