My dataset has the next structure
df=structure(list(Data = structure(c(12L, 13L, 14L, 15L, 16L, 17L,
18L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L), .Label = c("01.01.2018",
"02.01.2018", "03.01.2018", "04.01.2018", "05.01.2018", "06.01.2018",
"07.01.2018", "12.02.2018", "13.02.2018", "14.02.2018", "15.02.2018",
"25.12.2017", "26.12.2017", "27.12.2017", "28.12.2017", "29.12.2017",
"30.12.2017", "31.12.2017"), class = "factor"), sku = 1:18, metric = c(100L,
210L, 320L, 430L, 540L, 650L, 760L, 870L, 980L, 1090L, 1200L,
1310L, 1420L, 1530L, 1640L, 1750L, 1860L, 1970L), action = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L)), .Names = c("Data", "sku", "metric", "action"), class = "data.frame", row.names = c(NA,
-18L))
I need to delete observations that have certain dates.
But in this dataset there is action variable. The action column has only two values 0 and 1.
Observations on these certain dates should be deleted only for the zero category of action.
these dates are presented in a separate datase.
datedata=structure(list(Data = structure(c(18L, 19L, 20L, 21L, 22L, 5L,
7L, 9L, 11L, 13L, 15L, 17L, 23L, 1L, 2L, 3L, 4L, 6L, 8L, 10L,
12L, 14L, 16L), .Label = c("01.05.2018", "02.05.2018", "03.05.2018",
"04.05.2018", "05.03.2018", "05.05.2018", "06.03.2018", "06.05.2018",
"07.03.2018", "07.05.2018", "08.03.2018", "08.05.2018", "09.03.2018",
"09.05.2018", "10.03.2018", "10.05.2018", "11.03.2018", "21.02.2018",
"22.02.2018", "23.02.2018", "24.02.2018", "25.02.2018", "30.04.2018"
), class = "factor")), .Names = "Data", class = "data.frame", row.names = c(NA,
-23L))
how can i do it?
A solution is to use dplyr::filter as:
library(dplyr)
library(lubridate)
df %>% mutate(Data = dmy(Data)) %>%
filter(action==1 | (action==0 & !(Data %in% dmy(datedata$Data))))
# Data sku metric action
# 1 2017-12-25 1 100 0
# 2 2017-12-26 2 210 0
# 3 2017-12-27 3 320 0
# 4 2017-12-28 4 430 0
# 5 2017-12-29 5 540 0
# 6 2017-12-30 6 650 0
# 7 2017-12-31 7 760 0
# 8 2018-01-01 8 870 0
# 9 2018-01-02 9 980 1
# 10 2018-01-03 10 1090 1
# 11 2018-01-04 11 1200 1
# 12 2018-01-05 12 1310 1
# 13 2018-01-06 13 1420 1
# 14 2018-01-07 14 1530 1
# 15 2018-02-12 15 1640 1
# 16 2018-02-13 16 1750 1
# 17 2018-02-14 17 1860 1
# 18 2018-02-15 18 1970 1
I guess this will work. Fist use match to see weather there is a match in the day of df and the day in datedata, then filter it
library (dplyr)
df <- df %>% mutate (Data.flag = match(Data,datedata$Data)) %>%
filter(!is.na(Data.flag) & action == 0)
Related
df <- structure(list(ID = c(2L, 2L, 13L, 13L, 13L, 21L, 21L, 21L, 24L,
24L, 24L, 24L), mignr = c(1L, 0L, 1L, 2L, 0L, 0L, 2L, 1L, 2L,
3L, 0L, 1L), start = c(1387L, 903L, 1357L, 1391L, 1087L, 936L,
1367L, 1354L, 1363L, 1392L, 908L, 1361L), end = c(1401L, 1386L,
1390L, 1401L, 1356L, 1353L, 1399L, 1366L, 1391L, 1400L, 1360L,
1362L), staytime = c(15L, 484L, 34L, 11L, 270L, 418L, 33L, 13L,
29L, 9L, 453L, 2L)), row.names = c(NA, -12L), class = "data.frame")
My objective is to reset the sequences in this spell dataset so that they all restart at zero and for mignr greater than one increase by staytime. The data is grouped by IDs and mignr represents the sequence in the spell. I have set start == 0 end == 1 for the first spell (mignr == 0) and would like for each subsequent spell that
start is the previous end + 1
and end is start + (staytime - 1)
df <- df %>%
mutate(start = ifelse(mignr == 0, 0, start)) %>%
mutate(end = ifelse(mignr == 0, 1, end))
max_spell <- max(df$mignr)
for(i in seq_along(1:max_spell)){
j <- i-1
df <- df %>%
mutate(start = ifelse(mignr == i, (df[which(df$ID == ID & df$mignr == j),4]), start)) %>%
mutate(end = ifelse(mignr == i, start + (staytime - 1), end))
}
df
My attempt seems to work for mignr = 1 the start and end values are both as I would expect. However for subsequent values of mignr (>1) the start value is no longer what I would expect it to be. The way I am currently calculating the start is not very clean, but I can't quite tell why for subsequent iterations it is breaking.
I would use cumsum() on staytime, but for this to work, I'd change the first staytime of mignr == 0 to 1, since that is basically what setting the first start and end to 0 and 1 implies. Would that be acceptable?
library(tidyverse)
df %>%
group_by(ID) %>%
arrange(mignr, ,.by_group = TRUE) %>%
mutate(start = (row_number()- 1L),
staytime = ifelse(row_number() == 1, 1, staytime),
end = cumsum(staytime),
start = lag(end + 1, default = first(start)))
#> # A tibble: 12 x 5
#> # Groups: ID [4]
#> ID mignr start end staytime
#> <int> <int> <dbl> <dbl> <dbl>
#> 1 2 0 0 1 1
#> 2 2 1 2 16 15
#> 3 13 0 0 1 1
#> 4 13 1 2 35 34
#> 5 13 2 36 46 11
#> 6 21 0 0 1 1
#> 7 21 1 2 14 13
#> 8 21 2 15 47 33
#> 9 24 0 0 1 1
#> 10 24 1 2 3 2
#> 11 24 2 4 32 29
#> 12 24 3 33 41 9
Data from OP
df <- structure(list(ID = c(2L, 2L, 13L, 13L, 13L, 21L, 21L, 21L, 24L,
24L, 24L, 24L), mignr = c(1L, 0L, 1L, 2L, 0L, 0L, 2L, 1L, 2L,
3L, 0L, 1L), start = c(1387L, 903L, 1357L, 1391L, 1087L, 936L,
1367L, 1354L, 1363L, 1392L, 908L, 1361L), end = c(1401L, 1386L,
1390L, 1401L, 1356L, 1353L, 1399L, 1366L, 1391L, 1400L, 1360L,
1362L), staytime = c(15L, 484L, 34L, 11L, 270L, 418L, 33L, 13L,
29L, 9L, 453L, 2L)), row.names = c(NA, -12L), class = "data.frame")
Created on 2023-02-20 by the reprex package (v2.0.1)
I have the following data frame with ages binned in ranges of 5 years and the frequency of a condition happening in males/females. The problem is that there were no occurrences in either gender for example in the range 15-20.
structure(list(age = structure(c(1L, 2L, 3L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L), .Label = c("[0,5]",
"(5,10]", "(10,15]", "(15,20]", "(20,25]", "(25,30]", "(30,35]",
"(35,40]", "(40,45]", "(45,50]", "(50,55]", "(55,60]", "(60,65]",
"(65,70]", "(70,75]", "(75,80]", "(80,85]", "(85,90]", "(90,95]",
"(95,100]"), class = "factor"), male = c(2L, 1L, 1L, 4L, 8L,
9L, 20L, 33L, 49L, 104L, 112L, 176L, 159L, 140L, 94L, 72L, 32L,
6L, 2L), female = c(1L, 1L, NA, 7L, 7L, 4L, 23L, 39L, 44L, 74L,
94L, 111L, 124L, 129L, 110L, 92L, 76L, 30L, 7L)), row.names = c(NA,
-19L), groups = structure(list(age = structure(c(1L, 2L, 3L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L), .Label = c("[0,5]", "(5,10]", "(10,15]", "(15,20]",
"(20,25]", "(25,30]", "(30,35]", "(35,40]", "(40,45]", "(45,50]",
"(50,55]", "(55,60]", "(60,65]", "(65,70]", "(70,75]", "(75,80]",
"(80,85]", "(85,90]", "(90,95]", "(95,100]"), class = "factor"),
.rows = structure(list(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = c(NA, -19L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"))
If I check the levels it properly shows all levels.
what I would want is a data frame where all ranges of ages show up and when they don't exist substitute then by 0.
You can use complete :
library(dplyr)
library(tidyr)
df %>%
ungroup %>%
complete(age, fill = list(male = 0, female = 0))
# age male female
# <fct> <dbl> <dbl>
# 1 [0,5] 2 1
# 2 (5,10] 1 1
# 3 (10,15] 1 0
# 4 (15,20] 0 0
# 5 (20,25] 4 7
# 6 (25,30] 8 7
# 7 (30,35] 9 4
# 8 (35,40] 20 23
# 9 (40,45] 33 39
#10 (45,50] 49 44
#11 (50,55] 104 74
#12 (55,60] 112 94
#13 (60,65] 176 111
#14 (65,70] 159 124
#15 (70,75] 140 129
#16 (75,80] 94 110
#17 (80,85] 72 92
#18 (85,90] 32 76
#19 (90,95] 6 30
#20 (95,100] 2 7
I have a dataset that looks like this:
A tibble: 935 x 17
wage hours iq kww educ exper tenure age married black south urban sibs brthord meduc
<int> <int> <int> <int> <int> <int> <int> <int> <fctr> <fctr> <fctr> <fctr> <int> <int> <int>
1 769 40 93 35 12 11 2 31 1 0 0 1 1 2 8
2 808 50 119 41 18 11 16 37 1 0 0 1 1 NA 14
3 825 40 108 46 14 11 9 33 1 0 0 1 1 2 14
4 650 40 96 32 12 13 7 32 1 0 0 1 4 3 12
5 562 40 74 27 11 14 5 34 1 0 0 1 10 6 6
6 1400 40 116 43 16 14 2 35 1 1 0 1 1 2 8
7 600 40 91 24 10 13 0 30 0 0 0 1 1 2 8
8 1081 40 114 50 18 8 14 38 1 0 0 1 2 3 8
9 1154 45 111 37 15 13 1 36 1 0 0 0 2 3 14
10 1000 40 95 44 12 16 16 36 1 0 0 1 1 1 12
...
What can I run to see the distribution of wage (the first column). Specifically, I want to see how many people have a wage of under $300.
What ggplot function can I run?
You can get the cumulative histogram:
library(ggplot2)
ggplot(df,aes(wage))+geom_histogram(aes(y=cumsum(..count..)))+
stat_bin(aes(y=cumsum(..count..)),geom="line",color="green")
If you specifically want to know the count of entries with a certain condition, in base r you can use the following:
count(df[df$wage > 1000,])
## # A tibble: 1 x 1
## n
## <int>
## 1 3
Data:
df <- structure(list(wage = c(769L, 808L, 825L, 650L, 562L, 1400L,
600L, 1081L, 1154L, 1000L), hours = c(40L, 50L, 40L, 40L, 40L,
40L, 40L, 40L, 45L, 40L), iq = c(93L, 119L, 108L, 96L, 74L, 116L,
91L, 114L, 111L, 95L), kww = c(35L, 41L, 46L, 32L, 27L, 43L,
24L, 50L, 37L, 44L), educ = c(12L, 18L, 14L, 12L, 11L, 16L, 10L,
18L, 15L, 12L), exper = c(11L, 11L, 11L, 13L, 14L, 14L, 13L,
8L, 13L, 16L), tenure = c(2L, 16L, 9L, 7L, 5L, 2L, 0L, 14L, 1L,
16L), age = c(31L, 37L, 33L, 32L, 34L, 35L, 30L, 38L, 36L, 36L
), married = c(1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L), black = c(0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L), south = c(0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L), urban = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L), sibs = c(1L, 1L, 1L, 4L, 10L, 1L, 1L, 2L, 2L, 1L
), brthord = c(2L, NA, 2L, 3L, 6L, 2L, 2L, 3L, 3L, 1L), meduc = c(8L,
14L, 14L, 12L, 6L, 8L, 8L, 8L, 14L, 12L)), .Names = c("wage",
"hours", "iq", "kww", "educ", "exper", "tenure", "age", "married",
"black", "south", "urban", "sibs", "brthord", "meduc"), row.names = c(NA,
-10L), class = c("tbl_df", "tbl", "data.frame"))
Try this:
library(dplyr)
library(ggplot2)
df <- df %>% filter(wage < 300)
qplot(wage, data = df)
suppose I have two boxplots.
trial1 <- ggplot(completionTime, aes(fill=Condition, x=Scenario, y=Trial1))
trial1 + geom_boxplot()+geom_point(position=position_dodge(width=0.75)) + ylim(0, 160)
trial2 <- ggplot(completionTime, aes(fill=Condition, x=Scenario, y=Trial2))
trial2 + geom_boxplot()+geom_point(position=position_dodge(width=0.75)) + ylim(0, 160)
How can I plot trial 1 and trial 2 on the same plot and same respective X? they have the same range of y.
I looked at geom_boxplot(position="identity"), but that plots the two conditions(fill) on the same X.
I want to plot two y column on the same X.
Edit: the dataset
User Condition Scenario Trial1 Trial2
1 1 ME a 67 41
2 1 ME b 70 42
3 1 ME c 40 15
4 1 ME d 65 23
5 1 ME e 45 45
6 1 SE a 100 34
7 1 SE b 54 23
8 1 SE c 70 23
9 1 SE d 56 15
10 1 SE e 30 20
11 2 ME a 42 23
12 2 ME b 22 12
13 2 ME c 28 8
14 2 ME d 22 8
15 2 ME e 38 37
16 2 SE a 59 18
17 2 SE b 65 14
18 2 SE c 75 7
19 2 SE d 37 9
20 2 SE e 31 7
dput()
structure(list(User = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), Condition = structure(c(1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L), .Label = c("ME", "SE"), class = "factor"), Scenario =
structure(c(1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L), .Label = c("a", "b", "c", "d", "e"), class = "factor"),
Trial1 = c(67L, 70L, 40L, 65L, 45L, 100L, 54L, 70L, 56L,
30L, 42L, 22L, 28L, 22L, 38L, 59L, 65L, 75L, 37L, 31L), Trial2 = c(41L,
42L, 15L, 23L, 45L, 34L, 23L, 23L, 15L, 20L, 23L, 12L, 8L,
8L, 37L, 18L, 14L, 7L, 9L, 7L)), .Names = c("User", "Condition",
"Scenario", "Trial1", "Trial2"), class = "data.frame", row.names = c(NA,
-20L))
You could try using interaction to combine two of your factors and plot against a third. For example, assuming you want to fill by condition as in your original code:
library(tidyr)
completionTime %>%
gather(trial, value, -Scenario, -Condition, -User) %>%
ggplot(aes(interaction(Scenario, trial), value)) + geom_boxplot(aes(fill = Condition))
Result:
I have a list of 200 variables and I want to sum those that are highly correlated.
Assuming this is my data
mydata <- structure(list(APPLE= c(1L, 2L, 5L, 4L, 366L, 65L, 43L, 456L, 876L, 78L, 687L, 378L, 378L, 34L, 53L, 43L),
PEAR= c(2L, 2L, 5L, 4L, 366L, 65L, 43L, 456L, 876L, 78L, 687L, 378L, 378L, 34L, 53L, 41L),
PLUM = c(10L, 20L, 10L, 20L, 10L, 20L, 1L, 0L, 1L, 2010L,20L, 10L, 10L, 10L, 10L, 10L),
BANANA= c(2L, 10L, 31L, 2L, 2L, 5L, 2L, 5L, 1L, 52L, 1L, 2L, 52L, 6L, 2L, 1L),
LEMON = c(4L, 10L, 31L, 2L, 2L, 5L, 2L, 5L, 1L, 52L, 1L, 2L, 52L, 6L, 2L, 3L)),
.Names = c("APPLE", "PEAR", "PLUM", "BANANA", "LEMON"),
class = "data.frame", row.names = c(NA,-16L))
I have found this code which I am not sure how to tweak in order to leverage it for my purpose
https://stackoverflow.com/a/39484353/4797853
var.corelation <- cor(as.matrix(mydata), method="pearson")
library(igraph)
# prevent duplicated pairs
var.corelation <- var.corelation*lower.tri(var.corelation)
check.corelation <- which(var.corelation>0.62, arr.ind=TRUE)
graph.cor <- graph.data.frame(check.corelation, directed = FALSE)
groups.cor <- split(unique(as.vector(check.corelation)), clusters(graph.cor)$membership)
lapply(groups.cor,FUN=function(list.cor){rownames(var.corelation)[list.cor]})
The output that I am looking for is 2 data frames as follow:
DF1
GROUP1 GROUP2
3 16
4 40
ETC..
The values are the sum of the values within a group
DF2
ORIGINAL_VAR GROUP
APPLE 1
PEAR 1
PLUM 2
BANANA 2
LEMON 2
Try this (assuming that you have only clustered into 2 groups):
DF1 <- cbind.data.frame(GROUP1=rowSums(mydata[,groups.cor[[1]]]),
GROUP2=rowSums(mydata[,groups.cor[[2]]]))
DF1
GROUP1 GROUP2
1 3 16
2 4 40
3 10 72
4 8 24
5 732 14
6 130 30
7 86 5
8 912 10
9 1752 3
10 156 2114
11 1374 22
12 756 14
13 756 114
14 68 22
15 106 14
16 84 14
DF2 <- NULL
for (i in 1:2) {
DF2 <- rbind(DF2,
cbind.data.frame(ORIGINAL_VAR=rownames(var.corelation)[groups.cor[[i]]],
GROUP=i))
}
DF2
ORIGINAL_VAR GROUP
1 PEAR 1
2 APPLE 1
3 BANANA 2
4 LEMON 2
5 PLUM 2