library(reprex)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(ggplot2)
q1 <- c((64/183), (12/66), (32/65), (54/68), (5/24))
q1
#> [1] 0.3497268 0.1818182 0.4923077 0.7941176 0.2083333
q2 <- c((123/260), (22/109), (28/55), (36/47), (12/49))
q3 <- c((27/252), (7/52), (29/64), (34/48), (11/53))
q4 <- c((94/224), (11/66), (21/55), (30/41), (16/62))
nursing <- data.frame(q1,q2,q3,q4)
nursing
#> q1 q2 q3 q4
#> 1 0.3497268 0.4730769 0.1071429 0.4196429
#> 2 0.1818182 0.2018349 0.1346154 0.1666667
#> 3 0.4923077 0.5090909 0.4531250 0.3818182
#> 4 0.7941176 0.7659574 0.7083333 0.7317073
#> 5 0.2083333 0.2448980 0.2075472 0.2580645
str(nursing)
#> 'data.frame': 5 obs. of 4 variables:
#> $ q1: num 0.35 0.182 0.492 0.794 0.208
#> $ q2: num 0.473 0.202 0.509 0.766 0.245
#> $ q3: num 0.107 0.135 0.453 0.708 0.208
#> $ q4: num 0.42 0.167 0.382 0.732 0.258
# nursing <- data.frame(t(nursing))
# nursing
nursing <- as.matrix(nursing)
nursing
#> q1 q2 q3 q4
#> [1,] 0.3497268 0.4730769 0.1071429 0.4196429
#> [2,] 0.1818182 0.2018349 0.1346154 0.1666667
#> [3,] 0.4923077 0.5090909 0.4531250 0.3818182
#> [4,] 0.7941176 0.7659574 0.7083333 0.7317073
#> [5,] 0.2083333 0.2448980 0.2075472 0.2580645
colnames(nursing) <- c("Q1", "Q2", "Q3", "Q4")
rownames(nursing) <- c("R1", "R2", "R3",
"R4", "R5")
nursing
#> Q1 Q2 Q3 Q4
#> R1 0.3497268 0.4730769 0.1071429 0.4196429
#> R2 0.1818182 0.2018349 0.1346154 0.1666667
#> R3 0.4923077 0.5090909 0.4531250 0.3818182
#> R4 0.7941176 0.7659574 0.7083333 0.7317073
#> R5 0.2083333 0.2448980 0.2075472 0.2580645
library("reshape2")
nursing.mod <- melt(nursing)
nursing.mod
#> Var1 Var2 value
#> 1 R1 Q1 0.3497268
#> 2 R2 Q1 0.1818182
#> 3 R3 Q1 0.4923077
#> 4 R4 Q1 0.7941176
#> 5 R5 Q1 0.2083333
#> 6 R1 Q2 0.4730769
#> 7 R2 Q2 0.2018349
#> 8 R3 Q2 0.5090909
#> 9 R4 Q2 0.7659574
#> 10 R5 Q2 0.2448980
#> 11 R1 Q3 0.1071429
#> 12 R2 Q3 0.1346154
#> 13 R3 Q3 0.4531250
#> 14 R4 Q3 0.7083333
#> 15 R5 Q3 0.2075472
#> 16 R1 Q4 0.4196429
#> 17 R2 Q4 0.1666667
#> 18 R3 Q4 0.3818182
#> 19 R4 Q4 0.7317073
#> 20 R5 Q4 0.2580645
names(nursing.mod) <- c("Regions", "Quarters", "Chart_completeness")
nursing.mod
#> Regions Quarters Chart_completeness
#> 1 R1 Q1 0.3497268
#> 2 R2 Q1 0.1818182
#> 3 R3 Q1 0.4923077
#> 4 R4 Q1 0.7941176
#> 5 R5 Q1 0.2083333
#> 6 R1 Q2 0.4730769
#> 7 R2 Q2 0.2018349
#> 8 R3 Q2 0.5090909
#> 9 R4 Q2 0.7659574
#> 10 R5 Q2 0.2448980
#> 11 R1 Q3 0.1071429
#> 12 R2 Q3 0.1346154
#> 13 R3 Q3 0.4531250
#> 14 R4 Q3 0.7083333
#> 15 R5 Q3 0.2075472
#> 16 R1 Q4 0.4196429
#> 17 R2 Q4 0.1666667
#> 18 R3 Q4 0.3818182
#> 19 R4 Q4 0.7317073
#> 20 R5 Q4 0.2580645
str(nursing.mod)
#> 'data.frame': 20 obs. of 3 variables:
#> $ Regions : Factor w/ 5 levels "R1","R2","R3",..: 1 2 3 4 5 1 2 3 4 5 ...
#> $ Quarters : Factor w/ 4 levels "Q1","Q2","Q3",..: 1 1 1 1 1 2 2 2 2 2 ...
#> $ Chart_completeness: num 0.35 0.182 0.492 0.794 0.208 ...
library(ggplot2)
barplot(nursing.mod, aes(x = Regions, y = Chart_completeness, fill = Quarters)) +
geom_bar()
#> Warning in mean.default(width): argument is not numeric or logical:
#> returning NA
#> Error in barplot.default(nursing.mod, aes(x = Regions, y = Chart_completeness, : 'height' must be a vector or a matrixCreated on 2019-10-27 by the reprex package (v0.2.1)