{"id":955,"date":"2023-07-28T04:49:24","date_gmt":"2023-07-28T04:49:24","guid":{"rendered":"https:\/\/statorials.org\/th\/%e0%b8%9e%e0%b8%b4%e0%b8%a1%e0%b8%9e%e0%b9%8c-tibble-r-%e0%b8%97%e0%b8%b8%e0%b8%81%e0%b8%9a%e0%b8%a3%e0%b8%a3%e0%b8%97%e0%b8%b1%e0%b8%94\/"},"modified":"2023-07-28T04:49:24","modified_gmt":"2023-07-28T04:49:24","slug":"%e0%b8%9e%e0%b8%b4%e0%b8%a1%e0%b8%9e%e0%b9%8c-tibble-r-%e0%b8%97%e0%b8%b8%e0%b8%81%e0%b8%9a%e0%b8%a3%e0%b8%a3%e0%b8%97%e0%b8%b1%e0%b8%94","status":"publish","type":"post","link":"https:\/\/statorials.org\/th\/%e0%b8%9e%e0%b8%b4%e0%b8%a1%e0%b8%9e%e0%b9%8c-tibble-r-%e0%b8%97%e0%b8%b8%e0%b8%81%e0%b8%9a%e0%b8%a3%e0%b8%a3%e0%b8%97%e0%b8%b1%e0%b8%94\/","title":{"rendered":"\u0e27\u0e34\u0e18\u0e35\u0e1e\u0e34\u0e21\u0e1e\u0e4c tibble \u0e17\u0e38\u0e01\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19 r"},"content":{"rendered":"

<\/p>\n

\n

Tibble<\/strong> \u0e04\u0e37\u0e2d\u0e40\u0e1f\u0e23\u0e21\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e43\u0e19 R \u0e17\u0e35\u0e48\u0e21\u0e35\u0e27\u0e34\u0e18\u0e35\u0e01\u0e32\u0e23\u0e1e\u0e34\u0e21\u0e1e\u0e4c\u0e17\u0e35\u0e48\u0e44\u0e14\u0e49\u0e23\u0e31\u0e1a\u0e01\u0e32\u0e23\u0e1b\u0e23\u0e31\u0e1a\u0e1b\u0e23\u0e38\u0e07\u0e0b\u0e36\u0e48\u0e07\u0e41\u0e2a\u0e14\u0e07\u0e40\u0e09\u0e1e\u0e32\u0e30 10 \u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e41\u0e23\u0e01\u0e02\u0e2d\u0e07\u0e40\u0e1f\u0e23\u0e21\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25 \u0e17\u0e33\u0e43\u0e2b\u0e49\u0e01\u0e32\u0e23\u0e17\u0e33\u0e07\u0e32\u0e19\u0e01\u0e31\u0e1a\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e02\u0e19\u0e32\u0e14\u0e43\u0e2b\u0e0d\u0e48\u0e07\u0e48\u0e32\u0e22\u0e02\u0e36\u0e49\u0e19\u0e21\u0e32\u0e01\u0e41\u0e25\u0e30\u0e1b\u0e49\u0e2d\u0e07\u0e01\u0e31\u0e19\u0e44\u0e21\u0e48\u0e43\u0e2b\u0e49 R \u0e1e\u0e22\u0e32\u0e22\u0e32\u0e21\u0e41\u0e2a\u0e14\u0e07\u0e17\u0e38\u0e01\u0e41\u0e16\u0e27\u0e43\u0e19\u0e01\u0e23\u0e2d\u0e1a\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25<\/span><\/p>\n

\u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e40\u0e0a\u0e48\u0e19 \u0e25\u0e2d\u0e07\u0e1e\u0e34\u0e08\u0e32\u0e23\u0e13\u0e32\u0e15\u0e32\u0e23\u0e32\u0e07\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49\u0e0b\u0e36\u0e48\u0e07\u0e21\u0e35 80 \u0e41\u0e16\u0e27\u0e41\u0e25\u0e30 2 \u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c:<\/span><\/p>\n

 #load dplyr\nlibrary(dplyr)<\/span>\n\n#make this example reproducible\nset.seed(1)<\/span>\n\n#create tibble<\/span>\ndata <- tibble(a = rnorm(80),\n               b = rnorm(80))\n<\/strong>\n#view tibble\n<\/span>data\n\n# A tibble: 80 x 2\n        ab\n      \n 1 -0.626 -0.569\n 2 0.184 -0.135\n 3 -0.836 1.18 \n 4 1.60 -1.52 \n 5 0.330 0.594\n 6 -0.820 0.333\n 7 0.487 1.06 \n 8 0.738 -0.304\n 9 0.576 0.370\n10 -0.305 0.267\n# ... with 70 more rows<\/strong><\/pre>\n
 \u0e40\u0e21\u0e37\u0e48\u0e2d\u0e40\u0e23\u0e32\u0e1e\u0e34\u0e21\u0e1e\u0e4c\u0e0a\u0e37\u0e48\u0e2d tibble \u0e43\u0e19 R \u0e21\u0e31\u0e19\u0e08\u0e30\u0e41\u0e2a\u0e14\u0e07\u0e40\u0e09\u0e1e\u0e32\u0e30 10 \u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e41\u0e23\u0e01\u0e15\u0e32\u0e21\u0e04\u0e48\u0e32\u0e40\u0e23\u0e34\u0e48\u0e21\u0e15\u0e49\u0e19 \u0e2d\u0e22\u0e48\u0e32\u0e07\u0e44\u0e23\u0e01\u0e47\u0e15\u0e32\u0e21 \u0e2a\u0e34\u0e48\u0e07\u0e19\u0e35\u0e49\u0e1a\u0e2d\u0e01\u0e40\u0e23\u0e32\u0e27\u0e48\u0e32\u0e21\u0e35 \u0e41\u0e16\u0e27\u0e40\u0e1e\u0e34\u0e48\u0e21\u0e40\u0e15\u0e34\u0e21\u0e2d\u0e35\u0e01 70 \u0e41\u0e16\u0e27<\/strong> \u0e17\u0e35\u0e48\u0e44\u0e21\u0e48\u0e41\u0e2a\u0e14\u0e07<\/span><\/p>\n
 \u0e41\u0e15\u0e48\u0e43\u0e19\u0e1a\u0e32\u0e07\u0e01\u0e23\u0e13\u0e35\u0e04\u0e38\u0e13\u0e2d\u0e32\u0e08\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e40\u0e2b\u0e47\u0e19\u0e40\u0e2a\u0e49\u0e19 Tibble \u0e21\u0e32\u0e01\u0e01\u0e27\u0e48\u0e32 10 \u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14<\/span><\/p>\n
 \u0e2b\u0e21\u0e32\u0e22\u0e40\u0e2b\u0e15\u0e38:<\/strong> \u0e2b\u0e32\u0e01\u0e04\u0e38\u0e13\u0e22\u0e31\u0e07\u0e43\u0e2b\u0e21\u0e48\u0e01\u0e31\u0e1a tibbles \u0e08\u0e38\u0e14\u0e40\u0e23\u0e34\u0e48\u0e21\u0e15\u0e49\u0e19\u0e17\u0e35\u0e48\u0e14\u0e35\u0e04\u0e37\u0e2d \u0e1a\u0e17 tibbles<\/a> \u0e02\u0e2d\u0e07 R for Data Science<\/em><\/span><\/p>\n
 \u0e1e\u0e34\u0e21\u0e1e\u0e4c\u0e08\u0e33\u0e19\u0e27\u0e19\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e17\u0e35\u0e48\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e08\u0e32\u0e01 Tibble<\/strong><\/h3>\n
 \u0e04\u0e38\u0e13\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e1e\u0e34\u0e21\u0e1e\u0e4c\u0e08\u0e33\u0e19\u0e27\u0e19\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e02\u0e2d\u0e07 tibble \u0e44\u0e14\u0e49\u0e42\u0e14\u0e22\u0e01\u0e32\u0e23\u0e23\u0e30\u0e1a\u0e38\u0e15\u0e31\u0e27\u0e40\u0e25\u0e02\u0e43\u0e19\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19 print()<\/strong> :<\/span><\/p>\n
 #print first 20 rows of tibble<\/span>\nprint(data, n= 20<\/span> )\n<\/strong>\n# A tibble: 80 x 2\n         ab\n       \n 1 -0.626 -0.569\n 2 0.184 -0.135\n 3 -0.836 1.18 \n 4 1.60 -1.52 \n 5 0.330 0.594\n 6 -0.820 0.333\n 7 0.487 1.06 \n 8 0.738 -0.304\n 9 0.576 0.370\n10 -0.305 0.267\n11 1.51 -0.543\n12 0.390 1.21 \n13 -0.621 1.16 \n14 -2.21 0.700\n15 1.12 1.59 \n16 -0.0449 0.558\n17 -0.0162 -1.28 \n18 0.944 -0.573\n19 0.821 -1.22 \n20 0.594 -0.473\n# ... with 60 more rows<\/strong><\/pre>\n
 \u0e04\u0e38\u0e13\u0e22\u0e31\u0e07\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e43\u0e0a\u0e49\u0e15\u0e31\u0e27\u0e14\u0e33\u0e40\u0e19\u0e34\u0e19\u0e01\u0e32\u0e23\u0e44\u0e1b\u0e1b\u0e4c\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e43\u0e2b\u0e49\u0e44\u0e14\u0e49\u0e1c\u0e25\u0e25\u0e31\u0e1e\u0e18\u0e4c\u0e40\u0e14\u0e35\u0e22\u0e27\u0e01\u0e31\u0e19:<\/span><\/p>\n
 #print first 20 rows of tibble<\/span>\ndata %>% print(n= 20<\/span> )\n<\/strong>\n# A tibble: 80 x 2\n         ab\n       \n 1 -0.626 -0.569\n 2 0.184 -0.135\n 3 -0.836 1.18 \n 4 1.60 -1.52 \n 5 0.330 0.594\n 6 -0.820 0.333\n 7 0.487 1.06 \n 8 0.738 -0.304\n 9 0.576 0.370\n10 -0.305 0.267\n11 1.51 -0.543\n12 0.390 1.21 \n13 -0.621 1.16 \n14 -2.21 0.700\n15 1.12 1.59 \n16 -0.0449 0.558\n17 -0.0162 -1.28 \n18 0.944 -0.573\n19 0.821 -1.22 \n20 0.594 -0.473\n# ... with 60 more rows<\/strong><\/pre>\n
 \u0e1e\u0e34\u0e21\u0e1e\u0e4c Tibble \u0e17\u0e38\u0e01\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14<\/strong><\/h3>\n
 \u0e04\u0e38\u0e13\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e1e\u0e34\u0e21\u0e1e\u0e4c\u0e41\u0e15\u0e48\u0e25\u0e30\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e02\u0e2d\u0e07 tibble \u0e44\u0e14\u0e49\u0e42\u0e14\u0e22\u0e01\u0e32\u0e23\u0e23\u0e30\u0e1a\u0e38 n = Inf<\/strong> :<\/span><\/p>\n
 #print all rows of tibble<\/span>\ndata %>% print(n= Inf<\/span> )\n<\/strong>\n# A tibble: 80 x 2\n          ab\n         \n 1 -0.626 -0.569 \n 2 0.184 -0.135 \n 3 -0.836 1.18  \n 4 1.60 -1.52  \n 5 0.330 0.594 \n 6 -0.820 0.333 \n 7 0.487 1.06  \n 8 0.738 -0.304 \n 9 0.576 0.370 \n10 -0.305 0.267 \n11 1.51 -0.543 \n12 0.390 1.21  \n13 -0.621 1.16  \n14 -2.21 0.700 \n15 1.12 1.59  \n16 -0.0449 0.558 \n17 -0.0162 -1.28  \n18 0.944 -0.573 \n19 0.821 -1.22  \n20 0.594 -0.473 \n21 0.919 -0.620 \n22 0.782 0.0421\n23 0.0746 -0.911 \n24 -1.99 0.158 \n25 0.620 -0.655 \n26 -0.0561 1.77  \n27 -0.156 0.717 \n28 -1.47 0.910 \n29 -0.478 0.384 \n30 0.418 1.68  \n31 1.36 -0.636 \n32 -0.103 -0.462 \n33 0.388 1.43  \n34 -0.0538 -0.651 \n35 -1.38 -0.207 \n36 -0.415 -0.393 \n37 -0.394 -0.320 \n38 -0.0593 -0.279 \n39 1.10 0.494 \n40 0.763 -0.177 \n41 -0.165 -0.506 \n42 -0.253 1.34  \n43 0.697 -0.215 \n44 0.557 -0.180 \n45 -0.689 -0.100 \n46 -0.707 0.713 \n47 0.365 -0.0736\n48 0.769 -0.0376\n49 -0.112 -0.682 \n50 0.881 -0.324 \n51 0.398 0.0602\n52 -0.612 -0.589 \n53 0.341 0.531 \n54 -1.13 -1.52  \n55 1.43 0.307 \n56 1.98 -1.54  \n57 -0.367 -0.301 \n58 -1.04 -0.528 \n59 0.570 -0.652 \n60 -0.135 -0.0569\n61 2.40 -1.91  \n62 -0.0392 1.18  \n63 0.690 -1.66  \n64 0.0280 -0.464 \n65 -0.743 -1.12  \n66 0.189 -0.751 \n67 -1.80 2.09  \n68 1.47 0.0174\n69 0.153 -1.29  \n70 2.17 -1.64  \n71 0.476 0.450 \n72 -0.710 -0.0186\n73 0.611 -0.318 \n74 -0.934 -0.929 \n75 -1.25 -1.49  \n76 0.291 -1.08  \n77 -0.443 1.00  \n78 0.00111 -0.621 \n79 0.0743 -1.38  \n80 -0.590 1.87<\/strong><\/pre>\n
 \u0e04\u0e38\u0e13\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e04\u0e49\u0e19\u0e2b\u0e32\u0e1a\u0e17\u0e0a\u0e48\u0e27\u0e22\u0e2a\u0e2d\u0e19 R \u0e40\u0e1e\u0e34\u0e48\u0e21\u0e40\u0e15\u0e34\u0e21\u0e44\u0e14\u0e49 \u0e17\u0e35\u0e48\u0e19\u0e35\u0e48<\/a><\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
Tibble \u0e04\u0e37\u0e2d\u0e40\u0e1f\u0e23\u0e21\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e43\u0e19 R \u0e17\u0e35\u0e48\u0e21\u0e35\u0e27\u0e34\u0e18\u0e35\u0e01\u0e32\u0e23\u0e1e\u0e34\u0e21\u0e1e\u0e4c\u0e17\u0e35\u0e48\u0e44\u0e14\u0e49\u0e23\u0e31\u0e1a\u0e01\u0e32\u0e23\u0e1b […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-955","post","type-post","status-publish","format-standard","hentry","category-3"],"yoast_head":"\n\u0e27\u0e34\u0e18\u0e35\u0e1e\u0e34\u0e21\u0e1e\u0e4c Tibble \u0e17\u0e38\u0e01\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19 R - Statology<\/title>\n<meta name=\"description\" content=\"\u0e04\u0e33\u0e2d\u0e18\u0e34\u0e1a\u0e32\u0e22\u0e07\u0e48\u0e32\u0e22\u0e46 \u0e40\u0e01\u0e35\u0e48\u0e22\u0e27\u0e01\u0e31\u0e1a\u0e27\u0e34\u0e18\u0e35\u0e1e\u0e34\u0e21\u0e1e\u0e4c tibble 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