R codes for hypothesis test in Rmarkdown - r
I am really struggling on the coding part for the R markdown but have no one to ask...
The data I am working on is, dput(survey):
structure(list(Time = structure(c(5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 1L, 2L, 3L, 4L, 35L, 42L, 46L, 30L, 31L,
33L, 34L, 29L, 36L, 37L, 38L, 39L, 40L, 41L, 43L, 44L, 45L, 47L,
48L, 32L, 54L, 55L, 50L, 49L, 51L, 52L, 53L, 57L, 59L, 56L, 60L,
61L, 58L, 62L), .Label = c("2017/08/06 10:25:01 PM GMT+10", "2017/08/06 10:26:54 PM GMT+10",
"2017/08/06 10:38:13 PM GMT+10", "2017/08/06 10:51:58 PM GMT+10",
"2017/08/06 4:53:07 PM GMT+10", "2017/08/06 4:58:44 PM GMT+10",
"2017/08/06 5:01:05 PM GMT+10", "2017/08/06 5:03:25 PM GMT+10",
"2017/08/06 5:04:50 PM GMT+10", "2017/08/06 5:06:51 PM GMT+10",
"2017/08/06 5:06:54 PM GMT+10", "2017/08/06 5:10:57 PM GMT+10",
"2017/08/06 5:11:16 PM GMT+10", "2017/08/06 5:18:21 PM GMT+10",
"2017/08/06 5:23:46 PM GMT+10", "2017/08/06 5:34:02 PM GMT+10",
"2017/08/06 5:43:10 PM GMT+10", "2017/08/06 5:54:52 PM GMT+10",
"2017/08/06 6:04:06 PM GMT+10", "2017/08/06 7:11:00 PM GMT+10",
"2017/08/06 7:13:21 PM GMT+10", "2017/08/06 7:32:45 PM GMT+10",
"2017/08/06 7:33:58 PM GMT+10", "2017/08/06 7:50:31 PM GMT+10",
"2017/08/06 8:02:07 PM GMT+10", "2017/08/06 8:28:39 PM GMT+10",
"2017/08/06 8:36:46 PM GMT+10", "2017/08/06 9:14:14 PM GMT+10",
"2017/08/07 1:59:14 PM GMT+10", "2017/08/07 10:28:13 AM GMT+10",
"2017/08/07 11:05:40 AM GMT+10", "2017/08/07 11:44:09 PM GMT+10",
"2017/08/07 12:18:04 PM GMT+10", "2017/08/07 12:49:27 PM GMT+10",
"2017/08/07 12:55:41 AM GMT+10", "2017/08/07 2:04:49 PM GMT+10",
"2017/08/07 2:14:56 PM GMT+10", "2017/08/07 2:17:10 PM GMT+10",
"2017/08/07 4:47:38 PM GMT+10", "2017/08/07 4:57:15 PM GMT+10",
"2017/08/07 7:08:44 PM GMT+10", "2017/08/07 9:12:16 AM GMT+10",
"2017/08/07 9:18:11 PM GMT+10", "2017/08/07 9:22:59 PM GMT+10",
"2017/08/07 9:23:43 PM GMT+10", "2017/08/07 9:32:10 AM GMT+10",
"2017/08/07 9:46:41 PM GMT+10", "2017/08/07 9:55:01 PM GMT+10",
"2017/08/08 1:36:16 PM GMT+10", "2017/08/08 10:27:59 AM GMT+10",
"2017/08/08 3:36:15 PM GMT+10", "2017/08/08 4:15:12 PM GMT+10",
"2017/08/08 6:39:28 PM GMT+10", "2017/08/08 8:44:38 AM GMT+10",
"2017/08/08 9:03:07 AM GMT+10", "2017/08/09 1:00:16 PM GMT+10",
"2017/08/09 10:17:55 AM GMT+10", "2017/08/09 10:26:28 PM GMT+10",
"2017/08/09 11:50:50 AM GMT+10", "2017/08/09 3:02:39 PM GMT+10",
"2017/08/09 9:48:19 PM GMT+10", "2017/08/10 7:32:00 AM GMT+10"
), class = "factor"), ID = structure(c(48L, 57L, 38L, 9L, 8L,
42L, 41L, 58L, 31L, 27L, 60L, 34L, 13L, 37L, 40L, 29L, 53L, 28L,
16L, 20L, 47L, 18L, 51L, 3L, 36L, 10L, 32L, 11L, 54L, 22L, 61L,
15L, 35L, 2L, 25L, 55L, 17L, 5L, 14L, 21L, 49L, 45L, 6L, 30L,
26L, 4L, 19L, 50L, 44L, 56L, 43L, 59L, 24L, 12L, 52L, 23L, 1L,
39L, 7L, 62L, 46L, 33L), .Label = c("1907", "3456", "450181964",
"460061490", "A", "ABCABCABC", "adsad", "affordance", "alexxx",
"AliceJ", "blueberry11", "Bob", "byue7515", "Cameron Nichols",
"Coelacanth", "crocophile", "Donald trump ", "DS2012-LB-S", "Gir",
"goly", "Grace", "greyshirt", "grob6576", "hahahahaha", "Harry",
"Insidestella", "ja150", "jane", "Jiashu Wu", "jmc", "Joohee0214",
"kakinna", "Kimbo Slice", "lhar7524", "lizebin", "Lucy", "Magician1213",
"Matchey", "md123", "mia", "MP", "N52981227", "Nattt", "Pete",
"rcon", "Ryan_eats_p-values", "S123", "Salmon ", "smarcon", "smile",
"snail", "sonja kay", "Thelimitdoesnotexist", "Toflin", "Tony Stark ",
"UriLover420", "valerie", "Whatzup", "Winky", "xwn19960829",
"zilu2637", "ZXFAARON"), class = "factor"), Gender = structure(c(3L,
2L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 2L,
2L, 1L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L,
2L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
4L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 3L), .Label = c("crocodilian",
"Female", "Male", "Poisson"), class = "factor"), Postcode =structure(c(12L,
30L, 20L, 35L, 28L, 33L, 13L, 22L, 12L, 2L, 3L, 38L, 25L, 13L,
4L, 23L, 19L, 23L, 29L, 32L, 26L, 4L, 14L, 4L, 36L, 12L, 3L,
41L, 28L, 40L, 24L, 9L, 37L, 4L, 3L, 17L, 32L, 27L, 15L, 36L,
12L, 11L, 3L, 7L, 4L, 10L, 39L, 24L, 42L, 8L, 12L, 13L, 5L, 6L,
31L, 20L, 1L, 34L, 18L, 13L, 21L, 16L), .Label = c("14052", "2000",
"2007", "2008", "2020", "2021", "2022", "2026", "2031", "2037",
"2041", "2042", "2050", "2066", "2069", "2074", "2097", "2112",
"2117", "2131", "2134", "2136", "2137", "2138", "2140", "2144",
"2154", "2165", "2166", "2171", "2193", "2200", "2205", "2209",
"2216", "2220", "2228", "2756", "2762", "2765", "2780", "sydney"
), class = "factor"), StatsCourse = structure(c(4L, 4L, 4L, 4L,
4L, 4L, 1L, 4L, 4L, 4L, 3L, 4L, 4L, 5L, 4L, 4L, 5L, 6L, 4L, 4L,
4L, 4L, 5L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 4L, 2L, 5L, 4L, 5L, 6L, 4L, 2L,
2L, 4L, 4L, 5L, 7L, 5L, 1L, 4L, 4L, 4L), .Label = c("", "BUSS1020",
"MATH1001,MATH1002", "MATH1005", "MATH1015", "MATH1905", "none"
), class = "factor"), Clubs = structure(c(1L, 1L, 4L, 5L, 4L,
2L, 4L, 4L, 2L, 4L, 7L, 2L, 4L, 4L, 1L, 4L, 1L, 4L, 1L, 1L, 6L,
1L, 4L, 1L, 11L, 4L, 5L, 10L, 3L, 5L, 2L, 4L, 1L, 1L, 2L, 1L,
4L, 4L, 4L, 6L, 2L, 2L, 4L, 4L, 9L, 4L, 1L, 8L, 2L, 4L, 2L, 6L,
4L, 4L, 11L, 5L, 1L, 1L, 1L, 4L, 4L, 1L), .Label = c("0", "1",
"10+", "2", "3", "4", "5", "6", "7", "none", "None"), class = "factor"),
StudyTime = structure(c(24L, 3L, 26L, 27L, 17L, 2L, 10L,
14L, 23L, 7L, 19L, 3L, 17L, 29L, 23L, 22L, 10L, 10L, 28L,
23L, 6L, 14L, 20L, 7L, 17L, 28L, 5L, 16L, 20L, 3L, 21L, 3L,
23L, 7L, 17L, 10L, 1L, 18L, 10L, 17L, 10L, 7L, 13L, 5L, 15L,
3L, 8L, 17L, 19L, 17L, 3L, 30L, 31L, 1L, 4L, 3L, 20L, 9L,
14L, 11L, 12L, 25L), .Label = c("0", "05-Jun", "10", "11",
"12", "14", "15", "17", "2", "20", "20-24", "20-25?", "24",
"25", "28", "28 hours", "30", "31", "35", "4", "40", "49",
"5", "50", "6", "7", "70", "8", "8hr", "didn't start uni maybe 6h",
"not sure"), class = "factor"), StudyLoad = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L), .Label = c("", "full-time", "part-time"), class = "factor"),
SocialMedia = structure(c(1L, 5L, 1L, 1L, 1L, 7L, 1L, 1L,
7L, 7L, 2L, 1L, 2L, 1L, 1L, 8L, 6L, 2L, 1L, 7L, 1L, 4L, 1L,
8L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 7L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 5L, 5L, 1L, 1L, 2L, 2L,
1L, 3L, 1L, 2L, 2L, 1L, 2L, 1L, 1L), .Label = c("Facebook",
"Instragram", "none! (really)", "reddit", "Snapchat", "Tumblr",
"Twitter", "WeChat"), class = "factor"), Siblings = structure(c(2L,
4L, 4L, 1L, 4L, 1L, 2L, 4L, 5L, 2L, 1L, 2L, 2L, 1L, 4L, 1L,
1L, 4L, 2L, 2L, 8L, 2L, 2L, 3L, 1L, 1L, 2L, 5L, 2L, 7L, 1L,
4L, 2L, 6L, 1L, 6L, 2L, 5L, 1L, 1L, 4L, 4L, 2L, 2L, 1L, 2L,
1L, 1L, 4L, 4L, 2L, 9L, 1L, 2L, 10L, 2L, 4L, 2L, 2L, 1L,
2L, 2L), .Label = c("0", "1", "165", "2", "3", "4", "5",
"6", "none", "one"), class = "factor"), FBFriends = structure(c(49L,
43L, 6L, 3L, 28L, 2L, 9L, 13L, 21L, 19L, 30L, 40L, 37L, 20L,
35L, 32L, 53L, 47L, 30L, 22L, 8L, 45L, 14L, 15L, 38L, 16L,
45L, 31L, 35L, 43L, 34L, 23L, 52L, 18L, 34L, 27L, 33L, 11L,
42L, 24L, 51L, 26L, 17L, 50L, 39L, 19L, 10L, 12L, 4L, 44L,
46L, 29L, 45L, 36L, 54L, 20L, 7L, 5L, 41L, 25L, 1L, 48L), .Label = c("~300",
"10", "100", "1000", "1127", "115", "1192", "12", "120",
"121", "130", "148", "150", "1583", "165", "170", "174",
"190", "200", "213", "228", "229", "235", "240", "242", "256",
"259", "263", "27", "300", "308", "31", "382", "40", "400",
"431", "470", "5", "540", "548", "57", "572", "600", "664",
"700", "724", "800", "850", "90", "936", "978", "do not know",
"Don't have FB", "none (not in facebook)"), class = "factor"),
Grade = structure(c(18L, 19L, 11L, 31L, 33L, 14L, 22L, 18L,
6L, 9L, 19L, 18L, 22L, 23L, 24L, 30L, 28L, 16L, 2L, 14L,
3L, 12L, 21L, 2L, 12L, 12L, 6L, 29L, 12L, 27L, 17L, 6L, 12L,
17L, 17L, 15L, 24L, 20L, 7L, 14L, 12L, 10L, 22L, 34L, 24L,
17L, 16L, 12L, 24L, 32L, 26L, 25L, 26L, 13L, 4L, 12L, 1L,
5L, 12L, 8L, 24L, 35L), .Label = c("2.8", "50", "50-60",
"54", "6.25", "60", "61", "61.5", "62", "63", "64", "65",
"65.9", "66", "68", "69", "70", "72", "73", "73.2", "73.4",
"74", "74.6", "75", "8.7", "80", "82", "82.4", "83.2", "87",
"90", "90.1", "90.5", "91", "D"), class = "factor"), Pet = structure(c(3L,
2L, 3L, 1L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 3L,
2L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 3L,
2L, 3L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 2L,
3L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L,
3L), .Label = c("", "No", "Yes"), class = "factor"), Home = structure(c(2L,
3L, 3L, 1L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L,
2L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 3L,
3L), .Label = c("", "No", "Yes"), class = "factor"), ExerciseTime = structure(c(10L,
12L, 7L, 1L, 4L, 7L, 7L, 5L, 7L, 12L, 13L, 5L, 10L, 7L, 15L,
15L, 10L, 10L, 5L, 14L, 2L, 9L, 4L, 5L, 7L, 4L, 14L, 8L,
10L, 13L, 1L, 13L, 1L, 13L, 13L, 5L, 7L, 16L, 16L, 14L, 10L,
14L, 7L, 6L, 12L, 10L, 10L, 13L, 13L, 14L, 7L, 11L, 2L, 2L,
17L, 16L, 7L, 7L, 2L, 3L, 13L, 15L), .Label = c("", "0",
"05-Jun", "1", "10", "12", "2", "2 hours", "20", "3", "3.5",
"4", "5", "6", "7", "8", "none"), class = "factor"), Eyecolor = structure(c(9L,
7L, 5L, 1L, 8L, 2L, 8L, 3L, 3L, 8L, 3L, 7L, 7L, 7L, 7L, 7L,
3L, 4L, 7L, 3L, 11L, 8L, 11L, 2L, 8L, 2L, 2L, 2L, 8L, 7L,
1L, 7L, 2L, 7L, 3L, 4L, 10L, 7L, 8L, 7L, 7L, 6L, 7L, 3L,
8L, 2L, 8L, 7L, 4L, 8L, 9L, 3L, 7L, 5L, 7L, 8L, 12L, 7L,
7L, 8L, 3L, 8L), .Label = c("", "black", "Black", "blue",
"Blue", "Blue/Green", "brown", "Brown", "Brown ", "Brown/black",
"dark brown", "grey"), class = "factor"), Working = structure(c(2L,
8L, 2L, 1L, 4L, 2L, 2L, 8L, 2L, 24L, 2L, 13L, 5L, 3L, 26L,
2L, 8L, 13L, 24L, 2L, 12L, 2L, 9L, 8L, 2L, 2L, 2L, 11L, 2L,
10L, 1L, 4L, 21L, 2L, 2L, 15L, 14L, 21L, 26L, 18L, 4L, 2L,
7L, 27L, 12L, 2L, 20L, 2L, 19L, 25L, 8L, 2L, 2L, 17L, 23L,
16L, 2L, 6L, 2L, 13L, 13L, 22L), .Label = c("", "0", "1.5",
"10", "11", "12", "14", "15", "17", "18", "18 hours", "2",
"20", "24", "25", "26", "3", "3.5", "30", "38", "4", "40",
"44", "5", "6", "7", "8"), class = "factor"), Season = structure(c(2L,
3L, 2L, 1L, 5L, 2L, 3L, 4L, 4L, 3L, 4L, 4L, 3L, 3L, 4L, 3L,
3L, 3L, 3L, 5L, 3L, 3L, 2L, 5L, 5L, 4L, 2L, 2L, 5L, 2L, 3L,
2L, 2L, 3L, 2L, 4L, 2L, 3L, 5L, 3L, 4L, 5L, 3L, 4L, 4L, 4L,
3L, 4L, 4L, 4L, 3L, 2L, 2L, 2L, 3L, 4L, 4L, 3L, 2L, 4L, 4L,
3L), .Label = c("", "Autumn", "Spring", "Summer", "Winter"
), class = "factor")), .Names = c("Time", "ID", "Gender",
"Postcode", "StatsCourse", "Clubs", "StudyTime", "StudyLoad",
"SocialMedia", "Siblings", "FBFriends", "Grade", "Pet", "Home",
"ExerciseTime", "Eyecolor", "Working", "Season"), class = "data.frame", row.names = c(NA,
-62L))
And what I did so far is,
library(dplyr)
library(ggplot2)
library(tidyr)
library(knitr)
survey <- read.csv("STAT2012Survey.csv")
colnames(survey)
oldname = colnames(survey)
newname = c("Time", "ID", "Gender", "Postcode", "StatsCourse", "Clubs", "StudyTime",
"StudyLoad", "SocialMedia", "Siblings", "FBFriends", "Grade", "Pet", "Home",
"ExerciseTime", "Eyecolor", "Working", "Season")
colnames(survey) = newname
What I want to achieve is, I want to provide a hypothesis test about
"Is there any evidence that there is difference in exercise time between males and females?"
To do this, I need to get the mean and standard deviation sort of that stuffs in order to test the two-sample t-test but I do not know how to approach to it
Also, to visualize the data with graph, I tried,
ggplot(survey, aes(x = Gender, y = ExerciseTime, fill = Gender)) + geom_boxplot()
however it only showed some strange graph. I think it is because the "ExerciesTime" variable is not numeric, but I am stuck on it as well since ggplot2 does not deal with the data of class numeric...
Someone please help me...! I want to make more hypothesis tests towards multiple questions but I am stuck on the first question... I might be able to achieve the goal if I know how to do the first one! Thanks.
Before you can make a boxplot, you will need to make ExerciseTime a numeric variable. The problem you will have with that is some of the responses don't easily turn numeric (2 hours, for example, should probably be 2, but it will require an extra step to get rid of the text).
As a start, though, let's just do the easiest case of take anything that isn't a natural number and let it change to a missing value.
survey2 <-
survey %>%
mutate(ExerciseTime = as.character(ExerciseTime),
ExerciseTime = str_replace(ExerciseTime, "\\d{2}-\\w{3}", ""),
ExerciseTime = str_extract(ExerciseTime, "\\d{1,2}"),
ExerciseTime = as.numeric(ExerciseTime))
ggplot(data = survey,
mapping = aes(x = Gender,
y = ExerciseTime,
fill = Gender)) +
geom_boxplot()
Related
Ordering data to match ggplot font faces
I'm currently practicing my R using tidyverse/dpylr library.
So I re-used this example from this post. I try to train with dpylr so i wanted to put value of muni_o bigger than 10 in bold in the y axis. I tried the following code:
pop=structure(list(muni_o = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L,
34L, 35L, 36L, 37L, 38L, 39L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L,
22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L,
35L, 36L, 37L, 38L, 39L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L,
23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L,
36L, 37L, 38L, 39L), .Label = c("1", "2", "3", "4", "5", "6",
"7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17",
"18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28",
"29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39"
), class = "factor"), muni_d2 = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L
), .Label = c("0", "1", "2"), class = "factor"), Freq = c(6122.14220014045,
14014.8937908602, 3212.98371738453, 3762.86973933326, 2036.58101043117,
49935.7095757787, 5660.24459378729, 27133.6814011703, 8436.05010435109,
1244.59144510279, 8192.3151623091, 5125.775303556, 5520.96031514796,
971.04877757262, 13042.4320509421, 11677.9344908548, 23179.1486978947,
19342.6692995674, 11480.8298502747, 773.75470216998, 658.84762129725,
47303.8942738045, 12189.6600233976, 34138.2766548561, 2280.85569280618,
6648.00527768305, 13731.4673121252, 6822.03535863008, 1654.5719919344,
3408.63547537664, 8289.59920490436, 68250.529943451, 42919.3498777027,
11304.5876393861, 1371.59278771158, 281250.973114875, 11758.2956764985,
12160.5828034274, 4762.03601855022, 3774.00754913303, 17629.1897496886,
450.28749520739, 11504.661338085, 2732.14923854684, 40180.8293143395,
16562.6710623701, 43865.6430358497, 36647.9484766594, 7035.57607097289,
25651.323071728, 26432.811604918, 23743.6986932767, 597.5643668538,
124644.985934809, 29773.2098362482, 13788.9040158001, 24718.4440592384,
6526.85217011751, 474.70640264902, 5440.01365034166, 29930.306857298,
11072.6889810259, 101099.685991706, 254.95577591974, 11637.2716353329,
5367.72912672273, 2504.81681919538, 97.98256049428, 2258.20425774592,
8475.80585487552, 40564.9077830157, 59907.8345066714, 12858.502494956,
850.24914346085, 0, 14152.3410516667, 41777.9839790016, 2730.8225408641,
13662.2793985781, 46579.248451629, 5035.33799662119, 9167.68431269098,
15713.4248568278, 36391.6173102699, 36523.5128982161, 56158.4213063058,
17666.1932118543, 10319.0225229469, 14744.0900996851, 14519.6971048675,
14341.9635886829, 5193.52143006156, 267285.439969485, 10049.4551896091,
17861.0606322283, 56589.3203766755, 14836.888817694, 6069.4455916734,
18992.3441918275, 52074.0110108799, 88973.5164027747, 82777.2109430964,
2270.54970959312, 12030.813277725, 15414.1038338142, 3284.84133984456,
3101.73291583232, 10020.5318813645, 25286.7675500444, 114919.563601638,
185758.597625183, 28154.2996127091, 2873.4152126078, 3503521.52693064,
49555.0928217366, 35402.2559957372, 7917.49624385274)), class = "data.frame", row.names = c(NA,
-117L))
pop<-mutate(pop, COLORS=case_when(as.numeric(muni_o)>10~ 'bold',TRUE~ 'plain'))
pop %>%
group_by(muni_o) %>%
mutate(prop = Freq / sum(Freq)) %>%
ungroup() %>%
arrange(desc(muni_d2), prop) %>%
mutate(muni_o = factor(muni_o, levels = unique(muni_o))) %>%
ggplot(aes(muni_o, prop, fill = muni_d2)) +
geom_col() +
coord_flip()+
theme(axis.text.y = element_text(face =pop$COLORS,size=14))
The column I added COLORS seems correct. But I think my COLORS column is not modified to be associated with muni_o (because of the unique function use on muni_o) so the bold/plain order doesn't correspond. Am I doing something wrong?
The issue is that the data frame you use for plotting has a different order than the original data frame pop. Hence the assignment between COLORS and muni_o is messed up. To prevent this save the df after your data wrangling as e.g. pop1 and use pop1$COLORS: library(dplyr) library(ggplot2) pop<-mutate(pop, COLORS=case_when(as.numeric(muni_o)>10~ 'bold',TRUE~ 'plain')) pop1 <- pop %>% group_by(muni_o) %>% mutate(prop = Freq / sum(Freq)) %>% ungroup() %>% arrange(desc(muni_d2), prop) %>% mutate(muni_o = factor(muni_o, levels = unique(muni_o))) pop1 %>% ggplot(aes(muni_o, prop, fill = muni_d2)) + geom_col() + coord_flip()+ theme(axis.text.y = element_text(face =pop1$COLORS,size=14)) #> Warning: Vectorized input to `element_text()` is not officially supported. #> Results may be unexpected or may change in future versions of ggplot2.
How to create histograms for each unique combination of levels from two factors?
I cannot figure out how to use a loop to plot one histogram for each unique combination of levels from TWO factors.
Here is my data: https://www.dropbox.com/sh/exsjhu23fnpwf4r/AABvitLBN1nRMpXcyYMVIOIDa?dl=0
# perhaps need to have factors
df$freq <- as.factor(df$freq)
df$time <- as.factor(df$time)
I learned how to use a loop to plot histograms for ONE factor levels:
# space for plots
windows(width=19, height=10)
par(las=1, cex.lab=0.75, cex.axis=0.6, bty="n", mgp=c(1, 0.6, 0),
oma=c(2, 4, 2, 0) + 0.1, mar=c(4, 0, 3, 3) + 0.1)
a <- layout(matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21), nrow=3, ncol=7, byrow=T))
layout.show(a)
# loop
for (i in 1:length(unique(df$freq))) {
value <- subset(df, freq == unique (df$freq)[i])
hist(value$thr, main=paste0("freq: ", unique(df$freq)[i]))
}
I tried variations of this loop for TWO factors but that unfortunately does not work:
for (i in 1:length(unique(df[c("freq", "time")]))) {
value <- subset(df, freq == unique (df$freq)[i] & time == unique(df$time)[i])
hist(value$thr, main=paste0("freq: ", unique(df$freq)[i]))
}
I would also like to learn how to label each histogram based on the levels of TWO factors (not just one)...
It's more convenient to use by here.
For the titles we prefer characters to factors.
df1[c("freq", "time")] <- lapply(df1[c("freq", "time")], as.character)
Then open windows,
windows(width=19, height=10)
par(las=1, cex.lab=0.75, cex.axis=0.6, bty="n", mgp=c(1, 0.6, 0),
oma=c(2, 4, 2, 0) + 0.1, mar=c(4, 0, 3, 3) + 0.1)
a <- layout(matrix(1:21, 3, 7))
layout.show(a)
and plot.
by(df1, df1[c("freq", "time")], function(x)
hist(x$thr, main=paste("freq:", paste(x[1, c(1, 3)], collapse=","))))
Result
Edit
To get the specific order we probably have to do some more stuff.
df1[c("freq", "time")] <- lapply(df1[c("freq", "time")], as.character)
windows(width=19, height=10)
par(las=1, cex.lab=0.75, cex.axis=0.6, bty="n", mgp=c(1, 0.6, 0),
oma=c(2, 4, 2, 0) + 0.1, mar=c(4, 0, 3, 3) + 0.1)
a <- layout(matrix(1:21, 3, 7, byrow=TRUE)) # with byrow
layout.show(a)
l <- split(df1, df1[c("freq", "time")])
m <- t(sapply(l, function(x) x[1, c(1, 3)])) # matrix of first rows of each subset
m[, 2] <- sub("m", "", m[, 2]) # use the values...
m <- apply(m, 1:2, as.numeric) # ... make numeric
Now we obtain the histograms within a lapply over the list ordered by m.
lapply(l[order(m[, 2], m[, 1])], function(x)
hist(x$thr, main=paste("freq:", paste(x[1, c(1, 3)], collapse=","))))
New Result
Data
df1 <- structure(list(freq = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 1L, 2L, 3L, 4L, 5L, 6L, 7L), .Label = c("4",
"8", "12.5", "16", "20", "25", "31.5"), class = "factor"), thr = c(60L,
25L, 20L, 15L, 15L, 30L, 35L, 60L, 25L, 10L, 15L, 15L, 30L, 35L,
55L, 30L, 15L, 15L, 10L, 25L, 40L, 50L, 25L, 15L, 10L, 15L, 20L,
40L, 50L, 30L, 10L, 15L, 15L, 20L, 25L, 50L, 25L, 10L, 10L, 10L,
20L, 25L, 45L, 20L, 10L, 10L, 10L, 20L, 25L, 45L, 15L, 10L, 10L,
10L, 20L, 30L, 60L, 30L, 10L, 10L, 10L, 15L, 30L, 50L, 25L, 10L,
10L, 10L, 20L, 30L, 45L, 25L, 15L, 10L, 15L, 30L, 35L, 50L, 25L,
15L, 10L, 15L, 25L, 35L, 60L, 25L, 10L, 10L, 15L, 20L, 30L, 60L,
25L, 5L, 5L, 10L, 20L, 30L, 45L, 20L, 5L, 10L, 10L, 20L, 30L,
45L, 20L, 10L, 10L, 10L, 20L, 30L, 60L, 30L, 15L, 10L, 15L, 25L,
30L, 55L, 25L, 10L, 10L, 10L, 20L, 30L, 55L, 35L, 10L, 10L, 10L,
20L, 30L, 60L, 35L, 15L, 10L, 10L, 15L, 25L, 50L, 30L, 10L, 10L,
10L, 20L, 25L, 55L, 25L, 10L, 10L, 15L, 25L, 25L, 65L, 30L, 10L,
10L, 15L, 20L, 30L, 60L, 30L, 15L, 15L, 15L, 15L, 30L, 55L, 35L,
15L, 15L, 15L, 25L, 35L, 55L, 35L, 15L, 15L, 15L, 25L, 35L, 60L,
35L, 15L, 15L, 15L, 25L, 35L, 60L, 30L, 10L, 10L, 15L, 25L, 35L,
55L, 30L, 15L, 10L, 10L, 25L, 30L, 50L, 25L, 10L, 10L, 10L, 20L,
30L, 55L, 30L, 10L, 10L, 15L, 20L, 30L, 55L, 30L, 10L, 15L, 20L,
25L, 35L, 55L, 25L, 15L, 15L, 15L, 25L, 40L, 50L, 20L, 10L, 10L,
20L, 30L, 40L, 45L, 25L, 10L, 10L, 10L, 20L, 30L, 50L, 25L, 10L,
10L, 10L, 20L, 25L, 55L, 20L, 10L, 10L, 15L, 25L, 35L, 50L, 20L,
10L, 10L, 15L, 25L, 30L, 45L, 20L, 15L, 10L, 10L, 20L, 30L, 50L,
20L, 15L, 15L, 15L, 20L, 30L, 60L, 35L, 15L, 10L, 15L, 25L, 30L,
60L, 35L, 15L, 15L, 15L, 30L, 35L, 55L, 25L, 10L, 15L, 15L, 25L,
35L, 50L, 30L, 10L, 15L, 15L, 25L, 35L, 55L, 25L, 20L, 15L, 15L,
25L, 30L, 55L, 25L, 15L, 15L, 15L, 30L, 35L), time = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L), .Label = c("3m", "6m", "9m"), class = "factor")), row.names = c(NA,
-322L), class = "data.frame")
Weekday Factor displaying alphabetically
I'm trying to create boxplots using the code below. I've also included sample data. It's time-series data, I have a date-time field, and I've created a factor for Weekday. The problem I have is that my Weekdays when displayed in the boxplot are out of order. I think it's doing it alphabetically instead of chronologically. Does anyone know how to fix this? Also I'd like to rotate the x-axis labels like 45 degrees, so they'll all fit, and have them all displayed.
Code:
boxplot(OrderCnt ~ Weekday, data=icartdf_factor, main="Orders vs Weekday", xlab="Weekday", ylab="Orders")
Sample Data:
dput(droplevels(icartdf_factor[1:50,]))
structure(list(OrderCnt = c(1L, 1L, 0L, 0L, 0L, 2L, 5L, 12L,
16L, 30L, 27L, 21L, 23L, 27L, 37L, 36L, 35L, 30L, 27L, 17L, 8L,
2L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 6L, 9L, 19L, 28L, 27L, 28L,
22L, 29L, 41L, 35L, 43L, 42L, 27L, 16L, 10L, 2L, 1L, 0L, 0L,
0L), DateTime = structure(c(1398931200, 1398934800, 1398938400,
1398942000, 1398945600, 1398949200, 1398952800, 1398956400, 1398960000,
1398963600, 1398967200, 1398970800, 1398974400, 1398978000, 1398981600,
1398985200, 1398988800, 1398992400, 1398996000, 1398999600, 1399003200,
1399006800, 1399010400, 1399014000, 1399017600, 1399021200, 1399024800,
1399028400, 1399032000, 1399035600, 1399039200, 1399042800, 1399046400,
1399050000, 1399053600, 1399057200, 1399060800, 1399064400, 1399068000,
1399071600, 1399075200, 1399078800, 1399082400, 1399086000, 1399089600,
1399093200, 1399096800, 1399100400, 1399104000, 1399107600), class = c("POSIXct",
"POSIXt")), Weekday = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L), .Label = c("Friday",
"Saturday", "Thursday"), class = "factor"), hourcol = structure(c(2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 1L, 2L, 3L), .Label = c("00", "01",
"02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12",
"13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23"
), class = "factor")), .Names = c("OrderCnt", "DateTime", "Weekday",
"hourcol"), row.names = c(NA, 50L), class = "data.frame")
You just need to specify the desired order within the factor itself.
icartdf_factor$Weekday = factor(icartdf_factor$Weekday,
levels = c("Thursday", "Friday", "Saturday"))
boxplot(OrderCnt ~ Weekday, data=icartdf_factor,
main="Orders vs Weekday", xlab="Weekday", ylab="Orders")
To change the angle of the labels, take a look at How can I change the angle of the value labels on my axes?
plot group and category means with group_by
I am new to R and trying to figure out a way to plot means for individual samples as well as group means with ggplot.
I am following this articles on R-bloggers (last paragraph):
https://www.r-bloggers.com/plotting-individual-observations-and-group-means-with-ggplot2/
This is my code:
gd <- meanplot1 %>%
group_by(treatment, value) %>%
summarise(measurement = mean(measurement))
ggplot(meanplot1, aes(x=value, y=measurement, color=treatment)) +
geom_line(aes(group=sample), alpha=0.3) +
geom_line(data=gd, size=3, alpha=0.9) +
theme_bw()
Whilst the sample means are being shown, the group means aren´t. I get the error
geom_path: Each group consists of only one observation. Do you need
to adjust the group aesthetic?
Upon adding group=1, I get a weirdly mixed category mean, but not what I am looking for..
I scrolled through a lot of articles already, but couldnt find an answer - I would be so happy if somebody could help me out here!! :)
My data (meanplot1) is formatted like this:
treatment sample value measurement
1 control, control 1, initial, 20,
2 control, control 1, 26, NA,
3 control, control 1, 26', 28,
12 control, control 2, initial, 22,
13 control control 2, 26, NA,
14 control control 2, 26', 36,
15 control control 2, 28, 45,
67 stressed, stress 1, initial, 37,
68 stressed, stress 1, 26, NA,
69 stressed, stress 1, 26', 17,
78 stressed, stress 2, initial, 36,
79 stressed, stress 2, 26, NA,
80 stressed, stress 2, 26', 25,
I am hoping to see 6 lines, one mean for stress 1, stress 2, control 1 and control 2, and one mean for all treatment=control, and one for all treatment=stressed
output dput(gd):
structure(list(treatment = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), .Label = c("control", "stressed"), class = "factor"), value = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L), .Label = c("26", "26'", "28", "28'",
"30", "30'", "32", "32'", "34", "34'", "initial"), class = "factor"),
measurement = c(NA, 32.3333333333333, 39.5, 30.3333333333333,
31.8333333333333, 31.8333333333333, NA, 36, 34.6666666666667,
36, 24.6666666666667, NA, 25.3333333333333, 33.3333333333333,
32, 50.1666666666667, 39.1666666666667, NA, 33.5, 24.3333333333333,
27.3333333333333, 36)), class = c("grouped_df", "tbl_df",
"tbl", "data.frame"), row.names = c(NA, -22L), vars = list(treatment), drop = TRUE, .Names = c("treatment",
"value", "measurement"))
output dput(meanplot1):
structure(list(treatment = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("control",
"stressed"), class = "factor"), sample = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L), .Label = c("control 1",
"control 2", "control 3", "control 4", "control 5", "control 6",
"stress 1", "stress 2", "stress 3", "stress 4", "stress 5", "stress 6"
), class = "factor"), value = structure(c(11L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), .Label = c("26", "26'",
"28", "28'", "30", "30'", "32", "32'", "34", "34'", "initial"
), class = "factor"), measurement = c(20L, NA, 28L, 18L, 17L,
19L, 34L, NA, 23L, 29L, 27L, 22L, NA, 36L, 45L, 31L, 40L, 44L,
NA, 49L, 40L, 39L, 32L, NA, 35L, 57L, 30L, 37L, 29L, NA, 44L,
37L, 46L, 20L, NA, 39L, 27L, 30L, 40L, 25L, NA, 29L, 50L, 30L,
26L, NA, 28L, 45L, 47L, 27L, 35L, NA, 24L, 22L, 35L, 28L, NA,
28L, 45L, 27L, 28L, 24L, NA, 47L, 30L, 39L, 37L, NA, 17L, 29L,
29L, 31L, 29L, NA, 37L, 21L, 27L, 36L, NA, 25L, 41L, 51L, 66L,
50L, NA, 33L, 25L, 22L, 36L, NA, 33L, 45L, 26L, 72L, 59L, NA,
33L, 26L, 25L, 33L, NA, 21L, 33L, 25L, 29L, 21L, NA, 26L, 20L,
16L, 22L, NA, 30L, 27L, 28L, 57L, 41L, NA, 28L, 23L, 17L, 52L,
NA, 26L, 25L, 33L, 46L, 35L, NA, 44L, 31L, 57L)), .Names = c("treatment",
"sample", "value", "measurement"), class = "data.frame", row.names = c(NA,
-132L))
I suppose you are aiming to plot the treatment means. By default, since you are using a categorical x-axis, the grouping is set to the interaction between x and color. You only want to group by treatment, however. So we'll add the correct grouping to the call. ggplot(meanplot1, aes(x = value, y = measurement, color=treatment)) + geom_line(aes(group=sample), alpha=0.3) + geom_line(aes(group = treatment), gd, size=3, alpha=0.9) + theme_bw() Also note that ggplot(meanplot1, aes(x=value, y=measurement, color=treatment)) + geom_line(aes(group=sample), alpha=0.3) + stat_summary(aes(group = treatment), fun.y = mean, geom = 'line', size=3, alpha=0.9) + theme_bw() Gives the same plot, without the interruption.
Use dplyr to find genotype frequency across SNPs
To find genotype frequency across SNPs I need to find the proportion of a certain genotype (XX, YX, or YY) in the total number of samples (XX, YX, and YY). I think I would need to start my dplyr statement with
dat %>% group_by(Assay) %>%
but I don't know how to finish it. The data, dat, provided below and dput at the bottom.
Source: local data frame [143 x 3]
Groups: Assay
Assay Final n
1 One_apoe-83 Invalid 2
2 One_apoe-83 No Call 9
3 One_apoe-83 NTC 2
4 One_apoe-83 XX 4
5 One_apoe-83 YX 41
6 One_apoe-83 YY 134
7 One_CD9-269 Invalid 2
8 One_CD9-269 No Call 5
9 One_CD9-269 NTC 2
10 One_CD9-269 XX 99
.. ... ... ...
I could use a for loop across SNPs to get what I'm looking for with boolean patterning for each genotype but that would be very verbose.
for(i in seq(levels(dat$Assay))) {
storage_df[i,1] <- dat[dat$Assay == levels(dat$Assay)[i],]$XX / (dat[dat$Assay == levels(dat$Assay)[i],]$XX + dat[dat$Assay == levels(dat$Assay)[i],]$YX + dat[dat$Assay == levels(dat$Assay)[i],]$XY) ...
You get the point. How would I do this in dplyr? The whole object is below.
dat <- structure(list(Assay = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L,
7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L,
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L,
14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L,
16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L,
18L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L,
21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 23L,
23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L), .Label = c("One_apoe-83",
"One_CD9-269", "One_Cytb_26", "One_E2", "One_ghsR-66", "One_IL8r-362",
"One_KPNA-422", "One_lpp1-44", "One_MHC2_190", "One_MHC2_251",
"One_Prl2", "One_redd1-414", "One_STC-410", "One_STR07", "One_sys1-230",
"One_U1004-183", "One_U1105", "One_U1201-492", "One_U1203-175",
"One_U1209-111", "One_U1212-106", "One_U401-224", "One_vamp5-255",
"One_ZNF-61"), class = "factor"), Final = structure(c(1L, 2L,
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L), .Label = c("Invalid",
"No Call", "NTC", "XX", "YX", "YY"), class = "factor"), n = c(2L,
9L, 2L, 4L, 41L, 134L, 2L, 5L, 2L, 99L, 75L, 9L, 2L, 7L, 2L,
110L, 71L, 2L, 8L, 2L, 110L, 59L, 11L, 2L, 6L, 2L, 67L, 86L,
29L, 2L, 3L, 2L, 152L, 28L, 5L, 2L, 4L, 2L, 78L, 81L, 25L, 2L,
4L, 2L, 115L, 62L, 7L, 2L, 17L, 2L, 80L, 62L, 29L, 2L, 13L, 2L,
59L, 68L, 48L, 2L, 7L, 2L, 48L, 86L, 47L, 2L, 7L, 2L, 42L, 87L,
52L, 2L, 3L, 2L, 47L, 81L, 57L, 2L, 9L, 2L, 40L, 85L, 54L, 2L,
8L, 2L, 52L, 86L, 42L, 2L, 7L, 2L, 9L, 39L, 133L, 2L, 8L, 2L,
101L, 71L, 8L, 2L, 13L, 2L, 20L, 82L, 73L, 2L, 11L, 2L, 27L,
75L, 75L, 2L, 6L, 2L, 3L, 40L, 139L, 2L, 13L, 2L, 59L, 82L, 34L,
2L, 19L, 2L, 20L, 84L, 65L, 2L, 11L, 2L, 119L, 47L, 11L, 2L,
8L, 2L, 51L, 100L, 29L)), class = "data.frame", .Names = c("Assay",
"Final", "n"), row.names = c(NA, -143L))
Hope I am not misunderstanding. Are you looking for below:
Assume the data structure is:
df <- structure(list(Assay = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L), .Label = c("One_apoe-83", "One_CD9-269"), class = "factor"),
Final = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L
), .Label = c("Invalid", "No Call", "NTC", "XX", "YX", "YY"
), class = "factor"), n = c(2L, 9L, 2L, 4L, 41L, 134L, 2L,
5L, 2L, 99L)), .Names = c("Assay", "Final", "n"), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10"))
Code
df %>% group_by(Assay) %>% mutate(n_percent = n/sum(n)*100)
# Assay Final n n_percent
# 1 One_apoe-83 Invalid 2 1.041667
# 2 One_apoe-83 No Call 9 4.687500
# 3 One_apoe-83 NTC 2 1.041667
# 4 One_apoe-83 XX 4 2.083333
# 5 One_apoe-83 YX 41 21.354167
# 6 One_apoe-83 YY 134 69.791667
# 7 One_CD9-269 Invalid 2 1.851852
# 8 One_CD9-269 No Call 5 4.629630
# 9 One_CD9-269 NTC 2 1.851852
# 10 One_CD9-269 XX 99 91.666667
Option 2
Here is the code based on the comment. A line is added to filter out the elements you don't want.
df %>%
filter(! Final %in% c("Invalid", "No Call", "NTC")) %>%
group_by(Assay) %>%
mutate(n_percent = n/sum(n)*100)
# Source: local data frame [4 x 4]
# Groups: Assay
#
# Assay Final n n_percent
# 1 One_apoe-83 XX 4 2.234637
# 2 One_apoe-83 YX 41 22.905028
# 3 One_apoe-83 YY 134 74.860335
# 4 One_CD9-269 XX 99 100.000000