This is the code that I used (with a lot of help from the StackOverflow communitiy!) to create a simpler table using the same data:
library(here)
ANOVA_Relationship_Subset_sum <- ANOVA_Relationship_Subset %>%
dplyr::group_by(treatment) %>%
dplyr::summarize(
n=n(),
mean=mean(TotalComm),
`std. dev` = sd(TotalComm)
)
ANOVA_Relationship_Subset_sum
Now I'm on to something a little more complicated; how can I create a table like this:
If it helps, this is my data:
structure(list(study = c(4, 4, 4, 1, 1, 1), TREATMENT = c(0,
0, 0, 0, 0, 0), TREATMENT4 = c(0, 0, 0, 0, 0, 0), TREATMENT2 = c(0,
0, 0, 0, 0, 0), TREATMENT3 = c(0, 0, 0, 0, 0, 0), order = c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), id = c(279,
238, 239, 135, 143, 138), treatment = c(0, 0, 0, 0, 0, 0), treatment_condition = c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), control_condition = c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), m_check1 = c(1,
1, 1, 1, 1, 1), relationship = c(NA, NA, NA, 7, 6, 5), payment = c(NA,
NA, NA, 10, 3, 3), educ_level = c(14, 14, 12, 16, 16, 18), golf = c(3,
5, 3, 3, 2, 3), male = c(1, 0, 1, 0, 0, 1), Asian = c(0, 1, 0,
0, 0, 0), Black = c(0, 0, 0, 0, 0, 0), Latino = c(1, 0, 0, 0,
0, 0), White = c(0, 0, 1, 1, 1, 1), age = c(27, 53, 49, 25, 28,
24), English = c(1, 1, 1, 1, 1, 1), education = c(16, 16, 14,
14, 14, 16), enjoy = c(4, 1, 3.5, 4.25, 3.25, 3.5), RELATIONSHIP = c(4.33333349227905,
1, 4.33333349227905, 3.66666674613953, 3.5, 3.66666674613953),
anxiety = c(3, 3.40000009536743, 2.20000004768372, 1.25,
2, 1.25), BEH_SIM = c(3, 1, 3.75, 2.75, 2.5, 1.75), sptconf = c(3.33333325386047,
1.5, 4, 4.83333349227905, 4, 3.66666674613953), NEG_EFFICACY = c(4,
1.16666662693024, 3.66666674613953, 4.83333349227905, 4.16666650772095,
4.5), spteffort = c(3.16666674613953, 3.5, 4.16666650772095,
3.16666674613953, 3.16666674613953, 3.5), SPTEFFORT_OTHER = c(3.16666674613953,
3.5, 3.5, 3.16666674613953, 3, 3.33333325386047), SIM_VALUES = c(3.75,
1, 3.75, 3.75, 1.5, 2.25), COOP_MOTIV = c(2.33333325386047,
3, 2.66666674613953, 5, 2.5, 2.66666674613953), COMP_MOTIV = c(5,
5, 3.20000004768372, 4.40000009536743, 2.40000009536743,
4.40000009536743), presence = c(NA, NA, NA, 2.79999995231628,
1.79999995231628, 2.59999990463257), environ = c(NA, NA,
NA, 3, 4, 3), openresponse = c(NA, NA, NA, 94.25, 86, 60),
TotalOwnerCommission = c(300, 266.666656494141, 258.333343505859,
266.666656494141, 383.333343505859, 325), TotalRangerComm = c(258.333343505859,
233.33332824707, 291.666656494141, 258.333343505859, 175,
166.66667175293), TotalComm = c(279.166687011719, 250, 275,
262.5, 279.166687011719, 245.833343505859), merge = c(1,
1, 1, 0, 0, 0), Control = c(1, 1, 1, NA, NA, NA), treatment_Shoes = c(0,
0, 0, NA, NA, NA), treatment_Instructions_Only = c(0, 0,
0, NA, NA, NA), treatment_Info_Only = c(0, 0, 0, NA, NA,
NA), treatment_Info_Instructions = c(0, 0, 0, NA, NA, NA),
group = c("OwnerOnly", "OwnerOnly", "OwnerOnly", "", "",
""), race = c(4, 2, 5, NA, NA, NA), race_a = c("", "", "",
"", "", ""), RELATIONSHIP_2 = c(9.02055358886719, 1, 9.02055358886719,
7.02113246917725, 6.54790019989014, 7.02113246917725), TotalOwnerCommission_2 = c(5196.15234375,
4354.64794921875, 4152.12744140625, 4354.64794921875, 7505.24560546875,
5859.02099609375)), row.names = c(NA, -6L), class = c("tbl_df",
"tbl", "data.frame"))
Briefly, I do want to thank the SO community for all their help with R. I don't know how I would have gotten this far without all of your help.
Try the apaTables Package! Format your data as per the example, and use the apa.aov.table() function to transform your table to APA style.
Related
I have this data (listed as reproducible):
structure(list(age = c(62.84998, 60.33899, 52.74698, 42.38498
), death = c(0, 1, 1, 1), sex = c("male", "female", "female",
"female"), hospdead = c(0, 1, 0, 0), slos = c(5, 4, 17, 3), d.time = c(2029,
4, 47, 133), dzgroup = c("Lung Cancer", "Cirrhosis", "Cirrhosis",
"Lung Cancer"), dzclass = c("Cancer", "COPD/CHF/Cirrhosis", "COPD/CHF/Cirrhosis",
"Cancer"), num.co = c(0, 2, 2, 2), edu = c(11, 12, 12, 11), income = c("$11-$25k",
"$11-$25k", "under $11k", "under $11k"), scoma = c(0, 44, 0,
0), charges = c(9715, 34496, 41094, 3075), totcst = c(NA_real_,
NA_real_, NA_real_, NA_real_), totmcst = c(NA_real_, NA_real_,
NA_real_, NA_real_), avtisst = c(7, 29, 13, 7), race = c("other",
"white", "white", "white"), sps = c(33.8984375, 52.6953125, 20.5,
20.0976562), aps = c(20, 74, 45, 19), surv2m = c(0.262939453,
0.0009999275, 0.790893555, 0.698974609), surv6m = c(0.0369949341,
0, 0.664916992, 0.411987305), hday = c(1, 3, 4, 1), diabetes = c(0,
0, 0, 0), dementia = c(0, 0, 0, 0), ca = c("metastatic", "no",
"no", "metastatic"), prg2m = c(0.5, 0, 0.75, 0.899999619), prg6m = c(0.25,
0, 0.5, 0.5), dnr = c("no dnr", NA, "no dnr", "no dnr"), dnrday = c(5,
NA, 17, 3), meanbp = c(97, 43, 70, 75), wblc = c(6, 17.0976562,
8.5, 9.09960938), hrt = c(69, 112, 88, 88), resp = c(22, 34,
28, 32), temp = c(36, 34.59375, 37.39844, 35), pafi = c(388,
98, 231.65625, NA), alb = c(1.7998047, NA, NA, NA), bili = c(0.19998169,
NA, 2.19970703, NA), crea = c(1.19995117, 5.5, 2, 0.79992676),
sod = c(141, 132, 134, 139), ph = c(7.459961, 7.25, 7.459961,
NA), glucose = c(NA_real_, NA_real_, NA_real_, NA_real_),
bun = c(NA_real_, NA_real_, NA_real_, NA_real_), urine = c(NA_real_,
NA_real_, NA_real_, NA_real_), adlp = c(7, NA, 1, 0), adls = c(7,
1, 0, 0), sfdm2 = c(NA, "<2 mo. follow-up", "<2 mo. follow-up",
"no(M2 and SIP pres)"), adlsc = c(7, 1, 0, 0)), row.names = c(NA,
4L), class = "data.frame")
I am wanting to estimate the population proportion of individuals who had lung cancer listed as their primary disease group (dzgroup). How would I do this? My original thought was to just divide the total number that have lung cancer by the whole dataset population, but I do not believe this is correct.
If we want to get the proportion on the whole data, create a logical vector and get the mean as TRUE -> 1 and FALSE -> 0, the mean will be the proportion of 1s and multiplying by 100 gives the percentage
round(100 * mean(df1$dzgroup == "Lung Cancer", na.rm = TRUE), 2)
I have this reproducible DataFrame:
structure(list(age = c(62.84998, 60.33899, 52.74698, 42.38498,
79.88495, 93.01599, 62.37097, 86.83899, 85.65594, 42.25897),
death = c(0, 1, 1, 1, 0, 1, 1, 1, 1, 1), sex = c("male",
"female", "female", "female", "female", "male", "male", "male",
"male", "female"), hospdead = c(0, 1, 0, 0, 0, 1, 0, 0, 0,
0), slos = c(5, 4, 17, 3, 16, 4, 9, 7, 12, 8), d.time = c(2029,
4, 47, 133, 2029, 4, 659, 142, 63, 370), dzgroup = c("Lung Cancer",
"Cirrhosis", "Cirrhosis", "Lung Cancer", "ARF/MOSF w/Sepsis",
"Coma", "CHF", "CHF", "Lung Cancer", "Colon Cancer"), dzclass = c("Cancer",
"COPD/CHF/Cirrhosis", "COPD/CHF/Cirrhosis", "Cancer", "ARF/MOSF",
"Coma", "COPD/CHF/Cirrhosis", "COPD/CHF/Cirrhosis", "Cancer",
"Cancer"), num.co = c(0, 2, 2, 2, 1, 1, 1, 3, 2, 0), edu = c(11,
12, 12, 11, NA, 14, 14, NA, 12, 11), income = c("$11-$25k",
"$11-$25k", "under $11k", "under $11k", NA, NA, "$25-$50k",
NA, NA, "$25-$50k"), scoma = c(0, 44, 0, 0, 26, 55, 0, 26,
26, 0), charges = c(9715, 34496, 41094, 3075, 50127, 6884,
30460, 30460, NA, 9914), totcst = c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_), totmcst = c(NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_
), avtisst = c(7, 29, 13, 7, 18.666656, 5, 8, 6.5, 8.5, 8
), race = c("other", "white", "white", "white", "white",
"white", "white", "white", "black", "hispanic"), sps = c(33.8984375,
52.6953125, 20.5, 20.0976562, 23.5, 19.3984375, 17.296875,
21.5976562, 15.8984375, 2.2998047), aps = c(20, 74, 45, 19,
30, 27, 46, 53, 17, 9), surv2m = c(0.262939453, 0.0009999275,
0.790893555, 0.698974609, 0.634887695, 0.284973145, 0.892944336,
0.670898438, 0.570922852, 0.952880859), surv6m = c(0.0369949341,
0, 0.664916992, 0.411987305, 0.532958984, 0.214996338, 0.820922852,
0.498962402, 0.24899292, 0.887939453), hday = c(1, 3, 4,
1, 3, 1, 1, 1, 1, 1), diabetes = c(0, 0, 0, 0, 0, 0, 0, 1,
0, 0), dementia = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 0), ca = c("metastatic",
"no", "no", "metastatic", "no", "no", "no", "no", "metastatic",
"metastatic"), prg2m = c(0.5, 0, 0.75, 0.899999619, 0.899999619,
0, NA, 0.799999714, 0.049999982, NA), prg6m = c(0.25, 0,
0.5, 0.5, 0.8999996, 0, 0.6999998, 0.3999999, 0.0001249999,
NA), dnr = c("no dnr", NA, "no dnr", "no dnr", "no dnr",
"no dnr", "no dnr", "no dnr", "dnr after sadm", "no dnr"),
dnrday = c(5, NA, 17, 3, 16, 4, 9, 7, 2, 8), meanbp = c(97,
43, 70, 75, 59, 110, 78, 72, 97, 84), wblc = c(6, 17.0976562,
8.5, 9.09960938, 13.5, 10.3984375, 11.6992188, 13.5996094,
9.69921875, 11.2988281), hrt = c(69, 112, 88, 88, 112, 101,
120, 100, 56, 94), resp = c(22, 34, 28, 32, 20, 44, 28, 26,
20, 20), temp = c(36, 34.59375, 37.39844, 35, 37.89844, 38.39844,
37.39844, 37.59375, 36.59375, 38.19531), pafi = c(388, 98,
231.65625, NA, 173.3125, 266.625, 309.5, 404.75, 357.125,
NA), alb = c(1.7998047, NA, NA, NA, NA, NA, 4.7998047, NA,
NA, 4.6992188), bili = c(0.19998169, NA, 2.19970703, NA,
NA, NA, 0.39996338, NA, 0.39996338, 0.19998169), crea = c(1.19995117,
5.5, 2, 0.79992676, 0.79992676, 0.69995117, 1.59985352, 2,
1, 0.79992676), sod = c(141, 132, 134, 139, 143, 140, 132,
139, 143, 139), ph = c(7.459961, 7.25, 7.459961, NA, 7.509766,
7.65918, 7.479492, 7.509766, 7.449219, NA), glucose = c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_), bun = c(NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_), urine = c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_), adlp = c(7, NA, 1, 0, NA, NA, 0, NA, NA, 0), adls = c(7,
1, 0, 0, 2, 1, 1, 0, 7, NA), sfdm2 = c(NA, "<2 mo. follow-up",
"<2 mo. follow-up", "no(M2 and SIP pres)", "no(M2 and SIP pres)",
"<2 mo. follow-up", "no(M2 and SIP pres)", NA, NA, NA), adlsc = c(7,
1, 0, 0, 2, 1, 1, 0, 7, 0.4947999)), row.names = c(NA, 10L
), class = "data.frame")
I am needing to calculate the proportion of patients who died in the hospital in patients with an active DNR order on day 3 and in patients without an active DNR order on day 3. To group which patients had an active DNR on day 3 and which did not, I used the subset function below:
SB_xlsx1 = SB_xlsx[!is.na(SB_xlsx$dnrday), ]
YesDNR = subset(SB_xlsx1, dnrday <= 3)
NoDNR = subset(SB_xlsx1, dnrday > 3)
However, I don't know how to calculate the proportion of patients that died in the hospital for those with a DNR and without a DNR. The 'hospdead' variable has all 0s and 1s, where 0 = not dead and 1 = dead. However, I don't know how to get the proportion that died for having a DNR at day 3 and did not have a DNR at day 3. What code could I use for my desired result. SB_xlsx also just represents my DataFrame name.
There's a few ways to do this but the simplest is probably via the aggregate function.
> aggregate( hospdead ~ (dnrday<=3) , SB_xlsx1 , mean)
dnrday <= 3 hospdead
1 FALSE 0.1428571
2 TRUE 0.0000000
You may use tapply to group deaths by the condition dnrday <= 3, i.e. with an active DNR on day 3 and calculate the mean.
(res <- proportions(xtabs(death ~ dnrday <= 3, SB_xlsx)))
# dnrday <= 3
# FALSE TRUE
# 0.7142857 0.2857143
where
sum(res)
# [1] 1
EDIT: I apologize; I misread your post when providing my original answer. I've revised it below.
You referred to the hospdeath variable, but in the toy data set it has just one nonzero entry, so I'm using the death variable instead to demonstrate the principle.
First, abase R approach:
mean(SB_xlsx1[SB_xlsx1$death == 1, ]$dnrday <= 3)
mean(SB_xlsx1[SB_xlsx1$death == 1, ]$dnrday > 3)
The idea is to restrict to the subset of rows for which a death occurred, then perform a logical check to see which entries have dnrday greater than 3.
Note that if you have NA entries in death, you'll want to remove them first as you did with those in dnrday.
For a dplyr approach:
library(dplyr)
SB_xlsx1 %>%
filter(death == 1) %>%
summarize(mean(dnrday <= 3), mean(dnrday > 3))
or, for a slightly nicer-looking table,
SB_xlsx1 %>%
filter(death == 1) %>%
group_by(dnrday <= 3) %>%
summarize(prop = n() / nrow(.))
I am working on the following structure and the following plotting code:
structure(c(NA, 11, 9, 9, 21, 7, 2, 5, 3, 0, 0, 1, 31, NA, 3,
2, 1, 0, 0, 10, 3, 0, 0, 0, 31, 16, NA, 2, 2, 10, 0, 5, 0, 0,
0, 0, 59, 65, 1, NA, 2, 4, 0, 4, 0, 0, 0, 0, 156, 23, 7, 17,
NA, 3, 2, 4, 7, 0, 0, 0, 31, 84, 0, 10, 16, NA, 0, 6, 0, 0, 2,
0, 129, 0, 2, 1, 0, 0, NA, 0, 0, 0, 0, 0, 41, 41, 0, 3, 4, 5,
0, NA, 0, 0, 0, 1, 16, 4, 1, 2, 0, 0, 0, 3, NA, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 1, 12, 2, 0, 0, 6, 0, 0, 0, 0,
NA, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), .Dim = c(12L,
12L), .Dimnames = list(c("WILL_", "WOULD_", "MAY_", "MIGHT_",
"CAN_", "COULD_", "SHALL_", "SHOULD_", "MUST_", "OUGHT TO_",
"USED TO_", "HAVE TO_"), c("_WILL", "_WOULD", "_MAY", "_MIGHT",
"_CAN", "_COULD", "_SHALL", "_SHOULD", "_MUST", "_OUGHT TO",
"_USED TO", "_HAVE TO")))
breaks <- c(0,1,5,10,50,100,500,100000)
reshape2::melt(structure, value.name = "Freq") %>%
mutate(label = ifelse(is.na(Freq) | Freq == 0, "", as.character(Freq))) %>%
ggplot(aes(Var2, fct_rev(Var1))) +
geom_tile(aes(fill = Freq), color = "black") +
geom_text(aes(label = label), color = "black") +
scale_fill_steps(low = "white", high = "purple", breaks = breaks, na.value = "grey",trans = "log")+
scale_x_discrete(NULL, expand = c(0, 0), position="top") +
scale_y_discrete(NULL, expand = c(0, 0)) +
theme(axis.text.x = element_text(angle=60,vjust = 0.5, hjust = 0))
I am trying to tweak the code so that original NA values (seen on the plot as the tiles forming a diagonal line from the co-occurrence of WILL WILL to HAVE TO HAVE TO, and the X HAVE TO column) are represented as black tiles separately from the other tiles which I would like to keep as they are.
Looking for tips on how to do this as I think I'm doing something wrong with the representation of values at the beginning of my code.
All the best
Cameron
I'm new to R and am having trouble with a simple command. How do I find the proportion of demographic variables (for example, proportion of English speakers in my population, or proportion of White respondents)?
I'd like to create a large table with all of the proportions, and would hopefull include mean age and median education level, but am having trouble finding the command. This is what I've tried:
table2 <- table(VR_Data$English)
prop.table(table2)
table3 <- table(VR_Data$race)
prop.table(table3)
table4 <- table(VR_Data$male)
prop.table(table4)
If it helps, this is my data:
structure(list(study = c(4, 4, 4, 1, 1, 1), TREATMENT = c(0,
0, 0, 0, 0, 0), TREATMENT4 = c(0, 0, 0, 0, 0, 0), TREATMENT2 = c(0,
0, 0, 0, 0, 0), TREATMENT3 = c(0, 0, 0, 0, 0, 0), order = c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), id = c(279,
238, 239, 135, 143, 138), treatment = c(0, 0, 0, 0, 0, 0), treatment_condition = c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), control_condition = c(NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), m_check1 = c(1,
1, 1, 1, 1, 1), relationship = c(NA, NA, NA, 7, 6, 5), payment = c(NA,
NA, NA, 10, 3, 3), educ_level = c(14, 14, 12, 16, 16, 18), golf = c(3,
5, 3, 3, 2, 3), male = c(1, 0, 1, 0, 0, 1), Asian = c(0, 1, 0,
0, 0, 0), Black = c(0, 0, 0, 0, 0, 0), Latino = c(1, 0, 0, 0,
0, 0), White = c(0, 0, 1, 1, 1, 1), age = c(27, 53, 49, 25, 28,
24), English = c(1, 1, 1, 1, 1, 1), education = c(16, 16, 14,
14, 14, 16), enjoy = c(4, 1, 3.5, 4.25, 3.25, 3.5), RELATIONSHIP = c(4.33333349227905,
1, 4.33333349227905, 3.66666674613953, 3.5, 3.66666674613953),
anxiety = c(3, 3.40000009536743, 2.20000004768372, 1.25,
2, 1.25), BEH_SIM = c(3, 1, 3.75, 2.75, 2.5, 1.75), sptconf = c(3.33333325386047,
1.5, 4, 4.83333349227905, 4, 3.66666674613953), NEG_EFFICACY = c(4,
1.16666662693024, 3.66666674613953, 4.83333349227905, 4.16666650772095,
4.5), spteffort = c(3.16666674613953, 3.5, 4.16666650772095,
3.16666674613953, 3.16666674613953, 3.5), SPTEFFORT_OTHER = c(3.16666674613953,
3.5, 3.5, 3.16666674613953, 3, 3.33333325386047), SIM_VALUES = c(3.75,
1, 3.75, 3.75, 1.5, 2.25), COOP_MOTIV = c(2.33333325386047,
3, 2.66666674613953, 5, 2.5, 2.66666674613953), COMP_MOTIV = c(5,
5, 3.20000004768372, 4.40000009536743, 2.40000009536743,
4.40000009536743), presence = c(NA, NA, NA, 2.79999995231628,
1.79999995231628, 2.59999990463257), environ = c(NA, NA,
NA, 3, 4, 3), openresponse = c(NA, NA, NA, 94.25, 86, 60),
TotalOwnerCommission = c(300, 266.666656494141, 258.333343505859,
266.666656494141, 383.333343505859, 325), TotalRangerComm = c(258.333343505859,
233.33332824707, 291.666656494141, 258.333343505859, 175,
166.66667175293), TotalComm = c(279.166687011719, 250, 275,
262.5, 279.166687011719, 245.833343505859), merge = c(1,
1, 1, 0, 0, 0), Control = c(1, 1, 1, NA, NA, NA), treatment_Shoes = c(0,
0, 0, NA, NA, NA), treatment_Instructions_Only = c(0, 0,
0, NA, NA, NA), treatment_Info_Only = c(0, 0, 0, NA, NA,
NA), treatment_Info_Instructions = c(0, 0, 0, NA, NA, NA),
group = c("OwnerOnly", "OwnerOnly", "OwnerOnly", "", "",
""), race = c(4, 2, 5, NA, NA, NA), race_a = c("", "", "",
"", "", ""), RELATIONSHIP_2 = c(9.02055358886719, 1, 9.02055358886719,
7.02113246917725, 6.54790019989014, 7.02113246917725), TotalOwnerCommission_2 = c(5196.15234375,
4354.64794921875, 4152.12744140625, 4354.64794921875, 7505.24560546875,
5859.02099609375)), row.names = c(NA, -6L), class = c("tbl_df",
"tbl", "data.frame"))
How can I put all of these proportions into one table, with mean and medians? Is this possible? Thank you so much in advance.
If I understand your question correctly, this should help you.
library(dplyr)
VR_Data %>%
summarize(English_prop = sum(English) / n(),
White_prop = sum(White) / n(),
male_prop = sum(male) / n(),
age_avg = mean(age),
education_avg = mean(education))
Should give you this...
# A tibble: 1 x 5
English_prop White_prop male_prop age_avg education_avg
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 0.667 0.5 34.3 15
I'm trying to produce a proper table with stargazer in markdown. The linear model code is as follows:
k <- lm(mean_p ~ P*MA, data=d)
Calling the following stargazer-function however does not work, produces the error
length of NULL cannot be changed
and only produces html-code.
stargazer(k,type="html")
This is the Output of the dput(head(d))-call:
structure(list(
age = c(15.1666666666667, 14.9166666666667,
13.3333333333333, 12.0833333333333, 10.9166666666667, 13.75),
gender = c("girls", "girls", "boys", "girls", "boys", "girls"),
MA = c("NM", "NM", "NM", "NM", "NM", "NM"),
AFC = c(0, 0, 0, 0, 0, 0),
test_k_1 = c(3, 3, 3, 3, 0, 0), test_k_2 = c(6, 3, 6, 6, 3, 3),
test_k_3 = c(9, 0, 6, 3, 0, 0), test_k_4 = c(3, 3, 9, 6, 0, 0),
test_u_1 = c(3, 3, 6, 3, 0, 0), test_u_2 = c(6, 3, 0, 0, 0, 0),
test_u_3 = c(9, 0, 3, 0, 0, 0), test_u_4 = c(3,0, 0, 0, 0, 0),
test_n_3 = c(6, 3, 6, 0, 0, 3), test_n_4 = c(3,0, 9, 0, 0, 3),
k = c(6, 2, 7, 5, 1, 1), b = c(6,1, 1, 0, 0, 0),
d = c(6, 2, 8, 1, 0, 3),
s = c(6,1.66666666666667,5.33333333333333, 2, 0.333333333333333, 1.33333333333333),
mean_p = c(6, 1.66666666666667, 5.33333333333333,2, 0.333333333333333, 1.33333333333333),
zc = c(-0.669549727292442,
-0.669549727292442, -0.669549727292442, -0.669549727292442,
-0.669549727292442, -0.669549727292442),
znum = c(-0.643217641381636, -0.643217641381636, -0.643217641381636, -0.643217641381636, -0.643217641381636, -0.643217641381636),
zse = c(-0.708845108492594, -0.708845108492594, -0.708845108492594, -0.708845108492594, -0.708845108492594, -0.708845108492594),
m = c(-0.669549727292442,-0.669549727292442, -0.669549727292442, -0.669549727292442, -0.669549727292442, -0.669549727292442),
P = c(-0.416305415592902, -0.442252258358532, -0.499214827082544, -0.699404516110036, -0.39383368539238, -0.702020755840638)),
row.names = c(186L,187L, 195L, 197L, 199L, 200L), class = "data.frame")
Does anybody know how to solve that problem? Thanks in advance for any hints.
For me, this problem disappeared when I updated tidyverse, as described here: https://tidyverse.tidyverse.org/reference/tidyverse_update.html