I am trying to look at the baseball data from 1903 through 1960 from the Lahman database. I am doing this for my own research. I am wanting to use the batting table, which does not include batting average, slugging, OBP or OPS.
I want to calculate those, but I first need to get total bases. I am having trouble getting the program to calculate total bases with the X2B and X3B.
I've looked into trying as.numeric, but I couldn't get it to work. This is using R and R studio. I've tried putting quotes around X2B and X3B for the doubles and triples and without quotes.
batting_1960 <- batting_1903 %>%
filter(yearID <= 1960 & G >= 90) %>%
mutate(Batting_Average = H/AB, TB = (2*"X2B")+(3*"X3B")+HR+(H-"X2B"-"X3B"-HR)) %>%
arrange(yearID, desc(Batting_Average))
I expect that for each row of data, that the total bases will be calculated in a new column but I get the error:
Error in 2 * "X2B" : non-numeric argument to binary operator
This would be so that I could eventually calculated OPS, OBP and slugging.
Your code is trying to mutiply 2 by the literal string "X2B", which is not going to work. Column names should be unquoted in mutate().
Your error:
> tibble(X2B = 1:10) %>% mutate(TB = 2 * "X2B")
Error in 2 * "X2B" : non-numeric argument to binary operator
Should be, for example:
> tibble(X2B = 1:10) %>% mutate(TB = 2 * X2B)
# A tibble: 10 x 2
X2B TB
<int> <dbl>
1 1 2
2 2 4
3 3 6
4 4 8
5 5 10
6 6 12
7 7 14
8 8 16
9 9 18
10 10 20
Related
So I have a dataframe and I want to create a new variable randomly using other factors; my data contains this key variables:
iQ
Age
Educ_y
5
23
15
4
54
17
2
43
6
3
13
7
5
14
8
1
51
16
I want to generate a new variable (years of experience) randomly using this creterias:
If Age >= 15 & Iq<= 2 so "Exp_y" takes a randome number between (Age-15)/2 and Age-15.
If (Age >= 15 & (Iq==3 | Iq==4) so "Exp_y" takes a randome number between (Age-Educ_y-6)/2 and (Age-Educ_y-6).
And 0 otherwise.
I tried using this code :
Df <- Df %>%
rowwise() %>%
mutate(Exep_y = case_when(
Age > 14 & iq <= 2 ~ sample(seq((Age-15)/2, Age-15, 1), 1),
Age > 14 & between(iq, 3, 4) ~ sample(seq((Age-Educ_y-6)/2, Age-Educ_y-6, 1), 1),
TRUE ~ 0
))
But I end up with this Error message:
Error in `mutate()`:
! Problem while computing `Exep_y = case_when(...)`.
i The error occurred in row 3.
Caused by error in `seq.default()`:
! signe incorrect de l'argument 'by'
Any ideas please;
Best Regards
This error message is occurring because the case_when() statement evaluates all the right-hand-side expressions, and then selects based on the left-hand-side.. Therefore, even though, for example row 4 of your sample dataset will default to TRUE~0, the RHS side of the the first two conditions also gets evaluated. In this case, the first condition's RHS is seq((13-15)/2,13-15,1), which returns an error, because in this case from = -1 and to = -2, so the by argument cannot be 1 (it is the wrong sign).
seq((13-15)/2, 13-15, 1)
Error in seq.default((13 - 15)/2, 13 - 15, 1) :
wrong sign in 'by' argument
You could do something like this:
f <- function(i,a,e) {
if(i>4 | a<15) return(0)
if(i<=2) return(sample(seq((a-15)/2, a-15),1))
return(sample(seq((a-e-6)/2, a-e-6),1))
}
Df %>% rowwise() %>% mutate(Exep_y=f(iq,Age,Educ_y))
Output:
iq Age Educ_y Exep_y
<int> <int> <int> <dbl>
1 5 23 15 0
2 4 54 17 16.5
3 2 43 6 21
4 3 13 7 0
5 5 14 8 0
6 1 51 16 27
You could try using if_else() rather than case_when:
Documentation can be found here: https://dplyr.tidyverse.org/reference/if_else.html
the title is vague but let me explain:
I have a non-vectorized function that outputs a 15-row table of volume estimates for a tree. Each row is a different measurement unit or portion of the input tree. I have a Tables argument to help the user decide what units and measurement protocol they're looking to find, but in 99% of use case scenarios, the output for a single tree's volume estimate is a tibble with more than one row.
I've removed ~20 other arguments from the function for demonstration's sake. DBH is a tree's diameter at breast height. Vol column is arbitrary.
Est1 <- TreeVol(Tables = "All", DBH = 7)
Est1
# A tibble: 15 x 3
Tables DBH Vol
<chr> <dbl> <dbl>
1 1. Total_Above_Ground_Cubic_Volume 7 2
2 2. Gross_Inter_1/4inch_Vol 7 4
3 3. Net_Scribner_Vol 7 6
4 4. Gross_Merchantable_Vol 7 8
5 5. Net_Merchantable_Vol 7 10
6 6. Merchantable_Vol 7 12
7 7. Gross_SecondaryProduct_Vol 7 14
8 8. Net_SecondaryProduct_Vol 7 16
9 9. SecondaryProduct 7 18
10 10. Gross_Inter_1/4inch_Vol 7 20
11 11. Net_Inter_1/4inch_Vol 7 22
12 12. Gross_Scribner_SecondaryProduct 7 24
13 13. Net_Scribner_SecondaryProduct 7 26
14 14. Stump_Volume 7 28
15 15. Tip_Volume 7 30
the user can utilize the Tables argument as so:
Est2 <- TreeVol(Tables = "Scribner_BF", DBH = 7)
# A tibble: 3 x 3
Tables DBH Vol
<chr> <dbl> <dbl>
1 3. Net_Scribner_Vol 7 6
2 12. Gross_Scribner_SecondaryProduct 7 24
3 13. Net_Scribner_SecondaryProduct 7 26
The problem arises in that I'd like to write a vectorized version of this function that can calculate the volume for an entire .csv of tree inventory data. Ideally, I'd like the multi-row outputs that relate to a single tree to output as one long tibble, with each 15-row default output filtered by what the user passes to the Tables argument as so:
Est3 <- VectorizedTreeVol(Tables = "Scribner_BF", DBH = c(7, 21, 26))
# A tibble: 9 x 3
Tables DBH Vol
<chr> <dbl> <dbl>
1 3. Net_Scribner_Vol 7 6
2 12. Gross_Scribner_SecondaryProduct 7 24
3 13. Net_Scribner_SecondaryProduct 7 26
4 3. Net_Scribner_Vol 21 18
5 12. Gross_Scribner_SecondaryProduct 21 72
6 13. Net_Scribner_SecondaryProduct 21 76
7 3. Net_Scribner_Vol 26 8
8 12. Gross_Scribner_SecondaryProduct 26 78
9 13. Net_Scribner_SecondaryProduct 26 84
To achieve this, I wrote a for() loop that acts as the heart of the vectorized function. I've heard from multiple people that it's very inefficient (and I agree), but it works with the principle I'd like to achieve, in theory. Nothing I've found on this topic has suggested a better idea for application in a vectorized function like mine.
The general setup for the loop looks like this:
for(i in 1:length(DBH)){
Output <- VectorizedTreeVol(Tables = Tables[[i]], DBH = DBH[[i]]) %>%
purrr::reduce(dplyr::full_join, by = NULL) %>%
SuppressWarnings()
and in functions where the non-vectorized output is always a single row, the heart of its respective vectorized function doesn't need to be encased in a for() loop and looks like this:
Output <- OtherVectorizedFunction(Tables = Tables, DBH = DBH) %>%
purrr::reduce(dplyr::full_join, by = ColumnNames) %>% #ColumnNames is a vector with all of the output's column names
SuppressWarnings()
This specific call to reduce() has worked pretty well when I've used it to vectorize the other functions in the project, but I'm open to suggestions regarding how to join the output tables. I've been stuck on this dilemma for a few months now, and any help regarding how to achieve what this for() loop is striving for in theory would be awesome. Is having a vectorized function that outputs a tibble like Est3 even possible? Any feedback/comments are much appreciated.
Given this function:
TreeVol <- function(DBH) {
data.frame(Tables = c("Tree_Vol", "Intercapillary_transfusion", "Woodiness"),
Vol = c(DBH^2, sqrt(DBH) + 3, sin(DBH)),
DBH)
}
We could put our DBH parameters into purrr::map and then bind_rows to get a data.frame.
VecTreeVol <- function(DBH) {
DBH %>%
purrr::map(TreeVol) %>%
bind_rows()
}
Result
> VecTreeVol(DBH = 1:3)
Tables Vol DBH
1 Tree_Vol 1.0000000 1
2 Intercapillary_transfusion 4.0000000 1
3 Woodiness 0.8414710 1
4 Tree_Vol 4.0000000 2
5 Intercapillary_transfusion 4.4142136 2
6 Woodiness 0.9092974 2
7 Tree_Vol 9.0000000 3
8 Intercapillary_transfusion 4.7320508 3
9 Woodiness 0.1411200 3
In R, I perform dunn's test. The function I use has no option to group the input variables by their statistical significant differences. However, this is what I am genuinely interested in, so I tried to write my own function. Unfortunately, I am not able to wrap my head around it. Perhaps someone can help.
I use the airquality dataset that comes with R as an example. The result that I need could look somewhat like this:
> library (tidyverse)
> ozone_summary <- airquality %>% group_by(Month) %>% dplyr::summarize(Mean = mean(Ozone, na.rm=TRUE))
# A tibble: 5 x 2
Month Mean
<int> <dbl>
1 5 23.6
2 6 29.4
3 7 59.1
4 8 60.0
5 9 31.4
When I run the dunn.test, I get the following:
> dunn.test::dunn.test (airquality$Ozone, airquality$Month, method = "bh", altp = T)
Kruskal-Wallis rank sum test
data: x and group
Kruskal-Wallis chi-squared = 29.2666, df = 4, p-value = 0
Comparison of x by group
(Benjamini-Hochberg)
Col Mean-|
Row Mean | 5 6 7 8
---------+--------------------------------------------
6 | -0.925158
| 0.4436
|
7 | -4.419470 -2.244208
| 0.0001* 0.0496*
|
8 | -4.132813 -2.038635 0.286657
| 0.0002* 0.0691 0.8604
|
9 | -1.321202 0.002538 3.217199 2.922827
| 0.2663 0.9980 0.0043* 0.0087*
alpha = 0.05
Reject Ho if p <= alpha
From this result, I deduce that May differs from July and August, June differs from July (but not from August) and so on. So I'd like to append significantly differing groups to my results table:
# A tibble: 5 x 3
Month Mean Group
<int> <dbl> <chr>
1 5 23.6 a
2 6 29.4 ac
3 7 59.1 b
4 8 60.0 bc
5 9 31.4 a
While I did this by hand, I suppose it must be possible to automate this process. However, I don't find a good starting point. I created a dataframe containing all comparisons:
> ozone_differences <- dunn.test::dunn.test (airquality$Ozone, airquality$Month, method = "bh", altp = T)
> ozone_differences <- data.frame ("P" = ozone_differences$altP.adjusted, "Compare" = ozone_differences$comparisons)
P Compare
1 4.436043e-01 5 - 6
2 9.894296e-05 5 - 7
3 4.963804e-02 6 - 7
4 1.791748e-04 5 - 8
5 6.914403e-02 6 - 8
6 8.604164e-01 7 - 8
7 2.663342e-01 5 - 9
8 9.979745e-01 6 - 9
9 4.314957e-03 7 - 9
10 8.671708e-03 8 - 9
I thought that a function iterating through this data frame and using a selection variable to choose the right letter from letters() might work. However, I cannot even think of a starting point, because changing numbers of rows have to considered at the same time...
Perhaps someone has a good idea?
Perhaps you could look into cldList() function from rcompanion library, you can pipe the res results from the output od dunnTest() and create a table that specifies the compact letter display comparison per group.
Following the advice of #TylerRuddenfort , the following code will work. The first cld is created with rcompanion::cldList, and the second directly uses multcompView::multcompLetters. Note that to use multcompLetters, the spaces have to be removed from the names of the comparisons.
Here, I have used FSA:dunnTest for the Dunn test (1964).
In general, I recommend ordering groups by e.g. median or mean before running e.g. dunnTest if you plan on using a cld, so that the cld comes out in a sensible order.
library (tidyverse)
ozone_summary <- airquality %>% group_by(Month) %>% dplyr::summarize(Mean = mean(Ozone, na.rm=TRUE))
library(FSA)
Result = dunnTest(airquality$Ozone, airquality$Month, method = "bh")$res
### Use cldList()
library(rcompanion)
cldList(P.adj ~ Comparison, data=Result)
### Use multcompView
library(multcompView)
X = Result$P.adj <= 0.05
names(X) = gsub(" ", "", Result$Comparison)
multcompLetters(X)
I have some data in the form:
Person.ID Household.ID Composition
1 4593 1A_0C
2 4992 2A_1C
3 9843 1A_1C
4 8385 2A_2C
5 9823 8A_1C
6 3458 1C_9C
7 7485 2C_0C
: : :
We can think of the composition variable as a count of adults/children i.e. 2A_1C would equate to two adults and two children.
What I want to do is reduce the amount of possible levels of composition. For person 5 we have composition of 8A_1C, I am looking for a way to reduce this to 4+A_0C. So for example we would have 4+ for any composition value with greater than 4A.
Person.ID Household.ID Composition
5 9823 4+A_1C
6 3458 1A_4+C
: : :
I am unsure of how to do this in R, I am thinking of using filter() or select() from dyplyr. Otherwise I would need to use some sort of regular expression.
Any help would be appreciated. Thanks
Data:
Person.ID <- c(1,2,3,4,5,6,7,8)
Household.ID <- c(4593,4992,9843,8385,9823,3458,7485)
Composition <- c("1A_0C","2A_1C","1A_1C","2A_2C","8A_1C","1A_9C","2A_0C")
dat <- tibble(Person.ID, Household.ID, Composition)
Function:
above4 <- function(f){
ff <- gsub("[^0-9]","",f)
if(ff>4){return("4+")}
if(ff<=4){return(ff)}
}
Apply function (done on separated data, but can recombine after):
dat_ <- dat %>% tidyr::separate(., col=Composition,
into=c("Adults", "Children"),
sep="_") %>%
dplyr::mutate(Adults_ = unlist(lapply(Adults,above4)),
Children_ = unlist(lapply(Children,above4)))
You might then use select, filter to get your required dataset.
dat_ %>% dplyr::mutate(Composition_ = paste0(Adults_, "A_", Children_, "C")) %>%
dplyr::select(Person.ID, Household.ID, Composition=Composition_)
# A tibble: 7 x 3
Person.ID Household.ID Composition
<dbl> <dbl> <chr>
1 1. 4593. 1A_0C
2 2. 4992. 2A_1C
3 3. 9843. 1A_1C
4 4. 8385. 2A_2C
5 5. 9823. 4+A_1C
6 6. 3458. 1A_4+C
7 7. 7485. 2A_0C
We can use gsub:
df$Composition <- gsub("(?<!\\d)([5-9]|\\d{2,})(?=[AC])", "4+", df$Composition, perl = TRUE)
This assumes that 2 or more consecutive digits represent a number that's always greater than 4 (i.e. no 01, 02, or 001).
Output:
Person.ID Household.ID Composition
1 1 4593 1A_0C
2 2 4992 2A_1C
3 3 9843 1A_1C
4 4 8385 2A_2C
5 5 9823 4+A_1C
6 6 3458 1C_4+C
7 7 7485 2C_0C
I am using the RDS package for respondent-driven sampling survey data. I want to convert a regular R data frame to an rds.data.frame. To do so, I have been trying to use the as.rds.data.frame function from RDS.
Here is an excerpted section of my data frame, where the first case (id=1) is the 'seed' respondent (who has no recruiter). It contains the variables: id (respondent id number), recruit.id(id number of respondent who recruited him/her), netsize (respondent's network size) and population (estimate of whole population size).
df<-data.frame(id=c(1,2,3,4,5,6,7,8,9,10),
recruit.id=c(-1,1,1,2,2,4,5,3,8,3),
netsize=c(6,6,6,5,5,4,4,3,4,6), population=rep(22,000, 10))
I then (try to) apply the relevant function:
new.df <-as.rds.data.frame(df,id=df$id,
recruiter.id=df$recruit.id,
network.size=df$netsize,
population.size=df$population,
max.coupons=2)
I get the error message:
Error in as.rds.data.frame(df, id = df$id, recruiter.id = df$recruit.id,: Invalid id
and the warning
In addition: Warning message:In if (!(id %in% names(x))) stop("Invalid id") :
the condition has length > 1 and only the first element will be used
I have tried assigning various 'recruiter id' values for seed participants, including -1,0 or their own id number but I still get the same message. I have also tried eliminating function arguments (coupon.max, population) or deleting seed respondents, but I still get the same message.
Package documentation says the function will fail if recruitment information is incomplete. As far as I can tell, this is not the case.
I am new to this, so if anyone can point me in the right direction I would be really grateful.
This seems to work:
colnames(df)[2:4] <- c("recruiter.id", "network.size.variable", "population.size")
as.rds.data.frame(df,max.coupons=2)
This gives a result with a warning
as.rds.data.frame(df, id="id", recruiter.id="recruit.id",
network.size="netsize", population.size="population", max.coupons=2)
# An object of class "rds.data.frame"
#id: 1 2 3 4 5 6 7 8 9 10
#recruiter.id: -1 1 1 2 2 4 5 3 8 3
# id recruit.id netsize population
#1 1 -1 6 22
#2 2 1 6 22
#3 3 1 6 22
#4 4 2 5 22
#5 5 2 5 22
#6 6 4 4 22
#7 7 5 4 22
#8 8 3 3 22
#9 9 8 4 22
#10 10 3 6 22
# Warning message:
#In as.rds.data.frame(df, id = "id", recruiter.id = "recruit.id", :
#NAs introduced by coercion